Testing for similarity in area‐based spatial patterns: Alternative methods to Andresen's spatial point pattern test
Testing for similarity in area‐based spatial patterns: Alternative methods to Andresen's spatial point pattern test
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DOI：
10.1111/tgis.12341
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发表时间：
2018-01
影响因子：
2.4
通讯作者：
A. Wheeler;W. Steenbeek;Martin A. Andresen
中科院分区：
文献类型：
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作者：
A. Wheeler;W. Steenbeek;Martin A. Andresen
Andresen的空间点模式测试（SPPT）比较两个空间点模式定义的面积单位;它确定的空间点模式分歧的地区，并将这些地方（不）相似性聚集到一个全球性的措施。我们讨论了SPPT的局限性，并提供了两种替代方法来计算点模式的差异。在第一种方法中，我们使用的差异比例检验校正多重比较。我们展示了差异的大小是如何重要的，因为大的点模式，许多地区将被SPPT识别为统计上不同的，即使这些差异是微不足道的。第二种方法使用多项逻辑回归，可以扩展到识别连续时间内比例的差异。我们通过识别2006-2016年纽约市警察局行人停车与暴力犯罪不同的区域来展示这些方法。
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.