Applying Association Rules and Co-location Techniques on Geospatial Web Services

Abstract

Most contemporary GIS have only very basic spatial analysis and data mining functionality and many are confined to analysis that involves comparing maps and descriptive statistical displays like histograms or pie charts. Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of data integration and knowledge management applications. Geospatial data mining describes the combination of two key market intelligence software tools: Geographical Information Systems and Data Mining Systems.

This research aims to develop a Spatial Data Mining web service it uses rule association techniques and correlation methods to explore results of huge amounts of data generated from crises management integrated applications developed. It integrates between traffic systems, medical services systems, civil defense and state of the art Geographic Information Systems and Data Mining Systems functionality in an open, highly extensible, internet-enabled plug-in architecture. The Interoperability of geospatial data previously focus just on data formats and standards. The recent popularity and adoption of the Internet and Web Services has provided a new means of interoperability for geospatial information not just for exchanging data but for analyzing these data during exchange. An integrated, user friendly Spatial Data Mining System available on the internet via a web service offers exciting new possibilities for spatial decision making and geographical research to a wide range of potential users.

Keywords: Spatial Data Mining, Rule Association, Co-location, Web Services, Geospatial Data