Exploring Multivariate Geospatial Data Through Interactive Feature-Space Mapping and Visualization

Abstract. Geospatial objects are embedded in geographic space and simultaneously described by multiple thematic attributes. Conventional maps preserve spatial context but offer limited support for exploring thematic relations and multivariate similarity structures beyond geographic proximity. We present an interactive feature-space visualization approach that maps each geospatial object to a multidimensional feature vector and represents it as a particle in a two-dimensional reference plane. Particle positions are determined by thematic, analytical, and spatial magnets whose attraction strength depends on selected feature values or spatial relations. Starting from barycentric target positions, particles are iteratively arranged with collision-aware placement, revealing thematic neighborhoods, outliers, and latent groups independently of the original geographic layout. By configuring magnets interactively, analysts can formulate and test hypotheses about geospatial datasets. We demonstrate the approach using an urban tree inventory derived from mobile mapping LiDAR data and show how it supports the analysis of structural relations, isolated trees, similarity patterns, and terrain-dependent distributions. The results indicate that interactive feature-space visualization provides a useful complementary perspective for exploratory geovisualization and visual analytics.