Localized Outlying and Boundary Data Detection in Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
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A GIS provides a powerful collection of tools for the management, visualization and analysis of spatial data. These tools can be even more powerful when they are integrated with statistical methods for spatial data analysis and many GIS users are requesting this integration. The Geostatistical Analyst extension to ArcGIS was developed to integrate statistical methods with GIS tools for mapping and modeling spatially continuous data such as temperature or pollution. However, many GIS applications involve data that are aggregated over geographic regions and the analysis of this type of spatial data poses additional challenges. In this paper, we illustrate several different analytical goals that commonly arise in applications based on regional data. Many of these require a measure of local spatial dependence and this is commonly based on Moran's I index of spatial association. However, as we describe in this paper, other measures that more explicitly take into account the aggregated nature of the data may be preferred. Using county-level crime data in California we show how many different statistical methods for regional data analysis can be implemented within a GIS to provide a powerful set of interactive, analytical tools uniquely suited to the goals of regional analysis.