Salat, H., Carlino, D., Benitez-Paez, F. et al. (3 more authors) (2023) Synthetic population Catalyst: A micro-simulated population of England with circadian activities. Environment and Planning B: Urban Analytics and City Science, 50 (8). pp. 2309-2316. ISSN 2399-8083
Abstract
The Synthetic Population Catalyst (SPC) is an open-source tool for the simulation of populations. Building on previous efforts, synthetic populations can be created for any area in England, from a small geographical unit to the entire country, and linked to geolocalised daily activities. In contrast to most transport models, the output is focussed on the population itself and the way people socially interact together, rather than on a precise modelling of the volume of transport trips from one area to another. SPC is therefore particularly well suited, for example, to study the spread of a pandemic within a population. Other applications include identifying segregation patterns and potential causes of inequality of opportunity amongst individuals. It is fast, thanks to its Rust codebase. The outputs for each lieutenancy area in England are directly available without having to run the code.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
Keywords: | Population micro-simulation, social interactions, transport flows, synthetic data |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Oct 2024 12:11 |
Last Modified: | 23 Oct 2024 12:11 |
Published Version: | https://journals.sagepub.com/doi/10.1177/239980832... |
Status: | Published |
Publisher: | SAGE |
Identification Number: | 10.1177/23998083231203066 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218733 |