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Clusters in irregular areas and lattices

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Abstract Geographic areas of different sizes and shapes of polygons that represent counts or rate data are often encountered in social, economic, health, and other information. Often political or census boundaries are used to define these areas because the information is available only for those geographies. Therefore, these types of boundaries are frequently used to define neighborhoods in spatial analyses using geographic information systems and related approaches such as multilevel models. When point data can be geocoded, it is possible to examine the impact of polygon shape on spatial statistical properties, such as clustering. We utilized point data (alcohol outlets) to examine the issue of polygon shape and size on visualization and statistical properties. The point data were allocated to regular lattices (hexagons and squares) and census areas for zip‐code tabulation areas and tracts. The number of units in the lattices was set to be similar to the number of tract and zip‐code areas. A spatial clustering statistic and visualization were used to assess the impact of polygon shape for zip‐ and tract‐sized units. Results showed substantial similarities and notable differences across shape and size. The specific circumstances of a spatial analysis that aggregates points to polygons will determine the size and shape of the areal units to be used. The irregular polygons of census units may reflect underlying characteristics that could be missed by large regular lattices. Future research to examine the potential for using a combination of irregular polygons and regular lattices would be useful. WIREs Comp Stat 2012, 4:67–74. doi: 10.1002/wics.196 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis

Clusters for zip‐code‐sized polygons.

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Clusters for tract‐sized polygons.

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Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis
Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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