{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

\"FT

\n", "

Frogtown/Saint Paul Traffic Stop Code; 04/07/19

\n", "

By Frogtown Crusader (Abu Nayeem)

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents\n", "* [Introduction](#introduction)\n", "* [Data](#data)\n", "* [Methodology](#methodology)\n", "* [Analysis](#analysis)\n", " * [Standard](#standard)\n", " * [Longitudinal](#longitudinal)\n", " * [Commerical](#commerical)\n", " * [Geo-Spatial Prep](#geo_prep)\n", " * [Frogtown_Geo-Spatial](#fg_geo)\n", " * [Saint Paul Geo-Spatial](#sp_geo)\n", "* [Conclusion](#conclusion)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Disclaimer:** This is my Coursera Capstone Project for Data Science. Also, I will be using the term \"Black\" instead of African American because that is the race indicator provided from the dataset and the category also includes persons of African origin (substantial minority). \n", "\n", "### About Me:\n", "I'm a Frogtown resident, community advocate, programmer. I will like to use open-source data to be share stories and create action. Please follow me on [Github](https://github.com/sustainabu) \n", "\n", "### Purpose:\n", "\n", "Currently in the United States, there is alot of tension between law enforcement, and the public. I will be looking into the Traffic Stop Data for Saint Paul, Minnesota (USA) provided by the Saint Paul Police Department (SPPD). Analyzing the traffic stop data can provide evidence (or lack thereof) of systemic biases. The goal of my report to add insight on what is happening in my community, Thomas-Dale neighborhood aka Frogtown, as well as advocate citizens to use open source data and/or demand their public agencies to provide such data. \n", " \n", "### Executive Summary:\n", "\n", "There are certain parts of Frogtown that have greater frequency of traffic stops compared to rest of the neighborhood, particularly along University University Avenue.The data **suggests** targeting of Black drivers given that they are stopped more frequently, searched more frequently per stop, and less likelihood to receive a citation. Some other data insights include that moving violation stops are given mostly in the morning and have a higher citation rate. In contrast, during the late night hours, there are greater instances of equipment violations. Furthermore, there seems to be many communities, including Frogtown that have considerable instances of equipment violations.\n", " \n", "\n", "### Why prove the obvious?\n", "\n", "The results may be obvious, but proving it may be more challenging. As a researcher, my goal is to measure the impact, seek the truth, explore, and challenge my expectations. Data can be the great equalizer challenging our worldviews and/or reinforcing our existing perspective. Data reports can be used to share stories and information effectively. Furthermore, data is used as an evaluation tool to determine the effectiveness of programs and policies.\n", "\n", "Thus data practitioners, more broadly institutions, hold strong responsibility and influence in shaping the data in support a certain narrative. In our current political climate, the public perception on law enforcement is polarized and I hope these studies can shed light on the issues. This report and other will be available via open source, allowing others to contribute, replicate, use code for their own respective neighborhood.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## About Saint Paul\n", "\n", "The City of Saint Paul is the second largest city in Minnesota, USA, and is the capital city for the state. Saint Paul is often paired with their nearby city, Minneapolis, where they are aptly named, The Twin Cities. It has roughly over 300,000 people and the city itself is quite diverse. Minnesota has a high level of [racial inequity](http://www.citypages.com/news/minnesota-still-has-some-of-the-worst-racial-disparities-in-the-nation/504390741) ranking 47th of 51st compared to rest of the United States. Saint Paul is broken down to seventeen Planning Districts, created in 1979 to allow neighborhoods to participate in governance and use Community Development Block Grants. The Thomas/Dale neighborhood is one of the district planning councils. A few years ago, a tragic police fatal shooting of African-American male, [Philando Castile](https://en.wikipedia.org/wiki/Shooting_of_Philando_Castile), occurred during a traffic stop in the suburbs of Saint Paul, Falcon Heights, which has increased tension within the community between law enforcement and citizens. \n", "\n", "\n", "## About Thomas-Dale-Frogtown Neighborhood\n", "\n", "The Frogtown community has historically been a transitional community with new immigrant/refugee communities living in the neighborhood for short period of time. From my experience, Frogtown boasts considerable diversity respect to language, culture, and ethnicity. In recent times, it has been historically poor. Here is a snapshot of the community exported from [Minnesota Compass](https://www.mncompass.org/profiles/neighborhoods/st-paul/frogtown-thomas-dale) based on 2017 Census Demographic Data.\n", "\n", "![title](Images/fg_Race11.png)\n", "\n", "![title](Images/fgInc1.png)\n", "\n", "\n", "### Frogtown Community Information\n", "\n", "The image below displays the Frogtown Community using the police grid (matches well with actual boundaries). On the southern boundary of Frogtown is University Avenue, where the Light Rail Transportation runs along the boundary and it is a heavy residential street as well. I will emphasize more noticeable landmarks once plugging in the 4-square data. The two rightmost sectors are respectfully Mt. Airy and Capitol Heights. These two communities are considered distinct by community members. \n", "\n", "![title](Images/FG_Grid.png)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n", "### About the Datasets:\n", "\n", "The dataset contains SPPD traffic stop collected by SSPD from 2000 to 2018 via agreement of the Saint Paul chapter of the NAACP and can be accessed [here](https://information.stpaul.gov/Public-Safety/Traffic-Stop-Dataset/kkd6-vvns). The website have a lot of features and visualizations for basic analysis, but for advanced users data transformations are not available/ limited. I have chosen to select years from 2017 to 2018 based on data limitations.\n", "\n", "Data Features:\n", "* Individual Traffic Stop Data\n", "* Driver characteristics\n", " - Gender, Age (if recieve citation), and Race \n", " - Was the driver searched, vehicle searched, and/or recieve a citation?\n", "* GeoCoordinates of center of police grid and timestamp for\n", "\n", "Data limitation as explain on the website:\n", "* Reason for stop (available starting in 2017)\n", " - Include Moving Violation, Equipment Violation, Investigative Stop, and 911 call\n", "* Data reflects traffic stops originating by St. Paul Police Officers\n", "* Race is based on officers’ perceptions\n", "* Fields indicating “No Data” may be due to a variety of factors, including:\n", " - Age data is only collected when a citation is issued\n", " - Reason for stop data was not collected before 2017\n", " - Technology changes over time/ Technical Errors/ Lack of Available information\n", "\n", "* Supplemental Info Suggested by Author\n", " - [Traffic Crashes by Police Grid 2018](https://www.stpaul.gov/sites/default/files/Media%20Root/Police/Traffic%20Crashes%20in%20Saint%20Paul%202018.pdf)\n", " - [Traffic Stops by Police Grid 2018](https://www.stpaul.gov/sites/default/files/Media%20Root/Police/2018%20Traffic%20Stops.pdf)\n", " - [911 Calls by Police Grid 2018](https://www.stpaul.gov/sites/default/files/Media%20Root/Police/911%20Calls%20in%20Saint%20Paul%202018.pdf)\n", "\n", "The dataset consist of each record of driver being stopped, but the locations coordinates are limited to the police grid coordinates. There is maybe over 90 or so police grids! \n", "\n", "![title](Images/policegrid.png)\n", "\n", "#### Four-Square API Dataset\n", "\n", "I'll be using the Four-square API to get information on local businesesses. Some street/ and areas might be more active than others\n", "\n", "#### Minnesota Compass 2017 Census Survey Data\n", "\n", "The Minnesota Compass offers raw data for both Minneapolis and Saint Paul districts. They are a non-partisan group." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Prep\n", "\n", "The primary data will range from 2017-18. The longitudinal analysis will have data from 2001 to 2018." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hide_input": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n" ] }, { "data": { "text/plain": [ "(106978, 12)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime\n", "import warnings\n", "%matplotlib inline \n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "warnings.filterwarnings('ignore')\n", "import plotly\n", "from IPython.display import HTML\n", "from IPython.display import display\n", "import json # library to handle JSON files\n", "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n", "import requests # library to handle requests\n", "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n", "from sodapy import Socrata\n", "\n", "#New Upload Method Get Information from Socrata API\n", "client = Socrata(\"information.stpaul.gov\", None)\n", "\n", "#Data Load\n", "#df = pd.read_csv('Data/Traffic_Stop_Dataset.csv')\n", "#More familar column names Column Names\n", "#cols= ['Year','Date','Race','Gender','Driver_search','Vehicle_search','Citation','Age','Reason','Grid','GridLocation', 'Count']\n", "#df.columns= cols\n", "\n", "results = client.get(\"kkd6-vvns\", limit=1000000)\n", "results_df = pd.DataFrame.from_records(results)\n", "\n", "#rename columns\n", "cols= ['Gunk', 'Gunk2', 'Gunk3','Age','Citation', 'Count', 'Date','Driver_search','Gender','GridLocation', 'Grid','Race','Reason','Vehicle_search','Year']\n", "results_df.columns= cols\n", "results_df =results_df.iloc[:,3:]\n", "#change datatypes\n", "results_df = results_df.astype({\"Year\": int, \"Age\": float, \"Grid\":float, \"Count\":int})\n", "\n", "#We will be choosing from 2017 to 2018 because there a reason given for traffic stop\n", "df= results_df.query('Year in [2018,2017,2019,2020,2021]')\n", "\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeCitationCountDateDriver_searchGenderGridLocationGridRaceReasonVehicle_searchYear
0NaNNo12018-05-11T20:47:00.000NoMaleNaN991.0BlackMoving ViolationNo2018
1NaNNo12019-03-03T00:15:00.000NoMaleNaN991.0BlackEquipment ViolationNo2019
228.0Yes12019-02-13T13:21:00.000NoMaleNaN991.0BlackMoving ViolationNo2019
3NaNNo12019-07-27T21:23:00.000NoMaleNaN991.0WhiteEquipment ViolationNo2019
4NaNNo12018-01-20T21:19:00.000NoMaleNaN991.0WhiteMoving ViolationNo2018
5NaNNo12018-12-27T14:02:00.000NoMaleNaN991.0WhiteMoving ViolationNo2018
624.0Yes12018-03-13T13:51:00.000NoFemaleNaN991.0WhiteMoving ViolationNo2018
7NaNYes12019-03-02T22:12:00.000NoMaleNaN991.0WhiteEquipment ViolationNo2019
825.0Yes12019-06-07T22:08:00.000NoFemaleNaN991.0OtherMoving ViolationNo2019
9NaNNo12019-01-19T22:08:00.000NoMaleNaN991.0WhiteEquipment ViolationNo2019
10NaNNo12019-06-07T20:18:00.000NoMaleNaN991.0WhiteMoving ViolationNo2019
1122.0Yes12018-05-01T14:11:00.000NoMaleNaN991.0WhiteMoving ViolationNo2018
1221.0Yes12019-09-27T19:13:00.000NoFemaleNaN991.0BlackMoving ViolationNo2019
13NaNNo12019-07-20T21:10:00.000NoMaleNaN991.0WhiteEquipment ViolationNo2019
14NaNNo12019-05-09T16:56:00.000NoFemaleNaN999.0WhiteMoving ViolationNo2019
15NaNNo12018-11-21T18:56:00.000NoMaleNaN991.0AsianMoving ViolationNo2018
16NaNNo12018-08-01T14:14:00.000NoMaleNaN993.0WhiteMoving ViolationNo2018
17NaNNo12018-10-27T20:43:00.000NoMaleNaN991.0WhiteEquipment ViolationNo2018
18NaNNo12018-02-18T00:36:00.000NoMaleNaN996.0BlackMoving ViolationNo2018
19NaNNo12018-06-23T00:46:00.000NoMaleNaN993.0OtherMoving ViolationNo2018
20NaNNo12018-11-21T20:06:00.000NoMaleNaN991.0WhiteMoving ViolationNo2018
2154.0Yes12018-04-09T14:20:00.000NoFemaleNaN991.0WhiteMoving ViolationNo2018
22NaNNo12018-10-05T20:49:00.000NoMaleNaN991.0WhiteMoving ViolationNo2018
23NaNNo12018-10-05T20:35:00.000NoFemaleNaN991.0WhiteMoving ViolationNo2018
24NaNNo12018-10-27T21:29:00.000NoMaleNaN991.0LatinoMoving ViolationNo2018
25NaNYes12018-06-30T22:35:00.000NoMaleNaN994.0WhiteMoving ViolationNo2018
26NaNNo12018-01-13T00:51:00.000NoMaleNaN991.0WhiteEquipment ViolationNo2018
27NaNNo12018-05-11T21:56:00.000NoMaleNaN991.0WhiteMoving ViolationNo2018
28NaNNo12018-02-17T21:37:00.000NoFemaleNaN991.0BlackEquipment ViolationNo2018
2923.0Yes12018-04-12T11:34:00.000NoMaleNaN991.0WhiteMoving ViolationNo2018
.......................................
784687NaNNo12017-08-30T17:45:00.000NoFemale{'latitude': '44.959171113', 'longitude': '-93...94.0OtherMoving ViolationNo2017
784688NaNNo12017-11-02T22:46:00.000NoMale{'latitude': '44.959369203', 'longitude': '-93...87.0BlackMoving ViolationNo2017
784689NaNNo12017-10-25T03:00:00.000NoFemale{'latitude': '44.96658643', 'longitude': '-93....68.0BlackMoving ViolationNo2017
784690NaNNo12017-12-07T15:30:00.000NoMale{'latitude': '44.988221181', 'longitude': '-93...11.0WhiteMoving ViolationNo2017
78469134.0Yes12017-09-07T08:30:00.000NoFemale{'latitude': '44.928875372', 'longitude': '-93...267.0WhiteMoving ViolationNo2017
78469230.0Yes12017-09-29T18:33:00.000NoMale{'latitude': '44.959515062', 'longitude': '-93...91.0WhiteMoving ViolationNo2017
784693NaNYes12017-09-13T10:42:00.000NoFemale{'latitude': '44.973827765', 'longitude': '-93...51.0BlackMoving ViolationNo2017
784694NaNYes12017-07-27T08:06:00.000NoMale{'latitude': '44.973124945', 'longitude': '-93...47.0BlackMoving ViolationNo2017
784695NaNNo12017-09-20T22:34:00.000NoFemale{'latitude': '44.988314242', 'longitude': '-93...14.0BlackMoving ViolationNo2017
78469637.0Yes12017-09-28T09:39:00.000NoMale{'latitude': '44.980704001', 'longitude': '-93...32.0BlackMoving ViolationNo2017
784697NaNNo12017-07-25T13:50:00.000NoMale{'latitude': '44.954914788', 'longitude': '-93...112.0BlackMoving ViolationNo2017
784698NaNYes12017-12-06T10:20:00.000NoFemale{'latitude': '44.980999933', 'longitude': '-93...31.0BlackMoving ViolationNo2017
784699NaNYes12017-10-05T10:03:00.000NoFemale{'latitude': '44.973124945', 'longitude': '-93...47.0LatinoMoving ViolationNo2017
784700NaNYes12017-12-01T11:21:00.000NoMale{'latitude': '44.928875372', 'longitude': '-93...267.0WhiteMoving ViolationNo2017
784701NaNNo12017-10-11T15:12:00.000NoFemale{'latitude': '44.966688357', 'longitude': '-93...75.0BlackMoving ViolationNo2017
784702NaNNo12017-10-03T17:40:00.000YesMale{'latitude': '44.959923808', 'longitude': '-93...99.0WhiteMoving ViolationYes2017
784703NaNNo12017-09-29T17:45:00.000NoFemale{'latitude': '44.95086001', 'longitude': '-93....132.0WhiteMoving ViolationNo2017
78470427.0Yes12017-08-17T21:35:00.000NoMale{'latitude': '44.953679624', 'longitude': '-93...111.0OtherEquipment ViolationNo2017
784705NaNNo12017-07-29T01:17:00.000NoMale{'latitude': '44.980704001', 'longitude': '-93...32.0BlackEquipment ViolationNo2017
78470628.0Yes12017-09-25T08:37:00.000NoFemale{'latitude': '44.965443164', 'longitude': '-93...269.0BlackMoving ViolationNo2017
784707NaNNo12017-11-09T10:51:00.000NoFemale{'latitude': '44.952068692', 'longitude': '-93...105.0WhiteMoving ViolationNo2017
784708NaNNo12017-12-04T00:15:00.000YesMale{'latitude': '44.966741984', 'longitude': '-93...73.0BlackMoving ViolationYes2017
784709NaNNo12017-08-31T23:28:00.000NoFemale{'latitude': '44.973798199', 'longitude': '-93...50.0WhiteEquipment ViolationNo2017
784710NaNYes12017-09-07T10:09:00.000NoFemale{'latitude': '44.988189836', 'longitude': '-93...9.0WhiteMoving ViolationNo2017
78471125.0Yes12017-10-24T08:45:00.000NoMale{'latitude': '44.952726847', 'longitude': '-93...119.0BlackMoving ViolationNo2017
78471226.0Yes12017-08-22T07:39:00.000NoMale{'latitude': '44.952131905', 'longitude': '-93...107.0OtherMoving ViolationNo2017
78471382.0Yes12017-11-02T12:01:00.000NoFemale{'latitude': '44.916259517', 'longitude': '-93...206.0WhiteMoving ViolationNo2017
784714NaNYes12017-10-31T09:05:00.000NoFemale{'latitude': '44.908307731', 'longitude': '-93...223.0LatinoMoving ViolationNo2017
784715NaNNo12017-09-14T22:42:00.000NoMaleNaN24.0WhiteEquipment ViolationNo2017
784716NaNNo12017-11-22T11:10:00.000NoFemale{'latitude': '44.948648967', 'longitude': '-93...131.0WhiteMoving ViolationNo2017
\n", "

106978 rows × 12 columns

\n", "
" ], "text/plain": [ " Age Citation Count Date Driver_search Gender \\\n", "0 NaN No 1 2018-05-11T20:47:00.000 No Male \n", "1 NaN No 1 2019-03-03T00:15:00.000 No Male \n", "2 28.0 Yes 1 2019-02-13T13:21:00.000 No Male \n", "3 NaN No 1 2019-07-27T21:23:00.000 No Male \n", "4 NaN No 1 2018-01-20T21:19:00.000 No Male \n", "5 NaN No 1 2018-12-27T14:02:00.000 No Male \n", "6 24.0 Yes 1 2018-03-13T13:51:00.000 No Female \n", "7 NaN Yes 1 2019-03-02T22:12:00.000 No Male \n", "8 25.0 Yes 1 2019-06-07T22:08:00.000 No Female \n", "9 NaN No 1 2019-01-19T22:08:00.000 No Male \n", "10 NaN No 1 2019-06-07T20:18:00.000 No Male \n", "11 22.0 Yes 1 2018-05-01T14:11:00.000 No Male \n", "12 21.0 Yes 1 2019-09-27T19:13:00.000 No Female \n", "13 NaN No 1 2019-07-20T21:10:00.000 No Male \n", "14 NaN No 1 2019-05-09T16:56:00.000 No Female \n", "15 NaN No 1 2018-11-21T18:56:00.000 No Male \n", "16 NaN No 1 2018-08-01T14:14:00.000 No Male \n", "17 NaN No 1 2018-10-27T20:43:00.000 No Male \n", "18 NaN No 1 2018-02-18T00:36:00.000 No Male \n", "19 NaN No 1 2018-06-23T00:46:00.000 No Male \n", "20 NaN No 1 2018-11-21T20:06:00.000 No Male \n", "21 54.0 Yes 1 2018-04-09T14:20:00.000 No Female \n", "22 NaN No 1 2018-10-05T20:49:00.000 No Male \n", "23 NaN No 1 2018-10-05T20:35:00.000 No Female \n", "24 NaN No 1 2018-10-27T21:29:00.000 No Male \n", "25 NaN Yes 1 2018-06-30T22:35:00.000 No Male \n", "26 NaN No 1 2018-01-13T00:51:00.000 No Male \n", "27 NaN No 1 2018-05-11T21:56:00.000 No Male \n", "28 NaN No 1 2018-02-17T21:37:00.000 No Female \n", "29 23.0 Yes 1 2018-04-12T11:34:00.000 No Male \n", "... ... ... ... ... ... ... \n", "784687 NaN No 1 2017-08-30T17:45:00.000 No Female \n", "784688 NaN No 1 2017-11-02T22:46:00.000 No Male \n", "784689 NaN No 1 2017-10-25T03:00:00.000 No Female \n", "784690 NaN No 1 2017-12-07T15:30:00.000 No Male \n", "784691 34.0 Yes 1 2017-09-07T08:30:00.000 No Female \n", "784692 30.0 Yes 1 2017-09-29T18:33:00.000 No Male \n", "784693 NaN Yes 1 2017-09-13T10:42:00.000 No Female \n", "784694 NaN Yes 1 2017-07-27T08:06:00.000 No Male \n", "784695 NaN No 1 2017-09-20T22:34:00.000 No Female \n", "784696 37.0 Yes 1 2017-09-28T09:39:00.000 No Male \n", "784697 NaN No 1 2017-07-25T13:50:00.000 No Male \n", "784698 NaN Yes 1 2017-12-06T10:20:00.000 No Female \n", "784699 NaN Yes 1 2017-10-05T10:03:00.000 No Female \n", "784700 NaN Yes 1 2017-12-01T11:21:00.000 No Male \n", "784701 NaN No 1 2017-10-11T15:12:00.000 No Female \n", "784702 NaN No 1 2017-10-03T17:40:00.000 Yes Male \n", "784703 NaN No 1 2017-09-29T17:45:00.000 No Female \n", "784704 27.0 Yes 1 2017-08-17T21:35:00.000 No Male \n", "784705 NaN No 1 2017-07-29T01:17:00.000 No Male \n", "784706 28.0 Yes 1 2017-09-25T08:37:00.000 No Female \n", "784707 NaN No 1 2017-11-09T10:51:00.000 No Female \n", "784708 NaN No 1 2017-12-04T00:15:00.000 Yes Male \n", "784709 NaN No 1 2017-08-31T23:28:00.000 No Female \n", "784710 NaN Yes 1 2017-09-07T10:09:00.000 No Female \n", "784711 25.0 Yes 1 2017-10-24T08:45:00.000 No Male \n", "784712 26.0 Yes 1 2017-08-22T07:39:00.000 No Male \n", "784713 82.0 Yes 1 2017-11-02T12:01:00.000 No Female \n", "784714 NaN Yes 1 2017-10-31T09:05:00.000 No Female \n", "784715 NaN No 1 2017-09-14T22:42:00.000 No Male \n", "784716 NaN No 1 2017-11-22T11:10:00.000 No Female \n", "\n", " GridLocation Grid Race \\\n", "0 NaN 991.0 Black \n", "1 NaN 991.0 Black \n", "2 NaN 991.0 Black \n", "3 NaN 991.0 White \n", "4 NaN 991.0 White \n", "5 NaN 991.0 White \n", "6 NaN 991.0 White \n", "7 NaN 991.0 White \n", "8 NaN 991.0 Other \n", "9 NaN 991.0 White \n", "10 NaN 991.0 White \n", "11 NaN 991.0 White \n", "12 NaN 991.0 Black \n", "13 NaN 991.0 White \n", "14 NaN 999.0 White \n", "15 NaN 991.0 Asian \n", "16 NaN 993.0 White \n", "17 NaN 991.0 White \n", "18 NaN 996.0 Black \n", "19 NaN 993.0 Other \n", "20 NaN 991.0 White \n", "21 NaN 991.0 White \n", "22 NaN 991.0 White \n", "23 NaN 991.0 White \n", "24 NaN 991.0 Latino \n", "25 NaN 994.0 White \n", "26 NaN 991.0 White \n", "27 NaN 991.0 White \n", "28 NaN 991.0 Black \n", "29 NaN 991.0 White \n", "... ... ... ... \n", "784687 {'latitude': '44.959171113', 'longitude': '-93... 