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This variable also displays a high-clusters pattern, indicating that areas in which a high percentage of commuters use public transportation are clustered.
For the next set of tools, you will run them each twice: once with a polygon dataset of race by census tract and once with a point dataset on crime locations. The instructions for the point dataset are not yet available, but will be posted here on the wiki next week.
Percent White
- At the bottom of the Geoprocessing pane, click the Catalog tab.
- Within the Patterns.gdb geodatabase, right-click the CensusTracts_Race_2014 feature class and select Add To Current New Map.
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Again, a new layer has been added to your map and you may need to undo the layer render if your census tracts are showing as a single color. The resulting high-high and low-low clusters should fairly closely match the hot and cold spots in the prior analysis and are represented in light pink and blue. The areas of low white population within a cluster of high white population are represented in dark blue and the areas of high white population within a cluster of low white population are represented in dark red. In this case, many of these outliers are on the periphery of the hot and cold spots as they transition into areas which are not significant, which makes the results less interesting. Cluster and outlier analysis is often more telling at a smaller geographic unit. For example, if you were to rerun this same analysis at the census block level, you would see individual red blocks with a high white population within a huge neighborhood of light blue with a low white population. These outliers will prompt you to pose interesting questions to try to explain them. In one neighborhood we investigated in Houston, these high-low outliers, or red blocks, corresponded to blocks with high numbers of building permits. Those two pieces of information combined present a compelling picture of gentrification. Instructions for running the analysis on the block level data will be added to this wiki next week. Because the geographic units are much smaller, calculating the results takes more time.