Developing Capacitated p-median Location-allocation Model in the spopt Library to Allow UCL Student Teacher Placements Using Public Transport

Bearman, N., Xu, R., Roddy, P., Gaboard, J. D., Zhao, Q. , Chen, H. and Wolf, L. (2023) Developing Capacitated p-median Location-allocation Model in the spopt Library to Allow UCL Student Teacher Placements Using Public Transport. In: e 26th AGILE Conference on Geographic Information Science (AGILE 2023), Delft, Netherlands, 13-16 June 2023, (doi: 10.5194/agile-giss-4-20-2023)

Abstract

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Qunshan and Xu, Miss Rongbo
• Authors: Bearman, N., Xu, R., Roddy, P., Gaboard, J. D., Zhao, Q., Chen, H., and Wolf, L.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Copyright Holders: Copyright © Author(s) 2023
• First Published: First published in AGILE: GIScience Series, 4, 20, 2023
• Publisher Policy: Reproduced under a Creative Commons license

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
190698		Urban Big Data Research Centre	Nick Bailey	Economic and Social Research Council (ESRC)	ES/L011921/1	S&PS - Urban Big Data
304042		UBDC Centre Transition	Nick Bailey	Economic and Social Research Council (ESRC)	ES/S007105/1	S&PS - Administration