Evaluating Neighborhood Housing and the Impact of Crime

Evaluating the neighborhood’s housing units based on the impact by intensity of several crime types and their locations in relation with schools’ service areas. GIS software and its ability to present a final conclusion based on several overlapping analysis results in a particular area makes it suitable for projects that require different data impacts and their relation with one another visually showed on one united map.

Data Sources:

1. City of San Jose Planning Department’s schools, San Jose Roads and building foot prints shapefiles.

2. City of San Jose’s police department’s official web site of the San Jose Police Department’s crime data

First, time of the crime is from 7:00 am to 10:00am and 2:00pm to 8:00 pm, the actual raw data had a more diverse time frame. The hour limit chosen is related with hours that school children are actively present in the neighborhood.

Secondly, the crime types chosen are “Assault”, “Breaking and Entering”, and “Theft.”

Methodology:

In the first phase of the project, network analyst and spatial analyst extensions are activated. The network analyst tool calculates the school service areas and displays them as polygons while the spatial analyst tool rasterizes these polygons into light and dark blue areas. Secondly, the spatial analyst tool is calculating the Euclidean distance from the crime spots through using the distance toolbox, the further a location (a house) is from the crime the safer it is, these points are located in the dark purple areas. Rasterizing is necessary since overlapping the analysis results is done in raster image formats. Thirdly, the reclassification tool will make it clear that proximity to schools is more important than crime intensity by giving the location closer to schools a higher rank. The “Times” tool as a subset of “Math” toolbox will multiply the reclassified raster images and display areas which are both close and safe.

Most importantly, since the houses are in vector format as building footprint shapefiles, it is required to convert the final raster image into a vector format. This will make the spatial joint of the analysis results possible.

Findings:

The results are quite reasonable, not all of the houses which are close to schools are ranked as suitable if they are located within the range of an intensified crime spot, therefore there are shapefiles that are within a 1200 acres service area and yet are red, which is the least preferable color in out ranking. Only does houses which share both proximity and safety from crimes are in blue, most preferable houses. The shapefiles in purple are ranked as average. Unfortunately, it is shown in the final map that many locations, although, close to schools are red and in danger of several crime types. This requires more crime prevention policies in order to enhance the quality of the neighborhood. As for proximity of the residences to schools most of them are supported within a 2400 acres of each of the four displayed schools.

Written by: Maryam Sanieian