Crime Analysis & Mapping using GIS C N : H ) O ! / 8 % .

Crime Analysis & Mapping using GISC N : H ) O ! / 8 % . Laurie A.B. Garo GIS Access Bend, Oregon June, 2000

Overview • Objectives of Project • About Crime Analysis and Mapping (CAAM) with GIS • GIS Active Learning Objectives with CAAM • Data Sets and Selected Exercises • Selected Final Projects • Future Work

Objectives of GIS Access Project • To determine ways to use Charlotte’s crime and social data to help students “Actively” understand and practice various steps in GIS; • To discover some interesting and thoughtful ways to analyze and map this data at the neighborhood level; • To teach GIS to students from a variety of backgrounds, some with limited Spatial knowledge/training;

Objectives of GIS Access Project con’t • To help students to understand the power of GIS as an analytical tool and become excited about learning more. • To develop a series of exercises including database design, data input/preparation, queries, summaries, calculations, joins, and other analysis techniques, plus cartographic quality presentation.

About Crime Analysis and Mapping • Some objectives of Crime Analysis and Mapping are to analyze and illustrate spatial patterns and relationships of criminal activity within a given area or society. • GIS is an efficient tool for both the analysis and the mapping of crime activity.

Some Examples of Crime Analysis and Mapping are: • Pin/Point Maps, e.g., Geocoded point locations of Crime incidents; • Graduated Symbol Maps, e.g., Proportion of Male and Female victims of crime; • Choropleth Maps, e.g., Crime rate per neighborhood, proportion of juveniles involved in criminal activity, etc.; • Flow Line/Network maps, e.g., suspect movement; • Isoline maps e.g., outlines of crime target areas, crime density, or crime hotspots;

More Examples of Crime Analysis and Mapping: • 3-D surfaces, e.g., gang turfs, firearm incident areas; • Multivariate maps that combine 2 or more variables to determine and/or forcast crime patterns and to discover crime reduction strategies and solutions. These may require data on social and economic conditions, and other factors that may influence or be influenced by crime in an area, e.g., Dropout rate and number of juvenile victims of child abuse.

GIS Active Learning Objectives with Crime Analysis and Mapping • To use data sets of relevance and interest to all residents of Charlotte as a way to get students actively involved in hands-on learning about ArcView and about GIS analysis and mapping capabilities; • To get students to think and analyze their results and to understand that GIS can be used to find thoughtful explanations and solutions to societal problems. • To bring in experts in the field (CMPD GIS analysts) to demonstrate real-world application of CAAM using GIS and to inform them about potential jobs.

This Module includes Three Exercises: • 1. Crime Queries – ArcView basics; how to query a crime attribute database, convert results to new shapefiles and symbol the queries using crime point symbols; • 2. Map Design & Layout – to create map layouts of good quality cartographic standard; • 3. Crime Analysis and Mapping –a series of steps to follow in the analysis and mapping of juvenile crime in Charlotte neighborhoods. Students learn to prepare visual correlation between crime and various social factors. The project provides structure for students to then carry out their own crime analysis and mapping project.

1. Crime Queries Exercise The Main Steps Are: • Data Input & Preparation • Create Crime Queries • Convert Query Results to Shapefiles (Themes) • Symbolize using Crime.avp Point Symbols

Data Input and Preparationfor Crime Queries: • The objective of this portion of the project is to copy and organize relevant files into individual folders, and prepare the data for analysis and mapping. • The first step in preparation is to Clip the Mecklenburg County crime data with the Neighborhood boundaries to end up with all data at the neighborhood level:

Crime Incidents for Mecklenburg County, and Charlotte Neighborhood Boundaries

Crime Incidents Clipped to include only those within Charlotte Neighborhoods

Queries & Symbolization • Students begin with the query [offense] = “Homicide” • Results are mapped with the “Dead Body” symbol • Subsequent queries must have several components, e.g., offense, victim sex, victim age, victim race, etc.

Homicide Query B. A. A. Homicide Query B. Tabular Results C. Mapped Results C.

2. Map Design & Layout Exercise Three Thematic Maps are created: • A. Pictorial Point Symbol Maps depicting various crime queries (taken from Crime Query exercise) • B. Ranked Choropleth Map: Crime Status by Charlotte Neighborhood • C. Proportional or Graduated Symbol Map of Homicide by Victim Age .

Map Layouts & Cartographic Design • As part of the crime project, all students are trained in basic cartographic design principles. They must: • Create simple maps of query results by crime type using Crime.avp symbols • Create choropleth maps, experimenting somewhat with data classifications and color schemes • Combine area, line and point data in a cartographically clear manner • Practice Layouts: map scale, balance, type styles/ sizes, appropriate ArcView North arrows…...

3 Thematic Map Layouts A. B. C. A. Homicides B. Crime Status C. Age of Homicide Victims

3. Crime Analysis & Mapping Project • The purpose of this project is to give students experience in carrying out a fairly complex GIS project (minus graphical data input) using crime and social data. • The following slides describe: • Data Input/Preparation including Clipping, Queries, Attribute Data Modifications and Joins, Buffering and Select-by-Theme analysis. • Creation of single variable maps • Creation of multi-variable maps for analyzing potential correlations between criminal activity and social factors.

Data Input and Preparation Students learn more about Database design and Attribute Table Joins using the crime & social data: • 1999 Crime Incidents provided by the GIS unit of the Charlotte-Mecklenburg Police Department (CMPD); data is in ArcView format; • Social data provided from the Residential Quality of Life (QOL) study carried out in 1999-2000 by Dr. Owen Furuseth, Dept. of Geography & Earth Sciences, UNC-Charlotte.

