ethnic_enforcement_outliers.ipynb

{"nbformat_minor": 0, "cells": [{"source": "![](https://github.com/sfbrigade/CA_DOJ_OpenJustice/blob/master/figure/ca_openjustice.png?raw=true)\n\n[Data Science Working Group](http://datascience.codeforsanfrancisco.org/)  \n[Code for San Francisco, a CFA Brigade](http://codeforsanfrancisco.org/)  \n\n## Executive Summary\n\nThe following statistical analysis addresses the [CA DoJ's OpenJustice prompt #1](https://github.com/sfbrigade/CA_DOJ_OpenJustice) by revealing the outliers in the annual arrest rates of juveniles from a range of racial/ethnic groups, between California's counties. It finds that, of the roughly 34 counties represented, a handful of counties (5) have proven to outlie in their rates of arresting juveniles from one or more racial/ethnic groups over the past decade (2005-2014). \n\nOf these frequently outlying counties, San Francisco, Marin, and Kings are particularly interesting, for various reasons. San Francisco is an outlier for its arrest rates of the racially/ethnically \"Other,\" for all 10 years on record. Some possible explanations for this were explored by the researcher, but later research proved that this was very likely due to SFPD's lumping of Hispanic and Asian arrests into the \"Other\" category, thereby underreporting such arrests (see [SF Chronicle article by Shoshana Walter](https://goo.gl/W9hxw)). As for Kings county, it outlied for its arrest rates of juveniles from practically every racial/ethnic group, suggesting a more broadly systemic cause within that county. And finally, when it comes to nearby Marin county, it outlied for its arrest rates of Black and/or Hispanic juveniles for each year from 2005-2011 but not since.\n\n\n\n### Data Sources\nAll data was pulled from the California Department of Justice's OpenJustice Data Portal.\n+ [10 year arrest data (2005-2014 reported and anonymized)](https://openjustice.doj.ca.gov/data)  \n    \n## I. Data Exploration\n\n### Get the Relevant Arrests and Population Data from our Azure ML Workspace...", "cell_type": "markdown", "metadata": {}}, {"execution_count": 12, "cell_type": "code", "source": "## Prep the workspace\nlibrary(\"AzureML\")\nws <- workspace()\n\n## load the data: Get all 'arrests' file names.\n## Then, download those files from our Azure ML workspace and subset to 2005+,\n## because our population data (next) only begins in 2005.\nfile_names <- grep(\"^ca_doj_arrests.*csv\", ws$datasets$Name, value = T)\ndat_arrests <- do.call(rbind, download.datasets(ws, file_names[!grepl(\"200[0-4]\", file_names)]))\n\n## Remove row names\nrow.names(dat_arrests) <- NULL\n\n## Now, get county population data by race and gender.\ndat_pop <- download.datasets(ws, \"ca_county_population_by_race_gender_age_2005-2014_02-05-2016.csv\")", "outputs": [], "metadata": {"collapsed": false}}, {"source": "### Preview the arrests data...\n\nDimensions, followed by fields/variables, followed by the first 3 rows.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 13, "cell_type": "code", "source": "## Preview the arrests data\ndim(dat_arrests)\nnames(dat_arrests)\nhead(dat_arrests, 3)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "[1] 13928489       16", "text/markdown": "1. 13928489\n2. 16\n\n\n", "text/html": "<ol class=list-inline>\n\t<li>13928489<\/li>\n\t<li>16<\/li>\n<\/ol>\n", "text/latex": "\\begin{enumerate*}\n\\item 13928489\n\\item 16\n\\end{enumerate*}\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": " [1] \"county\"                   \"agency_name\"             \n [3] \"agency_code\"              \"arrest_year\"             \n [5] \"arrest_month\"             \"arrest_day\"              \n [7] \"race_or_ethnicity\"        \"gender\"                  \n [9] \"age_group\"                \"summary_offense_level\"   \n[11] \"offense_level\"            \"bcs_offense_code\"        \n[13] \"bcs_summary_offence_code\" \"fbi_offense_code\"        \n[15] \"status_type\"              \"disposition\"             ", "text/markdown": "1. 'county'\n2. 'agency_name'\n3. 'agency_code'\n4. 'arrest_year'\n5. 'arrest_month'\n6. 'arrest_day'\n7. 'race_or_ethnicity'\n8. 'gender'\n9. 'age_group'\n10. 'summary_offense_level'\n11. 'offense_level'\n12. 'bcs_offense_code'\n13. 'bcs_summary_offence_code'\n14. 'fbi_offense_code'\n15. 'status_type'\n16. 'disposition'\n\n\n", "text/html": "<ol class=list-inline>\n\t<li>'county'<\/li>\n\t<li>'agency_name'<\/li>\n\t<li>'agency_code'<\/li>\n\t<li>'arrest_year'<\/li>\n\t<li>'arrest_month'<\/li>\n\t<li>'arrest_day'<\/li>\n\t<li>'race_or_ethnicity'<\/li>\n\t<li>'gender'<\/li>\n\t<li>'age_group'<\/li>\n\t<li>'summary_offense_level'<\/li>\n\t<li>'offense_level'<\/li>\n\t<li>'bcs_offense_code'<\/li>\n\t<li>'bcs_summary_offence_code'<\/li>\n\t<li>'fbi_offense_code'<\/li>\n\t<li>'status_type'<\/li>\n\t<li>'disposition'<\/li>\n<\/ol>\n", "text/latex": "\\begin{enumerate*}\n\\item 'county'\n\\item 'agency\\_name'\n\\item 'agency\\_code'\n\\item 'arrest\\_year'\n\\item 'arrest\\_month'\n\\item 'arrest\\_day'\n\\item 'race\\_or\\_ethnicity'\n\\item 'gender'\n\\item 'age\\_group'\n\\item 'summary\\_offense\\_level'\n\\item 'offense\\_level'\n\\item 'bcs\\_offense\\_code'\n\\item 'bcs\\_summary\\_offence\\_code'\n\\item 'fbi\\_offense\\_code'\n\\item 'status\\_type'\n\\item 'disposition'\n\\end{enumerate*}\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "   county                      agency_name agency_code arrest_year arrest_month\n1 Alameda Alameda Co. Sheriff's Department        0100        2014            1\n2 Alameda Alameda Co. Sheriff's Department        0100        2014            1\n3 Alameda Alameda Co. Sheriff's Department        0100        2014            1\n  arrest_day race_or_ethnicity gender age_group summary_offense_level\n1          1             Black   male     adult           misdemeanor\n2          1          Hispanic   male     adult           misdemeanor\n3          1          Hispanic   male     adult           misdemeanor\n  offense_level bcs_offense_code bcs_summary_offence_code fbi_offense_code\n1   misdemeanor              856                       51               21\n2   misdemeanor              856                       51               21\n3   misdemeanor              397                       30               08\n  status_type                  disposition\n1      booked misdemeanor complaint sought\n2      booked misdemeanor complaint sought\n3      booked misdemeanor complaint sought", "text/latex": "\\begin{tabular}{r|llllllllllllllll}\n  & county & agency\\_name & agency\\_code & arrest\\_year & arrest\\_month & arrest\\_day & race\\_or\\_ethnicity & gender & age\\_group & summary\\_offense\\_level & offense\\_level & bcs\\_offense\\_code & bcs\\_summary\\_offence\\_code & fbi\\_offense\\_code & status\\_type & disposition\\\\\n\\hline\n\t1 & Alameda & Alameda Co. Sheriff's Department & 0100 & 2014 & 1 & 1 & Black & male & adult & misdemeanor & misdemeanor & 856 & 51 & 21 & booked & misdemeanor complaint sought\\\\\n\t2 & Alameda & Alameda Co. Sheriff's Department & 0100 & 2014 & 1 & 1 & Hispanic & male & adult & misdemeanor & misdemeanor & 856 & 51 & 21 & booked & misdemeanor complaint sought\\\\\n\t3 & Alameda & Alameda Co. Sheriff's Department & 0100 & 2014 & 1 & 1 & Hispanic & male & adult & misdemeanor & misdemeanor & 397 & 30 & 08 & booked & misdemeanor complaint sought\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>agency_name<\/th><th scope=col>agency_code<\/th><th scope=col>arrest_year<\/th><th scope=col>arrest_month<\/th><th scope=col>arrest_day<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>summary_offense_level<\/th><th scope=col>offense_level<\/th><th scope=col>bcs_offense_code<\/th><th scope=col>bcs_summary_offence_code<\/th><th scope=col>fbi_offense_code<\/th><th scope=col>status_type<\/th><th scope=col>disposition<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Alameda<\/td><td>Alameda Co. Sheriff's Department<\/td><td>0100<\/td><td>2014<\/td><td>1<\/td><td>1<\/td><td>Black<\/td><td>male<\/td><td>adult<\/td><td>misdemeanor<\/td><td>misdemeanor<\/td><td>856<\/td><td>51<\/td><td>21<\/td><td>booked<\/td><td>misdemeanor complaint sought<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Alameda<\/td><td>Alameda Co. Sheriff's Department<\/td><td>0100<\/td><td>2014<\/td><td>1<\/td><td>1<\/td><td>Hispanic<\/td><td>male<\/td><td>adult<\/td><td>misdemeanor<\/td><td>misdemeanor<\/td><td>856<\/td><td>51<\/td><td>21<\/td><td>booked<\/td><td>misdemeanor complaint sought<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Alameda<\/td><td>Alameda Co. Sheriff's Department<\/td><td>0100<\/td><td>2014<\/td><td>1<\/td><td>1<\/td><td>Hispanic<\/td><td>male<\/td><td>adult<\/td><td>misdemeanor<\/td><td>misdemeanor<\/td><td>397<\/td><td>30<\/td><td>08<\/td><td>booked<\/td><td>misdemeanor complaint sought<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"execution_count": 14, "cell_type": "code", "source": "## Load necessary libraries\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(grid)\nlibrary(stats)", "outputs": [], "metadata": {"collapsed": false}}, {"source": "### Subset to Juveniles and Summarize by Each County's Arrests per Ethnic Group...", "cell_type": "markdown", "metadata": {}}, {"execution_count": 62, "cell_type": "code", "source": "## Subset arrests to only juveniles.\ndat_juv <- dat_arrests[dat_arrests$age_group %in% \"juvenile\",]\n\n## Group by county, then by year, then by race/ethnicity and give me the counts.\ncty_ethnic <- summarise(group_by(dat_juv, county, arrest_year, race_or_ethnicity), total = n())\n\n## Now remove those records supressed due to privacy concern.\ncty_ethnic <- cty_ethnic[!(cty_ethnic$race_or_ethnicity %in% \"suppressed_due_to_privacy_concern\"),]\n\n#### !! Some counties are reporting only \"NA\"s in their arrest totals per ethnic group. :-\\\n## Let's remove those from our analysis...for now.\ncty_ethnic <- cty_ethnic[!is.na(cty_ethnic$total),]\n\n## Confirm via preview\ndim(cty_ethnic)\nhead(cty_ethnic)\ntail(cty_ethnic)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "[1] 1699    4", "text/markdown": "1. 1699\n2. 4\n\n\n", "text/html": "<ol class=list-inline>\n\t<li>1699<\/li>\n\t<li>4<\/li>\n<\/ol>\n", "text/latex": "\\begin{enumerate*}\n\\item 1699\n\\item 4\n\\end{enumerate*}\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "Source: local data frame [6 x 4]\nGroups: county, arrest_year\n\n   county arrest_year      race_or_ethnicity total\n1 Alameda        2005 Asian/Pacific Islander   483\n2 Alameda        2005                  Black  2299\n3 Alameda        2005               Hispanic  1465\n4 Alameda        2005                  Other   211\n5 Alameda        2005                  White  1342\n6 Alameda        2006 Asian/Pacific Islander   423", "text/latex": "\\begin{tabular}{r|llll}\n  & county & arrest\\_year & race\\_or\\_ethnicity & total\\\\\n\\hline\n\t1 & Alameda & 2005 & Asian/Pacific Islander & 483\\\\\n\t2 & Alameda & 2005 & Black & 2299\\\\\n\t3 & Alameda & 2005 & Hispanic & 1465\\\\\n\t4 & Alameda & 2005 & Other & 211\\\\\n\t5 & Alameda & 2005 & White & 1342\\\\\n\t6 & Alameda & 2006 & Asian/Pacific Islander & 423\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>arrest_year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Alameda<\/td><td>2005<\/td><td>Asian/Pacific Islander<\/td><td>483<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Alameda<\/td><td>2005<\/td><td>Black<\/td><td>2299<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Alameda<\/td><td>2005<\/td><td>Hispanic<\/td><td>1465<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Alameda<\/td><td>2005<\/td><td>Other<\/td><td>211<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Alameda<\/td><td>2005<\/td><td>White<\/td><td>1342<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Alameda<\/td><td>2006<\/td><td>Asian/Pacific Islander<\/td><td>423<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "Source: local data frame [6 x 4]\nGroups: county, arrest_year\n\n  county arrest_year      race_or_ethnicity total\n1   Yuba        2005                  White     3\n2   Yuba        2007 Asian/Pacific Islander     2\n3   Yuba        2007               Hispanic     3\n4   Yuba        2007                  White     5\n5   Yuba        2008                  White     2\n6   Yuba        2009               Hispanic     1", "text/latex": "\\begin{tabular}{r|llll}\n  & county & arrest\\_year & race\\_or\\_ethnicity & total\\\\\n\\hline\n\t1 & Yuba & 2005 & White & 3\\\\\n\t2 & Yuba & 2007 & Asian/Pacific Islander & 2\\\\\n\t3 & Yuba & 2007 & Hispanic & 3\\\\\n\t4 & Yuba & 2007 & White & 5\\\\\n\t5 & Yuba & 2008 & White & 2\\\\\n\t6 & Yuba & 2009 & Hispanic & 1\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>arrest_year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Yuba<\/td><td>2005<\/td><td>White<\/td><td>3<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Yuba<\/td><td>2007<\/td><td>Asian/Pacific Islander<\/td><td>2<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Yuba<\/td><td>2007<\/td><td>Hispanic<\/td><td>3<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Yuba<\/td><td>2007<\/td><td>White<\/td><td>5<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Yuba<\/td><td>2008<\/td><td>White<\/td><td>2<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Yuba<\/td><td>2009<\/td><td>Hispanic<\/td><td>1<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false}}, {"source": "### Panel Plot the By-County Summary of a Single Year (2014)...", "cell_type": "markdown", "metadata": {}}, {"execution_count": 64, "cell_type": "code", "source": "## Panel bar charts: ethnic breakdown of arrests, by county.\n## Note: this is sheerly by count (not rate).\nplot_ethnic <- ggplot(cty_ethnic[cty_ethnic$arrest_year %in% \"2014\",], aes(x = race_or_ethnicity, y = total, fill = race_or_ethnicity)) + \n                geom_bar(stat = \"identity\") + coord_flip() + facet_wrap(~county) +  \n                theme(axis.text.x=element_text(angle=-90,hjust=1,vjust=0.5, size = 8), axis.text.x=element_text(size = 8),\n                      legend.position = \"none\", strip.text=element_text(size = 8), axis.title.x=element_blank(),\n                      axis.title.y=element_blank()) +\n                ggtitle(\"Ethnic Breakdown of Arrest FREQ by County\\r\n2014 Only (test year)\")\n\n## Print plot\nsuppressWarnings(\n    print(plot_ethnic)\n)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "### Stacked Bar Chart of By-County Summary for a Single Year (2014)...", "cell_type": "markdown", "metadata": {}}, {"execution_count": 65, "cell_type": "code", "source": "## Stacked bar chart: ethnic breakdown of arrests, stacked between counties.\nplot_ethnic2 <- ggplot(cty_ethnic[cty_ethnic$arrest_year %in% \"2014\",], aes(x = race_or_ethnicity, y = total, fill = county)) + \n                geom_bar(stat = \"identity\") + coord_flip() + \n                theme(axis.text.x=element_text(angle=-90,hjust=1,vjust=0.5, size = 8), axis.text.x=element_text(size = 8),\n                      strip.text=element_text(size = 8), axis.title.x=element_blank(), axis.title.y=element_blank(),\n                      legend.text=element_text(size= 6), legend.key.height=unit(.4, \"cm\")) +\n                ggtitle(\"Cumulative Ethnic Breakdown of Arrest Freq by County\\r\n2014 Only (test year)\")\n\n## Print plot\nsuppressWarnings(\n    print(plot_ethnic2)\n)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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8ypQp+q5UF3VwfNbk48PU/WZpmBDYGYj91mG/e4yKXTpRxbrlK/0GZLcel5eX12IFtm7dOmPGjIqKCrFY7OLiwh6GIqJ79+5lZmbW8L0Fw4YNs7S0zMzMPH/+PBsEPzs7+9dffyWisWPH8tnYw2XZ2dlaOoRevHih40rFYjF/229NsG5/VWZmZkKhsKKiQnMX6JVZFfup2r59+xrX9/8JBIKWLVuOGzeuTZs2b7zxxsGDB8+ePav2EgJ2A7gqffeCAUfO8uXLiahz584RERGap0m2dqpsY1aamJWVRUSVjr5rbW3dtGnT/Px8lofhAzsiys/PT0hI8PLycnBwYOfaixcvzpkzJyUlJT09XSwWs37Hauug4142oLb6ys/PP3r0KBGxLjqem5ubv7//lStX9u3bN3v2bLWlNA8D7XP1Okh69+69dOnSxYsXnzx5ko254+HhERISMmPGDDc3N10bppUuw53UsBV6HZPV0lKO6t53cXHp16/fuXPndu3atXDhQiI6f/78o0ePrK2t33rrrWrXYsD5y9jHJ9Xs48PUcLOYDFyKNVDHjh2JqLCwkH+8yEj4x7J0TK9daWlpH3zwQUVFxfz583Nycu7fvx8TExMVFRUVFcWeluc4riblN2nShN17y3rpiOjAgQMKhcLT01P1egR7s4325+A+//zzmtTEAHrtgrrZX/oKDAxkj+D9/PPParM0x6/Say8YduSMGjVKJBLdunVr6dKl+ralqkOxtLRUxxJYz01sbGxJSUl0dLRSqWQhXbdu3Zo2bRodHc1xHDvT+/n5VTqAXM33su611dePP/7ICmfvJFV15coVquJqrPZhzGp4kBDR559/npiYuGLFisGDB9vY2Dx48GDNmjXe3t51+crUmreiKjX8etRezj//+U/6uxeW/r7+OGbMGHZDjnYGn7+Md3xSLX1J1mSzmAwEdgbiu+g1z4hasG8QNrKoKuO9tryGazx69KhcLu/evfuKFSv4J7yYGl6E5bGeucOHD5eVldHf99u9/fbbqnlYJ3wd3DvYALG2p6WlGal89pQfe3RAl5rouBcMO3JGjhy5a9cuoVC4ePFi1nunhvW+VFoCP2YYj11YfPTokWbmwsLCgoICPg/j6enp4OAgl8t/++03FsCxj7lIJAoICMjJybl165aW67A1pG9t9VXVXXS8mzdv3rp1y+DyGQM+qk5OTvPnzz916lROTs7FixcDAwPLy8unTZuWn59fw8oYTK9W6HVMVktLOWp7f/jw4XZ2dg8fPoyOjs7Pz2fP+qjd51cVA85f+h6fdX+yY2qyWUwGAjsDubq6sgcY165dm5OToyWnXC7np9kVwMePH6vluXbtmhHqWAtrZHeTdOrUSS29sLCw0hLY5TO9fqcGBQXZ2dnl5+efPHkyPT398uXL9L/XYYmoV69eRBQfH69L/FFbDGiLMfTs2ZOIrl69Wuu3TjLsm5pdJNVOr72g75HDGzdu3Pbt2wUCwcKFC7/77ju1uewut8uXL2s+8BEVFVVp5oSEBM1Nd+HCBY7jBAIBG1SFxzrtLl68qBbAVZVeiwyore4ePnzI7huLj4/PrQwbA6za4K9aNfmoCoXCPn36HD9+3NzcvKCgICEhgZ9Vxx9GvVqh1zFZrUov/rJy1J7wMDMzCw8PJ6KdO3dGRESUlJR07NiR1bxaBpy/9D0+jXey034w1GSzmAwEdoZbtmyZWCx+8uTJiBEjKv1xqVAoli1btnHjRj6FjXeq9iOppKRE8wRWW2q4RvYwgeYv12XLllV6jxR7hlGv32QSiYTd/RAREbF//36O41555RW1x5eGDBnSokULpVI5e/ZsdpVEjTGCHgPaYgwhISF2dnYlJSVqD0HXirNnz7JvXrUH8Sql117Q98hRNWnSpE2bNgkEgnnz5m3YsEF11tChQ83NzTMzM9VeqltYWKj6QWNCQkIsLCyKi4vXrVunmq5QKNgjxgMGDFB7JIVFbCdOnIiLi/Pw8ODvcGXpu3fvTklJYR142ptgAANqq7v//ve/RNS1a9euXbs2qwz7DO7du7fSR6R1p9dBUumNU1KplHX2sC58po4/jHq1Qq9jslqRkZE3btxQTbl169apU6eISPMuMXbZ8cCBA2xMUL36pfQ9f+l7fBrvZFftwVCTzWIaENgZrkePHuvWrRMIBBcvXvTy8lqxYkVCQkJOTk5OTk58fPyqVas8PT0/++wzhULBL8KG7T506BB7TSfHcbGxsQMHDjTeQxg1XCO7of7mzZsfffRRUVEREeXm5i5cuPCbb77hR0VX5e3tTUSHDx/W6yuY9c+dOHGC3Q+h1l1HRE2aNFm7di3LM2TIEDbOExHJ5fKrV6/OmzdPr1uty8vLs6vA2liTttQ6CwuLFStWENGmTZveeeedxMRElp6VlbVx40Y2MqoulEpl6d9KSkrS09N37NjBxvqysLDQ5ZExvfaCvkeOmmnTprHzxwcffMAGbGNsbW35wc9++OEH9uF68ODBkCFD2MUgVba2tmy8rq+++uq7775jd7s/evRo1KhR165dE4lEmnfysZ65uLi4iooK1W65V155xdLSMjY2lv6+5a7aJujLgNrqiPv7XUwjRoyoKk9oaKhEIpHJZL/88otha2H0OkjGjRs3derUc+fO8bH+48ePJ0+eXFxc3LRpU9ZXzdTxh1GvVuh1TFbLyspq2LBh586dY/9eunQpNDRUoVD4+/uzh7VVeXt79+rVq6ioKCEhQSwWs54qHel7/tL3+DTeya7ag6Emm8VEaLk5FHTx008/aXnqys/P7/bt23zmiooK1efn2Q9Te3v7EydOsJRKX5+ntkY2MkWlr0pkV75UF9FrjZUOUMw/QycUCu3t7dkQJO+8887EiROJ6JNPPlHNfOvWLbYKkUjUunVrJyenfv36aSmcUSqV/FOfQqGwqvcvrV+/XvXlaba2tvyDk2KxuNJF1FQ7QDH/IiPtbalq13Acx6qk+gJcvTJXtZWWLFnC31lsbW1twCvFqmJpaXn06FHV/FoqzOmzF/Q6cipd6erVq4lIIBD88MMPfGJJSQk/pIKFhQV7vs/CwoIfDVHtlWJ8P4dYLOZ7FCp9CRLHcUqlkh/ca+/evaqz+KeG586dq7mgXnu5KnrVVvcBis+fP8/KUf0u0sQGt+NfSqH9MKiVg4QNz8YfIXy4LJFI1AZM1vJhrIpe74qtSSs4PY9J7ZVZu3YtGzK6adOm/AZRe6WYKr6bcNiwYdWuQpNe5y+9js9aOdlxlX18dDkYarhZGjv02NXUW2+9lZKS8v333w8aNKhNmzbm5uZNmjRxc3MLDw8/depUbGys6m1GQqHwxIkTCxYscHZ2lkgkLVq0mDx5clxcnOatSLWl5mvcuXPnmjVrfHx8xGKxQqHo2bPnzp071UaA5Pn4+Jw5c2bgwIE2NjYymSwtLU3zHgtNAoGAvVWdiAIDAzVHeGHef//9P//8c9asWd7e3iKRqKCgwN7e/o033liyZMmff/6pY3N0Z1hbjORf//rXtWvXwsPD27dvX1ZWJhKJunbtOnfu3EqfMNCFlZVVly5d5s6de/v27X/84x+6L6j7XtDryKnUvHnzli1bxnHc5MmT+XOkVCo9c+bMihUrOnbsqFQqRSLRqFGjrl69WumQYxKJ5Mcff4yIiOjfv3/Tpk2LioratGkzfvz4GzduVHqBRiAQ9O7dm02r3Uindr+dMehbWx2xO+c8PDy0f+rZYLDHjh2rea+YjgfJypUrV6xYERwc7Ozs/OLFi7KyMjc3t3fffTc+Pp5VhlcvH0bdD3W9jkntHBwcrl+/PnPmzGbNmpWWlrZt2/b999+Pi4tj0Z4mfghlw44Qvc5feh2fxjvZ6XIw1HCzNHYCrr7vDQcAADA9MpmM/UzNzc2tlbEzNR06dGjUqFGOjo5seEVjrKIxesk3C3rsAAAAGiX2fMA777zzEoYvWrzkmwWBHQAAQOOzd+/eX3/9VSKRTJ8+vb7r0oBgs7yMwSwAAEAjlZWV9eqrrxYVFbEnTOfMmVO7rxxspLBZeAjsAAAAGo2Kioq0tDShUNihQ4dJkyYZY4TLxgibhYeHJwAAAABMBO6xAwAAADARCOwAwBQMGDBAIBCsX7/eSOWHhIQIBIKbN28aqfyXEMdxnTt3btKkyZPK3nwPAIZBYAfQEOXm5m7fvn3kyJEdOnQwNze3srLq3LnzRx999OjRo6oWSU9PnzFjhrOzs1QqdXR0HDFixJUrVzSz5eXlHTly5LPPPgsODrazsxMIBAKBID4+XpdayeVyb29vtogBIdSzZ8+WLl36+uuvOzg4mJubt2rVKjAwcNWqVZW+qrJBiYqKOnXq1NChQ1Vfc75jx45FixbFxcXVTR3qeHV1QCAQfPbZZyUlJV9++WV91wXAhNTviy8AQNPjx4+lUmmlH1hra+uTJ09qLnL16lV+BFR+6CahULhhwwa1nOy1oWpu3rypS8WWLFnCL1Lp2+G02L59u7W1daWNsre3P3LkiF6ladLyzrqae/XVV4no6tWrqon+/v5EtG3bNmOsUVMdr65uVFRUeHh4iESiP//8s77rAmAi0GMH0ODI5fLS0lJ3d/clS5ZER0enp6f/8ccfa9asad68eWFh4ahRo1JTU1XzFxQUvPnmm3l5ed27d4+Li5PL5RkZGeHh4UqlctasWb///rta+a1atRo6dOiiRYs2bNige60ePny4bNkyLy8v/k21uvv222/ffffdwsJCT0/P3bt3P3nypKys7NGjR1u2bGnbtm12dvbIkSP5l4Y1NNHR0bGxsZ07d37ttdfquy6mRigUTp48uaKi4vvvv6/vugCYivqOLAFAXXZ29oEDB5RKpVr61atXJRIJEc2fP181fenSpUTUrFmzp0+f8olKpTIgIICIBgwYoJpZoVDw0/wrL3XpsWOvOT937hx7x7nufWMxMTHsTd7BwcGqb/5mcnNz/fz8iMjKyurhw4c6lqnJeD12EyZMICL21lpV6LGrFampqQKBwMbG5sWLF/VdFwBTgMAOoDFhb6BXi9W8vLyIaPbs2WqZjx8/TkRCoVAmk1Vamu6BHXuR/NixYzmO0zewe+ONN4ioVatWubm5lWZITU21tLQkogkTJqimd+3alYgiIiKKi4s/++wzNzc3c3NzBweH8PDw1NRUtULUArvHjx+zaLLSphUXF7PrwpVe11ZVUFBgYWFBRImJiXxipZezicjT01N12bi4uIkTJzo5OZmbm9vY2Pj7+2/ZsqW8vFxtFXK5fN26db169bKxsZFIJA4ODl27dn3//fd/++03vVanyrDm61hhmUy2bt26kJAQDw8PS0tLKysrHx+fBQsWZGVlaa6L34l5eXnz5893d3eXSqWurq6qeXr27ElEe/bsqao5AKA7BHYAjUlYWBgR9e/fn095+vQpO81rxijFxcWsh2///v2VlqZjYJednW1vb29jY8N6BPUK7B4+fMhWsWLFCi3ZZs6cSUQSiaSgoIBPZDHBli1b2IRAIBAK/7p7pFWrVk+ePFEtQbPHLjQ0lIg++OADzdWxOLVNmzaq/ZeVOn36NBE5ODioJkZGRvbt27dp06