94.0 Other \n", "784688 {'latitude': '44.959369203', 'longitude': '-93... 87.0 Black \n", "784689 {'latitude': '44.96658643', 'longitude': '-93.... 68.0 Black \n", "784690 {'latitude': '44.988221181', 'longitude': '-93... 11.0 White \n", "784691 {'latitude': '44.928875372', 'longitude': '-93... 267.0 White \n", "784692 {'latitude': '44.959515062', 'longitude': '-93... 91.0 White \n", "784693 {'latitude': '44.973827765', 'longitude': '-93... 51.0 Black \n", "784694 {'latitude': '44.973124945', 'longitude': '-93... 47.0 Black \n", "784695 {'latitude': '44.988314242', 'longitude': '-93... 14.0 Black \n", "784696 {'latitude': '44.980704001', 'longitude': '-93... 32.0 Black \n", "784697 {'latitude': '44.954914788', 'longitude': '-93... 112.0 Black \n", "784698 {'latitude': '44.980999933', 'longitude': '-93... 31.0 Black \n", "784699 {'latitude': '44.973124945', 'longitude': '-93... 47.0 Latino \n", "784700 {'latitude': '44.928875372', 'longitude': '-93... 267.0 White \n", "784701 {'latitude': '44.966688357', 'longitude': '-93... 75.0 Black \n", "784702 {'latitude': '44.959923808', 'longitude': '-93... 99.0 White \n", "784703 {'latitude': '44.95086001', 'longitude': '-93.... 132.0 White \n", "784704 {'latitude': '44.953679624', 'longitude': '-93... 111.0 Other \n", "784705 {'latitude': '44.980704001', 'longitude': '-93... 32.0 Black \n", "784706 {'latitude': '44.965443164', 'longitude': '-93... 269.0 Black \n", "784707 {'latitude': '44.952068692', 'longitude': '-93... 105.0 White \n", "784708 {'latitude': '44.966741984', 'longitude': '-93... 73.0 Black \n", "784709 {'latitude': '44.973798199', 'longitude': '-93... 50.0 White \n", "784710 {'latitude': '44.988189836', 'longitude': '-93... 9.0 White \n", "784711 {'latitude': '44.952726847', 'longitude': '-93... 119.0 Black \n", "784712 {'latitude': '44.952131905', 'longitude': '-93... 107.0 Other \n", "784713 {'latitude': '44.916259517', 'longitude': '-93... 206.0 White \n", "784714 {'latitude': '44.908307731', 'longitude': '-93... 223.0 Latino \n", "784715 NaN 24.0 White \n", "784716 {'latitude': '44.948648967', 'longitude': '-93... 131.0 White \n", "\n", " Reason Vehicle_search Year \n", "0 Moving Violation No 2018 \n", "1 Equipment Violation No 2019 \n", "2 Moving Violation No 2019 \n", "3 Equipment Violation No 2019 \n", "4 Moving Violation No 2018 \n", "5 Moving Violation No 2018 \n", "6 Moving Violation No 2018 \n", "7 Equipment Violation No 2019 \n", "8 Moving Violation No 2019 \n", "9 Equipment Violation No 2019 \n", "10 Moving Violation No 2019 \n", "11 Moving Violation No 2018 \n", "12 Moving Violation No 2019 \n", "13 Equipment Violation No 2019 \n", "14 Moving Violation No 2019 \n", "15 Moving Violation No 2018 \n", "16 Moving Violation No 2018 \n", "17 Equipment Violation No 2018 \n", "18 Moving Violation No 2018 \n", "19 Moving Violation No 2018 \n", "20 Moving Violation No 2018 \n", "21 Moving Violation No 2018 \n", "22 Moving Violation No 2018 \n", "23 Moving Violation No 2018 \n", "24 Moving Violation No 2018 \n", "25 Moving Violation No 2018 \n", "26 Equipment Violation No 2018 \n", "27 Moving Violation No 2018 \n", "28 Equipment Violation No 2018 \n", "29 Moving Violation No 2018 \n", "... ... ... ... \n", "784687 Moving Violation No 2017 \n", "784688 Moving Violation No 2017 \n", "784689 Moving Violation No 2017 \n", "784690 Moving Violation No 2017 \n", "784691 Moving Violation No 2017 \n", "784692 Moving Violation No 2017 \n", "784693 Moving Violation No 2017 \n", "784694 Moving Violation No 2017 \n", "784695 Moving Violation No 2017 \n", "784696 Moving Violation No 2017 \n", "784697 Moving Violation No 2017 \n", "784698 Moving Violation No 2017 \n", "784699 Moving Violation No 2017 \n", "784700 Moving Violation No 2017 \n", "784701 Moving Violation No 2017 \n", "784702 Moving Violation Yes 2017 \n", "784703 Moving Violation No 2017 \n", "784704 Equipment Violation No 2017 \n", "784705 Equipment Violation No 2017 \n", "784706 Moving Violation No 2017 \n", "784707 Moving Violation No 2017 \n", "784708 Moving Violation Yes 2017 \n", "784709 Equipment Violation No 2017 \n", "784710 Moving Violation No 2017 \n", "784711 Moving Violation No 2017 \n", "784712 Moving Violation No 2017 \n", "784713 Moving Violation No 2017 \n", "784714 Moving Violation No 2017 \n", "784715 Equipment Violation No 2017 \n", "784716 Moving Violation No 2017 \n", "\n", "[106978 rows x 12 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, I will be creating some **functions** that will make data transformations easier; such as creating an AgeBin, Neighborhood Designation, and Policing District Designation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hide_input": false }, "outputs": [], "source": [ "#Community function\n", "def commun(x): \n", " if x in [67,68,87,88,89,90,91,92]:\n", " return 'Thomas_Frogtown'\n", " elif x in [5,6,7,8,25,26,27,28,45,46,47,48]:\n", " return 'Como'\n", " elif x in [107, 108, 109, 110,127,128,129,130]: \n", " return 'Summit_University'\n", " elif x in [101,102,103,104,105,106,122,123,124,125,126]: \n", " return 'Union_Park'\n", " elif x in [63,64,65,66,83,84,85,86]: \n", " return 'Midway' \n", " elif x in [142,143,144,145,146,162,163,164,165,166]:\n", " return 'Macalester_Groveland'\n", " elif x in [182,183,184,185,186,202,203,204,205,206,223,224,225,242,243,244,245,246]:\n", " return 'Highland_Park'\n", " elif x in [147,148,149,167,168]:\n", " return 'Summit_Hill'\n", " elif x in [1,2,21,22,43,44,61,62,81,82]:\n", " return 'St_Anthony'\n", " elif x in [226,207,187,188,189,267,268,169,170,171,249,150,151,230,367]:\n", " return 'West_7th'\n", " elif x in [209,210,211,212,213,214,215,192,193,194,195,172,173,174,175]:\n", " return 'West_Side'\n", " elif x in [111,112,131,132,133,152,153]:\n", " return 'Capital_River'\n", " elif x in [98,99,100,118,119,119,120,137,138,139,140,160,197,180,200,240,280]:\n", " return 'Battle_Creek'\n", " elif x in [76,95,96,97,115,116,117,138,114,136]:\n", " return 'Dayton_Bluff' \n", " elif x in [9,10,11,12,29,30,31,32,49,50,51,52,269,69,70,71,72]:\n", " return 'North_End'\n", " elif x in [13,14,15,16,33,34,35,36,53,54,55,56,73,74,75,93,94]:\n", " return 'Payne_Phalen'\n", " elif x in [17,18,19,20,37,38,39,40,56,57,58,59,60,77,78,79,80]:\n", " return 'Greater_East_Side'\n", " else: \n", " return 'NaN'\n", "\n", "#District Function \n", " \n", "def district(x):\n", " if x in [1,2,3,4,5,6,7,8,21,22,23,24,25,26,27,28,43,44,45,46,47,48,\\\n", " 61,62,63,64,65,66,67,68,269,81,82,83,84,85,86,87,88,89,\\\n", " 101,102,103,104,105,106,107,108,109,110,122,123,124,125,126,\\\n", " 127,128,129,130,142,143,144,145,146,147,148,149,149,\\\n", " 162,163,164,165,166,167,168,182,183,184,185,186,\\\n", " 202,203,204,205,206,223,224,225,242,243,244,245,246]:\n", " return 'Western'\n", " elif x in [9,10,11,12,29,30,32,31,49,50,51,52,69,70,71,72,\\\n", " 90,91,92,111,112,131,132,133,267,268,249,130,230,\\\n", " 150,151,152,153,169,170,171,172,173,174,175,207,209,226,\\\n", " 187,188,189,192,193,194,195,210,211,212,213,214,215]:\n", " return 'Central'\n", " elif x in [13,14,15,16,17,18,19,20,33,34,35,36,37,38,39,40,\\\n", " 53,54,55,56,57,58,59,60,73,74,75,76,77,78,79,80,\\\n", " 93,94,95,96,97,98,99,100,114,115,116,117,118,119,120,\\\n", " 136,137,138,139,140,160,197,180,200,240,280]:\n", " return 'Eastern'\n", " else:\n", " return 'NaN'\n", " \n", "# Define Age Function bins\n", "def agef(x):\n", " if x<19: \n", " return 'Teen <19'\n", " elif x>18 and x<25: \n", " return 'Young Adult 19-24'\n", " elif x>25 and x<31: \n", " return 'Adult 26-30'\n", " elif x>30 and x<46: \n", " return 'Middle Adult 31-45' \n", " elif x>45: \n", " return 'Older Adult 46+' \n", " else: \n", " return 'NaN' " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Cleaning/Wrangling\n", "\n", "I will be constructing several variables. Originally, I wanted to extract gridlocation coordinates from the dataset, but it makes more sense to connect the grid to a json file. The manipulations and additions are listed below:\n", "\n", "1. Convert time variable to datetime; Extract Month, DayofWeek, Weekend, Hour\n", "2. I've constructed a variable **LateNight** which denotes if a stop occured between 10:00PM to 5:00AM\n", "3. Converted several variables to integers; Note: Female is designated as 1 \n", "4. Converted some descriptive columns to dummy variables\n", "5. Extracted Latitude and Longitude in separate columns for each police grid\n", "\n", "\n", "**Initial Omissions**\n", "\n", "* The demographic 'Native American' was excluded because the numbers were too small.\n", "* There are empty cells under the 'No Data' Category\n", "* The two reasons, '911 call', and 'Investigative Stop' were excluded because they were small numbers and not relevant to study; See below \n", "* Finally, any data entries not belonging to a community were excluded. It's possible that some stops occurred outside Saint Paul jursidiction. These data points were excluded\n", "* Driver being search and vehicle being searched is strongly correlated, so exclude vehicle search in analysis\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Moving Violation 77989\n", "Equipment Violation 24475\n", "Investigative Stop 4191\n", "911 Call / Citizen Reported 183\n", "No Data 140\n", "Name: Reason, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Reason.value_counts()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2017 31290\n", "2018 29685\n", "2019 22843\n", "2020 18164\n", "Name: Year, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Year.value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hide_input": false }, "outputs": [ { "ename": "TypeError", "evalue": "Cannot compare types 'ndarray(dtype=int64)' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;31m#Replace variables with dummies\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Driver_search'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_replace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'No'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Yes'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Vehicle_search'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_replace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'No'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Yes'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Citation'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_replace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'No'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Yes'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m 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regex=regex)\n\u001b[0m\u001b[0;32m 6548\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6549\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# [NA, ''] -> 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mreplace_list\u001b[1;34m(self, src_list, dest_list, inplace, regex)\u001b[0m\n\u001b[0;32m 557\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_compare_or_regex_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 559\u001b[1;33m \u001b[0mmasks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcomp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mcomp\u001b[1;34m(s, regex)\u001b[0m\n\u001b[0;32m 555\u001b[0m return _compare_or_regex_search(maybe_convert_objects(values),\n\u001b[0;32m 556\u001b[0m getattr(s, 'asm8'), regex)\n\u001b[1;32m--> 557\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_compare_or_regex_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 559\u001b[0m \u001b[0mmasks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcomp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregex\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_compare_or_regex_search\u001b[1;34m(a, b, regex)\u001b[0m\n\u001b[0;32m 1949\u001b[0m raise TypeError(\n\u001b[0;32m 1950\u001b[0m \"Cannot compare types {a!r} and {b!r}\".format(a=type_names[0],\n\u001b[1;32m-> 1951\u001b[1;33m b=type_names[1]))\n\u001b[0m\u001b[0;32m 1952\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1953\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: Cannot compare types 'ndarray(dtype=int64)' and 'str'" ] } ], "source": [ "#Prepping the Primary Dataset\n", "\n", "#Add District Plannning Council and District columns from Functions\n", "df['Community']= df['Grid'].apply(commun)\n", "df['District']= df['Grid'].apply(district)\n", "df['AgeDemo']= df['Age'].apply(agef)\n", "\n", "#Add Time Variables\n", "df['Date']= pd.to_datetime(df['Date'])\n", "df['DayofWeek']=df['Date'].dt.dayofweek\n", "df['Weekend'] = df['DayofWeek'].apply(lambda x: 1 if (x>4) else 0)\n", "df['Month'] = df['Date'].dt.month\n", "df['Day'] = df['Date'].dt.day\n", "df['Hour'] = df['Date'].dt.hour\n", "df['LateNight'] = df['Hour'].apply(lambda x: 1 if (x>21 or x<5) else 0)\n", "\n", "#Screening\n", "df= df[df.Reason != '911 Call / Citizen Reported']\n", "df= df[df.Reason != 'No Data']\n", "df= df.loc[df['Reason'] != 'Investigative Stop']\n", "df= df.loc[df['Race'] != 'Native American']\n", "\n", "#Replace variables with dummies\n", "df['Driver_search'].replace(to_replace=['No','Yes'], value=[0,1],inplace=True) \n", "df['Vehicle_search'].replace(to_replace=['No','Yes'], value=[0,1],inplace=True) \n", "df['Citation'].replace(to_replace=['No','Yes'], value=[0,1],inplace=True) \n", "df['Gender'].replace(to_replace=['Male','Female'], value=[0,1],inplace=True) #FEMALE is 1\n", "\n", "df= pd.concat([df,pd.get_dummies(df['Reason'])], axis=1)\n", "df= pd.concat([df,pd.get_dummies(df['Race'])], axis=1)\n", "\n", "#Let Logitiude and Latitude\n", "## I conldn't figure out how to utilize the dictionary, so I just converted it to a string\n", "# Separate Latitude and Longitude \n", "df['GridLocation'] = df['GridLocation'].astype('str') \n", "\n", "#Get Latitude\n", "new=df['GridLocation'].str.split(\"',\", n = 1, expand = True) \n", "# making seperate first name column from new data frame\n", "lat=new[0].str.split(\" '\", n = 1, expand = True)\n", "df['Latitude']= pd.to_numeric(lat[1]) \n", "\n", "#Get Longtitude\n", "long= new[1].str.split(\": '\", n = 1, expand = True)\n", "#long[1]\n", "df['Longitude']= pd.to_numeric(long[1].str.rstrip(\"'}\"))\n", "\n", "#Use if need to change variables ot integers\n", "#df[['Dr_search', 'V_search', 'Citation']] = df[['Dr_search', 'V_search', 'Gender','Citation']].astype(int)\n", "\n", "# Remove any missing community data entries\n", "df= df[df.Community != 'NaN']\n", "\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeCitationCountDateDriver_searchGenderGridLocationGridRaceReason...HourLateNightEquipment ViolationMoving ViolationAsianBlackLatinoOtherWhiteLatitude
0NaN012018-05-11 20:47:0000nan991.0BlackMoving Violation...2000101000NaN
1NaN012019-03-03 00:15:0000nan991.0BlackEquipment Violation...011001000NaN
228.0112019-02-13 13:21:0000nan991.0BlackMoving Violation...1300101000NaN
3NaN012019-07-27 21:23:0000nan991.0WhiteEquipment Violation...2101000001NaN
4NaN012018-01-20 21:19:0000nan991.0WhiteMoving Violation...2100100001NaN
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5 rows × 29 columns

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" ], "text/plain": [ " Age Citation Count Date Driver_search Gender \\\n", "0 NaN 0 1 2018-05-11 20:47:00 0 0 \n", "1 NaN 0 1 2019-03-03 00:15:00 0 0 \n", "2 28.0 1 1 2019-02-13 13:21:00 0 0 \n", "3 NaN 0 1 2019-07-27 21:23:00 0 0 \n", "4 NaN 0 1 2018-01-20 21:19:00 0 0 \n", "\n", " GridLocation Grid Race Reason ... Hour LateNight \\\n", "0 nan 991.0 Black Moving Violation ... 20 0 \n", "1 nan 991.0 Black Equipment Violation ... 0 1 \n", "2 nan 991.0 Black Moving Violation ... 13 0 \n", "3 nan 991.0 White Equipment Violation ... 21 0 \n", "4 nan 991.0 White Moving Violation ... 21 0 \n", "\n", " Equipment Violation Moving Violation Asian Black Latino Other White \\\n", "0 0 1 0 1 0 0 0 \n", "1 1 0 0 1 0 0 0 \n", "2 0 1 0 1 0 0 0 \n", "3 1 0 0 0 0 0 1 \n", "4 0 1 0 0 0 0 1 \n", "\n", " Latitude \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeCitationCountDateDriver_searchGenderGridLocationGridRaceReason...HourLateNightEquipment ViolationMoving ViolationAsianBlackLatinoOtherWhiteLatitude
0NaN012018-05-11 20:47:0000nan991.0BlackMoving Violation...2000101000NaN
1NaN012019-03-03 00:15:0000nan991.0BlackEquipment Violation...011001000NaN
228.0112019-02-13 13:21:0000nan991.0BlackMoving Violation...1300101000NaN
3NaN012019-07-27 21:23:0000nan991.0WhiteEquipment Violation...2101000001NaN
4NaN012018-01-20 21:19:0000nan991.0WhiteMoving Violation...2100100001NaN
5NaN012018-12-27 14:02:0000nan991.0WhiteMoving Violation...1400100001NaN
624.0112018-03-13 13:51:0001nan991.0WhiteMoving Violation...1300100001NaN
7NaN112019-03-02 22:12:0000nan991.0WhiteEquipment Violation...2211000001NaN
825.0112019-06-07 22:08:0001nan991.0OtherMoving Violation...2210100010NaN
9NaN012019-01-19 22:08:0000nan991.0WhiteEquipment Violation...2211000001NaN
10NaN012019-06-07 20:18:0000nan991.0WhiteMoving Violation...2000100001NaN
1122.0112018-05-01 14:11:0000nan991.0WhiteMoving Violation...1400100001NaN
1221.0112019-09-27 19:13:0001nan991.0BlackMoving Violation...1900101000NaN
13NaN012019-07-20 21:10:0000nan991.0WhiteEquipment Violation...2101000001NaN
14NaN012019-05-09 16:56:0001nan999.0WhiteMoving Violation...1600100001NaN
15NaN012018-11-21 18:56:0000nan991.0AsianMoving Violation...1800110000NaN
16NaN012018-08-01 14:14:0000nan993.0WhiteMoving Violation...1400100001NaN
17NaN012018-10-27 20:43:0000nan991.0WhiteEquipment Violation...2001000001NaN
18NaN012018-02-18 00:36:0000nan996.0BlackMoving Violation...010101000NaN
19NaN012018-06-23 00:46:0000nan993.0OtherMoving Violation...010100010NaN
20NaN012018-11-21 20:06:0000nan991.0WhiteMoving Violation...2000100001NaN
2154.0112018-04-09 14:20:0001nan991.0WhiteMoving Violation...1400100001NaN
22NaN012018-10-05 20:49:0000nan991.0WhiteMoving Violation...2000100001NaN
23NaN012018-10-05 20:35:0001nan991.0WhiteMoving Violation...2000100001NaN
24NaN012018-10-27 21:29:0000nan991.0LatinoMoving Violation...2100100100NaN
25NaN112018-06-30 22:35:0000nan994.0WhiteMoving Violation...2210100001NaN
26NaN012018-01-13 00:51:0000nan991.0WhiteEquipment Violation...011000001NaN
27NaN012018-05-11 21:56:0000nan991.0WhiteMoving Violation...2100100001NaN
28NaN012018-02-17 21:37:0001nan991.0BlackEquipment Violation...2101001000NaN
2923.0112018-04-12 11:34:0000nan991.0WhiteMoving Violation...1100100001NaN
..................................................................