Data Input and Preparation • Next, students carry out some crime queries, convert them to shapefiles, and symbolize them as separate themes using the Crime.avp symbols. The example that follows is to analyze Juvenile Crime and Crime Against Juveniles (note:For this study a Juvenile is 16 years of age and under). • The query results will serve as overlays for correlation with selected social data.

First Query to Isolate all Crimes committed against Juveniles (victim age <= 16) 1. 2. 3. 1. Query all crimes against juveniles (with victims <=16) 2. Query Results (yellow) in table & view 3. Result converted to a shapefile in view

Next are a series of Queries to create themes on specific types of Crime Against Juveniles The above demonstrates a query to isolate and map all offenses listed as Crime Against Family - Abuse. The abuse is mapped using one of the Crime.avp symbols which students load from the symbol palette during each ArcView session. The next series of slides illustrate additional themes on Crime Against Juveniles.

Loading the Crime.avp Symbols & Views of 3 Selected Themes: Neglect of Children, Missing Children, and Teenage Suicide Missing Kids Teenage Suicide Neglect

QOL Data Format Conversion and Joining to Neighborhood Attribute Table • Next, Juvenile Crime Rate and other social data from the QOL study are opened in dbase format, and neighborhood names are modified to be exactly as listed in the Charlotte Neighborhoods attribute table (so that the join will work properly). • The modified social data are joined to the Charlotte Neighborhoods table by Neighborhood Name.

Preparing QOL data to Join with Charlotte Neighborhoods Attribute Table

Creating Maps on Juvenile Crime Rate and Crime Against Juveniles • Juvenile Crime Rate, one of the QOL data sets, is mapped by Choropleth technique, demonstrating which neighborhoods have a higher rate of juvenile crime. • Selected Crime Against Juveniles are then placed on the Juvenile Crime Rate base to view potential correlations between children as victims of crime and children committing crime.

Choropleth Map of Juvenile Crime Rate & Correlation with 3 “Crime Against Juveniles” Overlays Missing Children Neglect Child Abuse

Creating Maps on Crime Against Juveniles and Potential Correlation with Various Social Factors • Another series of maps attempts to correlate various social factors effecting juveniles with child abuse and other crimes against juveniles. • Such social factors include Adolescent Births, Kindergarten Scores, 9th Grade Dropouts, and 9th Grade Competency

% Adolescent Births & Child Abuse % 9th Grade Dropouts & Child Abuse % Passing 9th Grade, Missing Kids, & Teenage Suicide Avg. Kindergarten Scores & Neglect

Mapping Crime Against Juveniles occurring within .5 mile of a School • Add Schools Theme to View; • Buffer an area .5 mile from each school; • Select by Theme: All Crimes against Juveniles occurring completely within .5 mile of a school • Map the result, including buffers; • Map the result without buffers as an overlay on the Dropout Rate per neighborhood. • Is there a visual correlation between crime against children near schools and school drop outs?

Crime Against Juveniles within .5 mile of a School & Compared with Dropout Rate

Student Final Projects • Following the Crime Project, students carry out their own project on a topic of their choice (provided ArcView format data is available). • Almost all choose to do more analysis using the crime and social data. • The following are brief descriptions and maps of student final projects on Crime Mapping and Analysis from Fall, 2000.

Data Input and Preparation for Some Student Final Projects • Streets (lines), and Crime Types (Points) at the county level are clipped to the neighborhoods shapefile; • Several point features of relevance to crime analysis are clipped and queried, e.g., crime sites (convenience stores, adult entertainment, banks, etc.), and other cultural features (parks, schools, churches) so that various analyses can be carried out.

More Data Queries for Final Projects • Data Queries in preparation for analysis: • Convert Median Income “As String” to Median Income “As Number” so it can be classified numerically; • Spatially join crime types (points) with neighborhood boundaries (poly) so that attribute queries of crime types by neighborhood can be accomplished. • Calculate total crime per neighborhood and proportion of male and female victims

Examples of Final Project Analysis: • Location of selected crime types (points) and median household income per neighborhood • Proportion of Population over 64 and Crime Against Seniors • Auto Theft Against Women and Residential Quality of Life • Total Crime per Neighborhood and Proportion against Male vs Female

Examples of Final Project Analysis: • Spatial operations for Juvenile Crime and Youth Opportunities: • Buffers, e.g., 1/4 mi. buffers around youth program facilities; • Merge all youth program area buffers to identify a zone where local youth can walk to a program; • Identify all crime incidents (2 types mapped) that are “completely within” the buffer areas, convert to shapefiles • Reverse selection, convert to shapefile; charts • Illustrates 50% fewer of these crimes within buffer

Crime Analysis in a Mixed Income Neighborhood (Criminal Justice Major)

1. Youth Opportunities and Juvenile Crime Rates (Art Major) 2. Robberies in a Crime Hotspot (Geography Major) 2 1

A Study on Larceny within 3 Upper Income Neighborhoods in Charlotte (Earth Science Major)

Future Work • Collect more data on crime (suspect data) and social indicators • Carry out individual, more detailed and larger scale analysis, neighborhood by neighborhood (clip by neighborhood) • Get former students to present GIS work to my classes • Get my students involved in presenting results of GIS analysis and mapping at local conferences, e.g., GIS Day, November 14, 2001 Charlotte, NC

Crime Analysis & Mapping using GIS C N : H ) O ! / 8 % .