Modeling Spatial Dependencies for Mining Geospatial Data

Widespread use of spatial databases[24] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns[14, 17, 10, 22]. Efficient tools for extracting information from geo-spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial data sets. These organizations are spread across many domains including ecology and environment management, public safety, transportation, public health, business, travel and tourism[2, 12]. Classical data mining algorithms[1] often make assumptions (e.g. independent, identical distributions) which violate Tobler’s first law of Geography: everything is related to everything else but nearby things are more related than distant things[25]. In other words, the values of attributes of nearby spatial objects tend to systematically affect each other. In spatial statistics, an area within statistics devoted to the analysis of spatial data, this is called spatial autocorrelation[6]. Knowledge discovery techniques which ignore spatial autocorrelation typically perform poorly in the