Testing for similarity in area‐based spatial patterns: Alternative methods to Andresen's spatial point pattern test

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 matters, 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 by identifying areas where pedestrian stops by the New York City Police Department are different from violent crimes for 2006-2016.

Minimum geocoding match rates: an international study of the impact of data and areal unit sizes

“…In order to address our research question regarding the minimum acceptable match rate, we need to use a spatial statistical test that can identify change at the local level. For this purpose, we use the spatial point pattern test developed by Andresen (2009Andresen ( , 2016, and extended by Steenbeek et al (2018) and Wheeler et al (2018). This spatial point pattern test identifies spatial stability and/or differences in two (or more) spatial point patterns.…”

“…We use the partial bootstrap version of the test in the Monte Carlo simulation below, because we treat the percentages of events for each areal unit in the original event data sets as known (the base data sets) and perform a bootstrap on the subsequently sampled data sets as the test data sets. All versions of the test are available as an R library(Steenbeek et al 2018)-seeAndresen (2009Andresen ( , 2016,Wheeler et al (2018), andSteenbeek et al (2018) for more details regarding the test options.5 The basic context of the spatial point pattern test is as follows:1. Identify one data set as the base and calculate the percentage of events…”

Going Beyond the Blue: The Utility of Emergency Medical Services Data in Understanding Violent Crime

“…The SPPT has been utilized to assess differences in spatial patterns of different crime types (Andresen & Linning, 2012), in crimes across years (Hodgkinson & Andresen, 2019), in patterns at different units of analysis (Andresen & Malleson, 2011; de Melo et al, 2015), and across different sources of data (Hibdon et al, 2017). Recently, a strengthened, modified version of the SPPT was proposed, which uses a difference of proportions at each unique place rather than confidence intervals (see Wheeler et al, 2018). Here, we use the χ 2 estimations for proportion differences with no adjustment for p values to estimate the global S values.…”

Crime Concentration and Temporal Stability in Spatial Patterns of Crime in Nis, Serbia

“…These are often used in the spatial analysis of crime concentration (Hardyns et al, 2019) or its risk factors (Caplan, Kennedy, Miller, 2011;Barnum, Cambell, Trocchio, Caplan, Kennedy, 2017). One application of SPPT using the grid cells is known to the author, namely Wheeler, Steenbeek, and Andresen (2018), who used 500 by 500-metre grids in their analysis and concluded that it eliminates the problems of inconsistency between the two-point pattern geocoding and provides reasonably small areas. Some authors argue that it is important to consider grid cells as micro-level places for crime analysis as they can be seen as behavioural settings from a theoretical perspective (Hardyns et al, 2019, p. 13).…”

“…Andersen's spatial point pattern test (A. Wheeler et al, 2018). Finally, 100 by 100 metres were used as smaller spatial units, similarly to street segments in spatial crime analysis (Melo et al, 2015;Weisburd et al, 2004Weisburd et al, , 2009.…”