HiSpot: an integrated and accessible python framework for spatial optimization in Urban Analytics

Spatial optimization plays a pivotal role in urban analytics, underpinning critical decisions from facility siting to logistics routing. However, the practical application of these methods is often hindered by a fragmented software ecosystem: commercial GIS platforms offer usability but lack algorithmic flexibility, while specialized mathematical solvers provide computational power but require steep learning curves and extensive ‘glue code’ for data handling. To bridge this gap, we introduce HiSpot, an open-source Python framework designed as an integrated workbench for spatial optimization. Unlike disjointed tools, HiSpot unifies the entire workflow—data processing, model formulation, solution generation, and visualization—within a cohesive, object-oriented Application Programming Interface (API). The library supports a comprehensive suite of paradigms, including Facility Location Problems (FLP), Coverage Models (MCLP/LSCP), and Location-Routing Problems (LRP), seamlessly coupling them with standard geospatial data structures. Furthermore, HiSpot addresses the scarcity of standardized testing environments by incorporating the Spatial Optimization Benchmark Dataset, which includes multi-scale instances from real-world road networks. By lowering technical barriers and standardizing the analytical pipeline, HiSpot empowers researchers and practitioners to deploy robust, reproducible spatial optimization solutions for complex urban challenges.