ZE5OHh0fdv4eHhfJ4VK1YIBAJWWysrK77m/fv3V+2aUigUrCuUNd/R0ZG/vXLixIm6r06Tvs3XscIcx02bNo2vsK2tLZ+zXbt2SUlJauti+279+vXu7u58SOrs7KyaZ+7cuartBYCaQGAH0GgolUpnZ2cimjt3Lp947tw5drLU7MTiOM7Dw4OIvvzyy0oL1DGwe+edd1RjJr0Cu23btrFVaL/MeuHCBZbtl19+4RNZTGBvb+/s7HzixAm5XF5cXPzjjz+y3qZp06aplqAZ2B07doyI7OzsysrK1FYXGBhIRAsWLKi2/mz8+qFDh2rO0nJtdM+ePURkaWm5fPnyZ8+ecRxXUlJy+PDhtm3bEtGMGTP4nHv37iUia2vrQ4cO8X1jMpls69ata9eu1XF1ldKr+bpXmHp/C4UAACAASURBVOO4FStWrFq16u7duxUVFRzHyeXy6OhodgNiYGCg2rrYTrS1tW3ZsuXu3btzcnKKior4zkiG3V7p4uKiY9MAQAsEdgCNxn/+8x8iEolEd+7c4RPZKZmI5HK55iLsFD516tRKC9QlsIuKiiIiX19fdhbn9AzsWGeMtbW19my5ubmsJqrRDIsJJBLJ/fv3VTOvWrWKBXyqiZqBnUKhaN26NRH99NNPqjmTk5NZ19SDBw+qrX9wcDARffrpp5qzqoq0SktLHRwciOjw4cNqs65fvy4SiSQSSWZmJkuZNWsWEc2aNavamugb2OnefL0qXJWcnJwWLVoQ0d27d1XT2U4Ui8U3btyoatnbt2+zvZ+dna1j6wCgKngqFqBxuHv37ocffkhEc+bM8fb25tOLi4uJiJ19NZdq0qQJERUVFRm20rKysmnTpgmFws2bN/NX3PSSk5NDRCwW1KJZs2ZslBb2Am9VYWFhrN+RN3LkSCLKzs7OzMzUUqZIJGJ9jTt37lRN37lzJ8dxvXv3Vr04WBWZTKZL/VWdOXMmMzPT3d39zTffVJvl5+fn4+NTXl5+8eJFlsJ6HzMyMnQvX0e6N1+vClelefPmr7/+OhH99ttvmnNDQkJ8fX2rWtbW1pZNaN+hAKALcX1XAACq9/Tp09DQ0MLCwj59+ixbtqzO1rt8+fL79++/9957bCC3esG6fFS1adNGIBBwHJeXl8e6mqoyZcqU5cuXR0ZGPn78uE2bNkSkVCrZHWZTpkzRZe3Z2dmkEnnoIiYmhohyc3NZd6kaFsOlp6ezf4ODg7/++uuff/556NCh48ePDwwMdHR01H1d2unYfL0qzMTHx2/cuPHy5cuPHj1iPy14lb5Ggj30UxV+87KtDQA1gcAOoKHLzs4eOHBgcnLyK6+8cvz4cbWeOfY8aUVFRXl5uWan3YsXL4jIysrKgPXev3//m2++admyZU1CSXbO1uyHU5OXl6dQKKiyEIpdT1RlZmYmFApZk7UX6+Li0q9fv3Pnzu3atWvhwoVEdP78+UePHllbW7/11lu61J/Vih/zWRfscZbs7GwtvVxsvxBR7969ly5dunjx4pMnT548eZKIPDw8QkJCZsyY4ebmpvtKK6Vj8/WqMBFt3bp1xowZFRUVYrHYxcXFzs7OzMyMiO7du5eZmakW5zEtW7bUUk9+81a7QwGgWrgUC9Cg5eTkDBgw4M6dO507d/7ll1/Yo5Gq+Lin0p4SltiqVSsDVj1v3ryysrJFixZJJJIiFWyuXC4vKiqq9CyuqmPHjkRUWFiYlJSkJRv/TjPVq8wM/6imYf75z38S0Q8//MD+Zdclx4wZwy5SV8ve3p6I+FsAdaFUKolI+yOrn3/+OZ//888/T0xMXLFixeDBg21sbB48eLBmzRpvb+/9+/frvtKq6NJ8vSqclpb2wQcfVFRUzJ8/Pycn5/79+zExMVFRUVFRUQMGDCAijuM0q6E9MuY3L9vaAFATCOwAGq7c3NyBAwcmJCR4enqePXu20guCfCR09+5dtVklJSUpKSn0d3SlL/Z+ixkzZlj/L9b9Nm/ePGtraycnJ+2F8Ff3fv75Zy3ZDh06REQSiaRXr14GVFWL4cOH29nZPXz4MDo6Oj8///Dhw0Q0efJkHRdnDwSwOwV1xK4O37lzR/dFnJyc5s+ff+rUqZycnIsXLwYGBpaXl0+bNq3mb9HVpfl6Vfjo0aNyubx79+4rVqxQe0dcpT8tdMEHdmxrA0BNILADaKDy8vIGDhwYFxfn4uJy7ty5qm4mc3BwYHFbZGSk2qzz58+Xl5cLhcJK752qG25ubmzta9eurSpMefToEetSevvtt6t6n6zBzMzMwsPDiWjnzp0RERElJSUdO3bUPXzs0qULEfGj8aliIwBrdlCxwuPj45OTk/WtrVAo7NOnz/Hjx83NzQsKChISEqpdnXa6NF+vCrNb7jp16qSWXlhYeO3aNb3qxnvw4AER2dvbG9a1DACqENgBNEQFBQXBwcE3btxwcnI6f/48u/O9KmPHjiWiXbt2PXv2jE/kOG7lypVE1K9fP+1PGFTl9u3blV6VUx3uRJe73b/66iuRSPTkyZOxY8eWlZWpzc3Pz3/rrbeKioqaNGmieoGyFrHLkQcOHNi8eTPp011HROy1bLGxsZqz2GXxvLw8tfQhQ4a0aNFCqVTOnj2bXeVUo3pht9K7yqRSKbt2qbq5qlpdtaptvl4VZm8K1uzeW7ZsWbXX5avCNq+/v38NL7sDACGwA2iAiouLBw8efO3aNTs7u59++snS0jL7f6nd8jVr1ixHR8fc3NwhQ4bEx8dzHPfkyZNJkyZdunRJJBKxN8mq4svho4S8vDw+Ud8+oWr5+/uzxy9OnTrl6+u7b98+mUwml8szMjK2b9/epUuXa9euCYXCLVu26DL+iAG8vb179epVVFSUkJAgFotZD5aO+vTpIxQKExMTNZ//YBfBDx8+rBZsNWnSZO3atUR04sSJIUOGXL9+nW1SuVx+9erVefPmqT4VMW7cuKlTp547d46Pih4/fjx58uTi4uKmTZuyd21pX13Nm69XhQcOHEhEN2/e/Oijj9gNl7m5uQsXLvzmm2/0enZY1e+//04qV+0BoEb0HPcOAIwuOjpa+8fWyclJbZGrV6+yrhRSuVFdKBRu2LBBLWe1Dx5W9UZXnr7vimW2bNlS1cO5dnZ2hw4d0lyEf82o5ix2XfLWrVt8iuYAxap27NjB1jVs2DC9qs1xXFBQEBFt375dLf3WrVtsU4tEotatWzs5OfXr14+fu379en5HSKVSW1tbVme2g/hsISEh/M6yt7fnH46RSCQHDx7UfXXa6dJ8HSvMcdyECRNU68wGOHznnXcmTpxIRJ988olqZi07kcnLyzM3NxeLxeyFdQBQQ+ixAzAFr7322q1bt9577z0nJyehUNiyZcvhw4dfunRpxowZ9V21v0ydOjUxMXHx4sU9e/a0t7eXSCQtW7bs3bv3ihUrEhMTR4wYYdS180Pv6nUdlmGXMvft26eW7uPjc+bMmYEDB9rY2MhksrS0tMePH/Nz33///T///HPWrFne3t4ikaigoMDe3v6NN95YsmQJ/8IPIlq5cuWKFSuCg4OdnZ1fvHhRVlbm5ub27rvvxsfHs3GYdVxdzZuvY4WJaOfOnWvWrPHx8RGLxQqFomfPnjt37lQbBll3hw8fLisrGzJkSC0O4AfwMhNwtX3ZBQCgoTl06NCoUaMcHR3T09P1GpSOiMrLy11cXJ48eZKSktK+fXsj1dCoatJ8Y+vXr9+FCxd++eUX1jMKADWEHjsAMH3suYF33nnHgLBGIpEsWrRIqVSyh1Eao5o036h+//33Cxcu9O3bF1EdQG1Bjx0AmLi9e/eOHz9eIpEkJiYa1uVWUVHRuXPnlJSUlJSURnfFsObNN56hQ4eePHny999/79GjR33XBcBENKxfbwAAtSUrK+vVV18tKipiD7TOmTPH4LBGJBKdOHEiOTm5Ef0SrsXmGwnHcXPmzFm4cCGiOoBahB47ADBNMpmsVatWQqGwffv2kyZN+uyzz/jHPF8GL3nzAV5aCOwAapNSqWSjP4AxsO8rDGNrPBzHYfMaFb4iwNhwKRaMzuA3SJqZmdnb2xcVFRUUFNRulYzHysqKjdraKNjY2LDRj+VyeX3XRSdisVgkEmm+vqLBYu/8yMzMrO+K6EoqlSoUCoVCUd8V0Qm+IoiodevWtVgamAD8bgAAAAAwEeixAwAAgPoUFxd37NgxIho6dOgrr7xS39Vp3BDYAQAAQH2Ki4tbvHgxETk6OiKwqyFcigUAAAAwEQjsAAAAgG7fvh0eHt6uXTszMzM7O7s+ffpER0ezWbGxsSNHjnRwcGBveR4xYkRsbCy/YGBgoEAgEAgE+fn5ainsSZGwsDD27/nz58eMGdOsWbMWLVqMGzcuJyeHiHr27MneyExE06dPZznPnDkzc+ZMNn3jxg1+XT4+PgKBoEWLFo3lka+6h8AOAADgZRcZGfnKK6/s2bMnIyOjvLw8JycnOjo6ISGBiI4dO/b666///PPPz549UygUWVlZhw8ffv31148fP65WSLVj5fzjH//4+eefi4uLs7Oz9+3bN3PmTC2ZZ82axQrcsmULS7l169adO3eIKDw83MzMzODGmjYEdgAAAC+1ioqKKVOmsIGEPv30U5lMlpOTc+TIES8vL4VC8d577ykUCoFAsG/fvsLCwoiICIFAwKerllPtEH3dunXLzs7OyMhgIwEdOHCgoqLi6tWr27ZtYxk2bdrEcRzHcYMGDfLw8Bg8eDARRUREFBYWEtH+/ftZtnfffbe2t4HpQGAHAADwUouLi8vIyCAiX1/f5cuXOzg4NG/efNiwYQMGDIiLi3v69CkR9enT5+2337aysgoLC+vduzcRPXnyJD4+Xq8VLVy40MbGxsHBgT0hoVAotA90+uGHHxJRUVHR3r17iejHH38kol69enl7exvY1JcAAjsAAICX2rNnz9iEl5eX2qysrCw20a5dOz6Rf+8wv6Caqsa4dnFxYRPm5uZsQvt44wMHDuzUqRMRbdmyJTY2NikpidBdVx0EdgAAAC+1li1bsok///yzqlmPHj3iE9PS0lTn8lFaaWkpEXEcl5qaWumKxOK/BllTuxtPy815s2fPJqL4+PiPPvqIiKytrceMGVN9k15iCOwAAABear6+vm3btiWimzdvfvzxx48fP87Ozj569GhkZGT37t3ZW8uio6N3796dl5e3Z8+ey5cvE1Hr1q27detGRE5OTqycEydOENGmTZseP36sVwVsbW3ZxN27d9V6+8aPH29nZ0dEly5dIqKwsDBLS8uaNdfEIbADAAB4qYlEou3bt7OOt9WrV7dt27ZFixZvvvnmgwcPxGLx5s2bxWIxx3ETJkxo3rx5eHg4x3F8OhGNGzeOlTNp0iRra+v333/fwsJCrwr4+fmxov79739LJBJ+nBQisrCwmDp1Kp8T12GrhcAOAADgZRccHHz9+vXx48e3bdtWIpHY2Nj06tWrS5cuRBQaGhoTEzNixIgWLVqIxWIW8125ciU0NJQt27dv3+3bt3t4eJibm7u6uh44cKBHjx56rb19+/Y7duzw9PSsdBCT999/n4V9nTt31rfkl5CA47j6rgOYOO0PPWlhZmZmb29fVFRUUFBQu1UyHisrK/6HZsNnY2NjaWmZnZ3dWIb6FIvFIpFI+93WDQob0yEzM7O+K6IrqVSqUCiquvO9ocFXBBGx66SmLSEhoXv37hzHbdy4cfr06fVdnYYOPXYAAADQEB08eNDDw+PVV1/lOM7JyWnSpEn1XaNGAIEdAAAANER5eXkPHz4Ui8V9+/Y9deqUVCqt7xo1AuL6rgAAAABAJd599108LaEv9NgBAAAAmAgEdgAAAAAmApdioaGTLF1oR0REZdJHRFRifpuf9cI8SS3zXXv1xXMlf01kVfIQffWeiilZQkR0SGRF4l4k7lllVoHD3xMalSAizk49RWlbZVEVzfSrJa+8KRGRvLLRO0stQkqIiHq8ICJyK1adZ09EbYupZbGS/d+s5AWbsJIXsgmpIp/PLaYcEj3/6x9JluaqlGaZsvkTDWwCAADUAAI7AACAl1rZJ7OMUaz5iu+NUSxoh0uxAAAAACYCgR0AAACAiUBgBwAAAGAiENgBAAAAmAgEdgAAAFAXjhw5IhAI7t27x/7Ny8vz8vKq9bWsXr167dq1tV5sY4HADgAAAOpCRERESEhIREREfVfElCGwAwAAAKMrKiq6evXqhg0bfvzxR825QUFBvr6+Pj4+P/zwAxHJZDIfH58JEya4ubmNGTPm4sWLPXv2dHFxuXDhAst/8ODBHj16+Pr6hoeHl5WVEdGaNWu8vLz8/f0TEhIqLfMlgcAOAAAAjO7o0aPBwcFOTk4tWrS4ceOG2tz9+/fHxcXFxsZ+//33BQUFRHTv3r158+Y9fPgwKytr06ZNMTExERERixcvJqLU1NT169dHR0fHxcW5uLhs27bt3r17W7ZsuX79emRk5PXr16sq82WAAYoBAADA6CIiIj788EMiGj16dEREhJ+fn+rcDRs2nDlzRiwWp6enJycnOzo6uru7d+3alYi6devWqVMnoVDYvXv31NRUIoqKikpNTQ0ODiaikpKSfv36Xb58eejQoVZWVkQ0YsSISsvs1q1b3ba4fiCwAwAAAOPKyck5f/78rVu3BAJBRUWFQCBYtWoVPzcyMjImJubChQtmZmaDBg0qLS0lInNzczZXKBSyaaFQqFAoiIjjuEGDBm3evJkvYfv27RLJX2+QZBOVlvkywKVYAAAAMK6DBw+Gh4enpaWlpqamp6c7OztHR0fzc3Nzc52dnc3MzDIzM2NiYqotLTAw8NixY8nJyUSUl5eXlJQUEBBw/vz5iooKpVIZGRlpQJkmAz12AAAAYFwRERGffPIJ/+/IkSMjIiK6dOnC/g0NDd21a9fo0aOtrKx8fX2rLc3Z2XnTpk3Dhw8vLy+XSCTr1q0LDAwMCwsLDg52cHBo3769AWWaDAHHcfVdBzBxT548MWxBMzMze3t7/u3UZdJHRFRifpvP8MI8SW2Ru/bqheT+1TdPWWaG1OGpmJIlRESHRFYk7kXinlVmFTj8PaFRCSLi7NRTlLZVFlXRTL9a8sqbEhHJLSuZVWoRUkJE1OMFEZFbsfr8tsXUsljJppuVvGATVvJCNiFV5PM5xZRDoud//SPJ0lyV0ixTNn+iQQ2ohlgsFolE7Am4RsHBwYGIMjMz67siupJKpQqFgl3tavjYV0RRUVEjui/eysqqqKioFgts3bp1zQvhv2Zrl/mK741RLGiHS7EAAAAAJgKBHQAAAICJQGAHAAAAYCLw8AQAAMDLzdychKL6rgTUDgR2AAAALzXzxStJIKjvWkDtQGAHAADwUitYNNwYxTZdfMQYxYJ2uMcOAAAAwEQgsAMAAAAwEQjsAAAAAEwEAjsAAAAAE4HADgAAAIxLoVAIhUI3Nzc3N7cuXbpER0dXmu3FixcbN27UnAbdIbADAAAAozMzM0tMTExMTFy6dOmnn35aaR4EdjWH4U4AAACg7rx48aJ58+ZEdO/evffeey8qKoqIvvnmG6lUmpycnJKSEhgYGBAQUFBQwE9/9dVXBw8eXLlypUKh6NSp0/bt283Nzeu5GQ0VAjsAAAAwOrlc7uXlVVpampube+7cuUrzfPHFF+fPn2ehXnZ2Nj+dmpq6fv366Ohoc3PzL7/8ctu2bTNnzqzDujcmCOwAAADA6MzMzO7du0dEly9fHjdu3N27d3VfNioqKjU1NTg4mIhKSkr69etnrFo2fgjsAAAAoO4EBARkZ2c/fvxYLBYrlUqWKJfLpVJpVYtwHDdo0KDNmzfXVR0bMTw8AQAAAHXnxo0bCoXC0dGxVatWGRkZFRUVSqXywoULRGRlZVVYWMiyqU4HBgYeO3YsOTmZiPLy8pKSkuqr8g0feuwAAADA6MrKyjp06EBEFhYWu3fvNjMzMzMzmz59emBgYJs2bezs7IhIKpUGBQX5+PgMHDhwzZo1qtObNm0aPnx4eXm5RCJZt26dq6trPbenoUJgBwAAAMYlFos5jtNM//jjjz/++GPVlG3btlU6PWzYsGHDhhmvhiYDl2IBAAAATAQCOwAAAAATgcAOAAAAwETgHjsAAICXmtDBSSBCPGAisCMBAABealYz1tV3FaDWILADAAB4qWUuczNGsQ4LE41RLGiHe+wAAAAATAQCOwAAAAATgcAOAAAAwEQgsAMAAAAwEQjsAAAAwOhycnImTpzYunXrjh07Dh48+O7du7os9eLFi40bN9bBugxbUQOEwA4AAACMbuzYsa1bt3706NGff/65bNmyR48e6bJUpfEWx3FKpbJ214XADgAAAEAncXFxiYmJX3/9tVgsJqLu3bsPGjSIiLZu3dq5c2cfH5/FixcTkUwm8/LymjNnzvDhw0NCQkpLS5csWZKSkhIYGPj555/LZDJPT8/JkycPGTLk2bNnQUFBvr6+Pj4+P/zwgwHrysvLCwoK8vPz69q165EjR1RXRERVFd7wYRw7AAAAMK7bt293795dKPyf7qT79++vXr06NjZWKpUGBgb6+/v7+PikpKTMnj27Q4cOs2bNOnLkyBdffHH+/PmoqCgikslkSUlJx44d8/T0JKL9+/fb2tqWlJT4+/uPGDGiadOmeq3r8ePHnTt3/vbbb4mosLAwICCAX5GWwhs+9NgBAABAPbh8+fLQoUNtbGzMzc3DwsIuXbpERG5ubh06dCAiLy+v1NRUtUVcXV1ZVEdEGzZs8Pf3HzRoUHp6enJysr7r8vX1PXz48IIFC2JiYqytrdXy61V4g4LADgAAAIyrU6dOcXFx2m+MYyQSCZsQCoUKhUJtroWFBZuIjIyMiYm5cOHCxYsX/fz8SktL9V1X586dr1271rFjxwULFqxcuVJ1lpbCGz4EdgAAAGBcfn5+rq6uCxculMvlRBQTE3P8+PGAgICTJ0/m5+eXlZXt37+/b9++mgtaWVkVFhZqpufm5jo7O5uZmWVmZsbExBiwroyMDGtr6wkTJnz88cc3b95UXZGWwhs+3GMHAAAARrdv3745c+a0a9dOKpX6+Ph89913np6e8+bN69WrF8dxYWFh/fv3l8lkaktJpdKgoCAfH5+BAwd+8sknfHpoaOiuXbtGjx5tZWXl6+trwLqOHDnyr3/9SyQSSaXSLVu2qK7o66+/1lJ4AyfgOK6+6wAm7smTJ4YtaGZmZm9vX/bJLPZvmfQREZWY3+YzvDBPUlvkrr16Ibl/depTlpkhdXgqpmQJEdEhkRWJe5G4Z5VZBQ5/T2hUgog4O/UUpW2VRVU006+WvPKmRERyy0pmlVqElBAR9XhBRORWrD6/bTG1LP7rykWzkhdswkr+1+9XqSKfzymmHBI9/+sfSZbmqpRmmbL5Ew1qQDXEYrFIJCorKzNG4cbg4OBARJmZmfVdEV1JpVKFQqF5/athYl8RRUVFBQUF9V0XXVlZWRUVFdViga1bt655IZnL3GpeiCaHhYnGKBa0w6XYRunWrVuLFi1KS0vjU4qKihYtWrRq1SrVbNeuXVu0aNGzZ8+IKDIycvXq1VUVeOnSpWXLlrHplJSUy5cvG6fiAAAAYEQI7BolJycnIlIN7NLS0iQSSXFxcXZ2tmpikyZNWrRoUW2BlpaWfDYEdgAAAI0U7rFrlJo2bWpra6sW2Dk7O2dnZ6elpdnb/3Up8NGjR+3btxcIBNUW6Ofn5+fnZ6zqAgAAQJ1AYNdYOTk53blzR6lUsjEY09LSunTp0qRJk7S0NBaiPX/+vLCwkPXt8TIzM0+dOvXkyRNra+vXXnvttddeY+mXLl26fPnywoULz5w5c/XqVSJatGgRETVt2nTu3LlswfPnz6elpSkUilatWg0YMECtZAAAaKTM2vcgkaT6fNAYILBrrJycnG7evPn06dM2bdqUlpY+e/asffv2FhYWFy9eZBlYf55q+CWXy3/66Sc/Pz9/f/+7d++ePn3a1tbW3d1dtdi+ffsqlcqEhITp06cTEYsaZTLZjh07WrRoERoaamZmduPGjf/+979TpkxRu2m3oqKCPVuuSigUqg3/rTtd+hqhYTJ4p2snEAgEAoGRCjcGgUDAcVzjqnAj2sKsno2owlTbta2txx+bj99XK+VAQ4DArrHib7Nr06ZNWlqaWCxu3bp1kyZN8vPz8/LymjVrlpaWZm5u7ujoyC8il8sHDRrEIjl3d/dHjx7dvn1bLbCzsLCQSqUCgaBZs/9/MPPs2bNNmjSZOHGimZkZEbm5uW3evPnSpUthYWGqy6akpOzZs0etno6Oju+9915ttx4aOtUDD7A1jMrS0tLSsrInwRsqzZccGOzFixe1Uk7Kt0b5Fe08D8Nu1AMEdo1V8+bNmzZtmpaW9vrrr7PwTiQS2dnZWVpapqWlscCuffv2qj8NxWKxm9tfz7QLBIKWLVvm5+dXUfz/q6ioSE1N7dGjB4vq2LIeHh43btxQy9mkSRMXFxe1RFtbW4MHpxAKhfwQ5NC4GGlEEtafpMvg9Q0E+9Ro9mQ3WEKhkOO4xjIMFvuKqKioaCzjsxCRSCSqqKiordIaUcOhziCwa8ScnJwSExM5jktLS+Mjtvbt27MHKfLy8l555RXV/Kwrjv9XLBbr8qVQUlJSUVHx+++/x8bG8olKpVLzq79169YTJkzQLKGG49gZtizUr+fPn1efSX+NdBw7I20NY2iM49iVlJS8zOPYNaKX00PdQGDXiHXo0OHWrVsZGRlPnz7t168fS3RycoqNjWUvTq6V5xukUqlQKHz11VdfffXVmpcGAAAAxtNobjgFTSxuY2POtWvXjiW2b9/++fPnd+7ckUgkho1ILhKJVH+vi8XiDh06pKamNmvWzP5/1UYjAAAAoNYgsGvE7O3tLS0tHzx44OjoyN8Ax6YfPHjQtm1bkUhkQLEtW7ZUKBRXr159/PgxexVSUFBQbm7ujh074uPjk5OT79y5c/bs2bNnz9ZmYwAAwHQpFAqBQND2b/fv36/vGpksXIpt3JycnO7evat6yVUoFLZr1y4pKcng67Cenp5+fn6XLl0qKSmxtraeO3euo6Pj1KlTo6Kizp49W1paamlp2bp1a1yZBQAA3Zmbm2dkZKglsod1GtGANQ0fArvGbfTo0ZqJ4eHhmolBQUFBQUGqKaNGjeKn+/Tp06dPHzYtFApDQ0NDQ0NVM9vb26vmBwAAMJhMJuvbt6+/v//Tp0937tx5+fLllStXKhSKTp06bd++vaSkZPTo0c+fP1coFIsXL+7Zs2dgYODgwYNTU1PlcvmhQ4ekUunWrVv//e9/cxz31ltvffnll/XdoAYEgR0AAAAYXVlZWYcOHYioQ4cO+/fvT0pKOnbsq+AnaQAAIABJREFUmKenZ2pq6vr166Ojo83Nzb/88stt27ZZW1t37tz522+/JaLCwsLi4uKUlJTZs2d36NBh1qxZR44c6d69++rVq2NjY6VSaWBgoL+//4ABA+q5eQ0GAjsAAAAwOnNzczZiAxHJZDJXV1dPT08iioqKSk1NDQ4OJqKSkpJ+/fqNHTt28eLFZmZmoaGhr7/+enFxsZubGwsKvby8UlNTi4uLhw4damNjQ0RhYWGXLl1CYMdDYAcAAAB1zcLCgk1wHDdo0KDNmzerzr127dqpU6cWLFgQEhIyYcIEfrB6oVDYWMZZrC+4XREAAADqTWBg4LFjx5KTk4koLy8vKSkpIyPD2tp6woQJH3/88c2bNzUXCQgIOHnyZH5+fllZ2f79+/v27VvntW640GMHAAAA9cbZ2XnTpk3Dhw8vLy+XSCTr1q3Ly8v717/+JRKJpFLpli1bNBfx9PScN29er169OI4LCwvr379/3Ve7wUJgBwAAAMYlFotLS0v5fx0dHePj4/l/hw0bNmzYMNX8b775puq/fOb33nuPTUydOnXq1KnGqm5jhkuxAAAAACYCgR0AAACAiUBgBwAAAGAicI8dAADAS82q0zsCkVl91wJqBwI7AACAl1qLQTvruwpQaxDYAQAAvNRurRcYo9jOMzljFAva4R47AAAAABOBwA4AAADARCCwAwAAADARCOwAAAAATAQCOwAAADAuhUIhlUr5f1evXv3pp58aUI5MJuvWrZv2PHl5eV5eXmqJq1evXrt27YsXLzZu3GjAehsRBHYAAADwUkBgBwAAAGAs9+7dCwwMZNPffPPN2rVrZTKZj4/PhAkT3NzcxowZc/HixZ49e7q4uFy4cIFlKy8vf/vtt7t06fLmm28WFRUR0datWzt37uzj47N48WK18tesWePl5eXv75+QkEBES5YsSUlJCQwM/Pzzz+uukXULgR0AAAAYXVlZWYe/LVu2TEvOe/fuzZs37+HDh1lZWZs2bYqJiYmIiOCDtrt3786ePfuPP/5wd3f/7rvv7t+/v3r16suXL9+4cePMmTO//vqrajlbtmy5fv16ZGTk9evXieiLL75wdnaOior66quvjNrYeoQBigEAAMDozM3NU1NT2fTq1auzs7Oryunu7t61a1ci6tatW6dOnYRCYffu3fllnZ2de/bsSUTjx4//6KOP2rRpM3ToUBsbGyIKCwu7dOnSK6+8wnJevnx56NChVlZWRDRixAjjNa1BQY8dAAAA1A+xWKxUKtm0XC5nE+bm5mxCKBSyaaFQqFAoDChfIpGoTZg8BHYAAABQP1q1apWRkVFRUaFUKvm76LRLSUn57bffiGj37t19+vQJCAg4efJkfn5+WVnZ/v37+/bty+cMCAg4f/48KzwyMpKIrKysCgsLjdSWBgKXYgEAAKB+WFpaTp8+PTAwsE2bNnZ2dros4u3t/f3330+ZMsXd3X3v3r1WVlbz5s3r1asXx3FhYWH9+/fPy8tjOb28vMLCwoKDgx0cHNq3b09EUqk0KCjIx8dn4MCBa9asMWLD6o+A4/COXjCuJ0+eGLagmZmZvb192Sez2L9l0kdEVGJ+m8/wwjxJbZG79uqF5P7d+55lZkgdnoopWUJEdEhkReJeJO5ZZVaBw98TGpUgIk7jC0tpW2VRFc30qyWvvCkRkdyyklmlFiElREQ9XhARuRWrz29bTC2L/7og0qzkBZuwkv/101aqyOdziimHRM//+keSpbkqpVmmbP5EgxpQDbFYLBKJysrKjFG4MTg4OBBRZmZmfVdEV1KpVKFQGHbNq+6xr4iioqKCgoL6rouurKys2IOctaV169Y1L+TWekHNC9HUeSYCjHqAS7EAAAAAJgKBHQAAAICJQGAHAAAAYCLw8AQAAMBLreWrnwslVvVdC6gdCOwAAABeaol/GOU1DC18PzFGsaAdLsUCAAAAmAgEdgAAAAAmAoEdAAAAgIlAYAcAAABgIhDYAQAAAJgIBHYAAABgXAqFQiqV1nqxeXl5Xl5e+s4ybQjsAAAAoPHhOM7S0nLfvn31XZGGBYEdAAAA1BGZTObj4zNhwgQ3N7cxY8ZcvHixZ8+eLi4uFy5cYHPd3d0nTZo0evTokSNHFhUVEdHBgwd79Ojh6+sbHh5eVlYmk8k8PT0nT548ZMiQpKSksWPHspKDgoJ8fX19fHx++OGHemxgvUNgBwAAAHXn3r178+bNe/jwYVZW1qZNm2JiYiIiIhYvXszmJicnL1iw4KeffurRo8d3332Xmpq6fv366OjouLg4FxeXbdu2EVFSUtInn3xy+vRpR0dHvtj9+/fHxcXFxsZ+//33BQUF9dO2BgBvngAAAIC64+7u3rVrVyLq1q1bp06dhEJh9+7dU1NT2VwXFxcPDw8iCgkJmTNnTvv27VNTU4ODg4mopKSkX79+ROTq6urp6alW7IYNG86cOSMWi9PT05OTkzt06FCHbWpAENgBAABA3TE3N2cTQqGQTQuFQoVCwRLLy8tVJziOGzRo0ObNm/nFZTKZhYWFWpmRkZExMTEXLlwwMzMbNGhQaWmpsVvRYOFSLAAAADQUaWlpV65cIaL//ve/ffr0CQwMPHbsWHJyMhHl5eUlJSVVulRubq6zs7OZmVlmZmZMTEyd1riBQWAHAAAADYW3t/f69eu9vb2Tk5PnzJnj7Oy8adOm4cOHe3t79+3bNz09vdKlQkNDU1NTR48evWDBAl9f3zquc4OCS7EAAABgXGKxmF0edXR0jI+PZ4mrV6/m52ZkZLBpiUQSERGhuuywYcOGDRummsKX0KxZs3v37hFRkyZNTp06pbZSNutlgx47AAAAABOBwA4AAAAaBNX+PDAMAjsAAAAAE4F77AAAAF5qLq//WyAyq+9aQO1AYAcAAPBSi7v+gTGKDfGaaoxiQTtcigUAAAAwEQjsAAAAAEwEAjsAAAAAE4F77KChK//XsoKCgkpnmWukdDdaNb7XLZuVlVVRUZHRalEtucpfNbmaSTY2NpaWltnZ2XL5/yyi/Hui4P8nmqrMb0rUoUbVBAAA40CPHQAAAICJQGAHAAAAxqVQKKRSKZv+5ZdfXF1dk5KS6rdKpgqBHQAAANSRCxcuTJ8+/fTp066urlqycRynVCq1ZICqILADAACAunDlypXJkycfP37cw8ODpRw8eLBHjx6+vr7h4eFlZWUymczT03Py5MlDhgz5448/vLy85syZM3z48JCQkNLS0vqtfGOBwA4AAACMrry8PDQ09NChQ506dWIpqamp69evj46OjouLc3Fx2bZtGxElJSV98sknp0+fdnR0TElJmT179uHDh11dXY8cOVKv1W808FQsAAAAGJ1YLO7du/eOHTt8fX1ZSlRUVGpqanBwMBGVlJT069ePiFxdXT09PVkGNze3Dh06EJGXl1dqamq9VLvRQWAHAAAARicQCPbv3x8UFLRkyZIvvviCiDiOGzRo0ObNm/k8MpnMwsKC/1cikbAJoVCoUCjquMKNFC7FAgAAQF2wsLA4duzYgQMHtm7dSkSBgYHHjh1LTk4mory8PDwnWyvQYwcAAAB1pHnz5mfOnOndu7e9vf2IESM2bdo0fPjw8vJyiUSybt06Ly+v+q5goyfgOK6+6wAm7smTJ4YtaGZmZm9vX1RUVNWbJxqg+n7zhH6qevNEgyUWi0UiUVlZWX1XRFcODg5ElJmZWd8V0ZVUKlUoFI3lmhe+IoiodevWNS/k5C5BzQvRFDIRAUY9wKVYAAAAABOBwA4AAADAROAeO2joJEsXlrX89117IqIHlv8za679v0jgQORJSluqaEZEVP73u+rlliG5FmOzqF9akaPypyefD6rbWgMAANQDBHYAAAAvNb83DoslVvVdC6gdCOwAAABear9eHm6MYsePxcMT9QD32AEAAACYCAR2AAAAACYCgR0AAACAiUBgBwAAAGAiENgBAAAAmAgEdgAAAGBcCoVCKpWy6V9++cXV1TUpKSkvLw8vh611GO4EAAAA6siFCxemT59+5swZV1fXioqKffv21XeNTA167AAAAKAuXLlyZfLkycePH/fw8CCiwsLCsWPHEpFMJvPy8pozZ87w4cNDQkJKS0uJaPXq1R07dgwICAgPD1+7dm1eXl5QUJCfn1/Xrl2PHDlSzy1pwNBjBwAAAEZXXl4eGhr666+/durUSXNuSkrK7NmzO3ToMGvWrCNHjnTp0mXXrl03btwQCAQ9e/b08/M7evRo586dv/32WyIqLCys8+o3GgjsAP6PvbuPa/I8+8d/JCThKYBFBLRUCESIEURDByq0oFDIlweV1gJzo1vt3Dq7lVFnHf22W6373b91N3dFJz7A1qH93UILe4ncFRGdUKFUxYBa5YWdQBwWA9oSBAmBkOv3x7U7SwGRpxAIn/dfV87zvI7ruKJND8/zegAAAJPj8XjPPPPMhx9+KJPJhveKxWIvLy8ikkgkSqWyp6cnNjbWzs6OiDZs2EBEMpls165dAoEgISFhzZo105v7bIKlWAAAADA5DodTUFBw9erV9957b3gvn89nN7hcrk6nGz4gICDg0qVLS5cuzcjI+OMf/2jaXGczFHYAAAAwHWxtbUtKSgoLC3NyckYfuWbNmtLS0t7eXo1Gc+LECSK6c+eOg4PDSy+9tGPHjvr6+mnJd1bCUiwAAABMkyeeeKKsrOyZZ55xcXFZt27do4ZJpdLU1NQVK1a4uLgsXbrU0dHx8uXL77zzjpWVlY2NzeHDh6cz59kFhR0AAACYFo/HY+91JaInn3yyubmZ3W5sbCQid3f3K1eusC2vvvoqu/GLX/zizTff1Gq1UVFR7M2wGzdunPbEZx8UdgAAADDjpKWl1dfX9/X1bd68OTAw0NzpzBoo7AAAAGDGyc3NNXcKsxJungAAAACwEJixAwAAmNNiomusuAJzZwFTA4UdAADAnPb/VZrkeb/pmxhThIXRYSkWAAAAwEKgsAMAAACwECjsAAAAACwECjsAAAAAC4HCDgAAAMBCoLADAAAA09LpdDY2NpOJUFxczOFw2FeQTZharZZIJJOJMPOhsAMAAICZLj8/Py4uLj8/39yJzHQo7AAAAMAMcnJyAgIC/P39d+3aRURqtTo6OjooKCgwMLC4uNh4ZE9Pz4ULF7Kzsz/++GO2RaVSSSSS9PT0xMTEuLi4vr4+IsrMzFy6dGlYWFhqampWVhYRFRUVBQcHy2Sy1NRUrVZrHHNI1yhHn11Q2AEAAMB0u3nzZmZmZnV1tUKhKCsrO3v27IkTJwICAhQKxdWrVyMjI40HnzhxIiYmxtPTc8GCBQqFgm1saWlJS0s7fvy4j49PcXFxQ0PDkSNHFArFmTNnrl27RkRKpXL//v1VVVV1dXXe3t7GL58d3jXK0WcXvHkCAAAAplt1dXV8fLyTkxMRpaSknD9//sUXX9y1a5dAIEhISFiz5jsvw8jPz//Vr35FRElJSfn5+UFBQUQkFou9vLyISCKRKJXKnp6e2NhYOzs7ItqwYQMRVVZWKpXKmJgYItJoNOvWrTMEHN61efPmRx19dkFhBwAAAOYXEBBw6dKl0tLSjIyMuLi4N998k23/9ttvz5079+WXX3I4nMHBQQ6H85//+Z9ExOfz2QFcLlen0w0PyDCMXC4/dOiQoUWtVj+qi4hGPPqsg6VYAAAAmG5hYWEnT57s6urSarUFBQXh4eF37txxcHB46aWXduzYUV9fbxhZVFSUmpp6+/ZtpVLZ2toqEomqqqqGB1yzZk1paWlvb69Gozlx4gQRRURElJSUNDc3E5FarW5qajIMHt71qKPPOpixAwAAAJPTarUeHh7sdlpa2o4dO7Zv37569WqGYVJSUiIjI4uLi9955x0rKysbG5vDhw8bdszPz9+5c6fh4wsvvJCfn/+73/1uSHypVJqamrpixQoXF5elS5c6OjqKRKKDBw8mJiYODAzw+fy9e/fOnz+fHTy8S61Wj3j0WYfDMIy5cwAL19bWNrEdBQKBi4uLdufrba5/anAhIvrK/jsD3nB5hzhuRH6kd6bBeUREA47/6uu3j+u03XyP1t3ucdd/0va2fOInMB5CobCnp2d6jjV5Tk5O9vb29+/f7+/vN3cuY8Lj8aysrIbc2jaTubm5EVF7e7u5ExkrGxsbnU434qrWDMT+RPT09Dx48MDcuYzVlP9ELFq0aPJB9hRxJh9kuPRN011g9Pb22tnZabXaqKio/fv3BwYGTnMCMwFm7AAAAMASpKWl1dfX9/X1bd68eW5WdYTCDgAAACyD8QNN5izcPAEAAABgITBjBwAAMKf9JPaffJ7Q3FnA1EBhBwAAMKftLl9sirB/3Ii7M80AS7EAAAAAFgKFHQAAAICFQGEHAAAAYCFQ2AEAAABYCBR2AAAAYFo6nY7D4bz88svsR41G4+jomJKS8tgdMzMzs7KyTJydRUFhBwAAACbn6OhYX1/PvsDw008/FYlEUxKWYRi9Xj8loSwDCjsAAAAwOQ6HEx0dffr0aSL6+OOPk5OTDV3R0dEymczf3z8vL49t2bNnj0QiCQ0NvXr1KttSVFQUHBwsk8lSU1O1Wq1KpfLz89uyZUtsbGxHR8fwCHMWCjsAAACYDsnJyQUFBd3d3a2trVKp1NBeUFBQV1dXW1u7b9++Bw8eNDY2Hj58+PLly+Xl5ZcvXyYipVK5f//+qqqquro6b29v9tVhTU1NO3fuPHXqlLu7+5AIZjvDGQAPKAYAAIDpEBQU1NDQkJ+fv379euP27OzssrIyHo/X2tra3Nx8+fLl+Ph4oVBIRM8//zwRVVZWKpXKmJgYItJoNOvWrSMiHx8fPz+/ESOsWLFius9txkBhBwAAANMkNjY2IyPj4sWL169fZ1vKy8tramoqKioEAoFcLu/r6yMiPp/P9rIbDMPI5fJDhw4Z4qhUKltb21EizFlYigUAAIBpsnXr1t27d4vFYkNLZ2enSCQSCATt7e01NTVEFBYWdu7cucHBQb1eX15eTkQRERElJSXNzc1EpFarm5qajGMOjzCXYcYOAAAApomXl9e2bduMWxISEo4cOZKUlCQUCmUyGRFJJJKUlJSYmBg3N7fFixcTkUgkOnjwYGJi4sDAAJ/P37t3r0QiGSXCXIbCbnZraWn54osv7ty5o9VqHR0d/fz8nnnmGXt7e+MBX3/9dVhYmKGlvLz82rVrv/71r82RLwAAzEU8Hk+tVhu3bNy4cePGjURkZ2dXWlo6ZHx6enp6erpxy4YNGzZs2GDccuXKFXZjxAhzFgq7WeyLL744ffq0h4fHunXr7Ozs2tvbL126dOPGjR/96EcuLi7smJaWlkuXLhkXdgAAAGCpcI3dbNXa2lpeXr5s2bJXXnnl6aeflkqla9eu3bp168DAQGFhIcMw05CDTqebhqMAAADAGGHGbrb6/PPPrays4uLiOByOodHZ2XnVqlWVlZX/+Mc/fH19y8rKLly4QETvvvsuETk6Or7xxhvsyPb29tLS0ra2NgcHh5CQkJCQEEOQ9vb2c+fO3b59W6fTLVy4MCoqytPTk+1il3Gff/75v//97+3t7TKZLDY2dtpOGQAAAEaHwm5WYhimpaXFy8vLzs5uSNeyZcsqKytbWlp8fX3Dw8P1ev3Vq1d//vOfExGX+68J2v7+/k8++SQoKCg0NLShoeHUqVPOzs5LliwhIpVK9eGHHy5YsCAhIUEgECgUiqNHj77yyiuLFi1i9+3r6ystLWWvacWMHQAAwIyCwm5W0mq1Wq123rx5w7vYRva527a2tjY2NhwOZ8jI/v5+uVzOVnJLliz55z//ef36dfbjmTNn7OzsfvSjHwkEAiISi8WHDh06f/684VXNOp0uLi5uxHf8dXZ23rhxY0ijvb29j4/PxE7TyspqYjsOxz7ochpYW1tPz4GmBPuAKFtbW/aPe+bjcrlcLtfwgKuZj8vlMgwzbX/9Jo/H4+n1+tny5k32J4LP58+ib3hqfyIGBgamJM7/u15rxZ0dPwLwWCjs5iIej2d4hhCHw3F1de3q6iKiwcFBpVIZHBxs+N88h8Px9fVVKBSGfblcrpeX14hhv/nmm7Nnzw5pdHd3X7ly5ZSfwng5OjpO27FmV21HRMa3UcOU43A40/nXbw6ytraeXf/RTWG2vb29UxIn+ZRJvsCiuOm42huGQGE3K7E/ZENuHWexjaP/j4SdxjN85PF47KKqRqMZHBy8ePFibW2toVev1xvfimFnZ2e8rzF3d/cXX3xx+LE6Ozsff0oj4fF4Dg4OE9t3iAnnMF42Njaz6KHndnZ21tbW3d3ds2VVnZ2xmy3ZEpGTkxMRsf9wmhX4fD77VFhzJzIm7E9EX1+fRqMxdy5jNbU/ETqdbvgFOTDHobCblTgcjkgkampq0mg0hneqsBoaGohoxKXSx7KxseFyud/73ve+973vTWB3oVC4bNmy4e1tbW0TiEZEAoFgqgq7afvdt7KymkX/jxEIBNbW1lqttr+/39y5jAmPx7OystJqteZOZKzYf2LNor8SDMPodLrZUjqzPxE6nW4WfcOz6ycCZiM87mS2WrNmjU6nO3nypPF0Wmdn5xdffOHm5sZeMEdEVlZWY/+N5vF4Xl5eSqVy3rx5Lt819ScAAAAAUw0zdrPV4sWLo6Kizpw509XVtWLFCvYBxRcvXuTxeJs2bTKslrq6uup0ugsXLjz11FM8Hs/NzW30sNHR0R9++OGHH34YHBzs6Oio0WjY+bbnnnvO5KcEAAAAk4PCbhYLDQ1duHDhF198cfbs2f7+fkdHx+XLlz/zzDPGN4j5+fkFBQWdP39eo9E4ODgYnmP3KO7u7j/96U8rKyvPnDnT19dnb2+/aNGiia3MAgAAsHQ6HZ/P//GPf/zXv/6ViDQajZubW2xsbEFBweg7qtXqVatWNTY2TkualgCF3ezm7e3t7e09ygAul5uQkJCQkGBoiY6Ojo6ONh6zadMm448uLi5DWkbZFwAAYCwcHR3r6+v7+/sFAsGnn346lmvBGYaxt7c/duzYNKRnMXCNHQAAAJgch8OJjo4+ffo0EX388cfJycmGrujoaJlM5u/vn5eXR0QqlcrPz2/Lli2xsbFNTU2bN29mGyUSSXp6emJiYlxc3Cx6/sA0Q2EHAAAA0yE5ObmgoKC7u7u1tVUqlRraCwoK6urqamtr9+3bxz5gv6mpaefOnadOnXJ3dzcMa2lpSUtLO378uI+PT3FxsRlOYDbAUiwAAABMh6CgoIaGhvz8/PXr1xu3Z2dnl5WV8Xi81tbW5uZmd3d3Hx8fPz+/IbuLxWL2CfkSiUSpVE5X1rMMCjsAAACYJrGxsRkZGRcvXrx+/TrbUl5eXlNTU1FRIRAI5HI5u8Y65BGtLMPrBGfXg8qnGQo7AAAAmCZbt2598sknxWKxobDr7OwUiUQCgaC9vb2mpsa86VkAFHYAAAAwTby8vLZt22bckpCQcOTIkaSkJKFQKJPJzJWYxUBhBwAAAKbF4/GGvN9848aNGzduJCI7O7vS0tIh469cucJuzJs3j32Inbu7u6Hx1VdfNXnGsxbuigUAAACwECjsAAAAACwECjsAAAAAC4Fr7AAAAOa0edZu1lb25s4CpgYKOwAAgDktN+ouhzjmzgKmBgo7AACAOY1b5miKsIy82xRhYXS4xg4AAADAQqCwAwAAALAQKOwAAAAALAQKOwAAAAALgcIOAAAATEun03E4nJdffpn9qNFoHB0dU1JSpvxAmZmZWVlZUx52FkFhBwAAACbn6OhYX1/f399PRJ9++qlIJHrsLgzD6PV606dmUVDYAQAAgMlxOJzo6OjTp08T0ccff5ycnGzoKioqCg4OlslkqampWq1WpVL5+flt2bIlNja2o6OjsLAwMDAwMDBw06ZNI44noj179kgkktDQ0KtXr5rl7GYOFHYAAAAwHZKTkwsKCrq7u1tbW6VSKduoVCr3799fVVVVV1fn7e2dm5tLRE1NTTt37jx16lRvb+/OnTtPnz599erVnJycEcc3NjYePnz48uXL5eXlly9fNucZzgB4QDEAAABMh6CgoIaGhvz8/PXr1xsaKysrlUplTEwMEWk0mnXr1hGRj4+Pn58fEX322WcJCQnu7u5E5OzsPOL46urq+Ph4oVBIRM8//7w5zmwGQWEHAAAA0yQ2NjYjI+PixYvXr19nWxiGkcvlhw4dMoxRqVS2traPijB8/J///Gc+n89uGzbmLCzFAgAAwDTZunXr7t27xWKxoSUiIqKkpKS5uZmI1Gp1U1OT8fjw8PCSkpK7d+8S0b1790YcHxYWdu7cucHBQb1eX15ePq3nM/Ngxg4AAACmiZeX17Zt24xbRCLRwYMHExMTBwYG+Hz+3r17JRKJodfb2/v9999/7rnnGIaRSqWFhYXDx0dERKSkpMTExLi5uS1evHjaz2lm4TAMY+4cwMK1tbVNbEeBQODi4qLd+Xqb658aXIiIvrL/zoA3XN4hjhuRH+mdaXAeEdHA/77Kut8+rtN28z1ad7vHXf9J29vyiZ/AeAiFwp6enuk51uQ5OTnZ29vfv3+ffQDBzMfj8aysrNib4GYFNzc3Impvbzd3ImNlY2Oj0+l0Op25ExkT9ieip6fnwYMH5s5lrKb8J2LRokWTD8Ipc5h8kOEYebcpwsLosBQLAAAAYCFQ2AEAAABYCBR2AAAAABYCN08AAADMaQEOywQcgbmzgKmBwg4AAGBOuxZ6wdwpwJRBYQcAADCncc5GmyIsEzXXHylnFrjGDgAAAMBCoLADAAAAsBAo7AAAAAAsBAo7AAAAAAuBwg4AAABMS6fTcTicl19+mf2o0WgcHR1TUlLGuHtvb++BAwdMlp1FQWEHAAAAJufo6FhfX8++mfrTTz8ViURj33eMhR3DMHq9fuIpWgQUdgAAAGByHA4nOjr69OnTRPTxxx8nJye8WQ6OAAAgAElEQVQbunJycgICAvz9/Xft2kVEKpVKIpGkp6cnJibGxcX19fW99957LS0tERERb7/9NhEVFRUFBwfLZLLU1FStVqtSqfz8/LZs2RIbG9vR0TGk97e//W1WVhZ7oP/7f//v3r17zXH20weFHQAAAEyH5OTkgoKC7u7u1tZWqVTKNt68eTMzM7O6ulqhUJSVlZ09e5aIWlpa0tLSjh8/7uPjU1xc/Nvf/lYkElVWVv7+979XKpX79++vqqqqq6vz9vbOzc0loqampp07d546daqvr29I75YtW44ePUpEer2+oKDghz/8oRm/gWmABxQDAADAdAgKCmpoaMjPz1+/fr2hsbq6Oj4+3snJiYhSUlLOnz/v7+8vFou9vLyISCKRKJVK4yCVlZVKpTImJoaINBrNunXriMjHx8fPz2/EXi8vr/nz59fX17e3t69cuXL+/PnTdr5mgcIOAAAApklsbGxGRsbFixevX78+yjA+n89ucLlcnU5n3MUwjFwuP3TokKFFpVLZ2to+qpeIfvKTn+Tl5alUqi1btkzNacxgWIoFAACAabJ169bdu3eLxWJDS1hY2MmTJ7u6urRabUFBQXh4+PC9hEJhd3c3ux0REVFSUtLc3ExEarW6qanJeOSIvYmJiWVlZbW1texMnmVDYQcAAADTxMvLa9u2bcYtfn5+27dvX7169YoVK+RyeWRk5PC9bGxsoqOj/f3909PTRSLRwYMHExMTpVJpeHh4a2ur8cgRewUCwdq1a5OSkqysrEx6djMBh2EYc+cAFq6trW1iOwoEAhcXF+3O19tc/9TgQkT0lf13Brzh8g5x3Ij8SO9Mg/OIiAYc/9XXbx/Xabv5Hq273eOu/6TtbfnET2A8hEJhT0/P9Bxr8pycnOzt7e/fv88+gGDm4/F4VlZWWq3W3ImMlZubGxG1t7ebO5GxsrGx0el0Q1a+Ziz2J6Knp+fBgwfmzmWspvwnYtGiRZMPwjkbPfkgwzFR5aYIOwF6vV4mkxUWFi5ZssTcuZgcZuwAAADAYjU0NIjF4sjIyLlQ1RFungAAAAALJpVK2Uvu5gjM2AEAAABYCMzYAQAAzGn/Z/73hDxbc2cBUwOFHQAAwJxWuvL/MXcKMGVQ2AEAAMxpnHNbTRGWWZdrirAwOlxjBwAAAGAhUNgBAAAAWAgsxcJMN/DOf1g/+M1KIiJa+d2u5BGG9xttdBKRnqiNpunpxAAAAOaFGTsAAAAAC4HCDgAAAExLp9NxOJzt27ezHzMzM999912zZmSxUNgBAACAydna2hYVFd2/f9/ciVg4FHYAAABgcgKB4Cc/+cmePXuMG6Ojo2Uymb+/f15eHhGpVKolS5a8/PLLSUlJL7zwQk9Pz/AxMDoUdgAAADAdfvnLX3700UddXV2GloKCgrq6utra2n379j148ICImpubMzIyPvnkk+Dg4A8++GDEMTAKFHYAAAAwHebNm/fDH/5w3759hpbs7OzQ0FC5XN7a2trc3ExE3t7evr6+RBQXF1dVVTXiGBgFHncCAAAA0+SNN94ICgrasmULEZWXl9fU1FRUVAgEArlc3tfXR0QDAwPsSHZjxDEwCszYAQAAwDRxcXF54YUX/vKXvxBRZ2enSCQSCATt7e01NTXsgNu3b3/++edEdPTo0WeffXbEMTAKzNgBAADA9NmxY8eBAweIKCEh4ciRI0lJSUKhUCaTsb1SqXT//v1bt25dsmTJ7t27uVzu8DEwChR2AAAAYFo8Hk+tVrPbCxcuNKyolpaWGg9TqVR8Pj8/P9+4ccgYGB2WYgEAAAAsBAo7AAAAmBHc3d2vXLli7ixmNyzFwkzH3/3W/JHa1U7fmZzvtW4ab+QGl3En08kfofGeYNxxgIjuGv38NPOJiP5mJRw6iLd6hD25nsRdaLK8vovjRpzx/0UhImY+EdE/Gv7doneempQmYHDemIea+JbDhyP/wcV12g5vDO7997b44ShBhUTD/uaMjcdD8r3f868o/d3GXTa6riGDefQtEZHVNyPH4t8b40Ed/3dDL2gnItWbPxrjjgBjgcIOAABgTvvxwjUCDuoBC4E/SAAAgDktr73FFGEPS0wRFR4D19gBAAAAWAgUdgAAAAAWAoUdAAAAgIVAYQcAAABgIVDYAQAAAFgIFHYAAABgcvfu3UtOTn7qqaeeeuqpn/3sZ729vY/fB8YPhR0AAACY3Pe///2AgIB//vOfLS0tDMOkpaUZ9zIMo9frzZWbJUFhBwAAAKalUCiUSuVbb73F4XB4PN4HH3xQVFTU0dGhUqn8/Py2bNkSGxvb0dERHR0tk8n8/f3z8vKISKVSSSSS9PT0xMTEuLi4vr4+IsrMzFy6dGlYWFhqampWVhYRFRUVBQcHy2Sy1NRUrVZr3jM1OxR2AAAAYFo3btxYuXIll/uvqkMoFIrF4sbGRiJqamrauXPnqVOn3N3dCwoK6urqamtr9+3b9+DBAyJqaWlJS0s7fvy4j49PcXFxQ0PDkSNHFArFmTNnrl27RkRKpXL//v1VVVV1dXXe3t65ublmPM2ZAG+eAAAAgOnGMAy74ePj4+fnx25nZ2eXlZXxeLzW1tbm5mZ3d3exWOzl5UVEEolEqVT29PTExsba2dkR0YYNG4iosrJSqVTGxMQQkUajWbdunVlOZ+ZAYQcAAACmJZVKd+3apdfr2Um77u7uW7duSSQSvV5va2vLjikvL6+pqamoqBAIBHK5nF145fP5bC+Xy9XpdMMjMwwjl8sPHTo0Xacy02EpFgAAAEzr6aef9vT0fO+99/R6/cDAQHp6+gsvvODq6mo8prOzUyQSCQSC9vb2mpqaEeOsWbOmtLS0t7dXo9GcOHGCiCIiIkpKSpqbm4lIrVY3NTVNw+nMZCjsAAAAwOTy8/Nv3Ljh4eHh6enJ4XD27ds3ZEBCQoJSqUxKSsrIyJDJZCMGkUqlqampK1asiIyMXLp0qaOjo0gkOnjwYGJiolQqDQ8Pb21tNf2pzGhYigUAAACTc3NzKywsHNLo7u5+5coVdtvOzq60tHTIAEPvq6++ym784he/ePPNN7VabVRUVFBQEBFt2LCBvd4OCIUdAAAAzCJpaWn19fV9fX2bN28ODAw0dzozDgo7AAAAmDXwQJPR4Ro7AAAAAAuBGTsAAIA57T3ROgHHytxZwNRAYQcAADCn/VZ5wxRhdy5+xhRhYXRYigUAAACwECjsAAAAACwECjsAAAAAC4HCDgAAAMBCoLADAAAAsBAo7AAAAMC0dDodl8sVi8Xe3t4bNmzo7OxUq9USicTceVkgFHYAAABgcgKB4NatW01NTQ4ODnv27JmqsAzD6PX6qYpmAVDYAQAAwDThcDiRkZG3bt0yboyOjpbJZP7+/nl5eWxLYWFhYGBgYGDgpk2b2JaioqLg4GCZTJaamqrValUqlZ+f35YtW2JjYzs6Oqb5LGYyPKAYAAAApkl/f////M//yGQy48aCggJnZ2eNRhMaGvr888/fv39/586dNTU17u7u3377LREplcr9+/dXVVVZW1v/7ne/y83N3bRpU1NTU0lJiZ+fn5lOZYZCYQcAAAAmp9Vqvby8uFxuWFjYr371K51OZ+jKzs4uKyvj8Xitra3Nzc319fUJCQnu7u5E5OzsTESVlZVKpTImJoaINBrNunXriMjHxwdV3XAo7AAAAMDkrK2tlUql4aNarWY3ysvLa2pqKioqBAKBXC7v6+sbvi/DMHK5/NChQ4YWlUpla2tr4pRnJVxjBwAAAGbT2dkpEokEAkF7e3tNTQ0RhYeHl5SU3L17l4ju3btHRBERESUlJc3NzUSkVqubmprMm/NMhhk7AAAAMJuEhIQjR44kJSUJhUL22jtvb+/333//ueeeYxhGKpUWFhaKRKKDBw8mJiYODAzw+fy9e/fiUSmPgsIOAAAATIvH4w1ZY503b15jYyMR2dnZlZaWDhmflJSUlJRk3LJhw4YNGzYYt1y5csU0yc5uWIoFAAAAsBAo7AAAAAAsBAo7AAAAAAuBa+wAAADmtP1LnuVzMNFjIVDYAQAAzGm/uNVgirA/XbjMFGFhdKjQAQAAACwECjsAAAAAC4HCDgAAAMBCoLADAAAAsBAo7AAAAAAsBAo7AAAAMC2dTsflcsVisY+Pz/r16zs7O4lIrVZPyStfR4yTmZmZlZU1+eCzDgo7AAAAMDmBQHDr1q1bt245Ozvv2bOHiBwcHI4dOzbeOAzD6PV645aJxbFUKOwAAABgmnA4nPDw8H/84x9E1N3dvXnzZiL6zW9+k52dzQ549913MzMziaioqCg4OFgmk6Wmpmq1WpVK5efnt2XLltjY2K+++io6OjooKCgwMLC4uNgQh4j27NkjkUhCQ0OvXr3KtgyJY4Zznl4o7AAAAGCaDA4Onjp1yt/f37gxOTn5k08+YbcLCwuTk5OVSuX+/furqqrq6uq8vb1zc3OJqKmpaefOnadOnbp48WJAQIBCobh69WpkZKQhTmNj4+HDhy9fvlxeXn758mUiGjGOZcObJwAAAMDk+vv7JRJJe3u7p6fnRx99ZNy1cuXKjo6Otra2e/fuPfHEE0899VReXp5SqYyJiSEijUazbt06IvLx8fHz8yMimUy2a9cugUCQkJCwZs0atVrNxqmuro6PjxcKhUT0/PPPE1FlZeXwOJYNhR0AAACYnEAgaGxs1Gg0CQkJRUVFP/jBD4x7X3zxxaKiIpVKlZKSQkQMw8jl8kOHDhkGqFQqW1tbdjsgIODSpUulpaUZGRlxcXE//elPDcP4fL7xxvA4Fg9LsQAAADBNbG1t//SnP7399tv9/f3G7cnJyQUFBUVFRS+++CIRRURElJSUNDc3E5FarW5qajIefOfOHQcHh5deemnHjh319fWG9rCwsHPnzg0ODur1+vLy8sfGsUgo7Mzm0qVL77777v37940bHzx48O6771ZXV7Mfz58//x//8R/myM78RwcAAIu0dOnSVatWffjhh8aNy5Yt6+7uXrx4sZubGxGJRKKDBw8mJiZKpdLw8PDW1lbjwZcvX3766adXrFjx+9///je/+Y2hXSKRpKSkxMTEpKamLl68+LFxLBKWYmc0e3v7BQsWzM2jAwCAxeDxeH19fYaP+fn57EZjY6Oh8csvvzTeZcOGDRs2bDBuuXLlCruxcePGjRs3GncZ4qSnp6enp48ex7KhsJvRgoKCgoKC5ubRAQAAYLxQ2M1o58+fr66ufuutt4hIrVafPXtWqVRqNBo7O7snn3wyMTHR2tq6vLz82rVrGzZs+Pvf/37//n17e/tVq1atXr2ajdDR0XH+/PnW1taHDx86ODgsWbJk3bp1NjY2bC+774svvnjmzBmVSuXo6BgSEhISEjL86ETU3t5eUVFx+/btgYEBJyenFStWPPPMM9P+lQAAAMAjobAzM61Wazw7PcqzEz/55JPBwcHY2FgHB4fu7u6mpqbBwUG2S6PRnD59euPGjS4uLg0NDSdPnuTz+U8//TQRqdXqJ554YtmyZba2tmq1uqqqSqVSbdmyxRC2r6/v1KlTcrncxcXl+vXrp06dcnZ2XrJkyZCjt7W1/fWvf3V2dpbL5Y6Ojt988017e/tUfhEAAAAwaSjszGyMD0scHBy8e/fu+vXrpVIp22LYYHvlcrmHhwcRyWSyu3fvVlZWymQyLpfr6+vr6+trGOnu7n7o0KH29nb26lQi0ul0CQkJTz75JBGtWrXqypUr169fH17YnTlzxsbG5pVXXhEIBEQkEomGJ3n79u3i4uIhjQsWLEhOTh7LOQ7H4XAmtiMAwGxh+DWeAI1GMyU5nPR/VsDBzZQWAoWdmSUmJjo5ORk+Pnz4sLCwcPgwKysrNze3zz77rL+/XyQSubq6GvdyOBxvb2/DRx8fn9raWrVa7ezsPDg4eOnSpWvXrnV1dRnuLf/mm28MPyV8Pp+t6lhPPPFEV1fXkKPrdLrbt2+vWrWKreoeRa/XG88+srRaLeozAIBHmcwv5FT9usZ9eWNK4gzBhE+8ZoUJQ2FnZk8++aSLi4vh44MHDx41cvPmzZWVlVVVVadOnXJ0dFy9erXhQjpra2su99//2LKzs2NDOTs7nzlzRqFQREZGPvXUUwKBQKPRfPjhhzqdzjDY2tra+ChWVlbGvay+vj69Xu/o6Dj6uYhEop07dw5vb2trG33HRxEIBMZfDgCA5VGpVJPZ3d7efqoyAcuAwm7WcHR0XL9+PRF1dHTU19efPn3a0dFx2bJlRNTX1zcwMGB43DZbHbJ12LVr10JCQlatWsV2TazGsrGx4XK5oxSdAAAAMBNgTX32cXV1jY6O5vF4HR0dhsbr168btr/88ksHB4d58+YxDDMwMGC4B5aIbtyYyHw7j8fz9PT88ssvhzwoHAAAAGYUzNjNDmq1+m9/+1tAQMD8+fM5HM6NGzcGBwcN19UJBIKKigqtVsveFXvz5s34+Hh2cdbHx0ehUPj5+Tk5OV2/fv3q1asTSyA6OvrDDz/Mzc1ds2aNo6NjZ2dne3t7XFzclJ0hAAAATBoKu9nBxsbGxcXl0qVLXV1dVlZWCxYsSElJ8fT0ZHsFAkFSUtKpU6fa29vt7Oyio6PZZ50QUXx8/KlTp/7yl78wDLN48eKkpKQhb3EZo4ULF77yyisVFRWnT5/W6XTz5s1bsWLFlJ0eAAAATAUOwzDmzgEmhX3I8K9//WtzJ/JIk7x5Qrvz9RF71U6lxh97rcf9aueG8d+Y0ckfofHeaPcKwyPdNfp3ZTOfiOhvVsKhg3irR9iT60nchSbL67s4bsSZ0B08zPyhLXrnyaczQYPzzHboIR6O/AcX12k7vDG499/b4ocmScfjIfne72G3hf3dxl02uqHPB+DRt0REVt+MHIt/b7xH1wvaiUj15o/Gu6OxRYsWTWZ3Fuezs5MPMhwTHmX8MTMzMycnh4hsbGwOHDgQFhZmioOOS29vb15e3rZt28ydyFTCNXYAAABgWnV1dbm5uQqF4quvvjp79uzixYvHtTvDMHq9fsqz6u3tPXDgwJSHNS8UdgAAAGBaKpVqwYIFQqGQiFxdXdnCLjo6WiaT+fv75+XlscMKCwsDAwMDAwM3bdrE7uXn57dly5bY2NiOjo4h41Uqlb+//0svvSQWi5OTkz/77LNVq1Z5e3tXVFSw0YqKioKDg2UyWWpqqlarValUEokkPT09MTExLi6ur6/vvffea2lpiYiIePvtt4koJycnICDA399/165dZviOpgiWYsHksBQLI8JS7PTBUuwjYCmWNQ1LsRqNJiIiQq1WR0VFbdq0ae3atUT07bffOjs7azSa0NDQysrK+/fvR0VF1dTUuLu7s10qlcrDw+PGjRt+fn7Dx/f29np4eCgUiuXLl0dGRrq6uh47dqy2tnbnzp2VlZVKpfLHP/7x6dOnra2tf/e73y1YsGDTpk2enp43b9708vJ6/fXX16xZExUVFRERwT5W4ubNmwkJCbW1tTY2NhEREbt3746KinrUqc1kuHkCAAAATMvW1vbChQuXLl06d+5camrqW2+9tW3btuzs7LKyMh6P19ra2tzcXF9fn5CQ4O7uTkTOzv/6h5CPjw9b1RHRkPHu7u5LliwJDAwkohUrVixbtozL5a5cuVKpVBIRW9vFxMQQkUajWbduHRGJxWIvLy8ikkgk7DCD6urq+Ph49l1QKSkp58+fR2EHAAAAMDIOhxMSEhISEiKVSrOyssRicU1NTUVFhUAgkMvlw99IybK1/ddsbnl5+fDxhpcncblcdpvL5bLvT2IYRi6XHzp0yBBKpVIZnuRvGGZ5cI0dAAAAmFZjY6NCoSAihmEuXrzo6enZ2dkpEokEAkF7e3tNTQ0RhYeHl5SU3L17l4ju3Ru6tD18/OgiIiJKSkqam5uJSK1WNzWNcLmOUCjs7v7XEnxYWNjJkye7urq0Wm1BQUF4ePjkzthsMGMHAAAApjU4OLh9+/aWlhYejyeVSnNycpycnI4cOZKUlCQUCmUyGRF5e3u///77zz33HMMwUqm0sLDQOEJCQsKQ8aMTiUQHDx5MTExkX7m5d+9eiUQyZIyNjU10dLS/v/9zzz23Z8+e7du3r169mmGYlJSUyMjIKTz96YSbJ8DkcPMEjAg3T0wf3DzxCLh5gjU9z7GD6YGlWAAAAAALgcIOAAAAwEKgsAMAAACwELh5AgAAYE67+vQqoZWVubOAqYHCDgAAYE4LvPSVKcIyax9/7ypMOSzFAgAAAFgIFHYAAAAAFgKFHQAAAICFQGEHAAAAYCFQ2AEAAABYCBR2AAAAYHKZmZm+vr6+vr7Lly+vrq4e1746nY7L5YrFYrFYvHz58qqqKhMlycrMzMzKylKr1cNfLzvz4XEnAAAAYFp1dXW5ubkKhcLBwaGjo6Ovr2+8EQQCwa1bt4joxIkTv/nNbz7//PNRBjMMwzAMlzvW2asRxzs4OBw7dmy8eZodZuwAAADAtFQq1YIFC4RCIRG5urouXryYiKKjo2Uymb+/f15eHjtGIpGkp6cnJibGxcU9qvjr7e194okn2O2ioqLg4GCZTJaamqrValUqlZ+f35YtW2JjY69duzY82vAjGsZ3dHTs2bNHIpGEhoZevXqViLq7uzdv3vyoxHJycgICAvz9/Xft2mXyr288UNgBAACAaa1du3ZgYEAikbz22msVFRVsY0FBQV1dXW1t7b59+x48eEBELS0taWlpx48f9/HxKS4uNo7Q398vkUi8vLxeffXVd999l4iUSuX+/furqqrq6uq8vb1zc3OJqKmpaefOnadOnXJ3dx8ebfgRDePVavXhw4cvX75cXl5++fLlIfkPCXXz5s3MzMzq6mqFQlFWVnb27FmTf4NjhqVYAAAAMC1bW9sLFy5cunTp3Llzqampb7311rZt27Kzs8vKyng8Xmtra3Nzs7u7u1gs9vLyIiKJRKJUKo0jCASCxsZGIqqurv7BD37Q0NBQWVmpVCpjYmKISKPRrFu3joh8fHz8/PzYXYZHG35Ew/jq6ur4+Hh2TvH5558fkv+QUA8fPoyPj3dyciKilJSU8+fPR0VFmeq7GycUdgAAAGByHA4nJCQkJCREKpVmZWWJxeKampqKigqBQCCXy9n1TT6fzw7mcrk6nW7EOGFhYffv3//6668ZhpHL5YcOHTJ0qVQqW1tbw8ch0crLy4cfccTxho1HhZrE12ByWIoFAAAA02psbFQoFETEMMzFixc9PT07OztFIpFAIGhvb6+pqRl7KIVCodPp3N3dIyIiSkpKmpubiUitVjc1NY2+4+hHDAsLO3fu3ODgoF6vLy8vHz1UWFjYyZMnu7q6tFptQUFBeHj42PM3NczYAQAAgGkNDg5u3769paWFx+NJpdKcnBwnJ6cjR44kJSUJhUKZTPbYCFqtll0MtbW1/eijjwQCgUgkOnjwYGJi4sDAAJ/P37t37+hPJ0lISBjliBKJJCUlJSYmxs3Njb23YxR+fn7bt29fvXo1wzApKSmRkZGPzX/acBiGMXcOYOHa2tomtqNAIHBxcdHufH3EXrVTqfHHXuvH/FttuAaXcafUOXR6nojonmDccYCI7hr9u7KZT0T0Nyvh0EG81SPsyfUk7kKT5fVdHDfijP8vChEx84e26J0nn84EDc4z26GHeDjyH1xcp+3wxuDef2+LH5okHY+H5Hu/h90W9ncbd9nouoYM5tG3RERW34wci39vvEfXC9qJSPXmj8a7o7FFixZNZncWp6Ju8kGGY9Y+vlyDKYelWAAAAAALgcIOAAAAwEKgsAMAAACwELh5AgAAYE5ThQYIxvz2LZjhUNgBAADMae7nW00RlonyNkVYGB0qdAAAAAALgcIOAAAAwEKgsAMAAACwECjsAAAAACwECjsAAAAwuczMTF9fX19f3+XLl1dXV49rX51Ox+VyxWKxp6dnQEBAVlaWXq8nIrVaPfprxOYg3BULAAAAplVXV5ebm6tQKBwcHDo6Ovr6+sYbQSAQ3Lp1i4iUSmVycnJ3d/c777zj4OBw7NgxE+Q7i2HGDgAAAExLpVItWLBAKBQSkaur6+LFi4koOjpaJpP5+/vn5eWxYyQSSXp6emJiYlxc3KOKPy8vr4MHD/7pT38iou7u7s2bN7PtRUVFwcHBMpksNTVVq9USUWZm5tKlS8PCwlJTU7OysogoJycnICDA399/165d03Ha5oDCDgAAAExr7dq1AwMDEonktddeq6ioYBsLCgrq6upqa2v37dv34MEDImppaUlLSzt+/LiPj09xcfGjogUGBnZ2dn7zzTeGFqVSuX///qqqqrq6Om9v79zc3IaGhiNHjigUijNnzly7do2Ibt68mZmZWV1drVAoysrKzp49a+KTNg8UdgAAAGBatra2Fy5cOHr0qIeHR2pq6oEDB4goOzs7NDRULpe3trY2NzcTkVgs9vLyIiKJRKJUKh8VjWEYhmGMWyorK5VKZUxMTERERFlZ2ddff11TUxMbG2tnZ2dra7thwwYiqq6ujo+Pd3Jysra2TklJOX/+vCnP2GxwjR0AAACYHIfDCQkJCQkJkUqlWVlZYrG4pqamoqJCIBDI5XJ24ZXP57ODuVyuTqd7VKirV686OzvPnz9frVazLQzDyOXyQ4cOGcb8+c9/NuXZzFyYsQMAAADTamxsVCgURMQwzMWLFz09PTs7O0UikUAgaG9vr6mpGXuopqambdu2/fKXvzRujIiIKCkpYaf91Gp1U1PTmjVrSktLe3t7NRrNiRMniCgsLOzkyZNdXV1arbagoCA8PHxKT3GmwIwdAAAAmNbg4OD27dtbWlp4PJ5UKs3JyXFycjpy5EhSUpJQKJTJZI+NoNVqvby8BgYGnnjiiVdeeSUtLc24VyQSHTx4MDExcWBggM/n7927NyIiIjU1dcWKFS4uLkuXLnV0dPTz89u+ffvq1asZhifGXpUAACAASURBVElJSYmMjDTZ6ZoTZ8gqNcCUa2trm9iOAoHAxcVFu/P1EXvVTqXGH3utm8Ybv8Fl3Cl18kdovCcYdxwgortG/65s5hMR/c1KOHQQb/UIe3I9ibvQZHl9F8eNOOP/i0JEzPyhLXrnyaczQYPzzHboIR6O/AcX12k7vDG499/b4ocmScfjIfne72G3hf3dxl02uq4hg3n0LRGR1Tc0Iv698R5dL2gnItWbPxrvjsYWLVo0md1ZnLPNkw8yHBPlbYqwY9fb22tnZ6fVaqOiovbv3x8YGGjefKYHZuwAAADAAqWlpdXX1/f19W3evHmOVHWEwg4AAAAsUm5urrlTMAPcPAEAAABgITBjBwAAMKfZWXGsuRxzZwFTA4UdAADAnKaO8OJzUNhZCBR2AAAAc5qg7BG3+k4O838mdEc5TA6usQMAAACwEHiOHZjcJJ9j19PTw74celYQCoU9PT3mzmKsnJyc7O3t79+/39/fb+5cxoTH41lZWWm1WnMnMlZubm5E1N7ebu5ExsrGxkan043yKqcZBT8RNFXPsTt1f/JBhsOMnVlgxg4AAADAQqCwAwAAALAQKOwAAADA5DIzM319fX19fZcvX15dXT2ufXU6HYfD8fhfN2/enGQyarVaIpFMMsjMhLtiAQAAwLTq6upyc3MVCoWDg0NHR0dfX994I1hbW9+5c2dII8MwDMNwueOepXJwcDh27Nh495oVMGMHAAAApqVSqRYsWCAUConI1dV18eLFRBQdHS2Tyfz9/fPy8tgxEokkPT09MTExLi5ulOJPpVL5+flt2bIlNja2o6NjLHEKCwsDAwMDAwM3bdpERN3d3Zs3byYitVodHR0dFBQUGBhYXFw8fCQR5eTkBAQE+Pv779q1y7Rf01TAXbFgcrgrdsbCXbGmhrtiTQo/ETR77orVaDQRERFqtToqKmrTpk1r164lom+//dbZ2Vmj0YSGhlZWVvb29np6et68edPLy+v1119fs2ZNSkoKu7tOp+Pz+Z6enkTk5eVVUFDg4eFx48YNPz+/scQJDg6Oioqqqalxd3dnB6vV6lWrVjU2Nh45cuTatWv/9V//RUTd3d337t0bMvLmzZsJCQm1tbU2NjYRERG7d++Oiooyxdc1VTBjBwAAAKZla2t74cKFo0ePenh4pKamHjhwgIiys7NDQ0Plcnlra2tzczMRicViLy8vIpJIJEql0jiCtbW1UqlUKpWVlZVE5OPjw1Z1Y4nz2WefJSQkuLu7E5Gzs7NxWJlMdvz48YyMjJqaGgcHh+Ejq6ur4+PjnZycrK2tU1JSzp8/b9IvavJwjR0AAACYHIfDCQkJCQkJkUqlWVlZYrG4pqamoqJCIBDI5XJ2wZTP57ODuVzu6DPHtra27EZ5eflk4gQEBFy6dKm0tDQjIyMuLm7BggWTP1PzwowdAAAAmFZjY6NCoSAihmEuXrzo6enZ2dkpEokEAkF7e3tNTc2EI48lTnh4eElJyd27d4no3r17xl137txxcHB46aWXduzYUV9fP3xkWFjYyZMnu7q6tFptQUFBeHj4hFOdHpixAwAAANMaHBzcvn17S0sLj8eTSqU5OTlOTk5HjhxJSkoSCoUymWzCkRMSEh4bx9vb+/3333/uuecYhpFKpYWFhYauy5cvv/POO1ZWVjY2NocPHx4+0s/Pb/v27atXr2YYJiUlJTIycsKpTg/cPAEmh5snZizcPGFquHnCpPATQbPn5gmYNliKBQAAALAQWIqFmY6/+635jxujdip9VFevddPwxobv/jOyk0/3BBNIzSQ+t6W/WQmJt5p4q4iIOG7EecS/epnHfjFEeudRu1UjtA3Oe3xYIhpwHNrSb/+osXGdtkQU3Evih/9uXHd76LyFS+8dIuJZ/YP494io7W35mDIBAID/hRk7AAAAAAuBGTsAAIA5zcOGK+ByzJ0FTA0UdgAAAHNa69rRr9mA2QSFHQAAwJzGOaExRVhmg60pwsLocI0dAAAAgIVAYQcAAABgIVDYAQAAAFgIFHYAAAAAFgKFHQAAAJhcZmamr6+vr6/v8uXLq6urx7WvTqezsbGZwmTUarVEIpnCgDMH7ooFAAAA06qrq8vNzVUoFA4ODh0dHX19febNx8HB4dixY+bNwUQwYwcAAACmpVKpFixYIBQKicjV1XXx4sVEFB0dLZPJ/P398/Ly2DESiSQ9PT0xMTEuLu5RxV9OTk5AQIC/v/+uXbvYliFxiGjPnj0SiSQsLCw1NTUrK6uxsTEiIoLt+sMf/pCVldXd3b158+axH3QWQWEHAAAAprV27dqBgQGJRPLaa69VVFSwjQUFBXV1dbW1tfv27Xvw4AERtbS0pKWlHT9+3MfHp7i4eHicmzdvZmZmVldXKxSKsrKys2fPDo/T2NjIzg6Wl5fX1dU9NrfHHnR2QWEHAAAApmVra3vhwoWjR496eHikpqYeOHCAiLKzs0NDQ+VyeWtra3NzMxGJxWIvLy8ikkgkSqVyeJzq6ur4+HgnJydra+uUlJTz588Pj8OOsbe3t7Oze/755x+b22MPOrvgGjsAAAAwOQ6HExISEhISIpVKs7KyxGJxTU1NRUWFQCCQy+XsGiifz2cHc7lcnU43lrDl5eXD4wzB4/H0ej273d/fP+Q+jAkcdCbDjB0AAACYVmNjo0KhICKGYS5evOjp6dnZ2SkSiQQCQXt7e01NzRjjhIWFnTx5squrS6vVFhQUhIeHD48TFhb26aefPnz4sLe39/jx40S0cOHCO3fuDA4O6vV6w0KwpcKMHQAAAJjW4ODg9u3bW1paeDyeVCrNyclxcnI6cuRIUlKSUCiUyWSj7z4wMGBtbU1Efn5+27dvX716NcMwKSkpkZGRvb29Q+JIJJKtW7euXLly/vz5QUFBRGRvb//zn/88IiLiySefnD9//jScrxlxGIYxdw5g4dra2ia2o0AgcHFx0e58/bEj1U6lj+rqtW4a3tjg8p2PnXy6Jxh/fqbxuS39zUpIvNXEW0VExHEjjsvIQ5kx/DzpncedweC8MQ0bcBza0m//qLFxnbZEFNxL4of/blx3u2fIMJfeO0TEs/oH8e8RUdvb8iEDeDyelZWVVqsdU4YzgJubGxG1t7ebO5GxsrGx0el0s2U1iv2J6OnpYa+7nxWEQmFPz9C/+ZOxaNGiyQfhnNBMPshwzAbbqQpVWVn5xhtvjOVOiCEyMzN5PN6vfvWrqcpk5sOMHQAAAMxcf/zjHw8ePMjebwGPhcIOAAAAZq4333zzzTffnNi+v/71r6c2mZkPN08AAAAAWAjM2AEAAMxpq57gClEOWAr8SQIAAMxpXzxrbe4UYMqgsAMAAJjTOB+bJCyTbJKwMDpcYwcAAABgIVDYAQAAAFgIFHYAAAAAFgKFHQAAAICFQGEHAAAAJpeZmenr6+vr67t8+fLq6upx7avT6WxsbEYfo1arJRLJ6GPu3buXnJz81FNPPfXUUz/72c96e3uJSKVSrVixYryhZiwUdgAAAGBadXV1ubm5CoXiq6++Onv27OLFi6f8EA4ODseOHRt9zPe///2AgIB//vOfLS0tDMOkpaVNONSMhcIOAAAATEulUi1YsEAoFBKRq6srW9hFR0fLZDJ/f/+8vDx2jEQiSU9PT0xMjIuL6+vrGyVgY2NjREQEu/2HP/whKyuru7t78+bNRKRWq6Ojo4OCggIDA4uLiw27KBQKpVL51ltvcTgcHo/3wQcfFBUVdXR0ENHAwMD3v//95cuXb9y4saen51GhVCrVkiVLXn755aSkpBdeeKGnp4eIcnJyAgIC/P39d+3aZZLvbpxQ2AEAAIBprV27dmBgQCKRvPbaaxUVFWxjQUFBXV1dbW3tvn37Hjx4QEQtLS1paWnHjx/38fExrsnG5cSJEwEBAQqF4urVq5GRkYb2GzdurFy5ksv9V+UjFArFYnFjYyMRNTQ0pKWlXbt2bcmSJR988MEooZqbmzMyMj755JPg4OAPPvjg5s2bmZmZ1dXVCoWirKzs7NmzE8t5CqGwAwAAANOytbW9cOHC0aNHPTw8UlNTDxw4QETZ2dmhoaFyuby1tbW5uZmIxGKxl5cXEUkkEqVSObFjyWSy48ePZ2Rk1NTUODg4jDKSYRh2QyQSrVq1ioh++MMfVlVVjRLK29vb19eXiOLi4qqqqqqrq+Pj452cnKytrVNSUs6fPz+xnKcQCjsAAAAwOQ6HExISkpGRkZ2dXVhYWF5eXlNTU1FR8dlnnwUFBbELr3w+nx3M5XJ1Ot0o0Xg8nl6vZ7f7+/uNuwICAi5durR06dKMjIw//vGPhnapVFpXV2fYq7u7+9atW6PfJDE81MDAANtl2JhpUNgBAACAaTU2NioUCiJiGObixYuenp6dnZ0ikUggELS3t9fU1Iw34MKFC+/cuTM4OKjX6w1ru6w7d+44ODi89NJLO3bsqK+vN7Q//fTTnp6e7733nl6vHxgYSE9Pf+GFF1xdXYmopaXliy++IKKPPvro2WefHSXU7du3P//8cyI6evTos88+GxYWdvLkya6uLq1WW1BQEB4ePsEvaOrgXbEAAABgWoODg9u3b29paeHxeFKpNCcnx8nJ6ciRI0lJSUKhUCaTPTaCVqv18PBgt9PS0nbs2PHzn/88IiLiySefnD9/vvHIy5cvv/POO1ZWVjY2NocPHzbuys/P/8UvfsHGiYuL27t3L9sulUr37dv3yiuvLFmy5L//+78Nk4XDQ0ml0v3792/dunXJkiW7d+8WCoXbt29fvXo1wzApKSnGl/SZC8ewwAxgIm1tbRPbUSAQuLi4aHe+/tiRaqfSR3X1WjcNb2xw+c7HTj7dE4w/P9P43Jb+ZiUk3mrirSIi4rgRx2Xkocz8kduN6Z3HncHgvDENG3Ac2tJv/6ixcZ22RBTcS+KH/25cd7tnyDCX3jtExLP6B/HvEVHb2/IhA3g8npWVlVarHVOGM4CbmxsRtbe3mzuRsbKxsdHpdKMvgc0c7E9ET08Pe939rCAUCtlbKafKokWLJh+E8/HkY4yASTZJWDNSqVRyufzKlSvmTmQ0WIoFAAAAsBAo7AAAAAAez93dfYZP1xEKOwAAAACLgZsnAAAA5rQNT5IA8zyWAoUdAADAnFYcZu4MYOqgsAMAAJjT4vNMEvbTH5skLIwOc68AAAAAFgKFHQAAAICFQGEHAAAAYCFQ2AEAAABYCBR2AAAAYHKZmZm+vr6+vr7Lly+vrq4e1746nY7D4bz88svsR41G4+jomJKSMuLg3t7eAwcOTDbdWQuFHQAAAJhWXV1dbm6uQqH46quvzp49u3jx4vFGcHR0rK+v7+/vJ6JPP/1UJBI9aiQKOwAAAAATUqlUCxYsEAqFROTq6soWdtHR0TKZzN/fPy8vjx0jkUjS09MTExPj4uL6+vqMI3A4nOjo6NOnTxPRxx9/nJycbOgaEue9995raWmJiIh4++23iaioqCg4OFgmk6Wmpmq1WiLKyckJCAjw9/fftWvXdH0B0weFHQAAAJjW2rVrBwYGJBLJa6+9VlFRwTYWFBTU1dXV1tbu27fvwYMHRNTS0pKWlnb8+HEfH5/i4uIhQZKTkwsKCrq7u1tbW6VSqaF9SJzf/va3IpGosrLy97//vVKp3L9/f1VVVV1dnbe3d25u7s2bNzMzM6urqxUKRVlZ2dmzZ6ftS5geKOwAAADAtGxtbS9cuHD06FEPD4/U1FR2qTQ7Ozs0NFQul7e2tjY3NxORWCz28vIiIolEolQqhwQJCgpqaGjIz89fv369cfvwOAaVlZVKpTImJiYiIqKsrOzrr7+urq6Oj493cnKytrZOSUk5f/68Kc/bDPDmCQAAADA5DocTEhISEhIilUqzsrLEYnFNTU1FRYVAIJDL5ezCK5///7d372FN3Xn+wL+5EAKEiwgKSoEUhBSi1WAFF6vBS5p6GaS1lFppt7rbR9s+dV3qY3WtO7q60+2P6TI+ohaqVTttmamdUltveAkikwKaiFY70QqJhWJAKUFuAZKc3x9nNssmGKNwCBzerz/mOZzzPd/zOd+h4e25fONFN+ZyuRaLxbmThQsXbty4sbKy8urVq/SakpIS537sKIpSKpV79+61r9m3bx9TZzg84IodAAAAMEun02k0GkIIRVGVlZVRUVEtLS1isVggEDQ2NqrVajf7+ed//uf/+I//iI2Nta9x7kckErW1tdFb5XL5kSNH6Mt4JpOppqZm1qxZR48ebW1t7e7uLioqmjNnziCfqqfhit2IV1VVdezYMXqZw+H4+/tHRkbOnTs3ODiYXllWVlZeXr5p06aBHKWkpOTKlSvvvPPOQMsFAIDRx2q15uTk6PV6Pp+fkJBQUFAQGBh48ODBzMxMkUgkk8nc7Cc6OvqNN97ou2bJkiUO/QiFQoVCIZVKFyxY8N///d979uzJyMjo7e318vL6wx/+IJfLc3JyZs6cSVFUVlbWvHnzBv9sPQrBjiUWL14cEBBgs9mam5vVavWhQ4fWrFnj7e3t6boAAABIYmJiaWmpw0r7VQm76upqemH16tV91/P5fJPJ1HfN0qVLly5dSgjx9fV17qewsNC+nJ6enp6e3nfr66+//vrrrz/sKYwUCHYsER0dHRISQi/7+/v/5S9/qa+vj4mJ8WxVAAAAMJQQ7FjIx8eHEGKz2Zw3NTU1lZWV1dXVdXR0+Pv7T5o0ae7cuUKh0N6gsbFRpVLdunWrt7c3MDBw6tSpTz/9tHM/ly5d+vbbb+Vy+ezZs5k7EQAAAHgoCHYs0dvb29PTQ9+KValUAQEBUVFRzs1MJtOYMWMSExN9fHxMJtP58+eNRuPKlSvprQ0NDZ988klwcLBSqQwICGhubm5sbHTu5Pz58yqVavHixQ5PRXR2dhqNRofGAoGADpqPgM/H7+eo5vwsAY/H43JH0itfHA6H9Hciw5aXlxeXy+XxeJ4uxC30G5Q8Hm8EjTCfzx/Eaq1W62B1BayBP5ws8dFHH9mXx4wZ8/LLLwsEAudm9Pf02X8MCwvbu3dvY2Pj+PHjCSGnTp0SCoWrVq2i93X+whaKok6cOKHVal988cX4+HiHrQ0NDX/84x8dVoaFhTk8KgHgprFjx3q6hMHBmhMZnnx8fB75X48e4evrO1hddXZ2Dko/iyVE6DUoPYHnIdixxPPPPx8YGEhRVFtbW0VFxR//+MfXXnvN/mKsndVqraqqunLlSmtrK/2Ne4SQ5ubm8ePHWyyWW7dupaSk9JsICSE2m+2rr76qqanJzs7u92v+xo4dO3/+fIeVfn5+9Hzij4DH4/n5+T3avsACzr85XC73frNbDU/+/v4URbW3t3u6EHfx+XybzdbvgxzDEP0R0d3dTX9P1Ijg7e09iNX29vYOSkxs0g68j/4kMdMtuIRgxxLh4eH2lydiY2N///vfnzt3LiMjw6HZqVOnNBrNvHnzHnvsMYFA0NXVtX//fvrPpNlsttlsAQEB9ztET0/PjRs3oqKiIiIi+m0wZsyYWbNmOa9vaGh4tJMSCAQIdqOZcx7i8/k8Hm8E/RWnf4FHULATCoUWi2WkRGf6I6K3t3cEjTAZUb8PMBKNpKdVwE1CoTAgIKDfx+OuXLmSnJyckpIyceLE0NDQvg+xCYVCLpfr4uqaUCh8+eWXb926dfjw4ZHyD3oAAIBRBcGOhTo6OlpbW52vdVEU1dvb2/cd2GvXrtmX+Xx+VFTUDz/8YL9F6ywqKmrFihU3b9788ssv8dAuAADAcINgxxIGg+HGjRvXr1+/ePHioUOHrFbrjBkzHNpwOJyYmBiNRnPnzp2enh6tVnv58uW+DRQKhdlsLiwsvHTpUk1NzcWLF48ePerQSWRkZHZ2dm1tLbIdAADAcINn7Fjiu+++oxf8/PzGjx+fnZ39+OOPOzdbvHjx8ePH9+3bR1FUZGRkZmbm/v377VvDw8NXrVqlUqlOnjxpsViCgoKmTp3q3Mljjz2WnZ396aeffvnlly+88MJImRkBAACA9RDsRrwZM2Y4X5zra/bs2fZphEUi0QsvvNB3629/+9u+P4aFhb300kvOnSgUCoVCYf8xIiJi48aNj1wzAACMNrm5uQUFBYQQoVC4e/fufl+2c9DZ2XngwAGHL4d1ZrFYBAIBfTnD19c3Pz+/36n1+zIajUql0v4NZmyCYAcAAADM0mq1hYWFGo3G39+/qanJbDa7s1dnZ+fu3bsfGOwIIQKB4ObNm4SQb7755t133/3rX//qojFFUQ/1CiBFURRFjZTZ0UdGlQAAADByGY3G0NBQkUhECBk3bhw9GapCoZDJZFKp9MCBA3QbiUSybt26jIyMRYsWmc3mbdu26fV6uVy+efNm5/b96uzsHDNmDL3s3H98fPzKlSsXLlzY1NTU29v70ksvTZkyZenSpfQcNK7bu3P04QBX7AAAAIBZaWlpW7dulUgk8+fPX7ZsWVpaGiGkqKgoODi4q6srNTX1ueeeI4To9fq1a9dGR0e//fbbxcXFW7ZsOXv2bGlpKd2JQ/u+E6/29PRIJBKz2dzS0nLmzJl+2xNCampqjhw5Eh8fbzQaf/zxx3379qWkpKxfv/7DDz/csmWLi/aujz6s4IodAAAAMMvHx6eiouLQoUMRERHZ2dm7d+8mhOTn56empiqVyrq6utraWkJIbGxsdHQ0IUQikRgMBodOnNvbCQQCnU5nMBiOHj368ssv05M2OLePiYmxfx+mWCxOSUkhhKxYseL8+fMPbO/i6MMKrtgBAAAA4zgcTnJycnJyckJCQl5eXmxsrFqtVqlUAoFAqVTST915ef39O2udvzywpKTEub2zWbNm3b1795dfftHpdM7tXXytcL/929u7efThAFfsAAAAgFk6nU6j0RBCKIqqrKyMiopqaWkRi8UCgaCxsVGtVve7l0gkamtro5fdaU8I0Wg0FoslLCzsge31ev33339PCPn0009nz57tur2bRx8OcMUOAAAAmGW1WnNycvR6PZ/PT0hIKCgoCAwMPHjwYGZmpkgkkslk/e4lFAoVCoVUKl2wYMGOHTtctO/u7qbv4fr4+Hz66acCgWDJkiWu+09ISNi5c+eqVasmTZr02WefcblcF+0f2NvwgWAHAAAAzEpMTLS/A2F37NgxhzX2ieVWr15NLxQWFrpoT+Pz+RRFOaz09fV10X9YWFjfL9V8YD399jY84VYsAAAAAEsg2AEAAACwBIIdAAAAAEvgGTsAAIBRTT6D8HieLgIGCYIdAADAqFavYqTb1GmMdAuu4VYsAAAAAEsg2AEAAACwBIIdAAAAAEsg2AEAAACwBIIdAAAAAEsg2AEAAADjcnNz4+Li4uLipkyZUl5e7s4unZ2du3fvdqflr7/++uqrr06YMOGJJ5549tlnf/zxR5PJJJFIBlbyiITpTgAAAIBZWq22sLBQo9H4+/s3NTWZzWZ39qKD3RtvvPHAlsuXL582bdrPP//M5/MvXbr0888/T5gw4YF7URRFURSXy6qLXKw6GQAAABiGjEZjaGioSCQihIwbNy4yMpIQolAoZDKZVCo9cOAA3UYikaxbty4jI2PRokVms3nbtm16vV4ul2/evNm5vZ1Wq7158+aOHTv4fD4hZNq0aUqlsm8D5wPFx8evXLly4cKFTU1N9+t2hMIVOwAAAGBWWlra1q1bJRLJ/Pnzly1blpaWRggpKioKDg7u6upKTU197rnnCCF6vX7t2rXR0dFvv/12cXHxli1bzp49W1paSnfi0D4gIIBef/Xq1WnTprm48OZ8oJqamiNHjsTHx7vodoTCFTsAAABglo+PT0VFxaFDhyIiIrKzs+kn5/Lz81NTU5VKZV1dXW1tLSEkNjY2OjqaECKRSAwGg0Mnzu3d5LxjTEwMneoG0u3whCt2AAAAwDgOh5OcnJycnJyQkJCXlxcbG6tWq1UqlUAgUCqV9FN3Xl5edGMul2uxWPruXlJS4tyelpiYuHXrVpvN1u9Fu3539PHxeWC3IxSu2AEAAACzdDqdRqMhhFAUVVlZGRUV1dLSIhaLBQJBY2OjWq3udy+RSNTW1kYvu2iflJQUExOzadOmnp4eQoharf7222/tW10fyJ0yRhZcsQMAAABmWa3WnJwcvV7P5/MTEhIKCgoCAwMPHjyYmZkpEolkMlm/ewmFQoVCIZVKFyxYsGPHDhftP//883Xr1j322GNCoVAqlX744Yf2TUuWLHGxo+utIxGCHQAAADArMTHR/g6E3bFjxxzWVFdX0wurV6+mFwoLC120twsJCfn0008dVup0OkKIr6+viwP1u3VEw61YAAAAAJZAsAMAAABgCQQ7AAAAAJbAM3YAAACjWoqS8AWeLgIGCYIdAADAqFb/FSPdRv6WkW7BNdyKBQAAAGAJBDsAAAAAlkCwAwAAAGAJBDsAAAAAlkCwAwAAAGAJBDsAAABgXG5ublxcXFxc3JQpU8rLy93ZpbOzc/fu3e60/PXXX1999dUJEyY88cQTzz777I8//jiwYkcwTHcCAAAAzNJqtYWFhRqNxt/fv6mpyWw2u7MXHezeeOONB7Zcvnz5tGnTfv75Zz6ff+nSpZ9//jkhIYHeRFEURVFc7mi5koVgBwAAAMwyGo2hoaEikYgQMm7cOHqlQqG4e/duT0/PO++884//+I9Go1Eulz/77LMGg6Gnp+err77atm2bXq+Xy+WzZs3avn27Q3t751qt9ubNm8eOHaPT27Rp0+gjzpkzJzU19fbt2xs2bPjtb39bWlpKCHn//feFQmFYWNiuXbsIISaTSSAQaLXaoR4RxiDYAQAAALPS0tK2bt0qkUjmz5+/bNmytLQ0QkhRUVFwcHBXV1dqaupzzz1HCNHr9WvXro2Ojn777beLi4u3bNly9uxZOpA5tw8ICKDXX716ddq0ac7X5Gpqao4cORIfH6/T6Rw2ZWVlZWVlWa1WhUKx4L7wbgAAIABJREFUZs0aRs99iI2WK5MAAADgKT4+PhUVFYcOHYqIiMjOzqafnMvPz09NTVUqlXV1dbW1tYSQ2NjY6OhoQohEIjEYDA6dOLd3LSYmJj4+3kWDf/u3f5s6deqyZcse9bSGI1yxAwAAAMZxOJzk5OTk5OSEhIS8vLzY2Fi1Wq1SqQQCgVKppJ+68/LyohtzuVyLxdJ395KSEuf2tMTExK1bt9psNoeLdj4+PvQCn8+32Wz0ck9Pj1AoJIR8880333///ZkzZxg7Y8/AFTsAAABglk6n02g0hBCKoiorK6OiolpaWsRisUAgaGxsVKvV/e4lEona2troZRftk5KSYmJiNm3a1NPTQwhRq9Xffvtt3wbh4eH19fVWq9Vms6lUKkLIzZs3N2zYUFRUxOez7QoX284HAAAAhhur1ZqTk6PX6/l8fkJCQkFBQWBg4MGDBzMzM0UikUwm63cvoVCoUCikUumCBQt27Njhov3nn3++bt26xx57TCgUSqXSDz/8sO9WPz+/NWvWyOXyiRMnjh07lhCye/fujo6OF154gRAyadKkTz75hJnz9gAORVGergFYrqGh4dF2FAgEISEh3RvefmBLU+Cx+23q9K5xXvljyP/5scWL3BE8fH3M+KsP+YonIvyZhJ9CCCGc8YQT0n9TauyDu7MFP3QF1iC3mvUGOK7p8btf20UtPoSQGZ0ktuN/V8691e7QLKSznhDC5/1EvO4QQho2Kx0a8Pl8Ho/X3d3tVoXDwPjx4wkhjY2Nni7EXUKh0GKxONwCG7boj4j29vZ79+55uhZ3iUSi9nbH3/yBmDBhwsA7KfvtwPvox2xmugXXcCsWAAAAgCUQ7AAAAABYAsEOAAAAgCXw8gQAAMCoJn2ZcHieLgIGCYIdAADAqNZUYGOi2zH/D3cFPQCDDgAAAMASuGIHw13ve//pxlwG795vg3d/K6cNpCCXBj6XwYuE7Bysah4kMDDQz8+P/lLth9/beRcXnbQ4r3K+RNBE6ClUkh6+GAAAIARX7AAAAABYA8EOAAAAgCUQ7AAAAABYAsEOAAAAGJebmxsXFxcXFzdlypTy8nKj0Th16tSH6qGzs3P37t0MlccaCHYAAADALK1WW1hYqNFobty4cfr06cjIyEfoBMHOHQh2AAAAwCyj0RgaGioSiQgh48aNo4Od1Wr9p3/6J4lE8uyzz5rNZkKIQqGQyWRSqfTAgQOEEJPJpFAokpKSnnzyyeLi4m3btun1erlcvnnzZufGQEOwAwAAAGalpaX19vZKJJI333xTpVLRK//2t7+tWbNGp9OFhIQUFxcTQoqKirRa7YULF3bu3Hnv3r1vvvlm8uTJGo3m8uXL8+bN27Jli1gsLi0t3b59u3NjT57ecIJgBwAAAMzy8fGpqKg4dOhQREREdnY2fUc1NjY2KSmJEJKSkmIwGAgh+fn5qampSqWyrq6utrZWJpN9/fXXGzduVKvV/v7+Dn06NB7ycxqmEOwAAACAcRwOJzk5eePGjfn5+V9++SUhRCgU0pt4PJ7FYikpKVGr1SqV6ty5c0lJSWazefLkyVVVVU888cTGjRs/+OCDvr05N/bAKQ1L+OYJAAAAYJZOp+vo6EhKSqIoqrKyMioqyrlNS0uLWCwWCASNjY1qtZoQUl9fHxoa+sorrwQHB3/22Wcikaitre1+jYGGYAcAAADMslqtOTk5er2ez+cnJCQUFBQ4t1myZMnBgwczMzNFIpFMJiOEXLx48b333uPxeEKh8KOPPhIKhQqFQiqVLliwYMeOHQ6NgcahKMrTNQDLNTQ0PNqOAoEgJCSkvb19BD0VO/Dvih1KA/uuWA/g8/k8Hq+7u9vThbhr/PjxhJDGxkZPF+IuoVBosVgsFounC3ELPiIIIRMmTBh4J7r1zl/dPAgk/w+Pe3kArtjBcOf1H5t4gcc6vWv+Gk7uCAgh5F9D3iOEEG4iocYSWzCxBv1v645wQsiiFp8ZnSS24+/rlv9N3bA9eojLBgAAGHpI0wAAAAAsgWAHAAAAwBK4FQsAADCqPb6ey+F5uggYJAh2AAAAo9qv2xh55Stsl4iJbsE13IoFAAAAYAkEOwAAAACWQLADAAAAYAkEOwAAAACWQLADAAAAYAkEOwAAAGBcbm5uXFxcXFzclClTysvLnRuYTCaJRDL0hbEMpjsBAAAAZmm12sLCQo1G4+/v39TUZDabPV0Ra+GKHQAAADDLaDSGhoaKRCJCyLhx4yIjIwkhBQUFkydPlkqlW7dudWivUChkMplUKj1w4AC9u0QiWbduXUZGxqJFi+hc6GL30QzBDgAAAJiVlpbW29srkUjefPNNlUpFCLl+/Xpubm55eblGozlx4sTp06f7ti8qKtJqtRcuXNi5c+e9e/cIIXq9fu3atV9//XVMTExxcbHr3Ucz3IoFAAAAZvn4+FRUVFRVVZ09ezY7O3vTpk3e3t6LFy8ODAwkhGRlZZWVlU2fPt3ePj8//8SJE3w+v66urra2NiwsLDY2Njo6mhAikUgMBkNHR4fD7vPnz/fQyQ0vCHYAAADAOA6Hk5ycnJycnJCQkJeXt2LFivu1LCkpUavVKpVKIBAolUr6xquXlxe9lcvlWiyWISp6BMKtWAAAAGCWTqfTaDSEEIqiKisro6KiZs2adfTo0dbW1u7u7qKiojlz5tgbt7S0iMVigUDQ2NioVqv77dDF7qMcrtgBAAAAs6xWa05Ojl6v5/P5CQkJBQUF4eHhOTk5M2fOpCgqKytr3rx5JpOJbrxkyZKDBw9mZmaKRCKZTNZvh/Hx8Q67D+HZDGsIdgAAAMCsxMTE0tJSh5Wvv/7666+/bv8xKChIp9MRQnx9fY8dO+bQuLq6ml5YvXp1v7sDDbdiAQAAAFgCwQ4AAACAJRDsAAAAAFgCz9gBAACMauN+50d4ni4CBgmCHQAAwKjWmWNkoltRQTgT3YJruBULAAAAwBIIdgAAAAAsgWAHAAAAwBIIdgAAAAAsgWAHAAAAjMvNzY2Li4uLi5syZUp5ebmny2EtvBULAAAAzNJqtYWFhRqNxt/fv6mpyWw2e7oi1sIVOwAAAGCW0WgMDQ0ViUSEkHHjxkVGRhJCCgoKJk+eLJVKt27dSreRSCTr1q3LyMhYtGgRHf6c20il0ldeeSU2NvbFF188d+5cSkrK448/rlKp6AMpFAqZTCaVSg8cOOCpk/UsBDsAAABgVlpaWm9vr0QiefPNN+kQdv369dzc3PLyco1Gc+LEidOnTxNC9Hr92rVrv/7665iYmOLi4n7b6HS6nJycn3766c6dO3v27FGr1V988QUd+wghRUVFWq32woULO3fuvHfvngdP2VNwKxYAAACY5ePjU1FRUVVVdfbs2ezs7E2bNnl7ey9evDgwMJAQkpWVVVZWJpVKY2Njo6OjCSESicRgMHR0dDi3mTRp0pNPPkkImTp1amJiIpfLnTZtmsFgoA+Un59/4sQJPp9fV1dXW1s7depUT52ypyDYAQAAAOM4HE5ycnJycnJCQkJeXt6KFSuc23h5edELXC7XYrH024+3t7e9Db1sb1xSUqJWq1UqlUAgUCqVo/NJPtyKBQAAAGbpdDqNRkMIoSiqsrIyKipq1qxZR48ebW1t7e7uLioqmjNnjvNe7rTpq6WlRSwWCwSCxsZGtVrNyJkMe7hiBwAAAMyyWq05OTl6vZ7P5yckJBQUFISHh+fk5MycOZOiqKysrHnz5hmNjl9ZGx8f/8A2fS1ZsuTgwYOZmZkikUgmkzF5QsMXh6IoT9cALNfQ0PBoOwoEgpCQkO4Nb5sCj3V61/w1nNwREELIv4a8Rwgh3ERCjSW2YGIN+t99OsIJIYtafGZ0ktiOv69b/jd1w/boAZzBQxCJRO3t7UNzrIELDAz08/O7e/duT0+Pp2txC5/P5/F43d3dni7EXePHjyeENDY2eroQdwmFQovFcr9bYMMN/RHR3t4+gp6RH/SPiAkTJgy8k/bXbw+8E2eignAmugXXcCsWAAAAgCUQ7AAAAABYAsEOAAAAgCXw8gQAAMCoxhFyCJfj6SpgcCDYAQAAjGp+fwgjyHVsgWAHAAAwqllW6Zjolr9fwkS34BqesQMAAABgCQQ7AAAAAJZAsAMAAABgCQQ7AAAAAJZAsAMAAADG5ebmxsXFxcXFTZkypby8vLOzc/fu3e7vbjKZJJJ+3sa43/pRC8EOAAAAmKXVagsLCzUazY0bN06fPh0ZGfmwwc7f3//zzz9nrkLWQLADAAAAZhmNxtDQUJFIRAgZN25cZGTktm3b9Hq9XC7fvHkzIUShUMhkMqlUeuDAAbq9RCJZt25dRkbGokWLzGZzW1vb8uXLCSEmk0mhUCQlJT355JPFxcV9j+LQiU6nk8vl9Kb3338/Ly/Pxb6sgXnsAAAAgFlpaWlbt26VSCTz589ftmxZWlrali1bzp49W1paSjcoKioKDg7u6upKTU197rnnCCF6vX7t2rXR0dFvv/12cXGxUqmkW37zzTeTJ0/+/e9/Twhpa2uzWq32ozh34sBhX4ZP2jNwxQ4AAACY5ePjU1FRcejQoYiIiOzsbOebsPn5+ampqUqlsq6urra2lhASGxsbHR1NCJFIJAaDwd5SJpN9/fXXGzduVKvV/v7+rjtx4GJf1kCwAwAAAMZxOJzk5OSNGzfm5+d/+eWXfTeVlJSo1WqVSnXu3LmkpCSz2UwI8fLyordyuVyLxWJvPHny5KqqqieeeGLjxo0ffPCBi074fL7NZqO39vT0uNiXTRi5FXvkyBGtVpuSkmK/cOpaWVlZeXn5pk2bBuXobW1tH3744WuvvWY0Go8dO0avFAgEwcHB06dPl8lkXO6jx1mHUi9fvnzu3DmTySQQCN59992dO3fGxsYuXLjwofqsqqo6duzYW2+9FRIS4v6hB9GhQ4f4fD797AIAAMCg0+l0HR0dSUlJFEVVVlZGRUWJRCL7zdCWlhaxWCwQCBobG9Vqteuu6uvrQ0NDX3nlleDg4M8++8y+3rmT8PDw+vp6q9XK4XBUKlV6evr99mWTwQ92vb29165d8/Ly+uGHHxQKhTspys/PLzQ0dLAKuH79uq+v72OPPWY0GgkhixcvDggIMJvNV69e/e6770wm0/z58x+5876ldnR0HDlyJCkpacqUKXw+nxAyZswY+slQAAAAsLNarTk5OXq9ns/nJyQkFBQUCIVChUIhlUoXLFiwY8eOgwcPZmZmikQimUzmuquLFy++9957PB5PKBR+9NFH9vVLlixx6MTPz2/NmjVyuXzixIljx451sS+bDH6w0+l03d3dzz777PHjx2/evBkXF/fAXZKSkpKSkgaxgPj4eA6HQ/8YHR1NXwmbPHny3r17q6qq5s6d+8gX7fqW2tzcbLVap02bFh4eTq/Jzs4ecPkjicVioRMtAACAC4mJifb3JOwKCwvty/Y7bHbV1dX0wurVq+kFnU5HCFm6dOnSpUv7tqTX+/r6Oneyfv369evX913jsC/7DP5f5cuXL4eEhMyYMaO8vLy6urpvsDOZTKdPnzYYDF1dXb6+vhMnTszIyPD29u57k7GpqamsrKyurq6jo8Pf33/SpElz584VCoV0DyUlJVeuXHnhhRdOnTplNBoDAgKSk5OTk5Pth+ju7tbr9S+++KJzYRwOJyIiorGxsaurq6Ojw8VRCCGNjY0qlerWrVu9vb2BgYFTp059+umnSZ/7ocXFxfTvnEPknzFjhv1W7P06ce1+o9S3zQBH6dq1ayqVymQyjRkzJi0tzaGAxsbGs2fP3rp1y2KxhIeHz58/Pyoqqm/Pzz333JkzZxobG2Uy2cPedwYAAADmDHKwa2trq62tnT17NofDSUxMvHjxYldXl4+PD731z3/+s9VqXbhwob+/f1tbW01NTd+3lGl02khMTPTx8TGZTOfPnzcajStXrrQ3MJvNx48fVyqVISEhV69ePX78eHBw8KRJk+itP/30E4/He/zxx/str6Wlhcvlent7//LLLy6O0tDQ8MknnwQHByuVyoCAgObm5sbGRoeuFApFbGzs4cOHX375ZfvNWXriHPc76RfTo6TX6w8fPhwXF6dUKjs6Ok6ePGmz2ewXHY1G4/79+0NDQ5csWSIQCDQazaFDh1atWjVhwgR7z8eOHXvmmWfGjx/f92lWAAAA8LhBDnZXrlyx2WxSqZQQIpVKKyoqrl27Nn36dEKI1Wq9ffv2b37zm4SEBLqxfaEv+vtG7D+GhYXt3bu3sbFx/Pjx9BqLxbJkyZKJEycSQlJSUqqrq69evWoPdjqdLjY2tu/9wd7e3p6enu7u7itXrtTW1kokEj6f7/oop06dEgqFq1atEggEhBCxWOxcp6+vL/04nb+/f1BQEL2y7x1edzpxNgSjVFpaGhISkpWVRd+tHjt27Mcff2wPdqdOnfL19X311VfpsmNjY/fu3VtWVpaVlWXvedGiRf2ejtFoPH/+vMPKoKCgWbNmuXPuzgbymouDMWPGDFZXrvH5fPubXMMfXaq/v7/9xbFhjsvlcjgcX19fTxfiLvp3eMh+/QaOx+NRFDWCfh8IIUKhkMfjeboWdw3uRwT9pidAX4Mc7Kqrq8ePH08/0xYRETFmzJjq6mo62PF4vPHjx587d66np0csFo8bN67fHqxWa1VV1ZUrV1pbW+2/ss3NzfbI4uXlRecV2pgxY1pbW+37/vTTT4sWLerbof1WKYfDkUql9K1DF0exWCy3bt1KSUmhk82jeeROmB4liqJ++eWX1NRU+zOI9P9N9m4NBsOMGTPsZXM4nLi4OI1GY++Ky+XSEws5a29vv3btmsPKsLCwBQsWPMQQMMN+2XgIjKBgR3O40Q+Dbih//UYhPp8/sh72HcSPCIqiBqejYC/OiMnG8ACD+R9DQ0PDnTt3Zs+eTc9AQwiJj4+vqKhobm6m30ZZvnx5aWnp+fPnjx8/HhAQMHPmzJkzZzp0curUKY1GM2/evMcee0wgEHR1de3fv7/vLT+HP0I8Hs++Va/X9/b2Oryu8fzzzwcGBgoEgjFjxtj3dXEUs9lss9kCAgIGMhQD6YTRUerq6rJYLA6zMtp/7OrqslqtlZWVFy5csG+12Wx9Pzt8fX3todCBWCzesGGDw0oul0u/nvwIvLy86N+cgXvkGh6Wn59fR0fH0Bxr4Pz9/f38/Jqbm3t7ez1di1t4PB6PxxtBVynGjRtHUdSdO3c8XYi7vL29LRaL8+MfwxM9j1VHR8cI+gqBwf2IoChqUC5g83NjBt4JDBODGezolwnKysrKysr6rr98+fLcuXMJIQEBAb/5zW8IIU1NTZcuXTp58mRAQEBiYmLfxleuXElOTk5JSaF/bGhocL8AnU4XHR3d9x0IQkh4eLjz/HAujiIUCrlc7r1799w/rrOBdMLoKPn4+PD5fHvypnV1ddGDRpf91FNPPfXUU49QOY/H6/fKxCPf1hm0f4wOoIaHNYJuY9mNoJq5XO4Iqpb8z+/wyCp4BI0wXecIKpgM22pXPmDquEe0/x8Y6RZcGrRnmKxW69WrVyMiIv7x/woLC7t8+bLDX+hx48YpFAo+n9/U1NR3PUVRvb29fZOZ8629+6Eo6vr16xKJxJ2WLo7C5/OjoqJ++OGHgVwVGJROmBglDoczceLEmpoa+xqTydTc3GwvOzo62mAwBAUFhfxfj3wWAAAAMGQGLdjduHGjs7Nz+vTp0f/X9OnTW1tbDQaDyWTat29fVVVVTU1NbW3td999Z7VaHV5f5XA4MTExGo3mzp07PT09Wq328uXLbhbwyy+/tLW1uRPsHngUhUJhNpsLCwsvXbpUU1Nz8eLFo0ePuj8UA+mE6VEihMjlcoPBUFZWZjabm5ub//KXv/R9PEWhULS0tOzfv7+6urq2tvbatWunTp06deqU+/0DAACApwzardjLly97e3s73DEkhEil0pMnT16+fJmeeqOqqqq1tZXH44WGhmZlZdknSLNbvHjx8ePH9+3bR1FUZGRkZmbm/v373SlAp9NNmDDBzcfaXB8lPDx81apVKpXq5MmTFoslKCho6tSp7nTb16N1IhQKGR0lQohYLF62bBn9bXqBgYH/8A//0DfYhYWFvf7666WlpadOnTKbzX5+fhMmTHi0O7MAAAAwxDiD+BiTZ+3atWvKlCmzZ8/2dCHg6KEelOxLIBCEhIR0b3jbFHis07vmr+HkjoAQQv415D1CCOEmEmossQUTa9D/7tMRTghZ1OIzo5PE/s8Dysv/pm7YHj2AM3gIIpGovb19aI41cIGBgX5+fnfv3h0pryPw+Xwej9fd3e3pQtxFv6ju5hyWw4FQKLRYLCNlikr6I6K9vX2AT0UPpUH/iLBPMjogeMaORQbtVqzHvfXWW0h1AAAAw1Nubi49CeuUKVPKy8udG3R2du7evdt5GR4Ke4IdAAAADE9arbawsFCj0dy4ceP06dORkZHObR4h2A3Tt4w9aiRN6ggAAAAjkdFoDA0Npb+xiZ57X6fTrV69urS0lBDy/vvvC4XC2tpavV4vl8tnzZp17949+/L27dsPHz78wQcfWCyWxMTEjz/+uKWlZc6cOampqbdv3/7kk0/CwsI8e3bDCoIdAAAAMCstLW3r1q0SiWT+/PnLli1LS0tzbrNly5azZ8/SUe/u3bv2ZYPBsGvXrvPnz3t7e//7v/97YWHhsmXLampqjhw5Eh8fP7TnMQIg2AEAAACzfHx8Kioqqqqqzp49m52dvWnTJvqbC9xRWlpqMBieeeYZQkhXVxe9Y0xMDFJdvxDsAAAAgHEcDic5OTk5OTkhISEvL0+hUNgfj+vp6XH41qi+KIpSKpV79+61rzEajfgK5vvByxMAAADALJ1Op9FoCCEURVVWVkZFRYWHh9fX11utVpvNplKpCCEikcj+tb99l+Vy+ZEjR2prawkhJpOp75cngTMEOwAAAGCW1WrNycmJioqKjY394Ycffve73/n5+a1Zs0Yuly9fvnzs2LGEEKFQqFAopFLpunXr+i6LxeI9e/ZkZGQkJCTMmTOnrq7O02czrOFWLAAAADArMTGRfhOir/Xr169fv77vmsLCwn6X09PT09PT+7asrq4e/CpZAVfsAAAAAFgCwQ4AAACAJRDsAAAAAFgCz9gBAACMbnEBhM/xdBEwOBDsAAAARrd3pZ6uAAYNgh0AAMDo9s/fMtJt4RJGugWX8IwdAAAAAEsg2AEAAACwBIIdAAAAAEsg2AEAAACwBIIdAAAAMKi7uzsiIiIiIiIoKMjf359eNhgM9gYmk0kikXiuQFbBW7EAAADAIG9v7/r6ekJIXl5efX19bm7uI3RCURRFUVwuLkg9AAYIAAAAho5Op5PL5fTy+++/n5eX13erQqGQyWRSqfTAgQOEEKPRGB8fv3LlyoULFzY1NR0+fHjGjBkymSw7O7u7u3vIax8BEOwAAABguCgqKtJqtRcuXNi5c+e9e/cIITU1NRs2bDh+/LjZbN61a9f58+e1Wu3jjz9eWFjo6WKHI9yKBQAAgOEiPz//xIkTfD6/rq6utrY2LCwsJiYmPj6eEFJaWmowGJ555hlCSFdX19y5cz1d7HCEYAcAAABDh8/n22w2ermnp0coFNo3lZSUqNVqlUolEAiUSqXZbCaE+Pj40FspilIqlXv37h36mkcQ3IoFAACAoRMeHl5fX2+1Wm02m0ql6ruppaVFLBYLBILGxka1Wu2wo1wuP3LkSG1tLSHEZDLV1NQMXdEjB67YAQAAwNDx8/Nbs2aNXC6fOHHi2LFj+25asmTJwYMHMzMzRSKRTCZz2FEsFu/ZsycjI6O3t9fLy+sPf/hDTEzMEBY+MiDYAQAAwFD4l3/5F3ph/fr169ev77tJp9MRQnx9fY8dO+awV3V1tX05PT09PT2d4TJHNtyKBQAAAGAJBDsAAAAAlkCwAwAAAGAJPGMHAAAwuiVPJN7IAyyB/yMBAABGt39yfP8URi4EOwAAgNHtjf2MdLt7JSPdgkt4xg4AAACAJRDsAAAAAFgCwQ4AAACAJRDsAAAAAFgCwQ4AAACYlZaWdvLkSfuPeXl5a9aseeBenZ2du3fvZrIuFkKwAwAAAGa99NJLRUVF9h+LiopeeumlB+7lZrCjKMpmsw2oPhZBsAMAAABmLVu27OjRoz09PYQQg8HQ0NDw9NNPHz58eMaMGTKZLDs7u7u722g0SiSSdevWZWRkLFq0yGw2b9u2Ta/Xy+XyzZs363Q6uVxO9/b+++/n5eUZjcb4+PiVK1cuXLiwqalJoVDIZDKpVHrgwAEPnqnHIdgBAAAAs4KDg2fMmHH8+HFCSFFRUWZm5q1bt3bt2nX+/HmtVvv4448XFhYSQvR6/dq1a7/++uuYmJji4uItW7aIxeLS0tLt27f3221NTc2GDRuOHz8eFhZWVFSk1WovXLiwc+fOe/fuDenpDSeYoBgAAAAYR9+NTU9PLyoq2rdvX2lpqcFgeOaZZwghXV1dc+fOJYTExsZGR0cTQiQSicFgeGCfMTEx8fHx9HJ+fv6JEyf4fH5dXV1tbe3UqVOZO5fhDMEOAAAAGJeenr5u3TqtVtvZ2ZmUlHTlyhWlUrl37157A6PR6OXlRS9zuVyLxdJ3dz6fb3+QrqenRygUEkJ8fHzoNSUlJWq1WqVSCQQCpVJpNpuH4pSGJdyKBQAAAMaJRKK0tLSVK1fSr03I5fIjR47U1tYSQkwmU01NTb+7tLW10cvh4eH19fVWq9Vms6lUKoeWLS0tYrFYIBA0Njaq1WqGT2VYQ7ADAACAofDSSy9dvnyZDnZisXjPnj0ZGRkJCQlz5sypq6tzbi8UChUKhVQqXbdunZ+f35o1a+Ry+fLly8eOHevQcsmSJQaDITMzc+PGjTKZbChOZrjCrVgAAAAYCkuXLqUoyv5jenp6enp63wbV1dX0wurVq+kF+qUK2voE6RxOAAAJIklEQVT169evX99ve19f32PHjjFR84iDK3YAAAAALIFgBwAAAMASCHYAAAAALIFn7AAAAEa3uYmEz/N0ETA4EOwAAABGNVv5WSa65S6dzkS34BqCHQx3ve/9p/Xeu96EzP2fNS+6at7S53//roFEM1EYAADAcINn7AAAAABYAsEOAAAAgCUQ7AAAAABYAsEOAAAAgCUQ7AAAAIBZixYt+vzzz+nlsrKy6dOnW63Wvg1MJpNEIvFEaWyDYAcAAADM2rVr13vvvdfa2mqxWN566609e/bweJg5jxEIdgAAAMAssVi8atWqzZs35+XlPf3000899VRBQcHkyZOlUunWrVsdGrvYBA+EeewAAACAce+8805SUlJnZ6dGo7l+/Xpubu6FCxeEQqFcLk9NTZ0+/e+zGTtvmj9/vmcrH1lwxQ4AAAAYJxAIXnvttfT09KCgoPLy8sWLFwcGBnp7e2dlZZWVldmbudgE7kCwAwAAgKHA5XK5XAQPZmF8AQAAYEjNmjXr6NGjra2t3d3dRUVFc+bMcWcTuAPP2AEAAMCQio+Pz8nJmTlzJkVRWVlZ8+bNM5lM99vk2VJHHA5FUZ6uAViuoaHh0XYUCAQhISHt7e337t0b3JKYIxKJ2tvbPV2FuwIDA/38/O7evdvT0+PpWtzC5/N5PF53d7enC3HX+PHjCSGNjY2eLsRdQqHQYrFYLBZPF+IWfEQQQiZMmDDwTmz/8ruBd+KMm7eRiW7BNdyKBQAAAGAJBDsAAAAAlkCwAwAAAGAJBDsAAAAAlsBbsQAAAKMa3nJgE1yxAwAAAGAJTHcCw9ft27f//Oc/y2Syp59+2tO1uMtms42gedXLysouXbr04osvhoWFeboWt9CfVxwOx9OFuOvjjz/m8XivvfaapwtxF0VRI2h4f/nll8OHD0+fPj01NdXTtbhrZH1EwEiEW7EwfFkslpaWlq6uLk8X8hBG1kd2Z2dnS0uL1Wr1dCHuGkGZg9ba2srnj6SP2ZE1wr29vS0tLWaz2dOFPISR9REBIxF+wwAAAABYAsEOAAAAgCUQ7AAAAABYAsEOAAAAgCUQ7AAAAABYAtOdwPBlNpsbGhoCAwPHjh3r6VrYqbm5ubW1deLEid7e3p6uhZ1u3brF4XAiIyM9XQg7dXV13b59OygoKDg42NO1AAwXCHYAAAAALIFbsQAAAAAsgWAHAAAAwBIIdgAAAAAsgWAHADCYLBYLnl0GAE9BsAMYXa5evdrvd2tevXr1+vXrQ18P+2zfvr2oqMj5G3gLCgrUarVHSmKZb7755siRI87rL126VFFRMfT1AAwrCHYAo8vhw4dNJpPz+ubm5srKyqGvh5UaGhr+9Kc/OWS7yZMnX7161VMlsYler580aRK93N3dbf8HCZ/P12q1nqsLYFhAsAMYde7cudPgRCAQNDQ0eLo0lnj++ec7OzuLioosFot9ZXh4+N27dz1YFWu0tbX5+/vTy62trX/605/o5aCgoJaWFs/VBTAs8D1dAAAMta+++srTJbCcUCjMzs7+7LPPvvjii8zMTHr+587OTkwEPSh8fX07Ojro5fb2dpvNZjabhUJhT08Pn48/ajDa4b8BgFEnOzs7NDTU01WwnLe394oVK4qKigoKCubMmcPn80+dOhUbG+vputggMjKyoqJCLBZzudyKiooxY8aUlpZOmzatvLw8IiLC09UBeBiCHcCo4+fnFxAQ4Okq2E8gELz88stnz549evRoT0+PWCyeP3++p4tig7S0tEOHDv3Xf/0Xh8MJDAxcsWLFZ599VlFR4e/vn52d7enqADwMXykGMLoYDIYJEyYIBAJPF8Jav/76a2BgII/Hs6+hKMpisXh5eXmwKpYxm823bt2yWCyTJk0SCARWq7W1tTUoKIjLxYPjMNoh2AGMUlartbOzkxDi6+vbN4XAYMEIMw0jDOAMt2IBRp0ffvihoqLi9u3bNpuNEMLlcsPDw1NSUiZPnuzp0lgCI8w0jDDA/SDYAYwuarX6zJkzMpksNTXVz8+PENLR0aHX64uLi9vb22fOnOnpAkc8jDDTMMIALiDYAYwu33//fXp6+pQpU/quTEhIiIiIOHPmDP4oDhxGmGkYYQAX8JwpwOjS1dXV71wn48aNox9XggHCCDMNIwzgAoIdwOgSFRV15syZ9vb2vivb29vPnDkTFRXlqarYBCPMNIwwgAt4KxZgdGlpafniiy/u3LkTEhLi6+tLCOns7Lx7925oaOjy5cuDgoI8XeCIhxFmGkYYwAUEO4BRx2azGQyG+vp6+nuZ/Pz8IiIioqOjMQfYYMEIMw0jDHA/CHYAoxTmAGMaRphpGGEAZ3grFmDUwRxgTMMIMw0jDHA/CHYAowvmAGMaRphpGGEAFxDsAEYXzAHGNIww0zDCAC7gOVOA0QVzgDENI8w0jDCACwh2AKML5gBjGkaYaRhhABfwVizA6II5wJiGEWYaRhjABQQ7gFEHc4AxDSPMNIwwwP0g2AGMUpgDjGkYYaZhhAGc4a1YgFEHc4AxDSPMNIwwwP0g2AGMLpgDjGkYYaZhhAFcQLADGF0wBxjTMMJMwwgDuIDnTAFGF8wBxjSMMNMwwgAuINgBjC6YA4xpGGGmYYQBXMBbsQCjC+YAYxpGmGkYYQAXEOwARh3MAcY0jDDTMMIA94NgB8MXJqliFIaXaRhhpmGEAZzhrVgYjjBJFaMwvEzDCDMNIwxwPwh2MOxgkipGYXiZhhFmGkYYwAUEOxh2MEkVozC8TMMIMw0jDOACnjOFYQeTVDEKw8s0jDDTMMIALiDYwbCDSaoYheFlGkaYaRhhABfwViwMO5ikilEYXqZhhJmGEQZwAcEOhiNMUsUoDC/TMMJMwwgD3A+CHQAAAABL4B83AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACyBYAcAAADAEgh2AAAAACzx/wEH1PgGoXyZugAAAABJRU5ErkJggg=="}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "### Preview the Population Data...", "cell_type": "markdown", "metadata": {}}, {"execution_count": 18, "cell_type": "code", "source": "## Now, let's preview the population data\ndim(dat_pop)\nnames(dat_pop)\nhead(dat_pop)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "[1] 148680      6", "text/markdown": "1. 148680\n2. 6\n\n\n", "text/html": "<ol class=list-inline>\n\t<li>148680<\/li>\n\t<li>6<\/li>\n<\/ol>\n", "text/latex": "\\begin{enumerate*}\n\\item 148680\n\\item 6\n\\end{enumerate*}\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "[1] \"year\"       \"county\"     \"race\"       \"gender\"     \"age_group\" \n[6] \"population\"", "text/markdown": "1. 'year'\n2. 'county'\n3. 'race'\n4. 'gender'\n5. 'age_group'\n6. 'population'\n\n\n", "text/html": "<ol class=list-inline>\n\t<li>'year'<\/li>\n\t<li>'county'<\/li>\n\t<li>'race'<\/li>\n\t<li>'gender'<\/li>\n\t<li>'age_group'<\/li>\n\t<li>'population'<\/li>\n<\/ol>\n", "text/latex": "\\begin{enumerate*}\n\\item 'year'\n\\item 'county'\n\\item 'race'\n\\item 'gender'\n\\item 'age\\_group'\n\\item 'population'\n\\end{enumerate*}\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "  year  county         race       gender    age_group population\n1 2005 Alameda All Combined All Combined All Combined    1459881\n2 2005 Alameda All Combined All Combined     Juvenile     354877\n3 2005 Alameda All Combined All Combined        Adult    1105004\n4 2005 Alameda All Combined All Combined          0-9     192318\n5 2005 Alameda All Combined All Combined        10-14     101688\n6 2005 Alameda All Combined All Combined        15-17      60871", "text/latex": "\\begin{tabular}{r|llllll}\n  & year & county & race & gender & age\\_group & population\\\\\n\\hline\n\t1 & 2005 & Alameda & All Combined & All Combined & All Combined & 1459881\\\\\n\t2 & 2005 & Alameda & All Combined & All Combined & Juvenile & 354877\\\\\n\t3 & 2005 & Alameda & All Combined & All Combined & Adult & 1105004\\\\\n\t4 & 2005 & Alameda & All Combined & All Combined & 0-9 & 192318\\\\\n\t5 & 2005 & Alameda & All Combined & All Combined & 10-14 & 101688\\\\\n\t6 & 2005 & Alameda & All Combined & All Combined & 15-17 & 60871\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>year<\/th><th scope=col>county<\/th><th scope=col>race<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>2005<\/td><td>Alameda<\/td><td>All Combined<\/td><td>All Combined<\/td><td>All Combined<\/td><td>1459881<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>2005<\/td><td>Alameda<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>354877<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>2005<\/td><td>Alameda<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Adult<\/td><td>1105004<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>2005<\/td><td>Alameda<\/td><td>All Combined<\/td><td>All Combined<\/td><td>0-9<\/td><td>192318<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>2005<\/td><td>Alameda<\/td><td>All Combined<\/td><td>All Combined<\/td><td>10-14<\/td><td>101688<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>2005<\/td><td>Alameda<\/td><td>All Combined<\/td><td>All Combined<\/td><td>15-17<\/td><td>60871<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"execution_count": 19, "cell_type": "code", "source": "## Looks like it's already aggregated along a number of dimensions. \n## Let's subset only the juveniles.\ndat_pop_jv <- dat_pop[dat_pop$age_group %in% \"Juvenile\",]\n\n## Ok, now, let's look at arrests of both genders and ignore the 'all combined' county value.\ndat_pop_jv <- dat_pop_jv[dat_pop_jv$gender %in% \"All Combined\" & !(dat_pop_jv$county %in% \"All Combined\"),]\n\n## Let's also remove the race 'all combined.'\ndat_pop_jv <- dat_pop_jv[!(dat_pop_jv$race %in% \"All Combined\"),]\n\n## Confirm we did this right by previewing the head and tail.