784687NaN012017-08-30 17:45:0001{'latitude': '44.959171113', 'longitude': '-93...94.0OtherMoving Violation...170010001044.959171
784688NaN012017-11-02 22:46:0000{'latitude': '44.959369203', 'longitude': '-93...87.0BlackMoving Violation...221010100044.959369
784689NaN012017-10-25 03:00:0001{'latitude': '44.96658643', 'longitude': '-93....68.0BlackMoving Violation...31010100044.966586
784690NaN012017-12-07 15:30:0000{'latitude': '44.988221181', 'longitude': '-93...11.0WhiteMoving Violation...150010000144.988221
78469134.0112017-09-07 08:30:0001{'latitude': '44.928875372', 'longitude': '-93...267.0WhiteMoving Violation...80010000144.928875
78469230.0112017-09-29 18:33:0000{'latitude': '44.959515062', 'longitude': '-93...91.0WhiteMoving Violation...180010000144.959515
784693NaN112017-09-13 10:42:0001{'latitude': '44.973827765', 'longitude': '-93...51.0BlackMoving Violation...100010100044.973828
784694NaN112017-07-27 08:06:0000{'latitude': '44.973124945', 'longitude': '-93...47.0BlackMoving Violation...80010100044.973125
784695NaN012017-09-20 22:34:0001{'latitude': '44.988314242', 'longitude': '-93...14.0BlackMoving Violation...221010100044.988314
78469637.0112017-09-28 09:39:0000{'latitude': '44.980704001', 'longitude': '-93...32.0BlackMoving Violation...90010100044.980704
784697NaN012017-07-25 13:50:0000{'latitude': '44.954914788', 'longitude': '-93...112.0BlackMoving Violation...130010100044.954915
784698NaN112017-12-06 10:20:0001{'latitude': '44.980999933', 'longitude': '-93...31.0BlackMoving Violation...100010100044.981000
784699NaN112017-10-05 10:03:0001{'latitude': '44.973124945', 'longitude': '-93...47.0LatinoMoving Violation...100010010044.973125
784700NaN112017-12-01 11:21:0000{'latitude': '44.928875372', 'longitude': '-93...267.0WhiteMoving Violation...110010000144.928875
784701NaN012017-10-11 15:12:0001{'latitude': '44.966688357', 'longitude': '-93...75.0BlackMoving Violation...150010100044.966688
784702NaN012017-10-03 17:40:0010{'latitude': '44.959923808', 'longitude': '-93...99.0WhiteMoving Violation...170010000144.959924
784703NaN012017-09-29 17:45:0001{'latitude': '44.95086001', 'longitude': '-93....132.0WhiteMoving Violation...170010000144.950860
78470427.0112017-08-17 21:35:0000{'latitude': '44.953679624', 'longitude': '-93...111.0OtherEquipment Violation...210100001044.953680
784705NaN012017-07-29 01:17:0000{'latitude': '44.980704001', 'longitude': '-93...32.0BlackEquipment Violation...11100100044.980704
78470628.0112017-09-25 08:37:0001{'latitude': '44.965443164', 'longitude': '-93...269.0BlackMoving Violation...80010100044.965443
784707NaN012017-11-09 10:51:0001{'latitude': '44.952068692', 'longitude': '-93...105.0WhiteMoving Violation...100010000144.952069
784708NaN012017-12-04 00:15:0010{'latitude': '44.966741984', 'longitude': '-93...73.0BlackMoving Violation...01010100044.966742
784709NaN012017-08-31 23:28:0001{'latitude': '44.973798199', 'longitude': '-93...50.0WhiteEquipment Violation...231100000144.973798
784710NaN112017-09-07 10:09:0001{'latitude': '44.988189836', 'longitude': '-93...9.0WhiteMoving Violation...100010000144.988190
78471125.0112017-10-24 08:45:0000{'latitude': '44.952726847', 'longitude': '-93...119.0BlackMoving Violation...80010100044.952727
78471226.0112017-08-22 07:39:0000{'latitude': '44.952131905', 'longitude': '-93...107.0OtherMoving Violation...70010001044.952132
78471382.0112017-11-02 12:01:0001{'latitude': '44.916259517', 'longitude': '-93...206.0WhiteMoving Violation...120010000144.916260
784714NaN112017-10-31 09:05:0001{'latitude': '44.908307731', 'longitude': '-93...223.0LatinoMoving Violation...90010010044.908308
784715NaN012017-09-14 22:42:0000nan24.0WhiteEquipment Violation...2211000001NaN
784716NaN012017-11-22 11:10:0001{'latitude': '44.948648967', 'longitude': '-93...131.0WhiteMoving Violation...110010000144.948649
\n", "

101982 rows × 29 columns

\n", "
" ], "text/plain": [ " Age Citation Count Date Driver_search Gender \\\n", "0 NaN 0 1 2018-05-11 20:47:00 0 0 \n", "1 NaN 0 1 2019-03-03 00:15:00 0 0 \n", "2 28.0 1 1 2019-02-13 13:21:00 0 0 \n", "3 NaN 0 1 2019-07-27 21:23:00 0 0 \n", "4 NaN 0 1 2018-01-20 21:19:00 0 0 \n", "5 NaN 0 1 2018-12-27 14:02:00 0 0 \n", "6 24.0 1 1 2018-03-13 13:51:00 0 1 \n", "7 NaN 1 1 2019-03-02 22:12:00 0 0 \n", "8 25.0 1 1 2019-06-07 22:08:00 0 1 \n", "9 NaN 0 1 2019-01-19 22:08:00 0 0 \n", "10 NaN 0 1 2019-06-07 20:18:00 0 0 \n", "11 22.0 1 1 2018-05-01 14:11:00 0 0 \n", "12 21.0 1 1 2019-09-27 19:13:00 0 1 \n", "13 NaN 0 1 2019-07-20 21:10:00 0 0 \n", "14 NaN 0 1 2019-05-09 16:56:00 0 1 \n", "15 NaN 0 1 2018-11-21 18:56:00 0 0 \n", "16 NaN 0 1 2018-08-01 14:14:00 0 0 \n", "17 NaN 0 1 2018-10-27 20:43:00 0 0 \n", "18 NaN 0 1 2018-02-18 00:36:00 0 0 \n", "19 NaN 0 1 2018-06-23 00:46:00 0 0 \n", "20 NaN 0 1 2018-11-21 20:06:00 0 0 \n", "21 54.0 1 1 2018-04-09 14:20:00 0 1 \n", "22 NaN 0 1 2018-10-05 20:49:00 0 0 \n", "23 NaN 0 1 2018-10-05 20:35:00 0 1 \n", "24 NaN 0 1 2018-10-27 21:29:00 0 0 \n", "25 NaN 1 1 2018-06-30 22:35:00 0 0 \n", "26 NaN 0 1 2018-01-13 00:51:00 0 0 \n", "27 NaN 0 1 2018-05-11 21:56:00 0 0 \n", "28 NaN 0 1 2018-02-17 21:37:00 0 1 \n", "29 23.0 1 1 2018-04-12 11:34:00 0 0 \n", "... ... ... ... ... ... ... \n", "784687 NaN 0 1 2017-08-30 17:45:00 0 1 \n", "784688 NaN 0 1 2017-11-02 22:46:00 0 0 \n", "784689 NaN 0 1 2017-10-25 03:00:00 0 1 \n", "784690 NaN 0 1 2017-12-07 15:30:00 0 0 \n", "784691 34.0 1 1 2017-09-07 08:30:00 0 1 \n", "784692 30.0 1 1 2017-09-29 18:33:00 0 0 \n", "784693 NaN 1 1 2017-09-13 10:42:00 0 1 \n", "784694 NaN 1 1 2017-07-27 08:06:00 0 0 \n", "784695 NaN 0 1 2017-09-20 22:34:00 0 1 \n", "784696 37.0 1 1 2017-09-28 09:39:00 0 0 \n", "784697 NaN 0 1 2017-07-25 13:50:00 0 0 \n", "784698 NaN 1 1 2017-12-06 10:20:00 0 1 \n", "784699 NaN 1 1 2017-10-05 10:03:00 0 1 \n", "784700 NaN 1 1 2017-12-01 11:21:00 0 0 \n", "784701 NaN 0 1 2017-10-11 15:12:00 0 1 \n", "784702 NaN 0 1 2017-10-03 17:40:00 1 0 \n", "784703 NaN 0 1 2017-09-29 17:45:00 0 1 \n", "784704 27.0 1 1 2017-08-17 21:35:00 0 0 \n", "784705 NaN 0 1 2017-07-29 01:17:00 0 0 \n", "784706 28.0 1 1 2017-09-25 08:37:00 0 1 \n", "784707 NaN 0 1 2017-11-09 10:51:00 0 1 \n", "784708 NaN 0 1 2017-12-04 00:15:00 1 0 \n", "784709 NaN 0 1 2017-08-31 23:28:00 0 1 \n", "784710 NaN 1 1 2017-09-07 10:09:00 0 1 \n", "784711 25.0 1 1 2017-10-24 08:45:00 0 0 \n", "784712 26.0 1 1 2017-08-22 07:39:00 0 0 \n", "784713 82.0 1 1 2017-11-02 12:01:00 0 1 \n", "784714 NaN 1 1 2017-10-31 09:05:00 0 1 \n", "784715 NaN 0 1 2017-09-14 22:42:00 0 0 \n", "784716 NaN 0 1 2017-11-22 11:10:00 0 1 \n", "\n", " GridLocation Grid Race \\\n", "0 nan 991.0 Black \n", "1 nan 991.0 Black \n", "2 nan 991.0 Black \n", "3 nan 991.0 White \n", "4 nan 991.0 White \n", "5 nan 991.0 White \n", "6 nan 991.0 White \n", "7 nan 991.0 White \n", "8 nan 991.0 Other \n", "9 nan 991.0 White \n", "10 nan 991.0 White \n", "11 nan 991.0 White \n", "12 nan 991.0 Black \n", "13 nan 991.0 White \n", "14 nan 999.0 White \n", "15 nan 991.0 Asian \n", "16 nan 993.0 White \n", "17 nan 991.0 White \n", "18 nan 996.0 Black \n", "19 nan 993.0 Other \n", "20 nan 991.0 White \n", "21 nan 991.0 White \n", "22 nan 991.0 White \n", "23 nan 991.0 White \n", "24 nan 991.0 Latino \n", "25 nan 994.0 White \n", "26 nan 991.0 White \n", "27 nan 991.0 White \n", "28 nan 991.0 Black \n", "29 nan 991.0 White \n", "... ... ... ... \n", "784687 {'latitude': '44.959171113', 'longitude': '-93... 94.0 Other \n", "784688 {'latitude': '44.959369203', 'longitude': '-93... 87.0 Black \n", "784689 {'latitude': '44.96658643', 'longitude': '-93.... 68.0 Black \n", "784690 {'latitude': '44.988221181', 'longitude': '-93... 11.0 White \n", "784691 {'latitude': '44.928875372', 'longitude': '-93... 267.0 White \n", "784692 {'latitude': '44.959515062', 'longitude': '-93... 91.0 White \n", "784693 {'latitude': '44.973827765', 'longitude': '-93... 51.0 Black \n", "784694 {'latitude': '44.973124945', 'longitude': '-93... 47.0 Black \n", "784695 {'latitude': '44.988314242', 'longitude': '-93... 14.0 Black \n", "784696 {'latitude': '44.980704001', 'longitude': '-93... 32.0 Black \n", "784697 {'latitude': '44.954914788', 'longitude': '-93... 112.0 Black \n", "784698 {'latitude': '44.980999933', 'longitude': '-93... 31.0 Black \n", "784699 {'latitude': '44.973124945', 'longitude': '-93... 47.0 Latino \n", "784700 {'latitude': '44.928875372', 'longitude': '-93... 267.0 White \n", "784701 {'latitude': '44.966688357', 'longitude': '-93... 75.0 Black \n", "784702 {'latitude': '44.959923808', 'longitude': '-93... 99.0 White \n", "784703 {'latitude': '44.95086001', 'longitude': '-93.... 132.0 White \n", "784704 {'latitude': '44.953679624', 'longitude': '-93... 111.0 Other \n", "784705 {'latitude': '44.980704001', 'longitude': '-93... 32.0 Black \n", "784706 {'latitude': '44.965443164', 'longitude': '-93... 269.0 Black \n", "784707 {'latitude': '44.952068692', 'longitude': '-93... 105.0 White \n", "784708 {'latitude': '44.966741984', 'longitude': '-93... 73.0 Black \n", "784709 {'latitude': '44.973798199', 'longitude': '-93... 50.0 White \n", "784710 {'latitude': '44.988189836', 'longitude': '-93... 9.0 White \n", "784711 {'latitude': '44.952726847', 'longitude': '-93... 119.0 Black \n", "784712 {'latitude': '44.952131905', 'longitude': '-93... 107.0 Other \n", "784713 {'latitude': '44.916259517', 'longitude': '-93... 206.0 White \n", "784714 {'latitude': '44.908307731', 'longitude': '-93... 223.0 Latino \n", "784715 nan 24.0 White \n", "784716 {'latitude': '44.948648967', 'longitude': '-93... 131.0 White \n", "\n", " Reason ... Hour LateNight Equipment Violation \\\n", "0 Moving Violation ... 20 0 0 \n", "1 Equipment Violation ... 0 1 1 \n", "2 Moving Violation ... 13 0 0 \n", "3 Equipment Violation ... 21 0 1 \n", "4 Moving Violation ... 21 0 0 \n", "5 Moving Violation ... 14 0 0 \n", "6 Moving Violation ... 13 0 0 \n", "7 Equipment Violation ... 22 1 1 \n", "8 Moving Violation ... 22 1 0 \n", "9 Equipment Violation ... 22 1 1 \n", "10 Moving Violation ... 20 0 0 \n", "11 Moving Violation ... 14 0 0 \n", "12 Moving Violation ... 19 0 0 \n", "13 Equipment Violation ... 21 0 1 \n", "14 Moving Violation ... 16 0 0 \n", "15 Moving Violation ... 18 0 0 \n", "16 Moving Violation ... 14 0 0 \n", "17 Equipment Violation ... 20 0 1 \n", "18 Moving Violation ... 0 1 0 \n", "19 Moving Violation ... 0 1 0 \n", "20 Moving Violation ... 20 0 0 \n", "21 Moving Violation ... 14 0 0 \n", "22 Moving Violation ... 20 0 0 \n", "23 Moving Violation ... 20 0 0 \n", "24 Moving Violation ... 21 0 0 \n", "25 Moving Violation ... 22 1 0 \n", "26 Equipment Violation ... 0 1 1 \n", "27 Moving Violation ... 21 0 0 \n", "28 Equipment Violation ... 21 0 1 \n", "29 Moving Violation ... 11 0 0 \n", "... ... ... ... ... ... \n", "784687 Moving Violation ... 17 0 0 \n", "784688 Moving Violation ... 22 1 0 \n", "784689 Moving Violation ... 3 1 0 \n", "784690 Moving Violation ... 15 0 0 \n", "784691 Moving Violation ... 8 0 0 \n", "784692 Moving Violation ... 18 0 0 \n", "784693 Moving Violation ... 10 0 0 \n", "784694 Moving Violation ... 8 0 0 \n", "784695 Moving Violation ... 22 1 0 \n", "784696 Moving Violation ... 9 0 0 \n", "784697 Moving Violation ... 13 0 0 \n", "784698 Moving Violation ... 10 0 0 \n", "784699 Moving Violation ... 10 0 0 \n", "784700 Moving Violation ... 11 0 0 \n", "784701 Moving Violation ... 15 0 0 \n", "784702 Moving Violation ... 17 0 0 \n", "784703 Moving Violation ... 17 0 0 \n", "784704 Equipment Violation ... 21 0 1 \n", "784705 Equipment Violation ... 1 1 1 \n", "784706 Moving Violation ... 8 0 0 \n", "784707 Moving Violation ... 10 0 0 \n", "784708 Moving Violation ... 0 1 0 \n", "784709 Equipment Violation ... 23 1 1 \n", "784710 Moving Violation ... 10 0 0 \n", "784711 Moving Violation ... 8 0 0 \n", "784712 Moving Violation ... 7 0 0 \n", "784713 Moving Violation ... 12 0 0 \n", "784714 Moving Violation ... 9 0 0 \n", "784715 Equipment Violation ... 22 1 1 \n", "784716 Moving Violation ... 11 0 0 \n", "\n", " Moving Violation Asian Black Latino Other White Latitude \n", "0 1 0 1 0 0 0 NaN \n", "1 0 0 1 0 0 0 NaN \n", "2 1 0 1 0 0 0 NaN \n", "3 0 0 0 0 0 1 NaN \n", "4 1 0 0 0 0 1 NaN \n", "5 1 0 0 0 0 1 NaN \n", "6 1 0 0 0 0 1 NaN \n", "7 0 0 0 0 0 1 NaN \n", "8 1 0 0 0 1 0 NaN \n", "9 0 0 0 0 0 1 NaN \n", "10 1 0 0 0 0 1 NaN \n", "11 1 0 0 0 0 1 NaN \n", "12 1 0 1 0 0 0 NaN \n", "13 0 0 0 0 0 1 NaN \n", "14 1 0 0 0 0 1 NaN \n", "15 1 1 0 0 0 0 NaN \n", "16 1 0 0 0 0 1 NaN \n", "17 0 0 0 0 0 1 NaN \n", "18 1 0 1 0 0 0 NaN \n", "19 1 0 0 0 1 0 NaN \n", "20 1 0 0 0 0 1 NaN \n", "21 1 0 0 0 0 1 NaN \n", "22 1 0 0 0 0 1 NaN \n", "23 1 0 0 0 0 1 NaN \n", "24 1 0 0 1 0 0 NaN \n", "25 1 0 0 0 0 1 NaN \n", "26 0 0 0 0 0 1 NaN \n", "27 1 0 0 0 0 1 NaN \n", "28 0 0 1 0 0 0 NaN \n", "29 1 0 0 0 0 1 NaN \n", "... ... ... ... ... ... ... ... \n", "784687 1 0 0 0 1 0 44.959171 \n", "784688 1 0 1 0 0 0 44.959369 \n", "784689 1 0 1 0 0 0 44.966586 \n", "784690 1 0 0 0 0 1 44.988221 \n", "784691 1 0 0 0 0 1 44.928875 \n", "784692 1 0 0 0 0 1 44.959515 \n", "784693 1 0 1 0 0 0 44.973828 \n", "784694 1 0 1 0 0 0 44.973125 \n", "784695 1 0 1 0 0 0 44.988314 \n", "784696 1 0 1 0 0 0 44.980704 \n", "784697 1 0 1 0 0 0 44.954915 \n", "784698 1 0 1 0 0 0 44.981000 \n", "784699 1 0 0 1 0 0 44.973125 \n", "784700 1 0 0 0 0 1 44.928875 \n", "784701 1 0 1 0 0 0 44.966688 \n", "784702 1 0 0 0 0 1 44.959924 \n", "784703 1 0 0 0 0 1 44.950860 \n", "784704 0 0 0 0 1 0 44.953680 \n", "784705 0 0 1 0 0 0 44.980704 \n", "784706 1 0 1 0 0 0 44.965443 \n", "784707 1 0 0 0 0 1 44.952069 \n", "784708 1 0 1 0 0 0 44.966742 \n", "784709 0 0 0 0 0 1 44.973798 \n", "784710 1 0 0 0 0 1 44.988190 \n", "784711 1 0 1 0 0 0 44.952727 \n", "784712 1 0 0 0 1 0 44.952132 \n", "784713 1 0 0 0 0 1 44.916260 \n", "784714 1 0 0 1 0 0 44.908308 \n", "784715 0 0 0 0 0 1 NaN \n", "784716 1 0 0 0 0 1 44.948649 \n", "\n", "[101982 rows x 29 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=df.query(\"Year==2020\")\n", "a.Race.value_counts()\n", "df20x=df\n", "df20x" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "## Data Methodology " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'll be exploring the dataset through multiple angles: longitudinal, geo-spatial, and in-depth analysis from 2017-18. I will be focusing primarily on racial discrimination. I will be mostly be using data visualization, a predictive model would be inappropriate as the data is mostly binary.\n", "\n", "#### Standard Analysis\n", "\n", "There are several methods/considerations/limitations in testing for racial biases; : \n", "1. At the first layer (shallow level), are certain drivers being selected more than others? There can be many explainations for discrepancies that may not be discrimination\n", "2. At the second layer, are 'certain' drivers being treated differently. For example, do women drivers get less citations?\n", "3. Are certain racial groups treatment stand out compared to their peers?\n", "4. Are there external factors, such as venues, local bars, and congested traffic areas that may influence outcome?\n", "5. Racial identification is imperfect because it is determined by officers and certain persons are mixed heritage\n", "\n", "The primary analysis will focus on the second layer, I'll be create a **master table** that collects the groupby values; conditioned on race.\n", "\n", "My analysis will be focusing primarily on treatment:\n", "\n", "* **'Eq'** stands for Equipment Violation; and **'Mov'** indicates Moving Violation\n", "\n", "* Most of the values are normalized from [0 to 1] and conditioned on Racial identity; Examples provided below\n", "\n", "How to read results:\n", "\n", "* Eq_Margin of the Asian group indicates the percentage 'Equipment Violation' respect to all stops conditioned on being asian. So a value 0.24 would indicate that 24% of stops for Asians were for Equipment Violations.\n", "\n", "* Eq_Citation of 0.4 for Asian drivers indicates that 40% of Asian driver received a citation for equipment violations conditioned on being asian.\n", "\n", "* Mov_DriverSearch of 0.15 for Asians indicates that 15% of Asian drivers were searched during a Moving Violation conditioned on being Asian\n", "\n", "* Mov_Gender_F of 0.55 for Asians indicates that 55% of Asian women are stopped for Moving Violations conditioned on being women.\n", "\n", "* Eq_LateNight of 0.25 for Asians indicate that 25% of Asian drivers are stopped for Equipment Violation during latenight conditioned on being Asian\n", "\n", "* Morn_Citation of 0.2 for Asians indicate that 20% of Asian drivers received citations during the daytime conditioned of being Asian\n", "\n", "\n", "#### Longitudinal Analysis\n", "\n", "Are there any trends throughout the years?\n", "\n", "#### Commercial Analysis\n", "\n", "Does the number of stores and type of stores influence the neighborhood? We'll see this visually.\n", "\n", "#### Geo-Spatial Analysis\n", "\n", "How does Frogtown compare to their neighbors?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis \n", "\n", "### Standard Analysis \n", "My strategy below is to create to save the group by values into a single table; the sort index allows the data to be formated in a way to predict patterns. The original code was more bulky, you can see it in the excess section" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hide_input": false }, "outputs": [], "source": [ "#Empty Data table setup\n", "data = [['Asian',15,15,15,0.5,0.5,10,0.5,0.5,10,0.5,0.5,0.5,0.5,10,0.5,0.5,0.5,0.5],\\\n", " ['Black', 15,15,15,0.5,0.5,10,0.5,0.5,10,0.5,0.5,0.5,0.5,10,0.5,0.5,0.5,0.5],\\\n", " ['Latino', 15,15,15,0.5,0.5,10,0.5,0.5,10,0.5,0.5,0.5,0.5,10,0.5,0.5,0.5,0.5],\\\n", " ['Other', 15,15,15,0.5,0.5,10,0.5,0.5,10,0.5,0.5,0.5,0.5,10,0.5,0.5,0.5,0.5],\\\n", " ['White',15,15,15,0.5,0.5,10,0.5,0.5,10,0.5,0.5,0.5,0.5,10,0.5,0.5,0.5,0.5],\\\n", " ['Total/Average',15,15,15,0.5,0.5,10,0.5,0.5,10,0.5,0.5,0.5,0.5,10,0.5,0.5,0.5,0.5]] \n", " \n", "\n", "Race_Grp = pd.DataFrame(data, columns= ['Race','Tot_Count','Eq_Count','Mov_Count','Eq_Margin','Mov_Margin','Citation_Count','Eq_Citation',\\\n", " 'Mov_Citation','Driversearch_Count','Eq_DriverSearch','Mov_DriverSearch',\\\n", " 'Eq_Gender_F','Mov_Gender_F','LateNight_Count','Eq_LateNight','Mov_LateNight',\\\n", " 'Morn_Citation','Late_Citation'])\n", "\n", "Race_Grp.set_index('Race', inplace=True)\n", "\n", "#Specify Frogtown\n", "#rf=df.query(\"Community=='Thomas_Frogtown'\")\n", "rf=df.query(\"Year==2020\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** The code is sensitive to the ordering of columns" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tot_CountEq_CountMov_CountEq_MarginMov_MarginCitation_CountEq_CitationMov_CitationDriversearch_CountEq_DriverSearchMov_DriverSearchEq_Gender_FMov_Gender_FLateNight_CountEq_LateNightMov_LateNightMorn_CitationLate_Citation
