PySAL

Load real estate transaction data with spatial coordinates.

Construct a spatial weights matrix based on proximity.

Estimate a spatial regression model with house characteristics and neighborhood factors.

Analyze the spatial lag or spatial error coefficient to assess spatial autocorrelation.

Map the predicted property values and identify undervalued or overvalued areas.

Load disease incidence data with spatial coordinates.

Perform kernel density estimation to create a density map of disease cases.

Apply spatial clustering techniques to identify significant clusters of high incidence.

Map the clusters and visualize the spatial distribution of the disease.

Investigate potential risk factors associated with the identified clusters.

Load crime incident data with spatial coordinates.

Construct a spatial weights matrix based on street network proximity.

Calculate local indicators of spatial association (LISA) to identify crime hotspots.

Map the hotspots and visualize the spatial distribution of crime.

Implement targeted policing strategies in the identified hotspots.

Load customer location data and competitor store locations.

Perform spatial clustering analysis to identify areas with high customer density.

Construct a spatial weights matrix based on customer travel patterns.

Estimate a spatial interaction model to predict store performance based on location.

Select optimal locations for new stores based on the model predictions.

Load data on pollution sources and demographic characteristics.

Construct a spatial weights matrix based on proximity to pollution sources.

Calculate spatial autocorrelation statistics to assess the spatial distribution of pollution.

Estimate a spatial regression model to analyze the relationship between pollution and demographic factors.

Identify areas with disproportionate exposure to environmental hazards.