The size distribution of ‘cities’ delineated with a network theory-based method and mobile phone GPS data

Shota Fujishima; Naoya Fujiwara; Yuki Akiyama; Ryosuke Shibasaki; Ritsu Sakuramachi

doi:10.1111/ijet.12254

The size distribution of ‘cities’ delineated with a network theory-based method and mobile phone GPS data

Shota Fujishima, Naoya Fujiwara, Yuki Akiyama, Ryosuke Shibasaki, Ritsu Sakuramachi

INF - Human-Social Information Sciences

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

We delineate “cities” independent of administrative boundaries in Japan by using a network theory-based method and GPS-based human mobility data. We divide the country into approximately 1 × 1 km² cells and detect the partition of cells that is optimal from the perspective of information theory. The resulting groups of cells are specified as cities. We find that the combination of two lognormal distributions better fits the city-size distribution than a distribution with a Pareto upper tail. Moreover, we show that a jump diffusion process is the stochastic process of the city population underlying such a distribution.

Original language	English
Pages (from-to)	38-50
Number of pages	13
Journal	International Journal of Economic Theory
Volume	16
Issue number	1
DOIs	https://doi.org/10.1111/ijet.12254
Publication status	Published - 2020 Mar 1

Keywords

GPS data
city-size distribution
community detection
jump diffusion process
mixture of distributions
power law

ASJC Scopus subject areas

Economics and Econometrics

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10.1111/ijet.12254

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title = "The size distribution of {\textquoteleft}cities{\textquoteright} delineated with a network theory-based method and mobile phone GPS data",

abstract = "We delineate “cities” independent of administrative boundaries in Japan by using a network theory-based method and GPS-based human mobility data. We divide the country into approximately 1 × 1 km2 cells and detect the partition of cells that is optimal from the perspective of information theory. The resulting groups of cells are specified as cities. We find that the combination of two lognormal distributions better fits the city-size distribution than a distribution with a Pareto upper tail. Moreover, we show that a jump diffusion process is the stochastic process of the city population underlying such a distribution.",

keywords = "GPS data, city-size distribution, community detection, jump diffusion process, mixture of distributions, power law",

author = "Shota Fujishima and Naoya Fujiwara and Yuki Akiyama and Ryosuke Shibasaki and Ritsu Sakuramachi",

note = "Funding Information: We are grateful to Hiroyasu Inoue, Takaaki Aoki, Daisuke Oyama, and anonymous referees for helpful comments and discussions. We also would like to thank the participants at the European Meeting of the 7th Urban Economics Association and the seminar audience at the University of Tokyo and Kagawa University for useful comments. Shota Fujishima acknowledges financial support from the Kikawada Foundation and the Japan Society for the Promotion of Science (KAKENHI Grant Number 18K12776). Any remaining errors are our own. Publisher Copyright: {\textcopyright} 2020 IAET",

year = "2020",

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doi = "10.1111/ijet.12254",

language = "English",

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AU - Akiyama, Yuki

AU - Shibasaki, Ryosuke

AU - Sakuramachi, Ritsu

N1 - Funding Information: We are grateful to Hiroyasu Inoue, Takaaki Aoki, Daisuke Oyama, and anonymous referees for helpful comments and discussions. We also would like to thank the participants at the European Meeting of the 7th Urban Economics Association and the seminar audience at the University of Tokyo and Kagawa University for useful comments. Shota Fujishima acknowledges financial support from the Kikawada Foundation and the Japan Society for the Promotion of Science (KAKENHI Grant Number 18K12776). Any remaining errors are our own. Publisher Copyright: © 2020 IAET

PY - 2020/3/1

Y1 - 2020/3/1

N2 - We delineate “cities” independent of administrative boundaries in Japan by using a network theory-based method and GPS-based human mobility data. We divide the country into approximately 1 × 1 km2 cells and detect the partition of cells that is optimal from the perspective of information theory. The resulting groups of cells are specified as cities. We find that the combination of two lognormal distributions better fits the city-size distribution than a distribution with a Pareto upper tail. Moreover, we show that a jump diffusion process is the stochastic process of the city population underlying such a distribution.

AB - We delineate “cities” independent of administrative boundaries in Japan by using a network theory-based method and GPS-based human mobility data. We divide the country into approximately 1 × 1 km2 cells and detect the partition of cells that is optimal from the perspective of information theory. The resulting groups of cells are specified as cities. We find that the combination of two lognormal distributions better fits the city-size distribution than a distribution with a Pareto upper tail. Moreover, we show that a jump diffusion process is the stochastic process of the city population underlying such a distribution.

KW - GPS data

KW - city-size distribution

KW - community detection

KW - jump diffusion process

KW - mixture of distributions

KW - power law

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The size distribution of ‘cities’ delineated with a network theory-based method and mobile phone GPS data

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Keywords

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