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Spatial data mining

Spatial data mining definition

Spatial data mining extracts patterns and insights from location-based data. It employs methods like clustering or classification to uncover hidden correlations. Such an approach is crucial in fields like urban planning and environmental analysis.

See also: data mining

Use cases of spatial data mining

  • Urban planning. Spatial data mining is used to analyze traffic patterns and population density. This technique assists in designing more efficient public transportation routes.
  • Environmental science. Scientists employ spatial data mining to track changes in land use and vegetation cover, which aids in assessing environmental impacts.
  • Retail. Businesses use spatial data mining to understand customer distribution and buying habits. This process helps to optimize store locations and targeted marketing strategies.
  • Public health. Such a data mining technique helps analyze the spread of diseases by identifying geographical patterns in infection rates.
  • Real estate. Companies leverage spatial data mining to evaluate property values and market trends based on location-specific factors.