\nhead(dat_pop_jv)\ntail(dat_pop_jv)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "    year  county                   race       gender age_group population\n38  2005 Alameda               Hispanic All Combined  Juvenile     103616\n74  2005 Alameda                  Black All Combined  Juvenile      49014\n110 2005 Alameda                  White All Combined  Juvenile      94600\n146 2005 Alameda Asian/Pacific Islander All Combined  Juvenile      84980\n182 2005 Alameda        Native American All Combined  Juvenile        929\n218 2005 Alameda                  Other All Combined  Juvenile      21737", "text/latex": "\\begin{tabular}{r|llllll}\n  & year & county & race & gender & age\\_group & population\\\\\n\\hline\n\t38 & 2005 & Alameda & Hispanic & All Combined & Juvenile & 103616\\\\\n\t74 & 2005 & Alameda & Black & All Combined & Juvenile & 49014\\\\\n\t110 & 2005 & Alameda & White & All Combined & Juvenile & 94600\\\\\n\t146 & 2005 & Alameda & Asian/Pacific Islander & All Combined & Juvenile & 84980\\\\\n\t182 & 2005 & Alameda & Native American & All Combined & Juvenile & 929\\\\\n\t218 & 2005 & Alameda & Other & All Combined & Juvenile & 21737\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>year<\/th><th scope=col>county<\/th><th scope=col>race<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>38<\/th><td>2005<\/td><td>Alameda<\/td><td>Hispanic<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>103616<\/td><\/tr>\n\t<tr><th scope=row>74<\/th><td>2005<\/td><td>Alameda<\/td><td>Black<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>49014<\/td><\/tr>\n\t<tr><th scope=row>110<\/th><td>2005<\/td><td>Alameda<\/td><td>White<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>94600<\/td><\/tr>\n\t<tr><th scope=row>146<\/th><td>2005<\/td><td>Alameda<\/td><td>Asian/Pacific Islander<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>84980<\/td><\/tr>\n\t<tr><th scope=row>182<\/th><td>2005<\/td><td>Alameda<\/td><td>Native American<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>929<\/td><\/tr>\n\t<tr><th scope=row>218<\/th><td>2005<\/td><td>Alameda<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>21737<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "       year county                   race       gender age_group population\n148466 2014   Yuba               Hispanic All Combined  Juvenile       7234\n148502 2014   Yuba                  Black All Combined  Juvenile        568\n148538 2014   Yuba                  White All Combined  Juvenile      10187\n148574 2014   Yuba Asian/Pacific Islander All Combined  Juvenile       1506\n148610 2014   Yuba        Native American All Combined  Juvenile        307\n148646 2014   Yuba                  Other All Combined  Juvenile       1552", "text/latex": "\\begin{tabular}{r|llllll}\n  & year & county & race & gender & age\\_group & population\\\\\n\\hline\n\t148466 & 2014 & Yuba & Hispanic & All Combined & Juvenile & 7234\\\\\n\t148502 & 2014 & Yuba & Black & All Combined & Juvenile & 568\\\\\n\t148538 & 2014 & Yuba & White & All Combined & Juvenile & 10187\\\\\n\t148574 & 2014 & Yuba & Asian/Pacific Islander & All Combined & Juvenile & 1506\\\\\n\t148610 & 2014 & Yuba & Native American & All Combined & Juvenile & 307\\\\\n\t148646 & 2014 & Yuba & Other & All Combined & Juvenile & 1552\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>year<\/th><th scope=col>county<\/th><th scope=col>race<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>148466<\/th><td>2014<\/td><td>Yuba<\/td><td>Hispanic<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7234<\/td><\/tr>\n\t<tr><th scope=row>148502<\/th><td>2014<\/td><td>Yuba<\/td><td>Black<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>568<\/td><\/tr>\n\t<tr><th scope=row>148538<\/th><td>2014<\/td><td>Yuba<\/td><td>White<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10187<\/td><\/tr>\n\t<tr><th scope=row>148574<\/th><td>2014<\/td><td>Yuba<\/td><td>Asian/Pacific Islander<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1506<\/td><\/tr>\n\t<tr><th scope=row>148610<\/th><td>2014<\/td><td>Yuba<\/td><td>Native American<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>307<\/td><\/tr>\n\t<tr><th scope=row>148646<\/th><td>2014<\/td><td>Yuba<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1552<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "**Please note: It looks like the CA DoJ (or the reporting agencies) might want to do some recoding/reclassification, if we want to properly analyze Native American, Other, and/or Suppressed_due_to_privacy_concern populations.**", "cell_type": "markdown", "metadata": {}}, {"execution_count": 20, "cell_type": "code", "source": "## Show the race / ethnicity categories of each dataset (arrests vs. population)\nunique(dat_juv$race_or_ethnicity)\nunique(dat_pop_jv$race)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "[1] \"Black\"                             \"Hispanic\"                         \n[3] \"White\"                             \"Asian/Pacific Islander\"           \n[5] \"Other\"                             \"suppressed_due_to_privacy_concern\"", "text/markdown": "1. 'Black'\n2. 'Hispanic'\n3. 'White'\n4. 'Asian/Pacific Islander'\n5. 'Other'\n6. 'suppressed_due_to_privacy_concern'\n\n\n", "text/html": "<ol class=list-inline>\n\t<li>'Black'<\/li>\n\t<li>'Hispanic'<\/li>\n\t<li>'White'<\/li>\n\t<li>'Asian/Pacific Islander'<\/li>\n\t<li>'Other'<\/li>\n\t<li>'suppressed_due_to_privacy_concern'<\/li>\n<\/ol>\n", "text/latex": "\\begin{enumerate*}\n\\item 'Black'\n\\item 'Hispanic'\n\\item 'White'\n\\item 'Asian/Pacific Islander'\n\\item 'Other'\n\\item 'suppressed\\_due\\_to\\_privacy\\_concern'\n\\end{enumerate*}\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "[1] \"Hispanic\"               \"Black\"                  \"White\"                 \n[4] \"Asian/Pacific Islander\" \"Native American\"        \"Other\"                 ", "text/markdown": "1. 'Hispanic'\n2. 'Black'\n3. 'White'\n4. 'Asian/Pacific Islander'\n5. 'Native American'\n6. 'Other'\n\n\n", "text/html": "<ol class=list-inline>\n\t<li>'Hispanic'<\/li>\n\t<li>'Black'<\/li>\n\t<li>'White'<\/li>\n\t<li>'Asian/Pacific Islander'<\/li>\n\t<li>'Native American'<\/li>\n\t<li>'Other'<\/li>\n<\/ol>\n", "text/latex": "\\begin{enumerate*}\n\\item 'Hispanic'\n\\item 'Black'\n\\item 'White'\n\\item 'Asian/Pacific Islander'\n\\item 'Native American'\n\\item 'Other'\n\\end{enumerate*}\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "### Joining and Previewing the Population and Arrests Datasets...\nJoining the two datasets, while removing three counties that don't seem to be represented in the arrests dataset.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 21, "cell_type": "code", "source": "## Join the pop and arrests datasets.\n## Start by relabeling the 'race' variable in the pop table. Also, until we've bound all years together, \nnames(dat_pop_jv)[3] <- \"race_or_ethnicity\"\nnames(cty_ethnic)[2] <- \"year\"\ndat_joined <- right_join(cty_ethnic, dat_pop_jv, by = c(\"county\",\"year\",\"race_or_ethnicity\"))\n\n## Let's sub out those counties that aren't represented in the arrests file.\ndat_joined <- dat_joined[!(dat_joined$county %in% \"Alpine\" | \n                           dat_joined$county %in% \"Amador\" |\n                           dat_joined$county %in% \"Yuba\"),]\n\n## Preview to confirm. \nhead(dat_joined)\ntail(dat_joined)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [6 x 7]\n\n   county year      race_or_ethnicity total       gender age_group population\n1 Alameda 2005               Hispanic  1465 All Combined  Juvenile     103616\n2 Alameda 2005                  Black  2299 All Combined  Juvenile      49014\n3 Alameda 2005                  White  1342 All Combined  Juvenile      94600\n4 Alameda 2005 Asian/Pacific Islander   483 All Combined  Juvenile      84980\n5 Alameda 2005        Native American    NA All Combined  Juvenile        929\n6 Alameda 2005                  Other   211 All Combined  Juvenile      21737", "text/latex": "\\begin{tabular}{r|lllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population\\\\\n\\hline\n\t1 & Alameda & 2005 & Hispanic & 1465 & All Combined & Juvenile & 103616\\\\\n\t2 & Alameda & 2005 & Black & 2299 & All Combined & Juvenile & 49014\\\\\n\t3 & Alameda & 2005 & White & 1342 & All Combined & Juvenile & 94600\\\\\n\t4 & Alameda & 2005 & Asian/Pacific Islander & 483 & All Combined & Juvenile & 84980\\\\\n\t5 & Alameda & 2005 & Native American & NA & All Combined & Juvenile & 929\\\\\n\t6 & Alameda & 2005 & Other & 211 & All Combined & Juvenile & 21737\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Alameda<\/td><td>2005<\/td><td>Hispanic<\/td><td>1465<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>103616<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Alameda<\/td><td>2005<\/td><td>Black<\/td><td>2299<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>49014<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Alameda<\/td><td>2005<\/td><td>White<\/td><td>1342<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>94600<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Alameda<\/td><td>2005<\/td><td>Asian/Pacific Islander<\/td><td>483<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>84980<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Alameda<\/td><td>2005<\/td><td>Native American<\/td><td>NA<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>929<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Alameda<\/td><td>2005<\/td><td>Other<\/td><td>211<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>21737<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "Source: local data frame [6 x 7]\n\n  county year      race_or_ethnicity total       gender age_group population\n1   Yolo 2014               Hispanic   108 All Combined  Juvenile      19981\n2   Yolo 2014                  Black    32 All Combined  Juvenile        999\n3   Yolo 2014                  White   117 All Combined  Juvenile      16625\n4   Yolo 2014 Asian/Pacific Islander     8 All Combined  Juvenile       4069\n5   Yolo 2014        Native American    NA All Combined  Juvenile        215\n6   Yolo 2014                  Other     5 All Combined  Juvenile       2629", "text/latex": "\\begin{tabular}{r|lllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population\\\\\n\\hline\n\t1 & Yolo & 2014 & Hispanic & 108 & All Combined & Juvenile & 19981\\\\\n\t2 & Yolo & 2014 & Black & 32 & All Combined & Juvenile & 999\\\\\n\t3 & Yolo & 2014 & White & 117 & All Combined & Juvenile & 16625\\\\\n\t4 & Yolo & 2014 & Asian/Pacific Islander & 8 & All Combined & Juvenile & 4069\\\\\n\t5 & Yolo & 2014 & Native American & NA & All Combined & Juvenile & 215\\\\\n\t6 & Yolo & 2014 & Other & 5 & All Combined & Juvenile & 2629\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Yolo<\/td><td>2014<\/td><td>Hispanic<\/td><td>108<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>19981<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Yolo<\/td><td>2014<\/td><td>Black<\/td><td>32<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>999<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Yolo<\/td><td>2014<\/td><td>White<\/td><td>117<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>16625<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Yolo<\/td><td>2014<\/td><td>Asian/Pacific Islander<\/td><td>8<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>4069<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Yolo<\/td><td>2014<\/td><td>Native American<\/td><td>NA<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>215<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Yolo<\/td><td>2014<\/td><td>Other<\/td><td>5<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>2629<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "### Plot arrest rates by County for a Single Year (2014)", "cell_type": "markdown", "metadata": {}}, {"execution_count": 35, "cell_type": "code", "source": "### Plot Arrest Rates by County for a Single Year (2014)...\n## Let's remove post-join arrest total NAs from our analysis...for now.\ndat_joined <- dat_joined[!is.na(dat_joined$total),]\n\n## Actually add a column just for arrest rate by ethnic population per county.\ndat_joined$eth_arrest_rate <- round((dat_joined$total)/(dat_joined$population), 5)\n\n## Now, let's panel plot arrest rates by county.\nplot_ethnic_norm <- ggplot(dat_joined[!(dat_joined$race_or_ethnicity %in% \"Native American\") & dat_joined$year %in% \"2014\",], \n                        aes(x = race_or_ethnicity, y = eth_arrest_rate, fill = race_or_ethnicity), na.rm=T) + \n                        geom_bar(stat = \"identity\") + coord_flip() + facet_wrap(~county) +  \n                        theme(axis.text.x=element_text(angle=-90,hjust=1,vjust=0.5, size = 8), axis.text.x=element_text(size = 8),\n                        legend.position = \"none\", strip.text=element_text(size = 8), axis.title.x=element_blank(),\n                        axis.title.y=element_blank()) +\n                        ggtitle(\"Ethnic Breakdown of Arrest Rates by County\\r\n-2014 Only-\")\n\n## Print plot\nsuppressWarnings(print(plot_ethnic_norm))", "outputs": [{"output_type": "display_data", "data": {"image/png": 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"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "## II. Statistical Tests\n\nPerhaps the simplest way to detect outliers, where normal distributions are expected, is to simply compute the Z-score of each observation (i.e. number of standard deviations an observation is from a distribution's mean) and mark all observations that are equal to or greater than +/- 3 SDs (standard deviations) as outliers, because they only have a 0.01% of being natural to their respective systems. \n\nTo execute this, I simply computed the Z-score of each county's ethnic group arrest rate (vs. all counties) for that year. \n\n*Please note: I may soon be implementing generalized Extreme Studentized Deviate (ESD) tests (Rosen, 1983), instead of the simple Z-score approach, as these are a considered more robust way to detect outliers.*", "cell_type": "markdown", "metadata": {}}, {"source": "#### Compute Arrest Rate Probabilities and Z-Scores", "cell_type": "markdown", "metadata": {}}, {"execution_count": 24, "cell_type": "code", "source": "#### Looping approach (Please don't hate me, RNG! I'll vectorize asap :)) ####\n\n## Create empty dataframe\ndat_stats <- dat_joined[0,]\ndat_stats$rate_prob <- numeric(0)\ndat_stats$z_score <- numeric(0)\n\n## Nested loop (computing stats per race/ethnic group, per year)\nfor(i in unique(dat_joined$year)){\n    \n    ## Subset to iterative year\n    dat_year <- dat_joined[dat_joined$year %in% i,]\n    \n    for(j in unique(dat_year$race_or_ethnicity)){\n        \n        ## Subset to iterative race/ethnicity\n        dat_race <- dat_year[dat_year$race_or_ethnicity %in% j,]\n        \n        ## Compute the probability of the observed arrest rate\n        dat_race$percentile <- round(pnorm(dat_race$eth_arrest_rate, mean(dat_race$eth_arrest_rate, na.rm = T), \n                                    sd(dat_race$eth_arrest_rate, na.rm = T), lower.tail = T, log.p = F), 5)\n        \n        ## Compute the Z-score of the observed arrest rates\n        dat_race$z_score <- qnorm(dat_race$percentile, lower.tail = T, log.p = F)\n        \n        ## Bind to burgeoning dataframe\n        dat_stats <- rbind(dat_stats, dat_race)\n    }\n}\n\n## Now, preview those who have evidently been outliers in enforcement upon every ethnic group per every year.\npaste(\"Number of outlying instances over this time-span:\", nrow(dat_stats[dat_stats$z_score >= 3,]))\nhead(dat_stats[dat_stats$z_score >= 3,], 20)\ntail(dat_stats[dat_stats$z_score >= 3,], 20)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "[1] \"Number of outlying instances over this time-span: 33\"", "text/markdown": "'Number of outlying instances over this time-span: 33'", "text/html": "'Number of outlying instances over this time-span: 33'", "text/latex": "'Number of outlying instances over this time-span: 33'"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "Source: local data frame [20 x 10]\n\n          county year      race_or_ethnicity total       gender age_group\n1          Marin 2005                  Black   230 All Combined  Juvenile\n2          Kings 2005                  White   607 All Combined  Juvenile\n3  San Francisco 2005                  Other   495 All Combined  Juvenile\n4          Marin 2006                  Black   270 All Combined  Juvenile\n5          Kings 2006                  White   576 All Combined  Juvenile\n6  San Francisco 2006                  Other   539 All Combined  Juvenile\n7          Marin 2007                  Black   244 All Combined  Juvenile\n8          Kings 2007                  White   519 All Combined  Juvenile\n9  San Francisco 2007                  Other   513 All Combined  Juvenile\n10         Marin 2008               Hispanic   602 All Combined  Juvenile\n11         Marin 2008                  Black   219 All Combined  Juvenile\n12         Kings 2008                  White   500 All Combined  Juvenile\n13 San Francisco 2008                  Other   597 All Combined  Juvenile\n14         Marin 2009               Hispanic   659 All Combined  Juvenile\n15 San Francisco 2009                  Other   582 All Combined  Juvenile\n16         Marin 2010               Hispanic   536 All Combined  Juvenile\n17        Shasta 2010                  Black    95 All Combined  Juvenile\n18 San Francisco 2010                  Other   478 All Combined  Juvenile\n19         Marin 2011               Hispanic   546 All Combined  Juvenile\n20         Kings 2011 Asian/Pacific Islander    17 All Combined  Juvenile\nVariables not shown: population (int), eth_arrest_rate (dbl), percentile (dbl),\n  z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & Marin & 2005 & Black & 230 & All Combined & Juvenile & 1194 & 0.19263 & 0.99974 & 3.470237\\\\\n\t2 & Kings & 2005 & White & 607 & All Combined & Juvenile & 12285 & 0.04941 & 0.99986 & 3.633134\\\\\n\t3 & San Francisco & 2005 & Other & 495 & All Combined & Juvenile & 6834 & 0.07243 & 0.99988 & 3.672701\\\\\n\t4 & Marin & 2006 & Black & 270 & All Combined & Juvenile & 1172 & 0.23038 & 0.99984 & 3.598547\\\\\n\t5 & Kings & 2006 & White & 576 & All Combined & Juvenile & 12124 & 0.04751 & 0.99971 & 3.440799\\\\\n\t6 & San Francisco & 2006 & Other & 539 & All Combined & Juvenile & 7070 & 0.07624 & 1 & Inf\\\\\n\t7 & Marin & 2007 & Black & 244 & All Combined & Juvenile & 1146 & 0.21291 & 0.99984 & 3.598547\\\\\n\t8 & Kings & 2007 & White & 519 & All Combined & Juvenile & 11948 & 0.04344 & 0.99921 & 3.159574\\\\\n\t9 & San Francisco & 2007 & Other & 513 & All Combined & Juvenile & 7263 & 0.07063 & 1 & Inf\\\\\n\t10 & Marin & 2008 & Hispanic & 602 & All Combined & Juvenile & 10649 & 0.05653 & 0.9993 & 3.194651\\\\\n\t11 & Marin & 2008 & Black & 219 & All Combined & Juvenile & 1130 & 0.19381 & 0.99899 & 3.087276\\\\\n\t12 & Kings & 2008 & White & 500 & All Combined & Juvenile & 11648 & 0.04293 & 0.99913 & 3.131358\\\\\n\t13 & San Francisco & 2008 & Other & 597 & All Combined & Juvenile & 7348 & 0.08125 & 1 & Inf\\\\\n\t14 & Marin & 2009 & Hispanic & 659 & All Combined & Juvenile & 10916 & 0.06037 & 0.99993 & 3.808168\\\\\n\t15 & San Francisco & 2009 & Other & 582 & All Combined & Juvenile & 7716 & 0.07543 & 1 & Inf\\\\\n\t16 & Marin & 2010 & Hispanic & 536 & All Combined & Juvenile & 11434 & 0.04688 & 0.99906 & 3.108562\\\\\n\t17 & Shasta & 2010 & Black & 95 & All Combined & Juvenile & 447 & 0.21253 & 0.99966 & 3.397518\\\\\n\t18 & San Francisco & 2010 & Other & 478 & All Combined & Juvenile & 8199 & 0.0583 & 1 & Inf\\\\\n\t19 & Marin & 2011 & Hispanic & 546 & All Combined & Juvenile & 11659 & 0.04683 & 0.99994 & 3.846126\\\\\n\t20 & Kings & 2011 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1159 & 0.01467 & 0.9993 & 3.194651\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Marin<\/td><td>2005<\/td><td>Black<\/td><td>230<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1194<\/td><td>0.19263<\/td><td>0.99974<\/td><td>3.470237<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Kings<\/td><td>2005<\/td><td>White<\/td><td>607<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>12285<\/td><td>0.04941<\/td><td>0.99986<\/td><td>3.633134<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>San Francisco<\/td><td>2005<\/td><td>Other<\/td><td>495<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>6834<\/td><td>0.07243<\/td><td>0.99988<\/td><td>3.672701<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Marin<\/td><td>2006<\/td><td>Black<\/td><td>270<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1172<\/td><td>0.23038<\/td><td>0.99984<\/td><td>3.598547<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Kings<\/td><td>2006<\/td><td>White<\/td><td>576<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>12124<\/td><td>0.04751<\/td><td>0.99971<\/td><td>3.440799<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>San Francisco<\/td><td>2006<\/td><td>Other<\/td><td>539<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7070<\/td><td>0.07624<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>Marin<\/td><td>2007<\/td><td>Black<\/td><td>244<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1146<\/td><td>0.21291<\/td><td>0.99984<\/td><td>3.598547<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>Kings<\/td><td>2007<\/td><td>White<\/td><td>519<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11948<\/td><td>0.04344<\/td><td>0.99921<\/td><td>3.159574<\/td><\/tr>\n\t<tr><th scope=row>9<\/th><td>San Francisco<\/td><td>2007<\/td><td>Other<\/td><td>513<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7263<\/td><td>0.07063<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>10<\/th><td>Marin<\/td><td>2008<\/td><td>Hispanic<\/td><td>602<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10649<\/td><td>0.05653<\/td><td>0.9993<\/td><td>3.194651<\/td><\/tr>\n\t<tr><th scope=row>11<\/th><td>Marin<\/td><td>2008<\/td><td>Black<\/td><td>219<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1130<\/td><td>0.19381<\/td><td>0.99899<\/td><td>3.087276<\/td><\/tr>\n\t<tr><th scope=row>12<\/th><td>Kings<\/td><td>2008<\/td><td>White<\/td><td>500<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11648<\/td><td>0.04293<\/td><td>0.99913<\/td><td>3.131358<\/td><\/tr>\n\t<tr><th scope=row>13<\/th><td>San