Race
Asian159245811340.2877000.7123005940.1681000.4559001380.1223000.0723000.2293000.26814290.3974000.21780.4583000.142200
Black7780261051700.3355000.66450029020.2061000.4573007810.1310000.0849000.2962000.312422860.3946000.24290.4609000.161900
Latino11123507620.3147000.6853004860.2514000.522300690.0886000.0499000.2657000.28222620.3600000.17850.5212000.164100
Other330852450.2576000.7424001530.3059000.518400130.0706000.0286000.3059000.2857740.3529000.17960.5586000.135100
White7350192554250.2619000.73810034780.3283000.5246003000.0587000.0345000.3662000.380813200.2649000.14930.5430000.154500
Total/Average181645428127360.2988330.70116776130.2507370.49089213010.1009580.0591240.3135590.500043710.3459840.00000.5020660.157401
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" ], "text/plain": [ " Tot_Count Eq_Count Mov_Count Eq_Margin Mov_Margin \\\n", "Race \n", "Asian 1592 458 1134 0.287700 0.712300 \n", "Black 7780 2610 5170 0.335500 0.664500 \n", "Latino 1112 350 762 0.314700 0.685300 \n", "Other 330 85 245 0.257600 0.742400 \n", "White 7350 1925 5425 0.261900 0.738100 \n", "Total/Average 18164 5428 12736 0.298833 0.701167 \n", "\n", " Citation_Count Eq_Citation Mov_Citation Driversearch_Count \\\n", "Race \n", "Asian 594 0.168100 0.455900 138 \n", "Black 2902 0.206100 0.457300 781 \n", "Latino 486 0.251400 0.522300 69 \n", "Other 153 0.305900 0.518400 13 \n", "White 3478 0.328300 0.524600 300 \n", "Total/Average 7613 0.250737 0.490892 1301 \n", "\n", " Eq_DriverSearch Mov_DriverSearch Eq_Gender_F Mov_Gender_F \\\n", "Race \n", "Asian 0.122300 0.072300 0.229300 0.2681 \n", "Black 0.131000 0.084900 0.296200 0.3124 \n", "Latino 0.088600 0.049900 0.265700 0.2822 \n", "Other 0.070600 0.028600 0.305900 0.2857 \n", "White 0.058700 0.034500 0.366200 0.3808 \n", "Total/Average 0.100958 0.059124 0.313559 0.5000 \n", "\n", " LateNight_Count Eq_LateNight Mov_LateNight Morn_Citation \\\n", "Race \n", "Asian 429 0.397400 0.2178 0.458300 \n", "Black 2286 0.394600 0.2429 0.460900 \n", "Latino 262 0.360000 0.1785 0.521200 \n", "Other 74 0.352900 0.1796 0.558600 \n", "White 1320 0.264900 0.1493 0.543000 \n", "Total/Average 4371 0.345984 0.0000 0.502066 \n", "\n", " Late_Citation \n", "Race \n", "Asian 0.142200 \n", "Black 0.161900 \n", "Latino 0.164100 \n", "Other 0.135100 \n", "White 0.154500 \n", "Total/Average 0.157401 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#procedure: Run this line of code to check patterns \n", "#round(rf['Race'].value_counts().sort_index(level=1),4)\n", "\n", "RR= ['Asian','Black','Latino','Other','White']\n", "for i,j in enumerate(RR):\n", " Race_Grp.set_value(j,'Tot_Count', round(rf['Race'].value_counts().sort_index(level=1),4)[i])\n", " Race_Grp.set_value(j,'Eq_Count', round(rf.groupby(['Race'])['Reason'].value_counts().sort_index(level=1),4)[i])\n", " Race_Grp.set_value(j,'Mov_Count', round(rf.groupby(['Race'])['Reason'].value_counts().sort_index(level=1),4)[i+len(RR)])\n", " Race_Grp.set_value(j,'Eq_Margin', round(rf.groupby(['Race'])['Reason'].value_counts(normalize=True).sort_index(level=1),4)[i])\n", " Race_Grp.set_value(j,'Mov_Margin', round(rf.groupby(['Race'])['Reason'].value_counts(normalize=True).sort_index(level=1),4)[i +len(RR)])\n", " Race_Grp.set_value(j,'Citation_Count',round(rf.groupby(['Race'])['Citation'].value_counts().sort_index(level=1),4)[i+len(RR)])\n", " Race_Grp.set_value(j,'Eq_Citation', round(rf.groupby(['Race','Reason'])['Citation'].value_counts(normalize=True).sort_index(level=2),4)[2*i+10])\n", " Race_Grp.set_value(j,'Mov_Citation', round(rf.groupby(['Race','Reason'])['Citation'].value_counts(normalize=True).sort_index(level=2),4)[2*i+11])\n", " Race_Grp.set_value(j,'Driversearch_Count', round(rf.groupby(['Race'])['Driver_search'].value_counts().sort_index(level=1),4)[i+len(RR)]) \n", " Race_Grp.set_value(j,'Eq_DriverSearch', round(rf.groupby(['Race','Reason'])['Driver_search'].value_counts(normalize=True).sort_index(level=2),4)[i*2+10])\n", " Race_Grp.set_value(j,'Mov_DriverSearch', round(rf.groupby(['Race','Reason'])['Driver_search'].value_counts(normalize=True).sort_index(level=2),4)[i*2+11])\n", " Race_Grp.set_value(j,'Eq_Gender_F', round(rf.groupby(['Race','Reason'])['Gender'].value_counts(normalize=True).sort_index(level=2),4)[i*2+10])\n", " Race_Grp.set_value(j,'Mov_Gender_F', round(rf.groupby(['Race','Reason'])['Gender'].value_counts(normalize=True).sort_index(level=2),4)[i*2+11])\n", " Race_Grp.set_value(j,'LateNight_Count', round(rf.groupby(['Race'])['LateNight'].value_counts().sort_index(level=1),4)[i +len(RR)])\n", " Race_Grp.set_value(j,'Eq_LateNight', round(rf.groupby(['Race','Reason'])['LateNight'].value_counts(normalize=True).sort_index(level=2),4)[i*2+10])\n", " Race_Grp.set_value(j,'Mov_LateNight', round(rf.groupby(['Race','Reason'])['LateNight'].value_counts(normalize=True).sort_index(level=2),4)[i*2+11])\n", " Race_Grp.set_value(j,'Morn_Citation', round(rf.groupby(['Race','LateNight'])['Citation'].value_counts(normalize=True).sort_index(level=2),4)[i*2+10]) \n", " Race_Grp.set_value(j,'Late_Citation', round(rf.groupby(['Race','LateNight'])['Citation'].value_counts(normalize=True).sort_index(level=2),4)[i*2+11]) \n", "\n", "#Include Total/Avg Variables; #NOTE: the order\n", "E=rf[rf.Reason == 'Equipment Violation'].sum() #2 /3\n", "M=rf[rf.Reason == 'Moving Violation'].sum()#2 /3\n", "Mo= rf[rf.LateNight == 0].sum()\n", "L=rf[rf.LateNight == 1].sum() \n", " \n", "Race_Grp.set_value('Total/Average','Tot_Count',rf.count()[1]) \n", "Race_Grp.set_value('Total/Average','Eq_Count',rf[rf.Reason == 'Equipment Violation'].count()[1]) \n", "Race_Grp.set_value('Total/Average','Mov_Count',rf[rf.Reason == 'Moving Violation'].count()[1]) \n", "Race_Grp.set_value('Total/Average','LateNight_Count',rf[rf.LateNight == 1].count()[1]) \n", "Race_Grp.set_value('Total/Average','Citation_Count',rf[rf.Citation == 1].count()[1]) \n", "Race_Grp.set_value('Total/Average','Driversearch_Count',rf[rf.Driver_search == 1].count()[1])\n", "Race_Grp.set_value('Total/Average','Eq_Margin',E[2]/rf.count()[1])\n", "Race_Grp.set_value('Total/Average','Mov_Margin',M[2]/rf.count()[1])\n", "Race_Grp.set_value('Total/Average','Eq_Citation',E[1]/E[2])\n", "Race_Grp.set_value('Total/Average','Mov_Citation',M[1]/M[2])\n", "Race_Grp.set_value('Total/Average','Eq_DriverSearch',E[3]/E[2])\n", "Race_Grp.set_value('Total/Average','Mov_DriverSearch',M[3]/M[2])\n", "Race_Grp.set_value('Total/Average','Eq_Gender_F',E[4]/E[2])\n", "# Race_Grp.set_value('Total/Average','Mov_Gender_F',M[4]/M[2])\n", "Race_Grp.set_value('Total/Average','Eq_LateNight',E[19]/E[2])\n", "Race_Grp.set_value('Total/Average','Mov_LateNight',M[19]/M[2])\n", "Race_Grp.set_value('Total/Average','Morn_Citation',Mo[1]/Mo[2])\n", "Race_Grp.set_value('Total/Average','Late_Citation',L[1]/L[2])\n", "\n", "Race_Grp\n", "#round(rf['Race'].value_counts().sort_index(level=1),4)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tot_CountEq_CountMov_CountEq_MarginMov_MarginEq_Gender_FMov_Gender_F
Race
Asian159245811340.2877000.7123000.2293000.2681
Black7780261051700.3355000.6645000.2962000.3124
Latino11123507620.3147000.6853000.2657000.2822
Other330852450.2576000.7424000.3059000.2857
White7350192554250.2619000.7381000.3662000.3808
Total/Average181645428127360.2988330.7011670.3135590.5000
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" ], "text/plain": [ " Tot_Count Eq_Count Mov_Count Eq_Margin Mov_Margin \\\n", "Race \n", "Asian 1592 458 1134 0.287700 0.712300 \n", "Black 7780 2610 5170 0.335500 0.664500 \n", "Latino 1112 350 762 0.314700 0.685300 \n", "Other 330 85 245 0.257600 0.742400 \n", "White 7350 1925 5425 0.261900 0.738100 \n", "Total/Average 18164 5428 12736 0.298833 0.701167 \n", "\n", " Eq_Gender_F Mov_Gender_F \n", "Race \n", "Asian 0.229300 0.2681 \n", "Black 0.296200 0.3124 \n", "Latino 0.265700 0.2822 \n", "Other 0.305900 0.2857 \n", "White 0.366200 0.3808 \n", "Total/Average 0.313559 0.5000 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "F=['Tot_Count','Eq_Count','Mov_Count','Eq_Margin','Mov_Margin', 'Eq_Gender_F','Mov_Gender_F']\n", "Race_Grp[F]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Citation_CountEq_CitationMov_CitationDriversearch_CountEq_DriverSearchMov_DriverSearch
Race
Asian5940.1681000.4559001380.1223000.072300
Black29020.2061000.4573007810.1310000.084900
Latino4860.2514000.522300690.0886000.049900
Other1530.3059000.518400130.0706000.028600
White34780.3283000.5246003000.0587000.034500
Total/Average76130.2507370.49089213010.1009580.059124
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" ], "text/plain": [ " Citation_Count Eq_Citation Mov_Citation Driversearch_Count \\\n", "Race \n", "Asian 594 0.168100 0.455900 138 \n", "Black 2902 0.206100 0.457300 781 \n", "Latino 486 0.251400 0.522300 69 \n", "Other 153 0.305900 0.518400 13 \n", "White 3478 0.328300 0.524600 300 \n", "Total/Average 7613 0.250737 0.490892 1301 \n", "\n", " Eq_DriverSearch Mov_DriverSearch \n", "Race \n", "Asian 0.122300 0.072300 \n", "Black 0.131000 0.084900 \n", "Latino 0.088600 0.049900 \n", "Other 0.070600 0.028600 \n", "White 0.058700 0.034500 \n", "Total/Average 0.100958 0.059124 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G=['Citation_Count','Eq_Citation','Mov_Citation','Driversearch_Count','Eq_DriverSearch','Mov_DriverSearch',]\n", "Race_Grp[G]\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LateNight_CountEq_LateNightMov_LateNightMorn_CitationLate_Citation
Race
Asian4290.3974000.21780.4583000.142200
Black22860.3946000.24290.4609000.161900
Latino2620.3600000.17850.5212000.164100
Other740.3529000.17960.5586000.135100
White13200.2649000.14930.5430000.154500
Total/Average43710.3459840.00000.5020660.157401
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" ], "text/plain": [ " LateNight_Count Eq_LateNight Mov_LateNight Morn_Citation \\\n", "Race \n", "Asian 429 0.397400 0.2178 0.458300 \n", "Black 2286 0.394600 0.2429 0.460900 \n", "Latino 262 0.360000 0.1785 0.521200 \n", "Other 74 0.352900 0.1796 0.558600 \n", "White 1320 0.264900 0.1493 0.543000 \n", "Total/Average 4371 0.345984 0.0000 0.502066 \n", "\n", " Late_Citation \n", "Race \n", "Asian 0.142200 \n", "Black 0.161900 \n", "Latino 0.164100 \n", "Other 0.135100 \n", "White 0.154500 \n", "Total/Average 0.157401 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "H=['LateNight_Count','Eq_LateNight','Mov_LateNight','Morn_Citation','Late_Citation']\n", "Race_Grp[H]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting \n", "\n", "We will be taking the results from previous section and graphing it" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graph Based on Counts\n", "\n", "#Select Features\n", "Features= ['Tot_Count','Eq_Count', 'Mov_Count','LateNight_Count', 'Citation_Count','Driversearch_Count']\n", "df_t= Race_Grp[Features][:-1]\n", "df_t = df_t.transpose()\n", "#print(df_t)\n", "\n", "#Plotting\n", "ax= df_t.plot(kind='barh', figsize=(10, 6),alpha= 0.6)\n", "\n", "\n", "plt.title('Total Traffic Stops in Saint Paul from 2017-18')\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hide_input": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graph on Equipment Violations\n", "\n", "#Select Features\n", "Features= ['Eq_Margin','Eq_LateNight','Eq_Citation','Eq_DriverSearch','Eq_Gender_F']\n", "df_t= Race_Grp[Features][:-1]\n", "df_t = df_t.transpose()\n", "#print(df_t)\n", "\n", "#Plotting\n", "ax=df_t.plot(kind='barh', figsize=(10, 6),alpha= 0.6)\n", "\n", "plt.title('Equipment Violations in Saint Paul from 2017-18')\n", "plt.ylabel('')\n", "plt.xlabel('Percent')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "hide_input": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graph Based on Movement Violations\n", "\n", "#Select Features\n", "Features= ['Mov_Margin','Mov_Citation','Mov_DriverSearch','Mov_Gender_F','Mov_LateNight' ]\n", "df_t= Race_Grp[Features][:-1]\n", "df_t = df_t.transpose()\n", "#print(df_t)\n", "\n", "#Plotting\n", "ax=df_t.plot(kind='barh', figsize=(10, 6),alpha= 0.6)\n", "\n", "plt.title('Movement Violations in Saint Paul from 2017-18')\n", "plt.ylabel('')\n", "plt.xlabel('Percent')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hide_input": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graph Based on Morning and Latenight Citations\n", "\n", "#Select Features\n", "Features= ['Morn_Citation','Late_Citation']\n", "df_t= Race_Grp[Features][:-1]\n", "df_t = df_t.transpose()\n", "#print(df_t)\n", "\n", "#Plotting\n", "ax=df_t.plot(kind='barh', figsize=(10, 6),alpha= 0.6)\n", "\n", "plt.title('Morning vs LateNight Citations Margin in Saint Paul from 2017-18')\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hide_input": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Age Breakdown by Citation\n", "#bf= rf.query(\"AgeDemo != 'NaN'\")\n", "Features= ['Citation','AgeDemo']\n", "B= rf[Features].groupby(['AgeDemo']).sum()\n", "B=B.transpose()\n", "\n", "#print(B)\n", "ax=B.plot(kind='barh', figsize=(10, 6),alpha= 0.6)\n", "\n", "plt.title('Driver Age Citation Breakdown in Saint Paul from 2017-18')\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Insights**\n", "\n", "Recall in Frogtown the racial distribution is 1/3 Black, 1/3 Asian, and 1/5 white. With that said there is greater proportion of both Black and White drivers being stopped.\n", "\n", "* Blacks were stopped the most and by proportion have greater likelihood of being stopped for equipment violation ( \n", "* Asians were stopped less respect to their proportional population\n", "* Blacks were less likely to recieve citations despite being pulled over frequently\n", "* White drivers were more likely to recieve a citation for moving violations compared to other groups\n", "* Black drivers are searched much often than their peers despite low citations count\n", "* Female drivers via proportion are less likely to be stopped; though white females have the highest proportion\n", "* There are considerably more Equipment violations during latenight than in the morning and less Moving Violations\n", "* The citation rates are much higher during the daytime vs the nighttime. This makes sense because there is less drivers during late night. Though 1/3 of stops occur during latenight.\n", "* For the age distribution, nearly 1/3 of citations don't have the driver's age. Based on the available data, middle aged adults are most likely to get citations.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Time Based Analysis\n", "\n", "I will be checking out patterns for month, day of the week, and time of hour." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hide_input": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Traffic Stops by Day of Week (Count)\n", "\n", "Features= ['Count','LateNight','DayofWeek']\n", "B= rf[Features].groupby(['DayofWeek']).sum() #group by Function\n", "\n", "#print(B)\n", "ax=B.plot(kind='line')\n", "\n", "plt.title('Traffic Stops (Count) by DayofWeek in Frogtown from 2017-19')\n", "plt.ylabel('')\n", "plt.xlabel('Day of Week (Starting Monday)')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hide_input": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Traffic Stops by Day of Week\n", "\n", "Features= ['Count','Citation','Equipment Violation','Moving Violation','LateNight','DayofWeek']\n", "B= rf[Features].groupby(['DayofWeek']).sum() #group by Function\n", "B=B.div(B['Count'].values,axis=0) #divide by count to get normalization\n", "B.drop(B.columns[[0]], axis=1, inplace=True) #drop first group\n", "\n", "#print(B)\n", "ax=B.plot(kind='line')\n", "\n", "plt.title('Traffic Stops (Margin) by DayofWeek in Frogtown from 2017-19')\n", "plt.ylabel('')\n", "plt.xlabel('Day of Week (Starting Monday)')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The traffic stops are most frequent on Tuesday and less on the weekend\n", "* Late Night traffic stops increases on the weekend days includes Friday (makes sense)\n", "* There seems to be strong correlation between moving violation and number of citations\n", "* There seems to be strong correlation between equipment violation and latenight traffic stops\n", "* Less citations by proportion is less during the weekend" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hide_input": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Traffic Stops by Month\n", "\n", "Features= ['Count', 'LateNight','Month']\n", "B= rf[Features].groupby(['Month']).sum()\n", "\n", "#print(B)\n", "ax=B.plot(kind='line')\n", "\n", "plt.title('Monthly Traffic Stops (Count) in Frogtown from 2017-19')\n", "plt.ylabel('')\n", "plt.xlabel('Month')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hide_input": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Traffic Stops by Month\n", "\n", "Features= ['Count','Citation','Equipment Violation','Moving Violation','LateNight','Month']\n", "B= rf[Features].groupby(['Month']).sum()\n", "B=B.div(B['Count'].values,axis=0)\n", "B.drop(B.columns[[0]], axis=1, inplace=True)\n", "\n", "#print(B)\n", "ax=B.plot(kind='line')\n", "\n", "plt.title('Monthly Traffic Stops (margin) in Frogtown from 2017-19')\n", "plt.ylabel('')\n", "plt.xlabel('Month')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* There is less traffic stops during the winter months, and steadily more on Spring and Fall\n", "* There is a significant drop on Equipment and Latenight traffic stops during the summer month\n", "* There is an increase proportion of moving violations and citations during the summer months\n", "* Less citiations are given Late Fall and early Winter" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hide_input": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Traffic Stops by Hour\n", "\n", "Features= ['Count','Driver_search','Hour']\n", "B= rf[Features].groupby(['Hour']).sum()\n", "#print(B)\n", "ax= B.plot(kind='line')\n", "\n", "plt.title('Traffic Stops Hourly (counts) in Frogtown from 2017-19')\n", "plt.ylabel('')\n", "plt.xlabel('Hour')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "hide_input": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Traffic Stops by Hour\n", "\n", "Features= ['Count','Citation','Equipment Violation','Moving Violation','Driver_search','Hour']\n", "B= rf[Features].groupby(['Hour']).sum()\n", "B=B.div(B['Count'].values,axis=0)\n", "B.drop(B.columns[[0]], axis=1, inplace=True)\n", "#print(B)\n", "ax= B.plot(kind='line')\n", "\n", "plt.title('Traffic Stops (margin) Hourly in Frogtown from 2017-19')\n", "plt.ylabel('')\n", "plt.xlabel('Hour')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* There are signficiant more traffic stops during the night hours\n", "* Moving violations during the day, Equipment violations during the night\n", "* Very high frequency of citations during the daytime hour matched with moving violations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Longitudinal Analysis of Frogtown \n", "\n", "The full dataset ranges from 2001 to 2018 and has many missing components. For some years, 50% of the data collected are missing key information. The 'total count' will includes all instances of traffic stops in Frogtown even if there is missing supplmental information. Note the sum function ignores missing values." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "#Data Prep\n", "\n", "#From the masterdataset get subsample\n", "long= results_df.query('Grid in [87.0,88.0,89.0,67.0,68.0,90.0,91.0,92.0]')\n", "\n", "#Add Time Variables\n", "long['Date']= pd.to_datetime(long['Date'])\n", "long['DayofWeek']=long['Date'].dt.dayofweek\n", "long['Weekend'] = long['DayofWeek'].apply(lambda x: 1 if (x>4) else 0)\n", "long['Month'] = long['Date'].dt.month\n", "long['Day'] = long['Date'].dt.day\n", "long['Hour'] = long['Date'].dt.hour\n", "long['LateNight'] = long['Hour'].apply(lambda x: 1 if (x>21 or x<5) else 0)\n", "\n", "#Screening\n", "long=long.query(\"Race!='No Data'\")\n", "long=long.query(\"Driver_search !='No Data'\")\n", "long=long.query(\"Gender !='No Data'\")\n", "\n", "#Replace variables with dummies\n", "long['Driver_search'].replace(to_replace=['No','Yes'], value=[0,1],inplace=True) \n", "long['Vehicle_search'].replace(to_replace=['No','Yes'], value=[0,1],inplace=True) \n", "long['Citation'].replace(to_replace=['No','Yes'], value=[0,1],inplace=True) \n", "long['Gender'].replace(to_replace=['Male','Female'], value=[0,1],inplace=True) #FEMALE is 1\n", "long[['Driver_search','Gender']] = long[['Driver_search', 'Gender']].astype(int)\n", "long= pd.concat([long,pd.get_dummies(long['Reason'])], axis=1)\n", "long= pd.concat([long,pd.get_dummies(long['Race'])], axis=1)\n", "\n", "FG_long= long" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hide_input": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Line Graph Total Stops In Frogtown\n", "\n", "Features= ['Count','Year']\n", "B= FG_long[Features].groupby(['Year']).sum()\n", "ax= B.plot(kind='line')\n", "\n", "plt.title('Total Traffic Stops in Frogtown from 2001-19')\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hide_input": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Line graph COunt\n", "\n", "Features= ['Count','Citation','LateNight','Driver_search','Gender','Year']\n", "B= FG_long[Features].groupby(['Year']).sum() #group by Function\n", "\n", "#print(B)\n", "ax= B.plot(kind='line')\n", "\n", "plt.title('Trends Count in Frogtown from 2001-19')\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hide_input": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Line Graph Margin\n", "\n", "Features= ['Count','Citation','LateNight','Driver_search','Gender','Year']\n", "B= FG_long[Features].groupby(['Year']).sum() #group by Function\n", "B=B.div(B['Count'].values,axis=0) #divide by count to get normalization\n", "B.drop(B.columns[[0]], axis=1, inplace=True) #drop first group\n", "\n", "#print(B)\n", "ax= B.plot(kind='line')\n", "\n", "plt.title('Margin Trends in Frogtown from 2001-19')\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "hide_input": false }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Features= ['Count','Asian','Black','Latino','Other','White','Year']\n", "B= FG_long[Features].groupby(['Year']).sum() #group by Function\n", "B=B.div(B['Count'].values,axis=0) #divide by count to get normalization\n", "B.drop(B.columns[[0]], axis=1, inplace=True) #drop first group\n", "\n", "#print(B)\n", "ax= B.plot(kind='line')\n", "\n", "plt.title('Racial Breakdown Margin of Traffic Stops in Frogtown from 2001-19')\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Preliminary Longitudinal Analysis:**\n", "\n", "* There was plenty of past data that indicate a traffic, but there was no information given.Thus the graphs presented are missing many datapoints and can provide a skewed a picture\n", "* At around 2004 to 2005, a change of data practice/collections probably occured\n", "* The Citation rate has increased in the last couple of years\n", "* The racial demogrpahic of traffic stops have been steady with Blacks being overrepresentated despite some demographic shift in the neighborhood\n", "* Driver search rate has declined over time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Commercial Insight/Analysis \n", "\n", "I will be using the Four-Square API to get the nearby venues, within the radius of each police grid. I've separated the venue into three categories of interest. The map below indicates the venues within Frogtown (Upon running it again, I may have exhausted my queries):\n", "\n", "* Green indicates Restaurant\n", "* Blue indicates Bars\n", "* Yellow indicates Convenience/Corner Stores\n" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "hide_input": false }, "outputs": [], "source": [ "\n", "#Function that extracts information of many neighborhoods\n", "\n", "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n", " \n", " venues_list=[]\n", " for name, lat, lng in zip(names, latitudes, longitudes):\n", " print(name)\n", " \n", " # create the API request URL\n", " url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n", " CLIENT_ID, \n", " CLIENT_SECRET, \n", " VERSION, \n", " lat, \n", " lng, \n", " radius, \n", " LIMIT)\n", " \n", " # make the GET request\n", " results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n", " \n", " # return only relevant information for each nearby venue\n", " venues_list.append([(\n", " name, \n", " lat, \n", " lng, \n", " v['venue']['name'], \n", " v['venue']['location']['lat'], \n", " v['venue']['location']['lng'], \n", " v['venue']['categories'][0]['name']) for v in results])\n", "\n", " nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n", " nearby_venues.columns = ['Neighborhood', \n", " 'Neighborhood Latitude', \n", " 'Neighborhood Longitude', \n", " 'Venue', \n", " 'Venue_Latitude', \n", " 'Venue_Longitude', \n", " 'Venue_Category']\n", " \n", " return(nearby_venues)\n", " " ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "67.0\n", "68.0\n", "87.0\n", "88.0\n", "89.0\n", "90.0\n", "91.0\n", "92.0\n" ] } ], "source": [ "#Simplify the table\n", "ab=rf.groupby(['Grid','Latitude','Longitude'], as_index=False).count()\n", "Features= ['Grid','Latitude','Longitude']\n", "ab= ab[Features]\n", "\n", "# Get the location of Venues for bunch of locations \n", "Frogtown_venues = getNearbyVenues(names=ab['Grid'],\n", " latitudes=ab['Latitude'],\n", " longitudes=ab['Longitude']\n", " )\n", "\n", "Bar= ['Dive Bar','Bar','Liquor Store']\n", "Restaurant= ['Asian Restaurant', 'Thai Restaurant','Ethiopian Restaurant',' Vietnamese Restaurant','Middle Eastern Restaurant'\\\n", " ,'Chinese Restaurant','Ramen Restaurant','BBQ Joint','Fast Food Restaurant','Noodle House','Restaurant','Café']\n", "Convenience= ['Convenience Store','Grocery Store']\n", "\n", "#Select on mutiple values\n", "Frogtown_venuesB= Frogtown_venues.loc[Frogtown_venues['Venue_Category'].isin(Bar)]\n", "Frogtown_venuesR= Frogtown_venues.loc[Frogtown_venues['Venue_Category'].isin(Restaurant)]\n", "Frogtown_venuesC= Frogtown_venues.loc[Frogtown_venues['Venue_Category'].isin(Convenience)]\n", "\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NeighborhoodNeighborhood LatitudeNeighborhood LongitudeVenueVenue_LatitudeVenue_LongitudeVenue_Category
067.044.966603-93.141545Half Time Rec44.970362-93.143015Dive Bar
167.044.966603-93.141545Tobasi Stop44.963243-93.139395Convenience Store
267.044.966603-93.141545Pro-Image Beauty School44.963986-93.140867Cosmetics Shop
367.044.966603-93.141545Minnehaha Liquors44.963355-93.140359Liquor Store
467.044.966603-93.141545Sunrise Creative Gourmet44.964883-93.137208Market
\n", "
" ], "text/plain": [ " Neighborhood Neighborhood Latitude Neighborhood Longitude \\\n", "0 67.0 44.966603 -93.141545 \n", "1 67.0 44.966603 -93.141545 \n", "2 67.0 44.966603 -93.141545 \n", "3 67.0 44.966603 -93.141545 \n", "4 67.0 44.966603 -93.141545 \n", "\n", " Venue Venue_Latitude Venue_Longitude \\\n", "0 Half Time Rec 44.970362 -93.143015 \n", "1 Tobasi Stop 44.963243 -93.139395 \n", "2 Pro-Image Beauty School 44.963986 -93.140867 \n", "3 Minnehaha Liquors 44.963355 -93.140359 \n", "4 Sunrise Creative Gourmet 44.964883 -93.137208 \n", "\n", " Venue_Category \n", "0 Dive Bar \n", "1 Convenience Store \n", "2 Cosmetics Shop \n", "3 Liquor Store \n", "4 Market " ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Frogtown_venues.head()" ] }, { "cell_type": "code", "execution_count": 366, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 366, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n", "# Matplotlib and associated plotting modules\n", "import matplotlib.cm as cm\n", "import matplotlib.colors as colors\n", "# import k-means from clustering stage\n", "from sklearn.cluster import KMeans\n", "import folium # map rendering library\n", "\n", "#Create Frogtown GeoMap\n", "FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "\n", "# For Bars \n", "for lat, lng, borough, neighborhood in zip(Frogtown_venuesB['Venue_Latitude'], Frogtown_venuesB['Venue_Longitude'],Frogtown_venuesB['Venue'], Frogtown_venuesB['Venue_Category']):\n", " label = '{}, {}'.format(borough, neighborhood)\n", " label = folium.Popup(label, parse_html=True)\n", " folium.CircleMarker(\n", " [lat, lng],\n", " radius=5,\n", " popup=label,\n", " color='blue',\n", " fill=True,\n", " fill_color='#3186cc',\n", " fill_opacity=0.7,\n", " parse_html=False).add_to(FG_map) \n", "\n", "#Convenice Stores \n", "for lat, lng, borough, neighborhood in zip(Frogtown_venuesC['Venue_Latitude'], Frogtown_venuesC['Venue_Longitude'],Frogtown_venuesC['Venue'], Frogtown_venuesC['Venue_Category']):\n", " label = '{}, {}'.format(borough, neighborhood)\n", " label = folium.Popup(label, parse_html=True)\n", " folium.CircleMarker(\n", " [lat, lng],\n", " radius=5,\n", " popup=label,\n", " color='yellow',\n", " fill=True,\n", " fill_color='#3186cc',\n", " fill_opacity=0.7,\n", " parse_html=False).add_to(FG_map) \n", "\n", "# Restaurants \n", "for lat, lng, borough, neighborhood in zip(Frogtown_venuesR['Venue_Latitude'], Frogtown_venuesR['Venue_Longitude'],Frogtown_venuesR['Venue'], Frogtown_venuesR['Venue_Category']):\n", " label = '{}, {}'.format(borough, neighborhood)\n", " label = folium.Popup(label, parse_html=True)\n", " folium.CircleMarker(\n", " [lat, lng],\n", " radius=5,\n", " popup=label,\n", " color='green',\n", " fill=True,\n", " fill_color='#3186cc',\n", " fill_opacity=0.7,\n", " parse_html=False).add_to(FG_map) \n", " \n", " \n", "FG_map \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph above shows the stores within the Frogtown area. University Avenue has concentrated traffic. There are some neighborhood bars and convenience near the residential homes, we'll see a clearer picture with geo-spatial data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Geo-Graphing Prep " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Saint Paul Police department have a [json file](https://information.stpaul.gov/Public-Safety/Saint-Paul-Police-Grid-Shapefile/ykwt-ie3e) that maps out the police grid.\n", "\n", "To prepare the data for geo-spatial information, I would need to group by grid. I changed the variables to dummies early on, so I can sum them up during aggregation. \n", "\n", "For the neighborhood visualizations, I took in data from the Census on racial distribution " ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://information.stpaul.gov/resource/kkd6-vvns.geojson'" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sp_geo1" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "hide_input": false }, "outputs": [], "source": [ "#import Programs and police grid\n", "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n", "import matplotlib.cm as cm\n", "import matplotlib.colors as colors\n", "import folium # map rendering library\n", "\n", "sp_geo = r'Geo-Json\\Saint Paul Police Grid - Shapefile.geojson'\n", "\n", "#Socrata API IS NOT that good\n", "#sp_geo= 'https://information.stpaul.gov/resource/xfxz-iqwn.json'\n", "\n", "# Programmer Note: One of the challenges to get appropiate connection was to set the attribute Grid to be json value gridnum. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "hide_input": false }, "outputs": [], "source": [ "#All of Saint Paul Grid\n", "\n", "# The graph is determined by Grid so we will group along that\n", "Features= ['Grid','Count','Citation','Equipment Violation','Moving Violation','Driver_search','Vehicle_search','LateNight',\\\n", " 'Asian','Black','Latino','White','Other','Gender','Weekend']\n", "\n", "# Create a sum and divide by Count; and \n", "B= df[Features].groupby(['Grid']).sum()\n", "\n", "#Save Sum Values\n", "C= B[['Count','Citation','Equipment Violation','Moving Violation','LateNight']]\n", "\n", "#Divide by Count and then add new columns in tranformed table\n", "B=B.div(B['Count'].values,axis=0)\n", "B['Count']=C.iloc[:,0] \n", "B['Citation_count'] = C.iloc[:,1]\n", "B['Equipment Violation_count'] = C.iloc[:,2]\n", "B['Moving Violation_count'] = C.iloc[:,3]\n", "B['LateNight_count'] = C.iloc[:,4]\n", "\n", "# The GRID needs to follow exactly like the json file\n", "B=B.reset_index()\n", "B.Grid = B.Grid.astype(int)\n", "B.Grid = B.Grid.astype(str)\n", "#print(B.head())\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "#Frogtown Only:\n", "#FROGTOWN Value\n", "F= rf[Features].groupby(['Grid']).sum()\n", "C= F['Count']\n", "F=F.div(F['Count'].values,axis=0)\n", "F['Count']=C \n", "\n", "# The GRID needs to follow exactly like the json file\n", "F=F.reset_index()\n", "F.Grid = F.Grid.astype(int)\n", "F.Grid = F.Grid.astype(str)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#Add demographic Data\n", "SPDem = pd.read_csv('Data\\MSP Neighborhoods_2013-2017.csv', skiprows=[1])\n", "SPDem.columns = SPDem.columns.str.replace(' ', '')\n", "SPDem['City'].value_counts().to_frame()\n", "\n", "#Get Saint Paul only\n", "SPDem= SPDem.query('City in [\"St. Paul\"]')\n", "#SPDem.columns.values[i]\n", "\n", "#for i,square in enumerate(SPDem.columns.values): \n", "# print(str(i) + ' '+ square)\n", "#print(SPDem)\n", "\n", "#Specify columns of interest\n", "SPDem = SPDem.iloc[:,[1,2,27,39,43,47,63,71,107,123,127,131,135,159,215,243,267,271,275,279,363,370,374,378,\\\n", " 382,386,390,394,399,403,471,475,479,495,511,515,519,523]]\n", "col_names= ['TotHH','Neigh','Unemploy','\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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indexCommunityCountCitationEquipment ViolationMoving ViolationDriver_searchVehicle_searchLateNightAsian...WeekendCitation_countEquipment Violation_countMoving Violation_countLateNight_countWhite_DemoBlack_DemoAsian_DemoGridBlah
00Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158598268
11Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158599599
22Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158510032
33Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.231585118144
44Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.231585119682
55Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.231585120152
66Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158513739
77Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158513887
88Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158513965
99Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158514042
1010Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158516070
1111Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158518048
1212Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.2315851971
1313Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.2315852003
1414Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158524015
