New preprint: Testing for Similarity in Area-Based Spatial Patterns: Alternative Methods to Andresen’s Spatial Point Pattern Test

I just posted another pre-print to SSRN, Testing for Similarity in Area-Based Spatial Patterns: Alternative Methods to Andresen’s Spatial Point Pattern Test. This is work with Wouter Steenbeek and Martin Andresen. Below is the abstract:

Andresen’s spatial point pattern test (SPPT) compares two spatial point patterns on defined areal units: it identifies areas where the spatial point patterns diverge and aggregates these local (dis)similarities to one global measure. We discuss the limitations of the SPPT and provide two alternative methods to calculate differences in the point patterns. In the first approach we use differences in proportions tests corrected for multiple comparisons. We show how the size of differences matter, as with large point patterns many areas will be identified by SPPT as statistically different, even if those differences are substantively trivial. The second approach uses multinomial logistic regression, which can be extended to identify differences in proportions over continuous time. We demonstrate these methods on identifying areas where pedestrian stops by the New York City Police Department are different from violent crimes from 2006 through 2016.

And here is an example map using our proportion differences test and graduated circles to identify places with larger differences in the percentages:

This is opposed to the traditional SPPT output, which just identifies whether two areas are different and does not focus on the size of the difference, like below:

You can see with a large sample size, basically everything is statistically different! (This uses over 4 million stops and over 800,000 violent crimes). Focusing on the magnitude of the differences gives a much clear indication of patterns.

The paper includes a dropbox link to download the data and code used to estimate the different techniques (it includes code in SPSS, R, and Stata). If you have any feedback as always let me know. This was submitted as a GISScience presentation for the 2018 ESRI User conference in July in San Diego, so I should have news about that presentation in the near future as well.

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