Predhumeau, M. and Manley, E. orcid.org/0000-0002-8904-0513 (2023) Building an Environment for Spatial Modelling of Canadian Cities Using Open Data: A Replicable Workflow Applied to Winnipeg. In: Proceedings of The 18th International Conference on Computational Urban Planning and Urban Management. The 18th International Conference on Computational Urban Planning and Urban Management (CUPUM), 20-22 Jun 2023, Montreal, Canada. Center for Open Science
Abstract
Microscopic approaches, such as agent-based modelling, are increasingly used by urban planners to model cities. The development of accurate microscopic models is facilitated by the increasing availability of real-world open data, essential for agent-based models to be widely used and replicated. Whereas open data is gaining popularity in Canada, only few agent-based models have been built for urban planning using open data. This paper presents a workflow for building an environment for agent-based modelling of Canadian cities based exclusively on open data and tools. Combining data from Census, OpenStreetMap, and local portals, we propose a synthetic population and an environment model that form a basis for agent-based modelling of any Canadian city. The approach is implemented and validated on Winnipeg. Results indicate that the model offers a valuable basis for urban analysis. The proposed workflow has the potential to support human-centred urban planning and to facilitate replicable modelling in Canada.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Synthetic Population, Spatial Microsimulation, Public Data, Human-Centred, Urban Planning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/T012587/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Sep 2024 13:28 |
Last Modified: | 19 Dec 2024 14:30 |
Published Version: | https://osf.io/5zhp2 |
Status: | Published |
Publisher: | Center for Open Science |
Identification Number: | 10.17605/OSF.IO/6YR5V |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:217666 |