1515Battle_Creek22900.7222710.1174670.8825330.0327510.0305680.1541480.143668...0.1104801654269.02021.03530.3983830.2030790.23158528043
1616Capital_River49110.6970070.1087350.8912650.0299330.0256570.1457950.083690...0.1598453423534.04377.07160.6756630.1321140.089509111148
1717Capital_River49110.6970070.1087350.8912650.0299330.0256570.1457950.083690...0.1598453423534.04377.07160.6756630.1321140.0895091121007
1818Capital_River49110.6970070.1087350.8912650.0299330.0256570.1457950.083690...0.1598453423534.04377.07160.6756630.1321140.089509131452
1919Capital_River49110.6970070.1087350.8912650.0299330.0256570.1457950.083690...0.1598453423534.04377.07160.6756630.1321140.089509132230
2020Capital_River49110.6970070.1087350.8912650.0299330.0256570.1457950.083690...0.1598453423534.04377.07160.6756630.1321140.0895091332307
2121Capital_River49110.6970070.1087350.8912650.0299330.0256570.1457950.083690...0.1598453423534.04377.07160.6756630.1321140.089509152302
2222Capital_River49110.6970070.1087350.8912650.0299330.0256570.1457950.083690...0.1598453423534.04377.07160.6756630.1321140.089509153465
2323Como13280.6408130.1408130.8591870.0316270.0263550.1920180.100151...0.146084851187.01141.02550.7628980.0892300.054048533
2424Como13280.6408130.1408130.8591870.0316270.0263550.1920180.100151...0.146084851187.01141.02550.7628980.0892300.054048629
2525Como13280.6408130.1408130.8591870.0316270.0263550.1920180.100151...0.146084851187.01141.02550.7628980.0892300.054048713
2626Como13280.6408130.1408130.8591870.0316270.0263550.1920180.100151...0.146084851187.01141.02550.7628980.0892300.054048854
2727Como13280.6408130.1408130.8591870.0316270.0263550.1920180.100151...0.146084851187.01141.02550.7628980.0892300.05404825100
2828Como13280.6408130.1408130.8591870.0316270.0263550.1920180.100151...0.146084851187.01141.02550.7628980.0892300.0540482629
2929Como13280.6408130.1408130.8591870.0316270.0263550.1920180.100151...0.146084851187.01141.02550.7628980.0892300.0540482761
..................................................................
171171Union_Park31370.6592290.1415360.8584640.0315590.0267770.1842520.039528...0.1743702068444.02693.05780.7722430.0980120.037718126189
172172West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926150274
173173West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.02992615142
174174West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926169463
175175West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926170266
176176West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.02992617173
177177West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.02992618759
178178West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926188215
179179West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.0299261899
180180West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926207433
181181West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.02992622646
182182West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926230216
183183West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.02992624980
184184West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926267393
185185West_7th30380.7274520.0957870.9042130.0171170.0115210.1086240.035879...0.1431862210291.02747.03300.7214280.1088260.029926268469
186186West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498417261
187187West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.064984173193
188188West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.064984174245
189189West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.0649841751
190190West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498419297
191191West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.064984193154
192192West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.064984194202
193193West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498419535
194194West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.0649842096
195195West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.0649842105
196196West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498421122
197197West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498421234
198198West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498421342
199199West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498421477
200200West_Side11920.4337250.2793620.7206380.0838930.0780200.3531880.041946...0.283557517333.0859.04210.4533920.1358230.06498421518
\n", "

201 rows × 25 columns

\n", "" ], "text/plain": [ " index Community Count Citation Equipment Violation \\\n", "0 0 Battle_Creek 2290 0.722271 0.117467 \n", "1 1 Battle_Creek 2290 0.722271 0.117467 \n", "2 2 Battle_Creek 2290 0.722271 0.117467 \n", "3 3 Battle_Creek 2290 0.722271 0.117467 \n", "4 4 Battle_Creek 2290 0.722271 0.117467 \n", "5 5 Battle_Creek 2290 0.722271 0.117467 \n", "6 6 Battle_Creek 2290 0.722271 0.117467 \n", "7 7 Battle_Creek 2290 0.722271 0.117467 \n", "8 8 Battle_Creek 2290 0.722271 0.117467 \n", "9 9 Battle_Creek 2290 0.722271 0.117467 \n", "10 10 Battle_Creek 2290 0.722271 0.117467 \n", "11 11 Battle_Creek 2290 0.722271 0.117467 \n", "12 12 Battle_Creek 2290 0.722271 0.117467 \n", "13 13 Battle_Creek 2290 0.722271 0.117467 \n", "14 14 Battle_Creek 2290 0.722271 0.117467 \n", "15 15 Battle_Creek 2290 0.722271 0.117467 \n", "16 16 Capital_River 4911 0.697007 0.108735 \n", "17 17 Capital_River 4911 0.697007 0.108735 \n", "18 18 Capital_River 4911 0.697007 0.108735 \n", "19 19 Capital_River 4911 0.697007 0.108735 \n", "20 20 Capital_River 4911 0.697007 0.108735 \n", "21 21 Capital_River 4911 0.697007 0.108735 \n", "22 22 Capital_River 4911 0.697007 0.108735 \n", "23 23 Como 1328 0.640813 0.140813 \n", "24 24 Como 1328 0.640813 0.140813 \n", "25 25 Como 1328 0.640813 0.140813 \n", "26 26 Como 1328 0.640813 0.140813 \n", "27 27 Como 1328 0.640813 0.140813 \n", "28 28 Como 1328 0.640813 0.140813 \n", "29 29 Como 1328 0.640813 0.140813 \n", ".. ... ... ... ... ... \n", "171 171 Union_Park 3137 0.659229 0.141536 \n", "172 172 West_7th 3038 0.727452 0.095787 \n", "173 173 West_7th 3038 0.727452 0.095787 \n", "174 174 West_7th 3038 0.727452 0.095787 \n", "175 175 West_7th 3038 0.727452 0.095787 \n", "176 176 West_7th 3038 0.727452 0.095787 \n", "177 177 West_7th 3038 0.727452 0.095787 \n", "178 178 West_7th 3038 0.727452 0.095787 \n", "179 179 West_7th 3038 0.727452 0.095787 \n", "180 180 West_7th 3038 0.727452 0.095787 \n", "181 181 West_7th 3038 0.727452 0.095787 \n", "182 182 West_7th 3038 0.727452 0.095787 \n", "183 183 West_7th 3038 0.727452 0.095787 \n", "184 184 West_7th 3038 0.727452 0.095787 \n", "185 185 West_7th 3038 0.727452 0.095787 \n", "186 186 West_Side 1192 0.433725 0.279362 \n", "187 187 West_Side 1192 0.433725 0.279362 \n", "188 188 West_Side 1192 0.433725 0.279362 \n", "189 189 West_Side 1192 0.433725 0.279362 \n", "190 190 West_Side 1192 0.433725 0.279362 \n", "191 191 West_Side 1192 0.433725 0.279362 \n", "192 192 West_Side 1192 0.433725 0.279362 \n", "193 193 West_Side 1192 0.433725 0.279362 \n", "194 194 West_Side 1192 0.433725 0.279362 \n", "195 195 West_Side 1192 0.433725 0.279362 \n", "196 196 West_Side 1192 0.433725 0.279362 \n", "197 197 West_Side 1192 0.433725 0.279362 \n", "198 198 West_Side 1192 0.433725 0.279362 \n", "199 199 West_Side 1192 0.433725 0.279362 \n", "200 200 West_Side 1192 0.433725 0.279362 \n", "\n", " Moving Violation Driver_search Vehicle_search LateNight Asian \\\n", "0 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "1 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "2 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "3 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "4 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "5 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "6 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "7 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "8 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "9 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "10 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "11 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "12 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "13 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "14 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "15 0.882533 0.032751 0.030568 0.154148 0.143668 \n", "16 0.891265 0.029933 0.025657 0.145795 0.083690 \n", "17 0.891265 0.029933 0.025657 0.145795 0.083690 \n", "18 0.891265 0.029933 0.025657 0.145795 0.083690 \n", "19 0.891265 0.029933 0.025657 0.145795 0.083690 \n", "20 0.891265 0.029933 0.025657 0.145795 0.083690 \n", "21 0.891265 0.029933 0.025657 0.145795 0.083690 \n", "22 0.891265 0.029933 0.025657 0.145795 0.083690 \n", "23 0.859187 0.031627 0.026355 0.192018 0.100151 \n", "24 0.859187 0.031627 0.026355 0.192018 0.100151 \n", "25 0.859187 0.031627 0.026355 0.192018 0.100151 \n", "26 0.859187 0.031627 0.026355 0.192018 0.100151 \n", "27 0.859187 0.031627 0.026355 0.192018 0.100151 \n", "28 0.859187 0.031627 0.026355 0.192018 0.100151 \n", "29 0.859187 0.031627 0.026355 0.192018 0.100151 \n", ".. ... ... ... ... ... \n", "171 0.858464 0.031559 0.026777 0.184252 0.039528 \n", "172 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "173 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "174 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "175 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "176 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "177 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "178 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "179 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "180 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "181 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "182 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "183 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "184 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "185 0.904213 0.017117 0.011521 0.108624 0.035879 \n", "186 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "187 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "188 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "189 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "190 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "191 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "192 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "193 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "194 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "195 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "196 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "197 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "198 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "199 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "200 0.720638 0.083893 0.078020 0.353188 0.041946 \n", "\n", " ... Weekend Citation_count Equipment Violation_count \\\n", "0 ... 0.110480 1654 269.0 \n", "1 ... 0.110480 1654 269.0 \n", "2 ... 0.110480 1654 269.0 \n", "3 ... 0.110480 1654 269.0 \n", "4 ... 0.110480 1654 269.0 \n", "5 ... 0.110480 1654 269.0 \n", "6 ... 0.110480 1654 269.0 \n", "7 ... 0.110480 1654 269.0 \n", "8 ... 0.110480 1654 269.0 \n", "9 ... 0.110480 1654 269.0 \n", "10 ... 0.110480 1654 269.0 \n", "11 ... 0.110480 1654 269.0 \n", "12 ... 0.110480 1654 269.0 \n", "13 ... 0.110480 1654 269.0 \n", "14 ... 0.110480 1654 269.0 \n", "15 ... 0.110480 1654 269.0 \n", "16 ... 0.159845 3423 534.0 \n", "17 ... 0.159845 3423 534.0 \n", "18 ... 0.159845 3423 534.0 \n", "19 ... 0.159845 3423 534.0 \n", "20 ... 0.159845 3423 534.0 \n", "21 ... 0.159845 3423 534.0 \n", "22 ... 0.159845 3423 534.0 \n", "23 ... 0.146084 851 187.0 \n", "24 ... 0.146084 851 187.0 \n", "25 ... 0.146084 851 187.0 \n", "26 ... 0.146084 851 187.0 \n", "27 ... 0.146084 851 187.0 \n", "28 ... 0.146084 851 187.0 \n", "29 ... 0.146084 851 187.0 \n", ".. ... ... ... ... \n", "171 ... 0.174370 2068 444.0 \n", "172 ... 0.143186 2210 291.0 \n", "173 ... 0.143186 2210 291.0 \n", "174 ... 0.143186 2210 291.0 \n", "175 ... 0.143186 2210 291.0 \n", "176 ... 0.143186 2210 291.0 \n", "177 ... 0.143186 2210 291.0 \n", "178 ... 0.143186 2210 291.0 \n", "179 ... 0.143186 2210 291.0 \n", "180 ... 0.143186 2210 291.0 \n", "181 ... 0.143186 2210 291.0 \n", "182 ... 0.143186 2210 291.0 \n", "183 ... 0.143186 2210 291.0 \n", "184 ... 0.143186 2210 291.0 \n", "185 ... 0.143186 2210 291.0 \n", "186 ... 0.283557 517 333.0 \n", "187 ... 0.283557 517 333.0 \n", "188 ... 0.283557 517 333.0 \n", "189 ... 0.283557 517 333.0 \n", "190 ... 0.283557 517 333.0 \n", "191 ... 0.283557 517 333.0 \n", "192 ... 0.283557 517 333.0 \n", "193 ... 0.283557 517 333.0 \n", "194 ... 0.283557 517 333.0 \n", "195 ... 0.283557 517 333.0 \n", "196 ... 0.283557 517 333.0 \n", "197 ... 0.283557 517 333.0 \n", "198 ... 0.283557 517 333.0 \n", "199 ... 0.283557 517 333.0 \n", "200 ... 0.283557 517 333.0 \n", "\n", " Moving Violation_count LateNight_count White_Demo Black_Demo \\\n", "0 2021.0 353 0.398383 0.203079 \n", "1 2021.0 353 0.398383 0.203079 \n", "2 2021.0 353 0.398383 0.203079 \n", "3 2021.0 353 0.398383 0.203079 \n", "4 2021.0 353 0.398383 0.203079 \n", "5 2021.0 353 0.398383 0.203079 \n", "6 2021.0 353 0.398383 0.203079 \n", "7 2021.0 353 0.398383 0.203079 \n", "8 2021.0 353 0.398383 0.203079 \n", "9 2021.0 353 0.398383 0.203079 \n", "10 2021.0 353 0.398383 0.203079 \n", "11 2021.0 353 0.398383 0.203079 \n", "12 2021.0 353 0.398383 0.203079 \n", "13 2021.0 353 0.398383 0.203079 \n", "14 2021.0 353 0.398383 0.203079 \n", "15 2021.0 353 0.398383 0.203079 \n", "16 4377.0 716 0.675663 0.132114 \n", "17 4377.0 716 0.675663 0.132114 \n", "18 4377.0 716 0.675663 0.132114 \n", "19 4377.0 716 0.675663 0.132114 \n", "20 4377.0 716 0.675663 0.132114 \n", "21 4377.0 716 0.675663 0.132114 \n", "22 4377.0 716 0.675663 0.132114 \n", "23 1141.0 255 0.762898 0.089230 \n", "24 1141.0 255 0.762898 0.089230 \n", "25 1141.0 255 0.762898 0.089230 \n", "26 1141.0 255 0.762898 0.089230 \n", "27 1141.0 255 0.762898 0.089230 \n", "28 1141.0 255 0.762898 0.089230 \n", "29 1141.0 255 0.762898 0.089230 \n", ".. ... ... ... ... \n", "171 2693.0 578 0.772243 0.098012 \n", "172 2747.0 330 0.721428 0.108826 \n", "173 2747.0 330 0.721428 0.108826 \n", "174 2747.0 330 0.721428 0.108826 \n", "175 2747.0 330 0.721428 0.108826 \n", "176 2747.0 330 0.721428 0.108826 \n", "177 2747.0 330 0.721428 0.108826 \n", "178 2747.0 330 0.721428 0.108826 \n", "179 2747.0 330 0.721428 0.108826 \n", "180 2747.0 330 0.721428 0.108826 \n", "181 2747.0 330 0.721428 0.108826 \n", "182 2747.0 330 0.721428 0.108826 \n", "183 2747.0 330 0.721428 0.108826 \n", "184 2747.0 330 0.721428 0.108826 \n", "185 2747.0 330 0.721428 0.108826 \n", "186 859.0 421 0.453392 0.135823 \n", "187 859.0 421 0.453392 0.135823 \n", "188 859.0 421 0.453392 0.135823 \n", "189 859.0 421 0.453392 0.135823 \n", "190 859.0 421 0.453392 0.135823 \n", "191 859.0 421 0.453392 0.135823 \n", "192 859.0 421 0.453392 0.135823 \n", "193 859.0 421 0.453392 0.135823 \n", "194 859.0 421 0.453392 0.135823 \n", "195 859.0 421 0.453392 0.135823 \n", "196 859.0 421 0.453392 0.135823 \n", "197 859.0 421 0.453392 0.135823 \n", "198 859.0 421 0.453392 0.135823 \n", "199 859.0 421 0.453392 0.135823 \n", "200 859.0 421 0.453392 0.135823 \n", "\n", " Asian_Demo Grid Blah \n", "0 0.231585 98 268 \n", "1 0.231585 99 599 \n", "2 0.231585 100 32 \n", "3 0.231585 118 144 \n", "4 0.231585 119 682 \n", "5 0.231585 120 152 \n", "6 0.231585 137 39 \n", "7 0.231585 138 87 \n", "8 0.231585 139 65 \n", "9 0.231585 140 42 \n", "10 0.231585 160 70 \n", "11 0.231585 180 48 \n", "12 0.231585 197 1 \n", "13 0.231585 200 3 \n", "14 0.231585 240 15 \n", "15 0.231585 280 43 \n", "16 0.089509 111 148 \n", "17 0.089509 112 1007 \n", "18 0.089509 131 452 \n", "19 0.089509 132 230 \n", "20 0.089509 133 2307 \n", "21 0.089509 152 302 \n", "22 0.089509 153 465 \n", "23 0.054048 5 33 \n", "24 0.054048 6 29 \n", "25 0.054048 7 13 \n", "26 0.054048 8 54 \n", "27 0.054048 25 100 \n", "28 0.054048 26 29 \n", "29 0.054048 27 61 \n", ".. ... ... ... \n", "171 0.037718 126 189 \n", "172 0.029926 150 274 \n", "173 0.029926 151 42 \n", "174 0.029926 169 463 \n", "175 0.029926 170 266 \n", "176 0.029926 171 73 \n", "177 0.029926 187 59 \n", "178 0.029926 188 215 \n", "179 0.029926 189 9 \n", "180 0.029926 207 433 \n", "181 0.029926 226 46 \n", "182 0.029926 230 216 \n", "183 0.029926 249 80 \n", "184 0.029926 267 393 \n", "185 0.029926 268 469 \n", "186 0.064984 172 61 \n", "187 0.064984 173 193 \n", "188 0.064984 174 245 \n", "189 0.064984 175 1 \n", "190 0.064984 192 97 \n", "191 0.064984 193 154 \n", "192 0.064984 194 202 \n", "193 0.064984 195 35 \n", "194 0.064984 209 6 \n", "195 0.064984 210 5 \n", "196 0.064984 211 22 \n", "197 0.064984 212 34 \n", "198 0.064984 213 42 \n", "199 0.064984 214 77 \n", "200 0.064984 215 18 \n", "\n", "[201 rows x 25 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#All of SaintPaul Neighborhood\n", "\n", "# The graph is determined by Grid so we will group along that\n", "Features= ['Community','Count','Citation','Equipment Violation','Moving Violation','Driver_search','Vehicle_search','LateNight',\\\n", " 'Asian','Black','Latino','White','Other','Gender','Weekend']\n", "\n", "# Create a sum and divide by Count; and \n", "N= df[Features].groupby(['Community']).sum()\n", "\n", "#Save Sum Values\n", "C= N[['Count','Citation','Equipment Violation','Moving Violation','LateNight']]\n", "\n", "#Divide by Count and then add new columns in tranformed table\n", "N=N.div(N['Count'].values,axis=0)\n", "N['Count']=C.iloc[:,0] \n", "N['Citation_count'] = C.iloc[:,1]\n", "N['Equipment Violation_count'] = C.iloc[:,2]\n", "N['Moving Violation_count'] = C.iloc[:,3]\n", "N['LateNight_count'] = C.iloc[:,4]\n", "\n", "# Add the demographic info!