Francisco<\/td><td>2008<\/td><td>Other<\/td><td>597<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7348<\/td><td>0.08125<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>14<\/th><td>Marin<\/td><td>2009<\/td><td>Hispanic<\/td><td>659<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10916<\/td><td>0.06037<\/td><td>0.99993<\/td><td>3.808168<\/td><\/tr>\n\t<tr><th scope=row>15<\/th><td>San Francisco<\/td><td>2009<\/td><td>Other<\/td><td>582<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7716<\/td><td>0.07543<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>16<\/th><td>Marin<\/td><td>2010<\/td><td>Hispanic<\/td><td>536<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11434<\/td><td>0.04688<\/td><td>0.99906<\/td><td>3.108562<\/td><\/tr>\n\t<tr><th scope=row>17<\/th><td>Shasta<\/td><td>2010<\/td><td>Black<\/td><td>95<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>447<\/td><td>0.21253<\/td><td>0.99966<\/td><td>3.397518<\/td><\/tr>\n\t<tr><th scope=row>18<\/th><td>San Francisco<\/td><td>2010<\/td><td>Other<\/td><td>478<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8199<\/td><td>0.0583<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>19<\/th><td>Marin<\/td><td>2011<\/td><td>Hispanic<\/td><td>546<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11659<\/td><td>0.04683<\/td><td>0.99994<\/td><td>3.846126<\/td><\/tr>\n\t<tr><th scope=row>20<\/th><td>Kings<\/td><td>2011<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1159<\/td><td>0.01467<\/td><td>0.9993<\/td><td>3.194651<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "Source: local data frame [20 x 10]\n\n          county year      race_or_ethnicity total       gender age_group\n1          Marin 2009               Hispanic   659 All Combined  Juvenile\n2  San Francisco 2009                  Other   582 All Combined  Juvenile\n3          Marin 2010               Hispanic   536 All Combined  Juvenile\n4         Shasta 2010                  Black    95 All Combined  Juvenile\n5  San Francisco 2010                  Other   478 All Combined  Juvenile\n6          Marin 2011               Hispanic   546 All Combined  Juvenile\n7          Kings 2011 Asian/Pacific Islander    17 All Combined  Juvenile\n8  San Francisco 2011                  Other   426 All Combined  Juvenile\n9         Shasta 2012 Asian/Pacific Islander    17 All Combined  Juvenile\n10          Kern 2012                  Other   274 All Combined  Juvenile\n11 San Francisco 2012                  Other   348 All Combined  Juvenile\n12        Shasta 2013                  Black    59 All Combined  Juvenile\n13         Kings 2013 Asian/Pacific Islander    17 All Combined  Juvenile\n14          Kern 2013                  Other   216 All Combined  Juvenile\n15 San Francisco 2013                  Other   307 All Combined  Juvenile\n16        Shasta 2014                  Black    58 All Combined  Juvenile\n17         Kings 2014                  White   209 All Combined  Juvenile\n18         Kings 2014 Asian/Pacific Islander    45 All Combined  Juvenile\n19          Kern 2014                  Other   163 All Combined  Juvenile\n20 San Francisco 2014                  Other   257 All Combined  Juvenile\nVariables not shown: population (int), eth_arrest_rate (dbl), percentile (dbl),\n  z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & Marin & 2009 & Hispanic & 659 & All Combined & Juvenile & 10916 & 0.06037 & 0.99993 & 3.808168\\\\\n\t2 & San Francisco & 2009 & Other & 582 & All Combined & Juvenile & 7716 & 0.07543 & 1 & Inf\\\\\n\t3 & Marin & 2010 & Hispanic & 536 & All Combined & Juvenile & 11434 & 0.04688 & 0.99906 & 3.108562\\\\\n\t4 & Shasta & 2010 & Black & 95 & All Combined & Juvenile & 447 & 0.21253 & 0.99966 & 3.397518\\\\\n\t5 & San Francisco & 2010 & Other & 478 & All Combined & Juvenile & 8199 & 0.0583 & 1 & Inf\\\\\n\t6 & Marin & 2011 & Hispanic & 546 & All Combined & Juvenile & 11659 & 0.04683 & 0.99994 & 3.846126\\\\\n\t7 & Kings & 2011 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1159 & 0.01467 & 0.9993 & 3.194651\\\\\n\t8 & San Francisco & 2011 & Other & 426 & All Combined & Juvenile & 8722 & 0.04884 & 1 & Inf\\\\\n\t9 & Shasta & 2012 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1157 & 0.01469 & 0.99942 & 3.248537\\\\\n\t10 & Kern & 2012 & Other & 274 & All Combined & Juvenile & 6743 & 0.04063 & 0.99992 & 3.775012\\\\\n\t11 & San Francisco & 2012 & Other & 348 & All Combined & Juvenile & 9251 & 0.03762 & 0.9997 & 3.431614\\\\\n\t12 & Shasta & 2013 & Black & 59 & All Combined & Juvenile & 408 & 0.14461 & 0.99976 & 3.491676\\\\\n\t13 & Kings & 2013 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1018 & 0.0167 & 0.99986 & 3.633134\\\\\n\t14 & Kern & 2013 & Other & 216 & All Combined & Juvenile & 6844 & 0.03156 & 0.99988 & 3.672701\\\\\n\t15 & San Francisco & 2013 & Other & 307 & All Combined & Juvenile & 9862 & 0.03113 & 0.99985 & 3.6153\\\\\n\t16 & Shasta & 2014 & Black & 58 & All Combined & Juvenile & 404 & 0.14356 & 0.99996 & 3.9444\\\\\n\t17 & Kings & 2014 & White & 209 & All Combined & Juvenile & 8628 & 0.02422 & 0.99994 & 3.846126\\\\\n\t18 & Kings & 2014 & Asian/Pacific Islander & 45 & All Combined & Juvenile & 995 & 0.04523 & 1 & Inf\\\\\n\t19 & Kern & 2014 & Other & 163 & All Combined & Juvenile & 7011 & 0.02325 & 0.99907 & 3.111721\\\\\n\t20 & San Francisco & 2014 & Other & 257 & All Combined & Juvenile & 10500 & 0.02448 & 0.99956 & 3.326323\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Marin<\/td><td>2009<\/td><td>Hispanic<\/td><td>659<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10916<\/td><td>0.06037<\/td><td>0.99993<\/td><td>3.808168<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>San Francisco<\/td><td>2009<\/td><td>Other<\/td><td>582<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7716<\/td><td>0.07543<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Marin<\/td><td>2010<\/td><td>Hispanic<\/td><td>536<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11434<\/td><td>0.04688<\/td><td>0.99906<\/td><td>3.108562<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Shasta<\/td><td>2010<\/td><td>Black<\/td><td>95<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>447<\/td><td>0.21253<\/td><td>0.99966<\/td><td>3.397518<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>San Francisco<\/td><td>2010<\/td><td>Other<\/td><td>478<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8199<\/td><td>0.0583<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Marin<\/td><td>2011<\/td><td>Hispanic<\/td><td>546<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11659<\/td><td>0.04683<\/td><td>0.99994<\/td><td>3.846126<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>Kings<\/td><td>2011<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1159<\/td><td>0.01467<\/td><td>0.9993<\/td><td>3.194651<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>San Francisco<\/td><td>2011<\/td><td>Other<\/td><td>426<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8722<\/td><td>0.04884<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>9<\/th><td>Shasta<\/td><td>2012<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1157<\/td><td>0.01469<\/td><td>0.99942<\/td><td>3.248537<\/td><\/tr>\n\t<tr><th scope=row>10<\/th><td>Kern<\/td><td>2012<\/td><td>Other<\/td><td>274<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>6743<\/td><td>0.04063<\/td><td>0.99992<\/td><td>3.775012<\/td><\/tr>\n\t<tr><th scope=row>11<\/th><td>San Francisco<\/td><td>2012<\/td><td>Other<\/td><td>348<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9251<\/td><td>0.03762<\/td><td>0.9997<\/td><td>3.431614<\/td><\/tr>\n\t<tr><th scope=row>12<\/th><td>Shasta<\/td><td>2013<\/td><td>Black<\/td><td>59<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>408<\/td><td>0.14461<\/td><td>0.99976<\/td><td>3.491676<\/td><\/tr>\n\t<tr><th scope=row>13<\/th><td>Kings<\/td><td>2013<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1018<\/td><td>0.0167<\/td><td>0.99986<\/td><td>3.633134<\/td><\/tr>\n\t<tr><th scope=row>14<\/th><td>Kern<\/td><td>2013<\/td><td>Other<\/td><td>216<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>6844<\/td><td>0.03156<\/td><td>0.99988<\/td><td>3.672701<\/td><\/tr>\n\t<tr><th scope=row>15<\/th><td>San Francisco<\/td><td>2013<\/td><td>Other<\/td><td>307<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9862<\/td><td>0.03113<\/td><td>0.99985<\/td><td>3.6153<\/td><\/tr>\n\t<tr><th scope=row>16<\/th><td>Shasta<\/td><td>2014<\/td><td>Black<\/td><td>58<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>404<\/td><td>0.14356<\/td><td>0.99996<\/td><td>3.9444<\/td><\/tr>\n\t<tr><th scope=row>17<\/th><td>Kings<\/td><td>2014<\/td><td>White<\/td><td>209<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8628<\/td><td>0.02422<\/td><td>0.99994<\/td><td>3.846126<\/td><\/tr>\n\t<tr><th scope=row>18<\/th><td>Kings<\/td><td>2014<\/td><td>Asian/Pacific Islander<\/td><td>45<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>995<\/td><td>0.04523<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>19<\/th><td>Kern<\/td><td>2014<\/td><td>Other<\/td><td>163<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7011<\/td><td>0.02325<\/td><td>0.99907<\/td><td>3.111721<\/td><\/tr>\n\t<tr><th scope=row>20<\/th><td>San Francisco<\/td><td>2014<\/td><td>Other<\/td><td>257<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10500<\/td><td>0.02448<\/td><td>0.99956<\/td><td>3.326323<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false}}, {"source": "#### Investigating Interesting Patterns\nInteresting. There seem to be a lot of the same few counties represented. Let's dig deeper.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 25, "cell_type": "code", "source": "## Draw up a frequency table of the instances per outlying county from above.\ntable(dat_stats[dat_stats$z_score >= 3, \"county\"])", "outputs": [{"output_type": "display_data", "data": {"text/plain": "\n         Kern         Kings         Marin San Francisco        Shasta \n            3             8             8            10             4 "}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "#### Three Very Interesting Counties\nSan Francisco is very interesting, for its sheer volume, proximity, and, well, how strange it is to see here, given how \"cosmopolitan\" it's often considered to be. Kings and Marin are also interesting, for their volume and proximity. Thus, let's investigate a little further.\n\n*Note: in actuality, all 5 counties above should be considered interesting, given their propensity for outlying.*", "cell_type": "markdown", "metadata": {}}, {"source": "#### San Francisco County\nAH, in the table below, San Francisco proves to be an outlier for enforcement upon those classified as racially/ethnically 'Other' in every year within our dataset. There's two ways one can interpret this: Either San Francisco is indeed extremely cosmopolitan and the high number of 'Other' designees simply points to the proportion of mixed race/ethnicity one might assume such a city to have, OR, for some operational policy reason, SFPD is simply choosing to label a large segment of arrested juveniles as 'Other.'", "cell_type": "markdown", "metadata": {}}, {"execution_count": 26, "cell_type": "code", "source": "## Let's isolate those SF cases to see if there's a pattern there.\ndat_stats[dat_stats$z_score >= 3 & dat_stats$county %in% \"San Francisco\",]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [10 x 10]\n\n          county year race_or_ethnicity total       gender age_group population\n1  San Francisco 2005             Other   495 All Combined  Juvenile       6834\n2  San Francisco 2006             Other   539 All Combined  Juvenile       7070\n3  San Francisco 2007             Other   513 All Combined  Juvenile       7263\n4  San Francisco 2008             Other   597 All Combined  Juvenile       7348\n5  San Francisco 2009             Other   582 All Combined  Juvenile       7716\n6  San Francisco 2010             Other   478 All Combined  Juvenile       8199\n7  San Francisco 2011             Other   426 All Combined  Juvenile       8722\n8  San Francisco 2012             Other   348 All Combined  Juvenile       9251\n9  San Francisco 2013             Other   307 All Combined  Juvenile       9862\n10 San Francisco 2014             Other   257 All Combined  Juvenile      10500\nVariables not shown: eth_arrest_rate (dbl), percentile (dbl), z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & San Francisco & 2005 & Other & 495 & All Combined & Juvenile & 6834 & 0.07243 & 0.99988 & 3.672701\\\\\n\t2 & San Francisco & 2006 & Other & 539 & All Combined & Juvenile & 7070 & 0.07624 & 1 & Inf\\\\\n\t3 & San Francisco & 2007 & Other & 513 & All Combined & Juvenile & 7263 & 0.07063 & 1 & Inf\\\\\n\t4 & San Francisco & 2008 & Other & 597 & All Combined & Juvenile & 7348 & 0.08125 & 1 & Inf\\\\\n\t5 & San Francisco & 2009 & Other & 582 & All Combined & Juvenile & 7716 & 0.07543 & 1 & Inf\\\\\n\t6 & San Francisco & 2010 & Other & 478 & All Combined & Juvenile & 8199 & 0.0583 & 1 & Inf\\\\\n\t7 & San Francisco & 2011 & Other & 426 & All Combined & Juvenile & 8722 & 0.04884 & 1 & Inf\\\\\n\t8 & San Francisco & 2012 & Other & 348 & All Combined & Juvenile & 9251 & 0.03762 & 0.9997 & 3.431614\\\\\n\t9 & San Francisco & 2013 & Other & 307 & All Combined & Juvenile & 9862 & 0.03113 & 0.99985 & 3.6153\\\\\n\t10 & San Francisco & 2014 & Other & 257 & All Combined & Juvenile & 10500 & 0.02448 & 0.99956 & 3.326323\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>San Francisco<\/td><td>2005<\/td><td>Other<\/td><td>495<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>6834<\/td><td>0.07243<\/td><td>0.99988<\/td><td>3.672701<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>San Francisco<\/td><td>2006<\/td><td>Other<\/td><td>539<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7070<\/td><td>0.07624<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>San Francisco<\/td><td>2007<\/td><td>Other<\/td><td>513<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7263<\/td><td>0.07063<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>San Francisco<\/td><td>2008<\/td><td>Other<\/td><td>597<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7348<\/td><td>0.08125<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>San Francisco<\/td><td>2009<\/td><td>Other<\/td><td>582<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7716<\/td><td>0.07543<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>San Francisco<\/td><td>2010<\/td><td>Other<\/td><td>478<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8199<\/td><td>0.0583<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>San Francisco<\/td><td>2011<\/td><td>Other<\/td><td>426<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8722<\/td><td>0.04884<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>San Francisco<\/td><td>2012<\/td><td>Other<\/td><td>348<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9251<\/td><td>0.03762<\/td><td>0.9997<\/td><td>3.431614<\/td><\/tr>\n\t<tr><th scope=row>9<\/th><td>San Francisco<\/td><td>2013<\/td><td>Other<\/td><td>307<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9862<\/td><td>0.03113<\/td><td>0.99985<\/td><td>3.6153<\/td><\/tr>\n\t<tr><th scope=row>10<\/th><td>San Francisco<\/td><td>2014<\/td><td>Other<\/td><td>257<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10500<\/td><td>0.02448<\/td><td>0.99956<\/td><td>3.326323<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "#### Kings County\nVery interestingly, one will note below that Kings has historically been an outlier for arrests of Whites and Asians (vs. other counties). However, the picture gets stranger when we include \"mild outliers\" at 2 standard deviations from the mean. In the second table below, one will note that Kings County was at least a mild outlier for at least one ethnic group *every year*, and in only one of those years were they an outlier for blacks. **What is so different about Kings???**", "cell_type": "markdown", "metadata": {}}, {"execution_count": 27, "cell_type": "code", "source": "## Isolate Kings cases to see if there's a pattern.\ndat_stats[dat_stats$z_score >= 3 & dat_stats$county %in% \"Kings\",]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [8 x 10]\n\n  county year      race_or_ethnicity total       gender age_group population\n1  Kings 2005                  White   607 All Combined  Juvenile      12285\n2  Kings 2006                  White   576 All Combined  Juvenile      12124\n3  Kings 2007                  White   519 All Combined  Juvenile      11948\n4  Kings 2008                  White   500 All Combined  Juvenile      11648\n5  Kings 2011 Asian/Pacific Islander    17 All Combined  Juvenile       1159\n6  Kings 2013 Asian/Pacific Islander    17 All Combined  Juvenile       1018\n7  Kings 2014                  White   209 All Combined  Juvenile       8628\n8  Kings 2014 Asian/Pacific Islander    45 All Combined  Juvenile        995\nVariables not shown: eth_arrest_rate (dbl), percentile (dbl), z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & Kings & 2005 & White & 607 & All Combined & Juvenile & 12285 & 0.04941 & 0.99986 & 3.633134\\\\\n\t2 & Kings & 2006 & White & 576 & All Combined & Juvenile & 12124 & 0.04751 & 0.99971 & 3.440799\\\\\n\t3 & Kings & 2007 & White & 519 & All Combined & Juvenile & 11948 & 0.04344 & 0.99921 & 3.159574\\\\\n\t4 & Kings & 2008 & White & 500 & All Combined & Juvenile & 11648 & 0.04293 & 0.99913 & 3.131358\\\\\n\t5 & Kings & 2011 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1159 & 0.01467 & 0.9993 & 3.194651\\\\\n\t6 & Kings & 2013 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1018 & 0.0167 & 0.99986 & 3.633134\\\\\n\t7 & Kings & 2014 & White & 209 & All Combined & Juvenile & 8628 & 0.02422 & 0.99994 & 3.846126\\\\\n\t8 & Kings & 2014 & Asian/Pacific Islander & 45 & All Combined & Juvenile & 995 & 0.04523 & 1 & Inf\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Kings<\/td><td>2005<\/td><td>White<\/td><td>607<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>12285<\/td><td>0.04941<\/td><td>0.99986<\/td><td>3.633134<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Kings<\/td><td>2006<\/td><td>White<\/td><td>576<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>12124<\/td><td>0.04751<\/td><td>0.99971<\/td><td>3.440799<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Kings<\/td><td>2007<\/td><td>White<\/td><td>519<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11948<\/td><td>0.04344<\/td><td>0.99921<\/td><td>3.159574<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Kings<\/td><td>2008<\/td><td>White<\/td><td>500<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11648<\/td><td>0.04293<\/td><td>0.99913<\/td><td>3.131358<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Kings<\/td><td>2011<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1159<\/td><td>0.01467<\/td><td>0.9993<\/td><td>3.194651<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Kings<\/td><td>2013<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1018<\/td><td>0.0167<\/td><td>0.99986<\/td><td>3.633134<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>Kings<\/td><td>2014<\/td><td>White<\/td><td>209<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8628<\/td><td>0.02422<\/td><td>0.99994<\/td><td>3.846126<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>Kings<\/td><td>2014<\/td><td>Asian/Pacific Islander<\/td><td>45<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>995<\/td><td>0.04523<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false}}, {"execution_count": 28, "cell_type": "code", "source": "## Isolate Kings cases to see if there's a pattern at just 2 SDs from the mean.\ndat_stats[dat_stats$z_score >= 2& dat_stats$county %in% \"Kings\",]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [21 x 10]\n\n   county year      race_or_ethnicity total       gender age_group population\n1   Kings 2005               Hispanic  1075 All Combined  Juvenile      23908\n2   Kings 2005                  White   607 All Combined  Juvenile      12285\n3   Kings 2005                  Other    76 All Combined  Juvenile       1352\n4   Kings 2006               Hispanic  1103 All Combined  Juvenile      24535\n5   Kings 2006                  White   576 All Combined  Juvenile      12124\n6   Kings 2007                  White   519 All Combined  Juvenile      11948\n7   Kings 2008                  White   500 All Combined  Juvenile      11648\n8   Kings 2009                  White   351 All Combined  Juvenile      11101\n9   Kings 2009 Asian/Pacific Islander    20 All Combined  Juvenile       1152\n10  Kings 2010                  White   356 All Combined  Juvenile      10748\n..    ...  ...                    ...   ...          ...       ...        ...