Cartography and Geographic Information Science · 2001 · 37 citations · 0 references
ComputingGeographyGeoinformaticsData MiningSpatial AnalysisSpatial Data AnalysisGeodemographic AnalysisGeovisual AnalyticsSpatial Data AnalyticsSpatial Data MiningSpatial Data VisualizationUrban Spatial AnalysisDirect ManipulationGeographic Data VisualizationGeospatial Data MiningInteractive MapsInteractive Thematic MappingInteractive Thematic MapsInteractive Urban MappingSpatial Data Exploration
The paper introduces new methods for analyzing geo‑referenced statistical data. The methods integrate interactive thematic mapping (Descartes) with data‑mining tools (Kepler), enabling a steady visual–statistical interaction for exploring geo‑referenced data. The approach demonstrates that geography‑based classifications of urban districts can be linked to thematic characteristics using classification trees, attribute weighting, and subgroup discovery.
We present new methods for analyzing geo-referenced statistical data. These methods combine visualization and direct manipulation techniques of exploratory data analysis and algorithms for data mining. The methods have been implemented by integrating two hitherto separate software tools: Descartes for interactive thematic mapping, and the data mining toolbox Kepler. In using these tools, data analysis may proceed as a steady interaction between visual inspiration and insights gained from mathematical–statistical calculations. After introducing the various components of the methods and tools, the paper guides the reader through in-depth examples of using the tools in the context of analysis of urban demographic data. In particular, it is shown how geography-based classifications of urban districts can be related to available thematic characteristics by applying the data mining algorithms classification tree derivation, attribute weighting, and subgroup discovery.