Compare serious crime with poverty in Pittsburgh The criminology literature finds that much crime is related to poverty: the larger the population living in poverty, the higher the rate of certain kinds of crimes

I’m working on a Geographic Information exercise and need support.

Assignment 4-2 Compare serious crime with poverty in Pittsburgh The criminology literature finds that much crime is related to poverty: the larger the population living in poverty, the higher the rate of certain kinds of crimes. Let’s see if this relationship is evident in Pittsburgh at the census tract level. In the process, you’ll build a file geodatabase, import map layers into it, build a code table from scratch and join it to a map layer, join census data to a map layer, and carry out a spatial join to aggregate data. Get set up First, rename your assignment folder and create a map document. Rename the folder \EsriPress\GIST1\MyAssignments\Chapter4\Assignment4-2YourName\ to your name or student ID. Store all files that you produce for this assignment in this folder. Create a new map document called Assignment4-2YourName.mxd with relative paths. Use the following steps to project the Table of Contents layer to the state plane coordinate system, with coordinates in feet:Right-click Layers in the Table of Contents, click Properties > Coordinate System tab. Expand Projected Coordinate Systems > State Plane > NAD 1983 (US feet) and click NAD 1983 State Plane Pennsylvania South FIPS 3702 (US Feet) > OK. Build the map Create a new file geodatabase called Assignment4-2YourName.gdb. Import the following data into it: \EsriPress\GIST1\Data\Pittsburgh\Shapefiles\PittsburghSeriousCrimes2008.shp—point shapefile of serious crime offense locations in Pittsburgh during summer, 2008. Attributes include: CCN = police ID for offense, Address = location of the offense, DateOccur = date of the offense, Hierarchy = FBI hierarchy code for the offense (1 = Criminal Homicide, 2 = Forcible Rape, 3 = Robbery, 4 = Aggravated Assault, 5 = Burglary, 6 = Larceny-theft, 7 = Motor Vehicle Theft, 8 = Arson). \EsriPress\GIST1\Data\Pittsburgh\City.gdb\PghTracts—polygon features of Pittsburgh 2010 census tracts. \EsriPress\GIST1\Data\Pittsburgh\Shapefiles\PovertyTracts.xlsx with PghPovertyTracts worksheet—2010 tract data for poverty. Attributes include: GEOid = tract geocode (numeric data type), PopWithPovStatus = population for whom poverty status is known (presumably the same as total population), PopBelowPovLevel = population who are below the poverty level. Join PghPovertyTracts to PghTracts and make a choropleth map for population below the poverty line. Create a new code table in your file geodatabase: Right-click your file geodatabase in Catalog and click New > Table. Name the table UCRHierarchy with no alias. Click Next > Next. Create a field called UCR with the Short Integer data type. Create another field called Crime with the Text data type and length 20. Click Customize > Toolbars > Editor. On the editor toolbar, click Editor > Start Editing > Continue. Open UCRHierarchy in the Table of Contents, type 1 for UCR and Criminal Homicide for Crime in the first row. Finish up inputting rows found with codes and crime types from the description of PittsburghSeriousCrimes2008.shp above. When done, click Editor in the Editor toolbar, Save Edits, Stop editing,and close the Editor toolbar. Close your new table. Create the following query definition for PittsburghSeriousCrimes2008 and rename that layer in the Table of Contents to be PittsburghSeriousCrimesSummer2008: “DateOccur” >= date ‘2008-06-01’ AND “DateOccur” <= date ‘2008-08-31’. Join UCRHierarchy to PittsburghSeriousCrimesSummer2008. Symbolize PittsburghSeriousCrimesSummer2008 with the Crime attribute using unique values. Use different shapes and colors for point markers. This layer will display only when you zoom in. Spatially join PittsburghSeriousCrimesSummer2008 to PghTracts to get a count of serious crimes per tract in summer 2008. Call the output CrimeAggregatedByTracts. Use the results to symbolize PghTracts centroids with size-graduated point markers. Hint: Use Quantities, Graduated symbols for symbolizing the crime count (there is no need to create tract centroids in this case). Set threshold scales so that when zoomed in to about a fourth of Pittsburgh or farther, PittsburghSeriousCrimesSummer2008 turns on and CrimeAggregatedByTracts turns off. Create a layout of your own design for the map document, zoomed to full extent. If you have difficulty carrying out the spatial join of PittsburghSeriousCrimesSummer2008 to PghTracts, do the following. Right-click PghTracts, click Data > Export Data. Save the exported data and add to your map as CrimeAggregatedByTracts. Then spatially join PittsburghSeriouscrimesSummer2008 to CrimeAggregatedByTracts. Analyze the data Get set up to create a graph: Open the CrimeAggregatedByTracts property table, click the Fields tab, and give Count_ the alias Serious Crimes. Open the PghPovertyTracts table and give PopBelowPovLevel the alias Population Below Poverty Level. Join PittsburghPovertyTracts to CrimeAggregatedByTracts. Create a scatterplot of Serious Crimes versus Population Below Poverty Level: Use View > Graphs > Create Graph to create the scatterplot. Once created, resize its window so that the vertical and horizontal axes are the same length. Right-click the graph and click Copy As Graphic. On your layout, click Edit > Paste. Reposition and align the graph and other elements of your layout. Save your layout and export it as Assignment4-2YourName.jpg.

Gorr, Wilpen L.; Kurland, Kristen S.. GIS Tutorial 1 (p. 268-271). Esri Press. Kindle Edition.