\nVariables not shown: eth_arrest_rate (dbl), percentile (dbl), z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & Kings & 2005 & Hispanic & 1075 & All Combined & Juvenile & 23908 & 0.04496 & 0.99759 & 2.818824\\\\\n\t2 & Kings & 2005 & White & 607 & All Combined & Juvenile & 12285 & 0.04941 & 0.99986 & 3.633134\\\\\n\t3 & Kings & 2005 & Other & 76 & All Combined & Juvenile & 1352 & 0.05621 & 0.99612 & 2.662338\\\\\n\t4 & Kings & 2006 & Hispanic & 1103 & All Combined & Juvenile & 24535 & 0.04496 & 0.99231 & 2.423305\\\\\n\t5 & Kings & 2006 & White & 576 & All Combined & Juvenile & 12124 & 0.04751 & 0.99971 & 3.440799\\\\\n\t6 & Kings & 2007 & White & 519 & All Combined & Juvenile & 11948 & 0.04344 & 0.99921 & 3.159574\\\\\n\t7 & Kings & 2008 & White & 500 & All Combined & Juvenile & 11648 & 0.04293 & 0.99913 & 3.131358\\\\\n\t8 & Kings & 2009 & White & 351 & All Combined & Juvenile & 11101 & 0.03162 & 0.99306 & 2.460354\\\\\n\t9 & Kings & 2009 & Asian/Pacific Islander & 20 & All Combined & Juvenile & 1152 & 0.01736 & 0.9931 & 2.462428\\\\\n\t10 & Kings & 2010 & White & 356 & All Combined & Juvenile & 10748 & 0.03312 & 0.99809 & 2.892656\\\\\n\t11 & Kings & 2011 & White & 266 & All Combined & Juvenile & 9907 & 0.02685 & 0.99778 & 2.845082\\\\\n\t12 & Kings & 2011 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1159 & 0.01467 & 0.9993 & 3.194651\\\\\n\t13 & Kings & 2012 & Black & 187 & All Combined & Juvenile & 1451 & 0.12888 & 0.98246 & 2.107434\\\\\n\t14 & Kings & 2012 & White & 237 & All Combined & Juvenile & 9162 & 0.02587 & 0.9981 & 2.894304\\\\\n\t15 & Kings & 2013 & Hispanic & 644 & All Combined & Juvenile & 26441 & 0.02436 & 0.99812 & 2.897625\\\\\n\t16 & Kings & 2013 & White & 201 & All Combined & Juvenile & 8790 & 0.02287 & 0.99834 & 2.936443\\\\\n\t17 & Kings & 2013 & Asian/Pacific Islander & 17 & All Combined & Juvenile & 1018 & 0.0167 & 0.99986 & 3.633134\\\\\n\t18 & Kings & 2014 & Hispanic & 589 & All Combined & Juvenile & 26733 & 0.02203 & 0.9978 & 2.847963\\\\\n\t19 & Kings & 2014 & White & 209 & All Combined & Juvenile & 8628 & 0.02422 & 0.99994 & 3.846126\\\\\n\t20 & Kings & 2014 & Asian/Pacific Islander & 45 & All Combined & Juvenile & 995 & 0.04523 & 1 & Inf\\\\\n\t21 & Kings & 2014 & Other & 33 & All Combined & Juvenile & 1723 & 0.01915 & 0.99205 & 2.411203\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Kings<\/td><td>2005<\/td><td>Hispanic<\/td><td>1075<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>23908<\/td><td>0.04496<\/td><td>0.99759<\/td><td>2.818824<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Kings<\/td><td>2005<\/td><td>White<\/td><td>607<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>12285<\/td><td>0.04941<\/td><td>0.99986<\/td><td>3.633134<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Kings<\/td><td>2005<\/td><td>Other<\/td><td>76<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1352<\/td><td>0.05621<\/td><td>0.99612<\/td><td>2.662338<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Kings<\/td><td>2006<\/td><td>Hispanic<\/td><td>1103<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>24535<\/td><td>0.04496<\/td><td>0.99231<\/td><td>2.423305<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Kings<\/td><td>2006<\/td><td>White<\/td><td>576<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>12124<\/td><td>0.04751<\/td><td>0.99971<\/td><td>3.440799<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Kings<\/td><td>2007<\/td><td>White<\/td><td>519<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11948<\/td><td>0.04344<\/td><td>0.99921<\/td><td>3.159574<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>Kings<\/td><td>2008<\/td><td>White<\/td><td>500<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11648<\/td><td>0.04293<\/td><td>0.99913<\/td><td>3.131358<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>Kings<\/td><td>2009<\/td><td>White<\/td><td>351<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11101<\/td><td>0.03162<\/td><td>0.99306<\/td><td>2.460354<\/td><\/tr>\n\t<tr><th scope=row>9<\/th><td>Kings<\/td><td>2009<\/td><td>Asian/Pacific Islander<\/td><td>20<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1152<\/td><td>0.01736<\/td><td>0.9931<\/td><td>2.462428<\/td><\/tr>\n\t<tr><th scope=row>10<\/th><td>Kings<\/td><td>2010<\/td><td>White<\/td><td>356<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10748<\/td><td>0.03312<\/td><td>0.99809<\/td><td>2.892656<\/td><\/tr>\n\t<tr><th scope=row>11<\/th><td>Kings<\/td><td>2011<\/td><td>White<\/td><td>266<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9907<\/td><td>0.02685<\/td><td>0.99778<\/td><td>2.845082<\/td><\/tr>\n\t<tr><th scope=row>12<\/th><td>Kings<\/td><td>2011<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1159<\/td><td>0.01467<\/td><td>0.9993<\/td><td>3.194651<\/td><\/tr>\n\t<tr><th scope=row>13<\/th><td>Kings<\/td><td>2012<\/td><td>Black<\/td><td>187<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1451<\/td><td>0.12888<\/td><td>0.98246<\/td><td>2.107434<\/td><\/tr>\n\t<tr><th scope=row>14<\/th><td>Kings<\/td><td>2012<\/td><td>White<\/td><td>237<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9162<\/td><td>0.02587<\/td><td>0.9981<\/td><td>2.894304<\/td><\/tr>\n\t<tr><th scope=row>15<\/th><td>Kings<\/td><td>2013<\/td><td>Hispanic<\/td><td>644<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>26441<\/td><td>0.02436<\/td><td>0.99812<\/td><td>2.897625<\/td><\/tr>\n\t<tr><th scope=row>16<\/th><td>Kings<\/td><td>2013<\/td><td>White<\/td><td>201<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8790<\/td><td>0.02287<\/td><td>0.99834<\/td><td>2.936443<\/td><\/tr>\n\t<tr><th scope=row>17<\/th><td>Kings<\/td><td>2013<\/td><td>Asian/Pacific Islander<\/td><td>17<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1018<\/td><td>0.0167<\/td><td>0.99986<\/td><td>3.633134<\/td><\/tr>\n\t<tr><th scope=row>18<\/th><td>Kings<\/td><td>2014<\/td><td>Hispanic<\/td><td>589<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>26733<\/td><td>0.02203<\/td><td>0.9978<\/td><td>2.847963<\/td><\/tr>\n\t<tr><th scope=row>19<\/th><td>Kings<\/td><td>2014<\/td><td>White<\/td><td>209<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8628<\/td><td>0.02422<\/td><td>0.99994<\/td><td>3.846126<\/td><\/tr>\n\t<tr><th scope=row>20<\/th><td>Kings<\/td><td>2014<\/td><td>Asian/Pacific Islander<\/td><td>45<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>995<\/td><td>0.04523<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>21<\/th><td>Kings<\/td><td>2014<\/td><td>Other<\/td><td>33<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1723<\/td><td>0.01915<\/td><td>0.99205<\/td><td>2.411203<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "#### Marin County\nFinally, looking at nearby Marin, blacks and hispanics have historically been arrested at unusually high rates vs. other counties.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 29, "cell_type": "code", "source": "## Isolate Marin cases to see if there's a pattern.\ndat_stats[dat_stats$z_score >= 3 & dat_stats$county %in% \"Marin\",]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [8 x 10]\n\n  county year race_or_ethnicity total       gender age_group population\n1  Marin 2005             Black   230 All Combined  Juvenile       1194\n2  Marin 2006             Black   270 All Combined  Juvenile       1172\n3  Marin 2007             Black   244 All Combined  Juvenile       1146\n4  Marin 2008          Hispanic   602 All Combined  Juvenile      10649\n5  Marin 2008             Black   219 All Combined  Juvenile       1130\n6  Marin 2009          Hispanic   659 All Combined  Juvenile      10916\n7  Marin 2010          Hispanic   536 All Combined  Juvenile      11434\n8  Marin 2011          Hispanic   546 All Combined  Juvenile      11659\nVariables not shown: eth_arrest_rate (dbl), percentile (dbl), z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & Marin & 2005 & Black & 230 & All Combined & Juvenile & 1194 & 0.19263 & 0.99974 & 3.470237\\\\\n\t2 & Marin & 2006 & Black & 270 & All Combined & Juvenile & 1172 & 0.23038 & 0.99984 & 3.598547\\\\\n\t3 & Marin & 2007 & Black & 244 & All Combined & Juvenile & 1146 & 0.21291 & 0.99984 & 3.598547\\\\\n\t4 & Marin & 2008 & Hispanic & 602 & All Combined & Juvenile & 10649 & 0.05653 & 0.9993 & 3.194651\\\\\n\t5 & Marin & 2008 & Black & 219 & All Combined & Juvenile & 1130 & 0.19381 & 0.99899 & 3.087276\\\\\n\t6 & Marin & 2009 & Hispanic & 659 & All Combined & Juvenile & 10916 & 0.06037 & 0.99993 & 3.808168\\\\\n\t7 & Marin & 2010 & Hispanic & 536 & All Combined & Juvenile & 11434 & 0.04688 & 0.99906 & 3.108562\\\\\n\t8 & Marin & 2011 & Hispanic & 546 & All Combined & Juvenile & 11659 & 0.04683 & 0.99994 & 3.846126\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Marin<\/td><td>2005<\/td><td>Black<\/td><td>230<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1194<\/td><td>0.19263<\/td><td>0.99974<\/td><td>3.470237<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Marin<\/td><td>2006<\/td><td>Black<\/td><td>270<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1172<\/td><td>0.23038<\/td><td>0.99984<\/td><td>3.598547<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Marin<\/td><td>2007<\/td><td>Black<\/td><td>244<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1146<\/td><td>0.21291<\/td><td>0.99984<\/td><td>3.598547<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Marin<\/td><td>2008<\/td><td>Hispanic<\/td><td>602<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10649<\/td><td>0.05653<\/td><td>0.9993<\/td><td>3.194651<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Marin<\/td><td>2008<\/td><td>Black<\/td><td>219<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1130<\/td><td>0.19381<\/td><td>0.99899<\/td><td>3.087276<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Marin<\/td><td>2009<\/td><td>Hispanic<\/td><td>659<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10916<\/td><td>0.06037<\/td><td>0.99993<\/td><td>3.808168<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>Marin<\/td><td>2010<\/td><td>Hispanic<\/td><td>536<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11434<\/td><td>0.04688<\/td><td>0.99906<\/td><td>3.108562<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>Marin<\/td><td>2011<\/td><td>Hispanic<\/td><td>546<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>11659<\/td><td>0.04683<\/td><td>0.99994<\/td><td>3.846126<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false}}, {"source": "### Negative (Left-Tail) Outliers?\nOut of curiosity, are there any counties who arrest at least one ethnic group at an abnormally *low* rate (vs. other counties)?  \n\nUpon looking for true outliers (3 SDs), we find none. However, when we look for \"mild\" outliers, we find one: SF, for its rate of arresting Asians/Pacific Islanders.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 30, "cell_type": "code", "source": "dat_stats[dat_stats$z_score <= -3,]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [0 x 10]\n\nVariables not shown: county (chr), year (int), race_or_ethnicity (chr), total\n  (int), gender (chr), age_group (chr), population (int), eth_arrest_rate\n  (dbl), percentile (dbl), z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false}}, {"execution_count": 31, "cell_type": "code", "source": "dat_stats[dat_stats$z_score <= -2,]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [1 x 10]\n\n         county year      race_or_ethnicity total       gender age_group\n1 San Francisco 2009 Asian/Pacific Islander     6 All Combined  Juvenile\nVariables not shown: population (int), eth_arrest_rate (dbl), percentile (dbl),\n  z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & San Francisco & 2009 & Asian/Pacific Islander & 6 & All Combined & Juvenile & 37180 & 0.00016 & 0.01979 & -2.058106\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>San Francisco<\/td><td>2009<\/td><td>Asian/Pacific Islander<\/td><td>6<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>37180<\/td><td>0.00016<\/td><td>0.01979<\/td><td>-2.058106<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "### Testing Assumptions\n\nIt'd be a good idea to check whether our assumptions over a normal distribution (because we're employing z-scores) are true. I do this by, first, conducting a Shapiro-Wilkes test for normality (if significance of diff < .05, then not normal). Then, I plot the sheer distribution of observations, followed by the observations vs. the line of expected/theoretical values of a normal distribution.  \n\nTo make the case clearer, I do this twice: Once for a subset that contains no known outliers, and once for a subset that contains multiple known outliers.", "cell_type": "markdown", "metadata": {}}, {"source": "#### Testing Normality of Subset with No Known Outliers (Hispanics Juveniles, 2014)", "cell_type": "markdown", "metadata": {}}, {"execution_count": 45, "cell_type": "code", "source": "## Subset to Hispanics, 2014 (entire dataset is already subsetted to juveniles)\ndat_stats_test <- dat_stats[dat_stats$year %in% \"2014\" & dat_stats$race_or_ethnicity %in% \"Hispanic\",]\n\n## Test for difference between observed distribution and normal distribution (Shapiro-Wilk normality test). \n## If difference's p is < .05, then the observed distribution is not sufficiently normal.\nshapiro.test(dat_stats_test$eth_arrest_rate)\n\n## Plot the density w/outliers\nsuppressWarnings(\n    plot(density(dat_stats_test$eth_arrest_rate), main = \"Arrest Rate Density for Hispanics in 2014\\r\n(test ethnicity and year)\")\n)\n\n## Plot vs. purely normal distribution\nsuppressWarnings(\n    qqnorm(dat_stats_test$eth_arrest_rate, main = \"Arrest Rate Observations for Hispanics in 2014\\r\nvs. Theoretical Norm Estimates\")\n)\nqqline(dat_stats_test$eth_arrest_rate)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "\n\tShapiro-Wilk normality test\n\ndata:  dat_stats_test$eth_arrest_rate\nW = 0.9398, p-value = 0.06084\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"metadata": {}}], "metadata": {"collapsed": false}}, {"source": "#### Testing Normality of Subset with Multiple Outliers (Ethnic \"Other\" Juveniles, 2014)\n*Please note the p-value's scientific notation.*", "cell_type": "markdown", "metadata": {}}, {"execution_count": 46, "cell_type": "code", "source": "## Subset to \"Other,\" 2014 (entire dataset is already subsetted to juveniles)\ndat_stats_test <- dat_stats[dat_stats$year %in% \"2014\" & dat_stats$race_or_ethnicity %in% \"Other\",]\n\n## Test for difference between observed distribution and normal distribution (Shapiro-Wilk normality test). \n## If difference's p is < .05, then the observed distribution is not sufficiently normal.\nshapiro.test(dat_stats_test$eth_arrest_rate)\n\n## Plot the density w/outliers\nsuppressWarnings(\n    plot(density(dat_stats_test$eth_arrest_rate), \n     main = \"Arrest Rate Density for OTHER in 2014\\r\n(test ethnicity and year)\")\n)\n    \n## Plot vs. purely normal distribution\nsuppressWarnings(\n    qqnorm(dat_stats_test$eth_arrest_rate, main = \"Arrest Rate Observations for OTHER in 2014\\r\nvs. Theoretical Norm Estimates\")\n)\nqqline(dat_stats_test$eth_arrest_rate)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "\n\tShapiro-Wilk normality test\n\ndata:  dat_stats_test$eth_arrest_rate\nW = 0.6024, p-value = 2.898e-08\n"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"metadata": {}}], "metadata": {"collapsed": false}}, {"source": "### Save to Azure ML Cloud", "cell_type": "markdown", "metadata": {}}, {"execution_count": null, "cell_type": "code", "source": "## Upload full stats DF to Azure ML and save to local csv.\nupload.dataset(dat_stats, ws, name = \"juv-ethnic-arrests_stats_2005-2014\")\nwrite.csv(dat_stats, \"juv-ethnic-arrests_stats_2005-2014.csv\", row.names = F)\n\n## Upload outliers DF to Azure ML and save to local csv.\nupload.dataset(dat_stats[dat_stats$z_score > 2,], ws, name = \"juv-ethnic-arrests_outliers_2005-2014\")\nwrite.csv(dat_stats, \"juv-ethnic-arrests_outliers_2005-2014.csv\", row.names = F)\n\n## ^Please disregard the Azure ML upload status messages the system produces...", "outputs": [], "metadata": {"collapsed": true}}, {"source": "### Let's Compare \"Other\" Arrest Rates Between SF and LA Counties", "cell_type": "markdown", "metadata": {}}, {"execution_count": 47, "cell_type": "code", "source": "dat_stats[dat_stats$county %in% \"Los Angeles\" & dat_stats$race_or_ethnicity %in% \"Other\",]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [10 x 10]\n\n        county year race_or_ethnicity total       gender age_group population\n1  Los Angeles 2005             Other  1640 All Combined  Juvenile      65142\n2  Los Angeles 2006             Other  1548 All Combined  Juvenile      65925\n3  Los Angeles 2007             Other  1484 All Combined  Juvenile      66389\n4  Los Angeles 2008             Other  1374 All Combined  Juvenile      66797\n5  Los Angeles 2009             Other  1145 All Combined  Juvenile      66489\n6  Los Angeles 2010             Other  1140 All Combined  Juvenile      67853\n7  Los Angeles 2011             Other   810 All Combined  Juvenile      69238\n8  Los Angeles 2012             Other   538 All Combined  Juvenile      70493\n9  Los Angeles 2013             Other   459 All Combined  Juvenile      71814\n10 Los Angeles 2014             Other   383 All Combined  Juvenile      73150\nVariables not shown: eth_arrest_rate (dbl), percentile (dbl), z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & Los Angeles & 2005 & Other & 1640 & All Combined & Juvenile & 65142 & 0.02518 & 0.76431 & 0.7202355\\\\\n\t2 & Los Angeles & 2006 & Other & 1548 & All Combined & Juvenile & 65925 & 0.02348 & 0.78391 & 0.7854667\\\\\n\t3 & Los Angeles & 2007 & Other & 1484 & All Combined & Juvenile & 66389 & 0.02235 & 0.78988 & 0.8060049\\\\\n\t4 & Los Angeles & 2008 & Other & 1374 & All Combined & Juvenile & 66797 & 0.02057 & 0.71188 & 0.5588853\\\\\n\t5 & Los Angeles & 2009 & Other & 1145 & All Combined & Juvenile & 66489 & 0.01722 & 0.66138 & 0.4162323\\\\\n\t6 & Los Angeles & 2010 & Other & 1140 & All Combined & Juvenile & 67853 & 0.0168 & 0.76934 & 0.736675\\\\\n\t7 & Los Angeles & 2011 & Other & 810 & All Combined & Juvenile & 69238 & 0.0117 & 0.67939 & 0.4659937\\\\\n\t8 & Los Angeles & 2012 & Other & 538 & All Combined & Juvenile & 70493 & 0.00763 & 0.50679 & 0.01702083\\\\\n\t9 & Los Angeles & 2013 & Other & 459 & All Combined & Juvenile & 71814 & 0.00639 & 0.52603 & 0.0652939\\\\\n\t10 & Los Angeles & 2014 & Other & 383 & All Combined & Juvenile & 73150 & 0.00524 & 0.51171 & 0.02935683\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Los Angeles<\/td><td>2005<\/td><td>Other<\/td><td>1640<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>65142<\/td><td>0.02518<\/td><td>0.76431<\/td><td>0.7202355<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Los Angeles<\/td><td>2006<\/td><td>Other<\/td><td>1548<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>65925<\/td><td>0.02348<\/td><td>0.78391<\/td><td>0.7854667<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Los Angeles<\/td><td>2007<\/td><td>Other<\/td><td>1484<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>66389<\/td><td>0.02235<\/td><td>0.78988<\/td><td>0.8060049<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Los Angeles<\/td><td>2008<\/td><td>Other<\/td><td>1374<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>66797<\/td><td>0.02057<\/td><td>0.71188<\/td><td>0.5588853<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Los Angeles<\/td><td>2009<\/td><td>Other<\/td><td>1145<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>66489<\/td><td>0.01722<\/td><td>0.66138<\/td><td>0.4162323<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Los Angeles<\/td><td>2010<\/td><td>Other<\/td><td>1140<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>67853<\/td><td>0.0168<\/td><td>0.76934<\/td><td>0.736675<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>Los Angeles<\/td><td>2011<\/td><td>Other<\/td><td>810<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>69238<\/td><td>0.0117<\/td><td>0.67939<\/td><td>0.4659937<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>Los Angeles<\/td><td>2012<\/td><td>Other<\/td><td>538<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>70493<\/td><td>0.00763<\/td><td>0.50679<\/td><td>0.01702083<\/td><\/tr>\n\t<tr><th scope=row>9<\/th><td>Los Angeles<\/td><td>2013<\/td><td>Other<\/td><td>459<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>71814<\/td><td>0.00639<\/td><td>0.52603<\/td><td>0.0652939<\/td><\/tr>\n\t<tr><th scope=row>10<\/th><td>Los Angeles<\/td><td>2014<\/td><td>Other<\/td><td>383<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>73150<\/td><td>0.00524<\/td><td>0.51171<\/td><td>0.02935683<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"execution_count": 48, "cell_type": "code", "source": "dat_stats[dat_stats$county %in% \"San Francisco\" & dat_stats$race_or_ethnicity %in% \"Other\",]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [10 x 10]\n\n          county year race_or_ethnicity total       gender age_group population\n1  San Francisco 2005             Other   495 All Combined  Juvenile       6834\n2  San Francisco 2006             Other   539 All Combined  Juvenile       7070\n3  San Francisco 2007             Other   513 All Combined  Juvenile       7263\n4  