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A Weather-Aware Framework for Population Mobility Modelling (Short Paper)

Authors Vanessa Brum-Bastos , Kamil Smolak , Witold Rohm , Katarzyna Sila-Nowicka

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Author Details

Vanessa Brum-Bastos
  • Wroclaw University of Environmental and Life Sciences, Poland
  • University of Canterbury, Christchurch, New Zealand
Kamil Smolak
  • Wroclaw University of Environmental and Life Sciences, Poland
Witold Rohm
  • Wroclaw University of Environmental and Life Sciences, Poland
Katarzyna Sila-Nowicka
  • The University of Auckland, New Zealand
  • Wroclaw University of Environmental and Life Sciences, Poland
  • University of Glasgow, UK
  • www.science.auckland.ac.nz/people/profile/ksil287

Funding

  • Brum-Bastos, Vanessa: The Polish National Agency for Academic Exchange (NAWA) Ulam Fellowship PPN/ULM/2020/1/00207/DEC/1 and The Royal Society International Exchanges Grant IES/R1/201127.
  • Smolak, Kamil: The research was financed under the National Science Centre, Poland research grant "Explanation and mitigation of the bias in human mobility predictions" no. 2021/41/N/HS4/03084.
  • Sila-Nowicka, Katarzyna: Supported by the Marsden Fund Council from Government funding, managed by Royal Society Te Apārangi - UOA2037. Also supported by The Royal Society International Exchanges Grant IES/R1/201127.

Cite AsGet BibTex

Vanessa Brum-Bastos, Kamil Smolak, Witold Rohm, and Katarzyna Sila-Nowicka. A Weather-Aware Framework for Population Mobility Modelling (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 17:1-17:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.COSIT.2022.17

Abstract

The widespread availability of GPS-enabled mobile devices has contributed towards an unprecedented volume of data on human movement. Human mobility data are the key input for developing accurate mobility models that can support decision-making in, for example, urban planning, transportation planning and disease spread. However, the increasing geoprivacy concerns have been limiting the use of and access to such data. For this reason, the WHO-WHERE-WHEN (3W) model, a privacy-protective model for generating synthetic mobility data, has been developed. However, human mobility is affected by multiple factors that must be accounted for to produce synthetic mobility trajectories that accurately simulate the fluctuations of population in a study area. The 3W model already considers four main factors affecting human mobility: size and shape of activity spaces, circadian rhythm, and home and work locations. Yet, meteorological factors are known to affect human mobility patterns but, to our knowledge, there is not a model that accounts for weather conditions. In this paper, we propose a theoretical framework to extend the 3W model to a 4W model: WHO-WHERE-WHEN-WEATHER. We hypothesise that accounting for weather conditions in human mobility predictions will increase the overall accuracy of predicted mobility patterns.

Subject Classification

ACM Subject Classification
  • Applied computing
  • Human-centered computing
Keywords
  • movement analytics
  • human movement
  • mobility models
  • context-awareness

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