Spatial-Statistics

A recent addition to the local statistical modeling framework is the implementation of the Multiscale Geographically Weighted Regression (MGWR) model, a multiscale extension to the widely used approach for modeling process spatial heterogeneity, Geographically Weighted Regression (GWR).

The concept of ‘spatial scale’, or simply ‘scale’ is implicit in any discussion of global versus local models. The raison d’etre of local models is that a global scale (where here ‘global’ simply refers to all locations within a predefined area of interest) might be the incorrect scale at which to undertake any analysis of spatial processes; the alternative being a local scale (where here ‘local’ refers to individual locations).

Studies of spatially varying parameter estimates obtained in the calibration of various types of local statistical models are commonplace. The variation in such estimates is typically explained in terms of spatially varying processes.

Real estate market analysis has long been an active area of inquiry and one that reveals much about people’s preferences regarding housing attributes. It is well-known that house prices tend to exhibit strong spatial dependency and that they vary across space due to differences in structural and neighborhood characteristics.

Space-time Autocorrelation using NYC taxi data (2015) This project explores spatial and temporal autocorrelation. Open-source yellow taxi trip data for the year 2015 was aggregated to a 50m * 50m grid across NYC.

My first contribution to the CARTO environment was to develop materials for the Carto Academy depicting how to implement and interpret spatial tools on the web GIS platform. This has since been deprecated to accommodate for changes in the version of the current software.