San Francisco 2008             Other   597 All Combined  Juvenile       7348\n5  San Francisco 2009             Other   582 All Combined  Juvenile       7716\n6  San Francisco 2010             Other   478 All Combined  Juvenile       8199\n7  San Francisco 2011             Other   426 All Combined  Juvenile       8722\n8  San Francisco 2012             Other   348 All Combined  Juvenile       9251\n9  San Francisco 2013             Other   307 All Combined  Juvenile       9862\n10 San Francisco 2014             Other   257 All Combined  Juvenile      10500\nVariables not shown: eth_arrest_rate (dbl), percentile (dbl), z_score (dbl)", "text/latex": "\\begin{tabular}{r|llllllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate & percentile & z\\_score\\\\\n\\hline\n\t1 & San Francisco & 2005 & Other & 495 & All Combined & Juvenile & 6834 & 0.07243 & 0.99988 & 3.672701\\\\\n\t2 & San Francisco & 2006 & Other & 539 & All Combined & Juvenile & 7070 & 0.07624 & 1 & Inf\\\\\n\t3 & San Francisco & 2007 & Other & 513 & All Combined & Juvenile & 7263 & 0.07063 & 1 & Inf\\\\\n\t4 & San Francisco & 2008 & Other & 597 & All Combined & Juvenile & 7348 & 0.08125 & 1 & Inf\\\\\n\t5 & San Francisco & 2009 & Other & 582 & All Combined & Juvenile & 7716 & 0.07543 & 1 & Inf\\\\\n\t6 & San Francisco & 2010 & Other & 478 & All Combined & Juvenile & 8199 & 0.0583 & 1 & Inf\\\\\n\t7 & San Francisco & 2011 & Other & 426 & All Combined & Juvenile & 8722 & 0.04884 & 1 & Inf\\\\\n\t8 & San Francisco & 2012 & Other & 348 & All Combined & Juvenile & 9251 & 0.03762 & 0.9997 & 3.431614\\\\\n\t9 & San Francisco & 2013 & Other & 307 & All Combined & Juvenile & 9862 & 0.03113 & 0.99985 & 3.6153\\\\\n\t10 & San Francisco & 2014 & Other & 257 & All Combined & Juvenile & 10500 & 0.02448 & 0.99956 & 3.326323\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><th scope=col>percentile<\/th><th scope=col>z_score<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>San Francisco<\/td><td>2005<\/td><td>Other<\/td><td>495<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>6834<\/td><td>0.07243<\/td><td>0.99988<\/td><td>3.672701<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>San Francisco<\/td><td>2006<\/td><td>Other<\/td><td>539<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7070<\/td><td>0.07624<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>San Francisco<\/td><td>2007<\/td><td>Other<\/td><td>513<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7263<\/td><td>0.07063<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>San Francisco<\/td><td>2008<\/td><td>Other<\/td><td>597<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7348<\/td><td>0.08125<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>San Francisco<\/td><td>2009<\/td><td>Other<\/td><td>582<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7716<\/td><td>0.07543<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>San Francisco<\/td><td>2010<\/td><td>Other<\/td><td>478<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8199<\/td><td>0.0583<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>San Francisco<\/td><td>2011<\/td><td>Other<\/td><td>426<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8722<\/td><td>0.04884<\/td><td>1<\/td><td>Inf<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>San Francisco<\/td><td>2012<\/td><td>Other<\/td><td>348<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9251<\/td><td>0.03762<\/td><td>0.9997<\/td><td>3.431614<\/td><\/tr>\n\t<tr><th scope=row>9<\/th><td>San Francisco<\/td><td>2013<\/td><td>Other<\/td><td>307<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9862<\/td><td>0.03113<\/td><td>0.99985<\/td><td>3.6153<\/td><\/tr>\n\t<tr><th scope=row>10<\/th><td>San Francisco<\/td><td>2014<\/td><td>Other<\/td><td>257<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10500<\/td><td>0.02448<\/td><td>0.99956<\/td><td>3.326323<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "### How Much of the SF Population is Made up by the racially/ethnically \"Other\"?", "cell_type": "markdown", "metadata": {}}, {"execution_count": 49, "cell_type": "code", "source": "dat_pop[dat_pop$county %in% \"San Francisco\" & (dat_pop$race %in% \"Other\" | dat_pop$race %in% \"All Combined\") &\n        dat_pop$gender %in% \"All Combined\" & dat_pop$age_group %in% \"Juvenile\",]", "outputs": [{"output_type": "display_data", "data": {"text/plain": "       year        county         race       gender age_group population\n9578   2005 San Francisco All Combined All Combined  Juvenile     113840\n9794   2005 San Francisco        Other All Combined  Juvenile       6834\n24446  2006 San Francisco All Combined All Combined  Juvenile     111222\n24662  2006 San Francisco        Other All Combined  Juvenile       7070\n39314  2007 San Francisco All Combined All Combined  Juvenile     109469\n39530  2007 San Francisco        Other All Combined  Juvenile       7263\n54182  2008 San Francisco All Combined All Combined  Juvenile     107819\n54398  2008 San Francisco        Other All Combined  Juvenile       7348\n69050  2009 San Francisco All Combined All Combined  Juvenile     104765\n69266  2009 San Francisco        Other All Combined  Juvenile       7716\n83918  2010 San Francisco All Combined All Combined  Juvenile     108141\n84134  2010 San Francisco        Other All Combined  Juvenile       8199\n98786  2011 San Francisco All Combined All Combined  Juvenile     110763\n99002  2011 San Francisco        Other All Combined  Juvenile       8722\n113654 2012 San Francisco All Combined All Combined  Juvenile     113215\n113870 2012 San Francisco        Other All Combined  Juvenile       9251\n128522 2013 San Francisco All Combined All Combined  Juvenile     116086\n128738 2013 San Francisco        Other All Combined  Juvenile       9862\n143390 2014 San Francisco All Combined All Combined  Juvenile     118747\n143606 2014 San Francisco        Other All Combined  Juvenile      10500", "text/latex": "\\begin{tabular}{r|llllll}\n  & year & county & race & gender & age\\_group & population\\\\\n\\hline\n\t9578 & 2005 & San Francisco & All Combined & All Combined & Juvenile & 113840\\\\\n\t9794 & 2005 & San Francisco & Other & All Combined & Juvenile & 6834\\\\\n\t24446 & 2006 & San Francisco & All Combined & All Combined & Juvenile & 111222\\\\\n\t24662 & 2006 & San Francisco & Other & All Combined & Juvenile & 7070\\\\\n\t39314 & 2007 & San Francisco & All Combined & All Combined & Juvenile & 109469\\\\\n\t39530 & 2007 & San Francisco & Other & All Combined & Juvenile & 7263\\\\\n\t54182 & 2008 & San Francisco & All Combined & All Combined & Juvenile & 107819\\\\\n\t54398 & 2008 & San Francisco & Other & All Combined & Juvenile & 7348\\\\\n\t69050 & 2009 & San Francisco & All Combined & All Combined & Juvenile & 104765\\\\\n\t69266 & 2009 & San Francisco & Other & All Combined & Juvenile & 7716\\\\\n\t83918 & 2010 & San Francisco & All Combined & All Combined & Juvenile & 108141\\\\\n\t84134 & 2010 & San Francisco & Other & All Combined & Juvenile & 8199\\\\\n\t98786 & 2011 & San Francisco & All Combined & All Combined & Juvenile & 110763\\\\\n\t99002 & 2011 & San Francisco & Other & All Combined & Juvenile & 8722\\\\\n\t113654 & 2012 & San Francisco & All Combined & All Combined & Juvenile & 113215\\\\\n\t113870 & 2012 & San Francisco & Other & All Combined & Juvenile & 9251\\\\\n\t128522 & 2013 & San Francisco & All Combined & All Combined & Juvenile & 116086\\\\\n\t128738 & 2013 & San Francisco & Other & All Combined & Juvenile & 9862\\\\\n\t143390 & 2014 & San Francisco & All Combined & All Combined & Juvenile & 118747\\\\\n\t143606 & 2014 & San Francisco & Other & All Combined & Juvenile & 10500\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>year<\/th><th scope=col>county<\/th><th scope=col>race<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>9578<\/th><td>2005<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>113840<\/td><\/tr>\n\t<tr><th scope=row>9794<\/th><td>2005<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>6834<\/td><\/tr>\n\t<tr><th scope=row>24446<\/th><td>2006<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>111222<\/td><\/tr>\n\t<tr><th scope=row>24662<\/th><td>2006<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7070<\/td><\/tr>\n\t<tr><th scope=row>39314<\/th><td>2007<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>109469<\/td><\/tr>\n\t<tr><th scope=row>39530<\/th><td>2007<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7263<\/td><\/tr>\n\t<tr><th scope=row>54182<\/th><td>2008<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>107819<\/td><\/tr>\n\t<tr><th scope=row>54398<\/th><td>2008<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7348<\/td><\/tr>\n\t<tr><th scope=row>69050<\/th><td>2009<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>104765<\/td><\/tr>\n\t<tr><th scope=row>69266<\/th><td>2009<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7716<\/td><\/tr>\n\t<tr><th scope=row>83918<\/th><td>2010<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>108141<\/td><\/tr>\n\t<tr><th scope=row>84134<\/th><td>2010<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8199<\/td><\/tr>\n\t<tr><th scope=row>98786<\/th><td>2011<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>110763<\/td><\/tr>\n\t<tr><th scope=row>99002<\/th><td>2011<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>8722<\/td><\/tr>\n\t<tr><th scope=row>113654<\/th><td>2012<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>113215<\/td><\/tr>\n\t<tr><th scope=row>113870<\/th><td>2012<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9251<\/td><\/tr>\n\t<tr><th scope=row>128522<\/th><td>2013<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>116086<\/td><\/tr>\n\t<tr><th scope=row>128738<\/th><td>2013<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9862<\/td><\/tr>\n\t<tr><th scope=row>143390<\/th><td>2014<\/td><td>San Francisco<\/td><td>All Combined<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>118747<\/td><\/tr>\n\t<tr><th scope=row>143606<\/th><td>2014<\/td><td>San Francisco<\/td><td>Other<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10500<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"source": "## Looking specifically at SF arrests\nAHH! It looks like Hispanics and Asians are ridiculously underreported in the arrest totals, as you can see in 2005's arrest of just 2 Hispanics and 1 Asian/Pacific Islander. I then compare this to LA in the next table.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 51, "cell_type": "code", "source": "head(dat_joined[dat_joined$county %in% \"San Francisco\",], 20)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [20 x 8]\n\n          county year      race_or_ethnicity total       gender age_group\n1  San Francisco 2005               Hispanic     2 All Combined  Juvenile\n2  San Francisco 2005                  Black  1067 All Combined  Juvenile\n3  San Francisco 2005                  White   477 All Combined  Juvenile\n4  San Francisco 2005 Asian/Pacific Islander     1 All Combined  Juvenile\n5  San Francisco 2005                  Other   495 All Combined  Juvenile\n6  San Francisco 2006               Hispanic    10 All Combined  Juvenile\n7  San Francisco 2006                  Black  1334 All Combined  Juvenile\n8  San Francisco 2006                  White   559 All Combined  Juvenile\n9  San Francisco 2006 Asian/Pacific Islander     9 All Combined  Juvenile\n10 San Francisco 2006                  Other   539 All Combined  Juvenile\n11 San Francisco 2007               Hispanic     5 All Combined  Juvenile\n12 San Francisco 2007                  Black  1211 All Combined  Juvenile\n13 San Francisco 2007                  White   537 All Combined  Juvenile\n14 San Francisco 2007 Asian/Pacific Islander     1 All Combined  Juvenile\n15 San Francisco 2007                  Other   513 All Combined  Juvenile\n16 San Francisco 2008               Hispanic     6 All Combined  Juvenile\n17 San Francisco 2008                  Black  1237 All Combined  Juvenile\n18 San Francisco 2008                  White   542 All Combined  Juvenile\n19 San Francisco 2008 Asian/Pacific Islander     8 All Combined  Juvenile\n20 San Francisco 2008                  Other   597 All Combined  Juvenile\nVariables not shown: population (int), eth_arrest_rate (dbl)", "text/latex": "\\begin{tabular}{r|llllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate\\\\\n\\hline\n\t1 & San Francisco & 2005 & Hispanic & 2 & All Combined & Juvenile & 25772 & 8e-05\\\\\n\t2 & San Francisco & 2005 & Black & 1067 & All Combined & Juvenile & 10543 & 0.1012\\\\\n\t3 & San Francisco & 2005 & White & 477 & All Combined & Juvenile & 29079 & 0.0164\\\\\n\t4 & San Francisco & 2005 & Asian/Pacific Islander & 1 & All Combined & Juvenile & 41459 & 2e-05\\\\\n\t5 & San Francisco & 2005 & Other & 495 & All Combined & Juvenile & 6834 & 0.07243\\\\\n\t6 & San Francisco & 2006 & Hispanic & 10 & All Combined & Juvenile & 25414 & 0.00039\\\\\n\t7 & San Francisco & 2006 & Black & 1334 & All Combined & Juvenile & 10000 & 0.1334\\\\\n\t8 & San Francisco & 2006 & White & 559 & All Combined & Juvenile & 28091 & 0.0199\\\\\n\t9 & San Francisco & 2006 & Asian/Pacific Islander & 9 & All Combined & Juvenile & 40506 & 0.00022\\\\\n\t10 & San Francisco & 2006 & Other & 539 & All Combined & Juvenile & 7070 & 0.07624\\\\\n\t11 & San Francisco & 2007 & Hispanic & 5 & All Combined & Juvenile & 25229 & 2e-04\\\\\n\t12 & San Francisco & 2007 & Black & 1211 & All Combined & Juvenile & 9568 & 0.12657\\\\\n\t13 & San Francisco & 2007 & White & 537 & All Combined & Juvenile & 27612 & 0.01945\\\\\n\t14 & San Francisco & 2007 & Asian/Pacific Islander & 1 & All Combined & Juvenile & 39661 & 3e-05\\\\\n\t15 & San Francisco & 2007 & Other & 513 & All Combined & Juvenile & 7263 & 0.07063\\\\\n\t16 & San Francisco & 2008 & Hispanic & 6 & All Combined & Juvenile & 25009 & 0.00024\\\\\n\t17 & San Francisco & 2008 & Black & 1237 & All Combined & Juvenile & 9121 & 0.13562\\\\\n\t18 & San Francisco & 2008 & White & 542 & All Combined & Juvenile & 27201 & 0.01993\\\\\n\t19 & San Francisco & 2008 & Asian/Pacific Islander & 8 & All Combined & Juvenile & 39005 & 0.00021\\\\\n\t20 & San Francisco & 2008 & Other & 597 & All Combined & Juvenile & 7348 & 0.08125\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>San Francisco<\/td><td>2005<\/td><td>Hispanic<\/td><td>2<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>25772<\/td><td>8e-05<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>San Francisco<\/td><td>2005<\/td><td>Black<\/td><td>1067<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10543<\/td><td>0.1012<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>San Francisco<\/td><td>2005<\/td><td>White<\/td><td>477<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>29079<\/td><td>0.0164<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>San Francisco<\/td><td>2005<\/td><td>Asian/Pacific Islander<\/td><td>1<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>41459<\/td><td>2e-05<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>San Francisco<\/td><td>2005<\/td><td>Other<\/td><td>495<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>6834<\/td><td>0.07243<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>San Francisco<\/td><td>2006<\/td><td>Hispanic<\/td><td>10<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>25414<\/td><td>0.00039<\/td><\/tr>\n\t<tr><th scope=row>7<\/th><td>San Francisco<\/td><td>2006<\/td><td>Black<\/td><td>1334<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>10000<\/td><td>0.1334<\/td><\/tr>\n\t<tr><th scope=row>8<\/th><td>San Francisco<\/td><td>2006<\/td><td>White<\/td><td>559<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>28091<\/td><td>0.0199<\/td><\/tr>\n\t<tr><th scope=row>9<\/th><td>San Francisco<\/td><td>2006<\/td><td>Asian/Pacific Islander<\/td><td>9<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>40506<\/td><td>0.00022<\/td><\/tr>\n\t<tr><th scope=row>10<\/th><td>San Francisco<\/td><td>2006<\/td><td>Other<\/td><td>539<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7070<\/td><td>0.07624<\/td><\/tr>\n\t<tr><th scope=row>11<\/th><td>San Francisco<\/td><td>2007<\/td><td>Hispanic<\/td><td>5<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>25229<\/td><td>2e-04<\/td><\/tr>\n\t<tr><th scope=row>12<\/th><td>San Francisco<\/td><td>2007<\/td><td>Black<\/td><td>1211<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9568<\/td><td>0.12657<\/td><\/tr>\n\t<tr><th scope=row>13<\/th><td>San Francisco<\/td><td>2007<\/td><td>White<\/td><td>537<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>27612<\/td><td>0.01945<\/td><\/tr>\n\t<tr><th scope=row>14<\/th><td>San Francisco<\/td><td>2007<\/td><td>Asian/Pacific Islander<\/td><td>1<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>39661<\/td><td>3e-05<\/td><\/tr>\n\t<tr><th scope=row>15<\/th><td>San Francisco<\/td><td>2007<\/td><td>Other<\/td><td>513<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7263<\/td><td>0.07063<\/td><\/tr>\n\t<tr><th scope=row>16<\/th><td>San Francisco<\/td><td>2008<\/td><td>Hispanic<\/td><td>6<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>25009<\/td><td>0.00024<\/td><\/tr>\n\t<tr><th scope=row>17<\/th><td>San Francisco<\/td><td>2008<\/td><td>Black<\/td><td>1237<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>9121<\/td><td>0.13562<\/td><\/tr>\n\t<tr><th scope=row>18<\/th><td>San Francisco<\/td><td>2008<\/td><td>White<\/td><td>542<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>27201<\/td><td>0.01993<\/td><\/tr>\n\t<tr><th scope=row>19<\/th><td>San Francisco<\/td><td>2008<\/td><td>Asian/Pacific Islander<\/td><td>8<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>39005<\/td><td>0.00021<\/td><\/tr>\n\t<tr><th scope=row>20<\/th><td>San Francisco<\/td><td>2008<\/td><td>Other<\/td><td>597<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>7348<\/td><td>0.08125<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], "metadata": {"collapsed": false}}, {"execution_count": 52, "cell_type": "code", "source": "head(dat_joined[dat_joined$county %in% \"Los Angeles\",])", "outputs": [{"output_type": "display_data", "data": {"text/plain": "Source: local data frame [6 x 8]\n\n       county year      race_or_ethnicity total       gender age_group\n1 Los Angeles 2005               Hispanic 28028 All Combined  Juvenile\n2 Los Angeles 2005                  Black 12302 All Combined  Juvenile\n3 Los Angeles 2005                  White  6967 All Combined  Juvenile\n4 Los Angeles 2005 Asian/Pacific Islander   659 All Combined  Juvenile\n5 Los Angeles 2005                  Other  1640 All Combined  Juvenile\n6 Los Angeles 2006               Hispanic 31367 All Combined  Juvenile\nVariables not shown: population (int), eth_arrest_rate (dbl)", "text/latex": "\\begin{tabular}{r|llllllll}\n  & county & year & race\\_or\\_ethnicity & total & gender & age\\_group & population & eth\\_arrest\\_rate\\\\\n\\hline\n\t1 & Los Angeles & 2005 & Hispanic & 28028 & All Combined & Juvenile & 1589070 & 0.01764\\\\\n\t2 & Los Angeles & 2005 & Black & 12302 & All Combined & Juvenile & 232002 & 0.05303\\\\\n\t3 & Los Angeles & 2005 & White & 6967 & All Combined & Juvenile & 495362 & 0.01406\\\\\n\t4 & Los Angeles & 2005 & Asian/Pacific Islander & 659 & All Combined & Juvenile & 251585 & 0.00262\\\\\n\t5 & Los Angeles & 2005 & Other & 1640 & All Combined & Juvenile & 65142 & 0.02518\\\\\n\t6 & Los Angeles & 2006 & Hispanic & 31367 & All Combined & Juvenile & 1576626 & 0.0199\\\\\n\\end{tabular}\n", "text/html": "<table>\n<thead><tr><th><\/th><th scope=col>county<\/th><th scope=col>year<\/th><th scope=col>race_or_ethnicity<\/th><th scope=col>total<\/th><th scope=col>gender<\/th><th scope=col>age_group<\/th><th scope=col>population<\/th><th scope=col>eth_arrest_rate<\/th><\/tr><\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>Los Angeles<\/td><td>2005<\/td><td>Hispanic<\/td><td>28028<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1589070<\/td><td>0.01764<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>Los Angeles<\/td><td>2005<\/td><td>Black<\/td><td>12302<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>232002<\/td><td>0.05303<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>Los Angeles<\/td><td>2005<\/td><td>White<\/td><td>6967<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>495362<\/td><td>0.01406<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>Los Angeles<\/td><td>2005<\/td><td>Asian/Pacific Islander<\/td><td>659<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>251585<\/td><td>0.00262<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>Los Angeles<\/td><td>2005<\/td><td>Other<\/td><td>1640<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>65142<\/td><td>0.02518<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>Los Angeles<\/td><td>2006<\/td><td>Hispanic<\/td><td>31367<\/td><td>All Combined<\/td><td>Juvenile<\/td><td>1576626<\/td><td>0.0199<\/td><\/tr>\n<\/tbody>\n<\/table>\n"}, "metadata": {}}], 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