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DOE OSTI2022

spopt: a python package for solving spatial optimization problems in PySAL

Spatial optimization is a major spatial analytical tool in management and planning, the significance of which cannot be overstated. Spatial optimization models play an important role in designing and managing effective and efficient service systems such as transportation, education, public health, environmental protection, and commercial investment among others. To this end, spopt (spatial optimization) is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions (Feng et al., 2021). Spopt is a submodule in the open-source spatial analysis library PySAL (Python Spatial Analysis Library) founded by Dr. Sergio J. Rey and Dr. Luc Anselin in 2005 (Rey et al., 2015, 2021; Rey & Anselin, 2007). The goal of developing spopt is to provide management and decision-making support to all relevant practitioners and to further promote the appropriate and meaningful application of spatial optimization models in practice.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Developing capacitated p-median location-allocation model in the spopt library to allow UCL student teacher placements using public transport

Location-allocation is a key tool within the GIS and network analysis toolbox. In this paper we discuss the real world application of a location-allocation case study (approx 800 students, 500 schools) from UCL using public transport. The use of public transportation is key for this case study, as many location-allocation approaches only make use of drive-time or walking-time distances, but the location of UCL in Greater London, UK makes the inclusion of public transport vital for this case study. The location-allocation is implemented as a capacitated p-median location-allocation model, using the spopt library, part of the Python Spatial Analysis Library (PySAL). The capacitated variation of the p-median location-allocation problem is a new addition to the spopt library, which this work will present. The results from the initial version of the capacitated p-median location-allocation problem has shown a marked improvement on public transport travel time, with public transport travel time reduced by 891 minutes overall for an initial sample of 93 students (9.58 minutes per student). Results will be presented below and plans for further improvement shared.

Bearman, Nick↗

DOE OSTI2025

Location-allocation and public transit: An update on UCL student teacher placements

Location-allocation is a key application of GIS, with many varied applications. We present further development of a location-allocation tool for a real-world case study with UCL using public transport. The location of UCL in Greater London (UK) means that the inclusion of public transport is vital for this case study. The location-allocation is implemented as a capacitated p-median location-allocation model, using spopt, a library in the Python Spatial Analysis Library (PySAL) ecosystem. The initial results of this work are promising, with calculation times reduced by up to 60%, and the majority of students allocated shorter journey times, with further testing ongoing.

Bearman, Nick [University College London (UCL), UK↗