\n", "N['White_Demo']= SPDem.iloc[:,0].values #.values was used\n", "N['Black_Demo']= SPDem.iloc[:,1].values \n", "N['Asian_Demo']= SPDem.iloc[:,2].values \n", "N=N.reset_index()\n", "\n", "#Merging two dataframes; where you create a distinct dataframe of just grid and community, then merge to neighbrhood file\n", "A= df.groupby(['Grid','Community']).size().reset_index(name='Blah')\n", "#A= df[['Grid','Community',]].groupby('Community').reset_index()\n", "C=A\n", "N=pd.merge(N, C, on='Community')\n", "\n", "# The GRID needs to follow exactly like the json file\n", "N=N.reset_index()\n", "N.Grid = N.Grid.astype(int)\n", "N.Grid = N.Grid.astype(str)\n", "N" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Frogtown Geo-Spatial Data " ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Total Traffic Incidents in Frogtown by Grid" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'Frogtown_venues' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# I've included the markers from previous graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mlat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlng\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mborough\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mneighborhood\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFrogtown_venues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Venue_Latitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFrogtown_venues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Venue_Longitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mFrogtown_venues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Venue'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFrogtown_venues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Venue_Category'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'{}, {}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mborough\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mneighborhood\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfolium\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPopup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparse_html\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'Frogtown_venues' is not defined" ] } ], "source": [ "#Create Frogtown GeoMap\n", "FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "\n", "\n", "# I've included the markers from previous graph \n", "for lat, lng, borough, neighborhood in zip(Frogtown_venues['Venue_Latitude'], Frogtown_venues['Venue_Longitude'],Frogtown_venues['Venue'], Frogtown_venues['Venue_Category']):\n", " label = '{}, {}'.format(borough, neighborhood)\n", " label = folium.Popup(label, parse_html=True)\n", " folium.CircleMarker(\n", " [lat, lng],\n", " radius=5,\n", " popup=label,\n", " color='green',\n", " fill=True,\n", " fill_color='#3186cc',\n", " fill_opacity=0.1,\n", " parse_html=False).add_to(FG_map) \n", " \n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=F,\n", " columns=['Grid','Count'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Total Traffic Stops',\n", " highlight= True,\n", " name= 'Clean Map' \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", "\n", "\n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Citation Margin in Frogtown by Grid" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#sp_geo = r'Geo-Json\\Saint Paul Police Grid - Shapefile.geojson'\n", "\n", "FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=F,\n", " columns=['Grid','Citation'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Citation Margin',\n", " highlight= True,\n", " name= 'Clean Map' \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Equipment Violation Margin in Frogtown by Grid" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=F,\n", " columns=['Grid','Equipment Violation'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Equipment Violation Margin',\n", " highlight= True,\n", " name= 'Clean Map' \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": { "hide_input": false }, "source": [ "### LateNight Traffic Stop Margin in Frogtown by Grid" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=F,\n", " columns=['Grid','LateNight'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='LateNight Stop Margin',\n", " highlight= True,\n", " name= 'Clean Map' \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Driver Searched Margin In Frogtown" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=F,\n", " columns=['Grid','Driver_search'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Driver Search Margin',\n", " highlight= True,\n", " name= 'Clean Map' \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown Summary Stats\n", "\n", "* For total incidents, high volume of incidents are around University ave around Dale\n", "* The citation margin is not too high even in the spaces where there is greater frequency of stops\n", "* The Equipment Violation density is high and located in the same high density rate\n", "* The LateNight Stop Margin is really high in the University area and the vehicle search rate is also high\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saint Paul Geospatial Data \n", "\n", "* For margin specifications, a police grid must have more than 100 total traffic stops. A smaller number creates bigger imbalance on margins\n", "* The downtown district was excluded when graphing total numbers under grid data because it has very high frequency of stops; influencing the legend gradient.\n", "* The data is from 2017 to 2018.\n", "* There is one police grid that has zero traffic stops (try to find it)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Total Traffic Incidents in Saint Paul by Neighborhood" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Count'],\n", " #nan_fill_color='purple',\n", " #nan_fill_opacity=0.4,\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Total Traffic Stops',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Total Traffic Incidents in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "#messed_up_data.loc[4, 'Count'] = float('nan')\n", "C=B.query('Count < 2000')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Count'],\n", " #nan_fill_color='purple',\n", " #nan_fill_opacity=0.4,\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Total Traffic Stops',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Total Citation Count in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "hide_input": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count < 2000')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Citation_count'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Citation Counts',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Citation Margin in Saint Paul by Neighborhood" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Citation'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Citation Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Citation Margin in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "hide_input": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count > 100')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Citation'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Citation Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Equipment Violation Count in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count < 2000')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Equipment Violation_count'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Total Equipment Violation Stops',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Equipment Violation Margin in Saint Paul by Neighborhood" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Equipment Violation'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Equipment Violation Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Equipment Violation Margin in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "hide_input": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count > 100')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Equipment Violation'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Equipment Violation Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Moving Violation Count in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "hide_input": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count < 2000')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Moving Violation_count'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Total Moving Violation Counts',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### LateNight Traffic Stop Margin in Saint Paul by Neighborhood" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','LateNight'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='LateNight Traffic Stop Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LateNight Traffic Stop Margin in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "hide_input": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count > 100')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','LateNight'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='LateNight Traffic Stop Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Driver Searched Margin in Saint Paul by Neighborhood" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Driver_search'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Driver Search Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Driver Searched Margin in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count >100')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Driver_search'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Driver Search Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### White proportion in Saint Paul by Neighborhood(Census)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','White_Demo'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='White Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### White Driver Margin (Diff) in Saint Paul by Neighborhood\n", "\n", "I map out the difference between actual demographic distribution of whites from the margin white drivers. For legend, if lighter indicates over-representation and darker indicates under-representation " ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "N['Diff']= N['White_Demo']- N['White']\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Diff'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='White: Difference',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### White Driver Margin in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count >100')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','White'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='White Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Black proportion in Saint Paul by Neighborhood(Census)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Black_Demo'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Black Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Black Driver Margin (Diff) in Saint Paul by Neighborhood\n", "\n", "I map out the difference between actual demographic distribution of whites from the margin white drivers. For legend, Black drivers are by default over-representated!" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "N['Diff']= N['Black_Demo']- N['Black']\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Diff'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Black: Difference',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Black proportion in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count >100')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Black'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Black Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asian proportion in Saint Paul by Neighborhood(Census)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Asian_Demo'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Asian Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asian Driver Margin (Diff) in Saint Paul by Neighborhood\n", "\n", "I map out the difference between actual demographic distribution of whites from the margin white drivers. For legend, Asian drivers are by default underrepresentated." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "N['Diff']= N['Asian_Demo']- N['Asian']\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Diff'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Asian: Difference',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asian proportion in Saint Paul by Grid" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "C= B.query('Count >100')\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=C,\n", " columns=['Grid','Asian'],\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Asian Margin',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "folium.LayerControl().add_to(FG_map)\n", " \n", "# display map\n", "FG_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quick Analysis**\n", "\n", "* The Frogtown community does not have heaviest density of traffic stops, but the cluster is very apparent to nearby grids\n", "* Despite greater frequency of traffic stops in certain areas, the margin of citations is lower\n", "* Many lower social economic areas have more equipment violations, which is also representated by their margin\n", "* Many drivers in lower social economic areas are searched by margin and are stopped more regularly late night\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions/Summary \n", "\n", "After digging into the data, we get a better grasp on how traffic stops are administered and the citation rate. Within Frogtown, the data shows that movement violations are more frequent during the daytime, which have a high citation rate. During late night, equipment violation is more frequent in the community. When expanding outward to the Saint Paul, we see that traffic stops are more frequent along university avenue, which is both commerical and the light transit runs through the avenue as well. The Frogtown area have more traffic stops than their neighboring community, with greater concentration from Western Ave to Lexington Ave. Within Saint Paul, there are similar communities like Frogtown.\n", " \n", "In regards to the racial question within Frogtown, Black and White drivers are over-representated, while Asians are under-representated for total citations given. Black drivers are at least twice more likely to be searched than their white counterparts for moving violations, despite having low citation rates. During the late night hours, black drivers are more likely to be stopped and have greater rates of moving violations. On the other hand, white drivers have greater citation rate during the morning time. Within gender, white female drivers get stopped. Though it's worth nothing that this can be due to the wealth gap between different racial lines. At the city level, we see that black drivers are over-representated on total traffic stops in all neighborhoods. On contrast, Asian drivers are under-representated respect to total traffic stops.\n", " \n", "This report created more questions than answers. Why is there discrepancies in the data? What is the criteria for an equipment violation; does crashes count? There is simply not enough volume of 911 calls and invesitgative stops to account for the imbalance.\n", " \n", "There is a lot more information that can be gleamed from the results. Please feel free to email me if you have any questions or thoughts.\n", "\n", "Please check out the numbers for your respective community in the Appendix.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Appendix (Neighborhood Tables)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hide_input": false }, "outputs": [ { "data": { "text/html": [ "
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CountCitation_countCitationEquipment Violation_countEquipment ViolationMoving Violation_countMoving Violation
Community
Battle_Creek229016540.7223%269.0000%0.1175%2021.0000%0.8825%
Capital_River491134230.6970%534.0000%0.1087%4377.0000%0.8913%
Como13288510.6408%187.0000%0.1408%1141.0000%0.8592%
Dayton_Bluff25689340.3637%753.0000%0.2932%1815.0000%0.7068%
Greater_East_Side396820730.5224%810.0000%0.2041%3158.0000%0.7959%
Highland_Park337229600.8778%76.0000%0.0225%3296.0000%0.9775%
Macalester_Groveland185114370.7763%130.0000%0.0702%1721.0000%0.9298%
Midway247612880.5202%491.0000%0.1983%1985.0000%0.8017%
North_End856148830.5704%1854.0000%0.2166%6707.0000%0.7834%
Payne_Phalen1213641850.3448%3573.0000%0.2944%8563.0000%0.7056%
St_Anthony3472230.6427%94.0000%0.2709%253.0000%0.7291%
Summit_Hill8515850.6874%74.0000%0.0870%777.0000%0.9130%
Summit_University247711330.4574%573.0000%0.2313%1904.0000%0.7687%
Thomas_Frogtown545822190.4066%1654.0000%0.3030%3804.0000%0.6970%
Union_Park313720680.6592%444.0000%0.1415%2693.0000%0.8585%
West_7th303822100.7275%291.0000%0.0958%2747.0000%0.9042%
West_Side11925170.4337%333.0000%0.2794%859.0000%0.7206%
\n", "
