Prediction of Spatiotemporal Distributions of Transient Urban Populations with Statistics Gathered by Cell Phones

Toshihiro Osaragi, Ryo Hayasaka

2020

Abstract

There is a growing demand for data that facilitate highly accurate understanding of the spatiotemporal distribution of both moving and static occupants in urban areas. Currently, a large amount of population data are available, however none of the data provide an accurate understanding of the numbers and departure/arrival locations of moving people using detailed units of space and time. In this paper, after evaluating the advantages and disadvantages of existing population statistics, including Mobile Spatial Statistics, Konzatsu-tokei®, and Person Trip survey data, we propose a method based on maximum likelihood method is investigated for using their strengths to best advantage and compensating for weaknesses. The proposed method is then validated by comparing with another flow data, which featured spatiotemporal data including departure/arrival locations, and demonstrate that the present procedure provides accurate estimates for population flows. This study makes it possible to analyse urban regions from new and never-before employed points of view by identifying the number of transient occupants and their travel directions at any time on high level of detail.

Paper Citation

in Harvard Style

Osaragi T. and Hayasaka R. (2020). Prediction of Spatiotemporal Distributions of Transient Urban Populations with Statistics Gathered by Cell Phones.In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-425-1, pages 33-44. DOI: 10.5220/0009325700330044

in Bibtex Style

@conference{gistam20,
author={Toshihiro Osaragi and Ryo Hayasaka},
title={Prediction of Spatiotemporal Distributions of Transient Urban Populations with Statistics Gathered by Cell Phones},
booktitle={Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2020},
pages={33-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009325700330044},
isbn={978-989-758-425-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Prediction of Spatiotemporal Distributions of Transient Urban Populations with Statistics Gathered by Cell Phones
SN - 978-989-758-425-1
AU - Osaragi T.
AU - Hayasaka R.
PY - 2020
SP - 33
EP - 44
DO - 10.5220/0009325700330044