" ], "text/plain": [ " Count Citation_count Citation \\\n", "Community \n", "Battle_Creek 2290 1654 0.7223% \n", "Capital_River 4911 3423 0.6970% \n", "Como 1328 851 0.6408% \n", "Dayton_Bluff 2568 934 0.3637% \n", "Greater_East_Side 3968 2073 0.5224% \n", "Highland_Park 3372 2960 0.8778% \n", "Macalester_Groveland 1851 1437 0.7763% \n", "Midway 2476 1288 0.5202% \n", "North_End 8561 4883 0.5704% \n", "Payne_Phalen 12136 4185 0.3448% \n", "St_Anthony 347 223 0.6427% \n", "Summit_Hill 851 585 0.6874% \n", "Summit_University 2477 1133 0.4574% \n", "Thomas_Frogtown 5458 2219 0.4066% \n", "Union_Park 3137 2068 0.6592% \n", "West_7th 3038 2210 0.7275% \n", "West_Side 1192 517 0.4337% \n", "\n", " Equipment Violation_count Equipment Violation \\\n", "Community \n", "Battle_Creek 269.0000% 0.1175% \n", "Capital_River 534.0000% 0.1087% \n", "Como 187.0000% 0.1408% \n", "Dayton_Bluff 753.0000% 0.2932% \n", "Greater_East_Side 810.0000% 0.2041% \n", "Highland_Park 76.0000% 0.0225% \n", "Macalester_Groveland 130.0000% 0.0702% \n", "Midway 491.0000% 0.1983% \n", "North_End 1854.0000% 0.2166% \n", "Payne_Phalen 3573.0000% 0.2944% \n", "St_Anthony 94.0000% 0.2709% \n", "Summit_Hill 74.0000% 0.0870% \n", "Summit_University 573.0000% 0.2313% \n", "Thomas_Frogtown 1654.0000% 0.3030% \n", "Union_Park 444.0000% 0.1415% \n", "West_7th 291.0000% 0.0958% \n", "West_Side 333.0000% 0.2794% \n", "\n", " Moving Violation_count Moving Violation \n", "Community \n", "Battle_Creek 2021.0000% 0.8825% \n", "Capital_River 4377.0000% 0.8913% \n", "Como 1141.0000% 0.8592% \n", "Dayton_Bluff 1815.0000% 0.7068% \n", "Greater_East_Side 3158.0000% 0.7959% \n", "Highland_Park 3296.0000% 0.9775% \n", "Macalester_Groveland 1721.0000% 0.9298% \n", "Midway 1985.0000% 0.8017% \n", "North_End 6707.0000% 0.7834% \n", "Payne_Phalen 8563.0000% 0.7056% \n", "St_Anthony 253.0000% 0.7291% \n", "Summit_Hill 777.0000% 0.9130% \n", "Summit_University 1904.0000% 0.7687% \n", "Thomas_Frogtown 3804.0000% 0.6970% \n", "Union_Park 2693.0000% 0.8585% \n", "West_7th 2747.0000% 0.9042% \n", "West_Side 859.0000% 0.7206% " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Features= ['Community','Count','Citation','Equipment Violation','Moving Violation','Driver_search','Vehicle_search','LateNight',\\\n", " 'Asian','Black','Latino','White','Other','Gender','Weekend']\n", "\n", "#pd.options.display.float_format = '{:.4f}%'.format\n", "\n", "# Create a sum and divide by Count; and \n", "Ne= df[Features].groupby(['Community']).sum()\n", "\n", "#Save Sum Values\n", "C= Ne[['Count','Citation','Equipment Violation','Moving Violation','LateNight']]\n", "\n", "#Divide by Count and then add new columns in tranformed table\n", "Ne=Ne.div(Ne['Count'].values,axis=0)\n", "Ne['Count']=C.iloc[:,0] \n", "Ne['Citation_count'] = C.iloc[:,1]\n", "Ne['Equipment Violation_count'] = C.iloc[:,2]\n", "Ne['Moving Violation_count'] = C.iloc[:,3]\n", "Ne['LateNight_count'] = C.iloc[:,4]\n", "\n", "Ne['White_Demo']= SPDem.iloc[:,0].values #.values was used\n", "#print(N['White_Demo'])\n", "Ne['Black_Demo']= SPDem.iloc[:,1].values \n", "Ne['Asian_Demo']= SPDem.iloc[:,2].values \n", "Ne=Ne.reset_index()\n", "\n", "Ne[['Community','Count', 'Citation_count','Citation','Equipment Violation_count','Equipment Violation','Moving Violation_count','Moving Violation']].set_index('Community')\n", "\n", "#df" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Driver_searchVehicle_searchCitation_countLateNightGenderWeekend
Community
Battle_Creek0.03%0.03%16540.15%0.42%0.11%
Capital_River0.03%0.03%34230.15%0.41%0.16%
Como0.03%0.03%8510.19%0.43%0.15%
Dayton_Bluff0.09%0.09%9340.35%0.33%0.23%
Greater_East_Side0.07%0.06%20730.27%0.38%0.17%
Highland_Park0.00%0.00%29600.02%0.44%0.03%
Macalester_Groveland0.01%0.01%14370.09%0.42%0.09%
Midway0.06%0.05%12880.25%0.40%0.18%
North_End0.06%0.05%48830.24%0.37%0.18%
Payne_Phalen0.11%0.10%41850.38%0.31%0.24%
St_Anthony0.03%0.02%2230.39%0.28%0.39%
Summit_Hill0.03%0.03%5850.13%0.42%0.14%
Summit_University0.08%0.07%11330.32%0.35%0.25%
Thomas_Frogtown0.10%0.09%22190.40%0.34%0.22%
Union_Park0.03%0.03%20680.18%0.38%0.17%
West_7th0.02%0.01%22100.11%0.39%0.14%
West_Side0.08%0.08%5170.35%0.32%0.28%
\n", "
" ], "text/plain": [ " Driver_search Vehicle_search Citation_count \\\n", "Community \n", "Battle_Creek 0.03% 0.03% 1654 \n", "Capital_River 0.03% 0.03% 3423 \n", "Como 0.03% 0.03% 851 \n", "Dayton_Bluff 0.09% 0.09% 934 \n", "Greater_East_Side 0.07% 0.06% 2073 \n", "Highland_Park 0.00% 0.00% 2960 \n", "Macalester_Groveland 0.01% 0.01% 1437 \n", "Midway 0.06% 0.05% 1288 \n", "North_End 0.06% 0.05% 4883 \n", "Payne_Phalen 0.11% 0.10% 4185 \n", "St_Anthony 0.03% 0.02% 223 \n", "Summit_Hill 0.03% 0.03% 585 \n", "Summit_University 0.08% 0.07% 1133 \n", "Thomas_Frogtown 0.10% 0.09% 2219 \n", "Union_Park 0.03% 0.03% 2068 \n", "West_7th 0.02% 0.01% 2210 \n", "West_Side 0.08% 0.08% 517 \n", "\n", " LateNight Gender Weekend \n", "Community \n", "Battle_Creek 0.15% 0.42% 0.11% \n", "Capital_River 0.15% 0.41% 0.16% \n", "Como 0.19% 0.43% 0.15% \n", "Dayton_Bluff 0.35% 0.33% 0.23% \n", "Greater_East_Side 0.27% 0.38% 0.17% \n", "Highland_Park 0.02% 0.44% 0.03% \n", "Macalester_Groveland 0.09% 0.42% 0.09% \n", "Midway 0.25% 0.40% 0.18% \n", "North_End 0.24% 0.37% 0.18% \n", "Payne_Phalen 0.38% 0.31% 0.24% \n", "St_Anthony 0.39% 0.28% 0.39% \n", "Summit_Hill 0.13% 0.42% 0.14% \n", "Summit_University 0.32% 0.35% 0.25% \n", "Thomas_Frogtown 0.40% 0.34% 0.22% \n", "Union_Park 0.18% 0.38% 0.17% \n", "West_7th 0.11% 0.39% 0.14% \n", "West_Side 0.35% 0.32% 0.28% " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ne[['Community','Driver_search','Vehicle_search', 'Citation_count','LateNight','Gender','Weekend']].set_index('Community')" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AsianAsian_DemoBlackBlack_DemoWhiteWhite_Demo
Community
Battle_Creek0.1436680.2315850.2921400.2030790.4301310.398383
Capital_River0.0836900.0895090.3029930.1321140.5159850.675663
Como0.1001510.0540480.3064760.0892300.4917170.762898
Dayton_Bluff0.1464170.3238170.4193930.1405530.3060750.348776
Greater_East_Side0.1844760.2976780.3369460.1552620.3366940.358690
Highland_Park0.0293590.0710760.1476870.1409590.7265720.652471
Macalester_Groveland0.0264720.0376230.1274990.1265500.7406810.768102
Midway0.0508890.0393930.3412760.0208930.4983840.871761
North_End0.1609630.3552030.3978510.2314650.3385120.259136
Payne_Phalen0.2115190.3584010.4001320.1286240.2760380.307874
St_Anthony0.0835730.1120200.2219020.0942200.5504320.711481
Summit_Hill0.0399530.0274510.1327850.0508390.7403060.839548
Summit_University0.0613650.0826620.4804200.3243490.3467900.491559
Thomas_Frogtown0.1258700.3273180.4923050.3297690.2722610.223661
Union_Park0.0395280.0377180.2591650.0980120.5999360.772243
West_7th0.0358790.0299260.1800530.1088260.6899280.721428
West_Side0.0419460.0649840.2810400.1358230.4530200.453392
\n", "
" ], "text/plain": [ " Asian Asian_Demo Black Black_Demo White \\\n", "Community \n", "Battle_Creek 0.143668 0.231585 0.292140 0.203079 0.430131 \n", "Capital_River 0.083690 0.089509 0.302993 0.132114 0.515985 \n", "Como 0.100151 0.054048 0.306476 0.089230 0.491717 \n", "Dayton_Bluff 0.146417 0.323817 0.419393 0.140553 0.306075 \n", "Greater_East_Side 0.184476 0.297678 0.336946 0.155262 0.336694 \n", "Highland_Park 0.029359 0.071076 0.147687 0.140959 0.726572 \n", "Macalester_Groveland 0.026472 0.037623 0.127499 0.126550 0.740681 \n", "Midway 0.050889 0.039393 0.341276 0.020893 0.498384 \n", "North_End 0.160963 0.355203 0.397851 0.231465 0.338512 \n", "Payne_Phalen 0.211519 0.358401 0.400132 0.128624 0.276038 \n", "St_Anthony 0.083573 0.112020 0.221902 0.094220 0.550432 \n", "Summit_Hill 0.039953 0.027451 0.132785 0.050839 0.740306 \n", "Summit_University 0.061365 0.082662 0.480420 0.324349 0.346790 \n", "Thomas_Frogtown 0.125870 0.327318 0.492305 0.329769 0.272261 \n", "Union_Park 0.039528 0.037718 0.259165 0.098012 0.599936 \n", "West_7th 0.035879 0.029926 0.180053 0.108826 0.689928 \n", "West_Side 0.041946 0.064984 0.281040 0.135823 0.453020 \n", "\n", " White_Demo \n", "Community \n", "Battle_Creek 0.398383 \n", "Capital_River 0.675663 \n", "Como 0.762898 \n", "Dayton_Bluff 0.348776 \n", "Greater_East_Side 0.358690 \n", "Highland_Park 0.652471 \n", "Macalester_Groveland 0.768102 \n", "Midway 0.871761 \n", "North_End 0.259136 \n", "Payne_Phalen 0.307874 \n", "St_Anthony 0.711481 \n", "Summit_Hill 0.839548 \n", "Summit_University 0.491559 \n", "Thomas_Frogtown 0.223661 \n", "Union_Park 0.772243 \n", "West_7th 0.721428 \n", "West_Side 0.453392 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ne[['Community','Asian','Asian_Demo', 'Black','Black_Demo','White','White_Demo']].set_index('Community')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Excess Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": false }, "outputs": [], "source": [ "\n", "#Long Strect of code\n", "\n", "#Programmer Note: The sort index made the values of the output easier to predice to allow more predictable results\n", "\n", "#Group by Race to get count\n", "A=round(rf['Race'].value_counts().sort_index(level=1),4)\n", "Race_Grp.set_value('Asian','Tot_Count', A[0])\n", "Race_Grp.set_value('Black','Tot_Count', A[1])\n", "Race_Grp.set_value('Latino','Tot_Count', A[2])\n", "Race_Grp.set_value('Other','Tot_Count', A[3])\n", "Race_Grp.set_value('White','Tot_Count', A[4])\n", "\n", "#Group by Race and Reason to get counts\n", "A= round(rf.groupby(['Race'])['Reason'].value_counts().sort_index(level=1),4)\n", "Race_Grp.set_value('Asian','Eq_Count', A[0])\n", "Race_Grp.set_value('Black','Eq_Count', A[1])\n", "Race_Grp.set_value('Latino','Eq_Count', A[2])\n", "Race_Grp.set_value('Other','Eq_Count', A[3])\n", "Race_Grp.set_value('White','Eq_Count', A[4])\n", "Race_Grp.set_value('Asian','Mov_Count', A[5])\n", "Race_Grp.set_value('Black','Mov_Count', A[6])\n", "Race_Grp.set_value('Latino','Mov_Count', A[7])\n", "Race_Grp.set_value('Other','Mov_Count', A[8])\n", "Race_Grp.set_value('White','Mov_Count', A[9])\n", "\n", "#Group by Race and Reason but normalized counts\n", "A= round(rf.groupby(['Race'])['Reason'].value_counts(normalize=True).sort_index(level=1),4)\n", "Race_Grp.set_value('Asian','Eq_Margin', A[0])\n", "Race_Grp.set_value('Black','Eq_Margin', A[1])\n", "Race_Grp.set_value('Latino','Eq_Margin', A[2])\n", "Race_Grp.set_value('Other','Eq_Margin', A[3])\n", "Race_Grp.set_value('White','Eq_Margin', A[4])\n", "Race_Grp.set_value('Asian','Mov_Margin', A[5])\n", "Race_Grp.set_value('Black','Mov_Margin', A[6])\n", "Race_Grp.set_value('Latino','Mov_Margin', A[7])\n", "Race_Grp.set_value('Other','Mov_Margin', A[8])\n", "Race_Grp.set_value('White','Mov_Margin', A[9])\n", "\n", "#Group by Race and Citation Counts\n", "A=round(rf.groupby(['Race'])['Citation'].value_counts().sort_index(level=1),4)\n", "Race_Grp.set_value('Asian','Citation_Count', A[5])\n", "Race_Grp.set_value('Black','Citation_Count', A[6])\n", "Race_Grp.set_value('Latino','Citation_Count', A[7])\n", "Race_Grp.set_value('Other','Citation_Count', A[8])\n", "Race_Grp.set_value('White','Citation_Count', A[9])\n", "\n", "#Group by Race, Reason, and Citation Normalized Counts\n", "A= round(rf.groupby(['Race','Reason'])['Citation'].value_counts(normalize=True).sort_index(level=2),4)\n", "Race_Grp.set_value('Asian','Eq_Citation', A[10])\n", "Race_Grp.set_value('Black','Eq_Citation', A[12])\n", "Race_Grp.set_value('Latino','Eq_Citation', A[14])\n", "Race_Grp.set_value('Other','Eq_Citation', A[16])\n", "Race_Grp.set_value('White','Eq_Citation', A[18])\n", "Race_Grp.set_value('Asian','Mov_Citation', A[11])\n", "Race_Grp.set_value('Black','Mov_Citation', A[13])\n", "Race_Grp.set_value('Latino','Mov_Citation', A[15])\n", "Race_Grp.set_value('Other','Mov_Citation', A[17])\n", "Race_Grp.set_value('White','Mov_Citation', A[19])\n", "\n", "#Group by Race and Search Counts\n", "A=round(rf.groupby(['Race'])['Driver_search'].value_counts().sort_index(level=1),4)\n", "Race_Grp.set_value('Asian','Driversearch_Count', A[5])\n", "Race_Grp.set_value('Black','Driversearch_Count', A[6])\n", "Race_Grp.set_value('Latino','Driversearch_Count', A[7])\n", "Race_Grp.set_value('Other','Driversearch_Count', A[8])\n", "Race_Grp.set_value('White','Driversearch_Count', A[9])\n", "\n", "#Group by Race and Citation Counts\n", "A= round(rf.groupby(['Race','Reason'])['Driver_search'].value_counts(normalize=True).sort_index(level=2),4)\n", "Race_Grp.set_value('Asian','Eq_DriverSearch', A[10])\n", "Race_Grp.set_value('Black','Eq_DriverSearch', A[12])\n", "Race_Grp.set_value('Latino','Eq_DriverSearch', A[14])\n", "Race_Grp.set_value('Other','Eq_DriverSearch', A[16])\n", "Race_Grp.set_value('White','Eq_DriverSearch', A[18])\n", "Race_Grp.set_value('Asian','Mov_DriverSearch', A[11])\n", "Race_Grp.set_value('Black','Mov_DriverSearch', A[13])\n", "Race_Grp.set_value('Latino','Mov_DriverSearch', A[15])\n", "Race_Grp.set_value('Other','Mov_DriverSearch', A[17])\n", "Race_Grp.set_value('White','Mov_DriverSearch', A[19])\n", "\n", "#Group by Race,Reason, and Gender Normalized Counts\n", "A=round(rf.groupby(['Race','Reason'])['Gender'].value_counts(normalize=True).sort_index(level=2),4)\n", "Race_Grp.set_value('Asian','Eq_Gender_F', A[10])\n", "Race_Grp.set_value('Black','Eq_Gender_F', A[12])\n", "Race_Grp.set_value('Latino','Eq_Gender_F', A[14])\n", "Race_Grp.set_value('Other','Eq_Gender_F', A[16])\n", "Race_Grp.set_value('White','Eq_Gender_F', A[18])\n", "Race_Grp.set_value('Asian','Mov_Gender_F', A[11])\n", "Race_Grp.set_value('Black','Mov_Gender_F', A[13])\n", "Race_Grp.set_value('Latino','Mov_Gender_F', A[15])\n", "Race_Grp.set_value('Other','Mov_Gender_F', A[17])\n", "Race_Grp.set_value('White','Mov_Gender_F', A[19])\n", "\n", "#Group by Race,LateNight Counts\n", "A=round(rf.groupby(['Race'])['LateNight'].value_counts().sort_index(level=1),4)\n", "Race_Grp.set_value('Asian','LateNight_Count', A[5])\n", "Race_Grp.set_value('Black','LateNight_Count', A[6])\n", "Race_Grp.set_value('Latino','LateNight_Count', A[7])\n", "Race_Grp.set_value('Other','LateNight_Count', A[8])\n", "Race_Grp.set_value('White','LateNight_Count', A[9])\n", "\n", "#Group by Race,Reason, and Latenight Normalized Counts\n", "A=round(rf.groupby(['Race','Reason'])['LateNight'].value_counts(normalize=True).sort_index(level=2),4)\n", "Race_Grp.set_value('Asian','Eq_LateNight', A[10])\n", "Race_Grp.set_value('Black','Eq_LateNight', A[12])\n", "Race_Grp.set_value('Latino','Eq_LateNight', A[14])\n", "Race_Grp.set_value('Other','Eq_LateNight', A[16])\n", "Race_Grp.set_value('White','Eq_LateNight', A[18])\n", "Race_Grp.set_value('Asian','Mov_LateNight', A[11])\n", "Race_Grp.set_value('Black','Mov_LateNight', A[13])\n", "Race_Grp.set_value('Latino','Mov_LateNight', A[15])\n", "Race_Grp.set_value('Other','Mov_LateNight', A[17])\n", "Race_Grp.set_value('White','Mov_LateNight', A[19])\n", "\n", "#Group by Race,Latenight, and Citation Normalized Counts\n", "#1/3 of data is done during latenight activities\n", "A=round(rf.groupby(['Race','LateNight'])['Citation'].value_counts(normalize=True).sort_index(level=2),4)\n", "Race_Grp.set_value('Asian','Morn_Citation', A[10])\n", "Race_Grp.set_value('Black','Morn_Citation', A[12])\n", "Race_Grp.set_value('Latino','Morn_Citation', A[14])\n", "Race_Grp.set_value('Other','Morn_Citation', A[16])\n", "Race_Grp.set_value('White','Morn_Citation', A[18])\n", "Race_Grp.set_value('Asian','Late_Citation', A[11])\n", "Race_Grp.set_value('Black','Late_Citation', A[13])\n", "Race_Grp.set_value('Latino','Late_Citation', A[15])\n", "Race_Grp.set_value('Other','Late_Citation', A[17])\n", "Race_Grp.set_value('White','Late_Citation', A[19])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Construct for every neighborhood\n", "\n", "d_North_End=Race_Grp\n", "d_West_Side=Race_Grp\n", "d_Battle_Creek=Race_Grp\n", "d_Dayton_Bluff=Race_Grp\n", "d_Payne_Phalen=Race_Grp\n", "d_Capital_River=Race_Grp\n", "d_Union_Park=Race_Grp\n", "d_Summit_University=Race_Grp\n", "d_Greater_East_Side=Race_Grp\n", "d_Thomas_Frogtown=Race_Grp\n", "d_St_Anthony=Race_Grp\n", "d_Summit_Hill=Race_Grp\n", "d_Midway=Race_Grp\n", "d_West_7th=Race_Grp\n", "d_Como=Race_Grp\n", "d_Highland_Park=Race_Grp\n", "d_Macalester_Groveland=Race_Grp\n", "\n", "A= df.Community.unique()\n", "Dict= {A[0]:d_North_End, A[1]:d_West_Side, A[2]:d_Battle_Creek, A[3]:d_Dayton_Bluff, A[4]:d_Payne_Phalen, A[5]:d_Capital_River,\n", " A[6]:d_Union_Park, A[7]:d_Summit_University, A[8]:d_Greater_East_Side, A[9]:d_Thomas_Frogtown, A[10]:d_St_Anthony,\n", " A[11]:d_Summit_Hill, A[12]:d_Midway, A[13]:d_West_7th, A[14]:d_Como, A[15]:d_Highland_Park, A[16]:d_Macalester_Groveland}\n", "\n", "\n", "RR= ['Asian','Black','Latino','Other','White']\n", "for k,p in enumerate(Dict):\n", " rf= df[df['Community']==p]\n", " for i,j in enumerate(RR):\n", " Dict[p].set_value(j,'Tot_Count', round(rf['Race'].value_counts().sort_index(level=1),4)[i])\n", " Dict[p].set_value(j,'Eq_Count', round(rf.groupby(['Race'])['Reason'].value_counts().sort_index(level=1),4)[i])\n", " Dict[p].set_value(j,'Mov_Count', round(rf.groupby(['Race'])['Reason'].value_counts().sort_index(level=1),4)[i+len(RR)])\n", "\n", " \n", "#print(Dict['Capital_River'])\n", "print(Dict['West_Side'])\n", "#DR[1] " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "LIMIT = 100 # limit of number of venues returned by Foursquare API\n", "radius = 500 # define radius\n", "url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n", " CLIENT_ID, \n", " CLIENT_SECRET, \n", " VERSION, \n", " grid_latitude, \n", " grid_longitude, \n", " radius, \n", " LIMIT)\n", "url # display URL\n", "\n", "#Get respective json\n", "results = requests.get(url).json()\n", "\n", "# Find venues in one location\n", "venues = results['response']['groups'][0]['items']\n", "nearby_venues = json_normalize(venues) # flatten JSON\n", "\n", "# filter columns\n", "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n", "nearby_venues =nearby_venues.loc[:, filtered_columns]\n", "# filter the category for each row\n", "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n", "# clean columns\n", "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n", "#nearby_venues\n", "\n", "Frogtown_venues.head()\n", "#Frogtown_venues.groupby('Neighborhood').count()\n", "Frogtown_venues.Venue_Category.unique()\n", "\n", "#Specify the Venues of interest to store and display\n", "x=['Dive Bar', 'Convenience Store', 'Liquor Store', 'Restaurant', 'Middle Eastern Restaurant','Vietnamese Restaurant',\\\n", " 'Asian Restaurant', 'Thai Restaurant','Café','Fast Food Restaurant','Noodle House','Ethiopian Restaurant','Bar',\\\n", " 'Ramen Restaurant', 'BBQ Joint', 'Grocery Store', 'Chinese Restaurant']\n", "#Frogtown_venues[Frogtown_venues['Venue_Category'].isin(x)]\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }