research-article

Exploiting mobile phone data for multi-category land use classification in Africa

Authors:

Huina Mao,

Gautam Thakur,

Budhendra BhaduriAuthors Info & Claims

UrbanGIS '16: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

Article No.: 9, Pages 1 - 6

https://doi.org/10.1145/3007540.3007549

Published: 31 October 2016 Publication History

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Abstract

In the context of Smart Africa Initiative, we present a method to infer multiple land use in Africa. Such information is usually scarce in developing countries due to the constrained resources. Timely land use information is a critical input to smart urban planning that improves efficiency for the public to access to resources. The mobile phone usage is almost universal, which creates a valuable data source for land use inference. In this paper, we demonstrate that the temporal mobile phone call pattern and call network features can be combined to infer ten-category land use including residential, commercial-industrial/office, commercial-business/retail/leisure, high- and low- density commercial, high- and low- density residential, mixed land use areas as well as commercial and residential hubs of the city. In low income countries where land use surveys are rare, our approach create an alternative for measuring land use.

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Wang JGao CWang MZhang Y(2023)Identification of Urban Functional Areas and Urban Spatial Structure Analysis by Fusing Multi-Source Data Features: A Case Study of Zhengzhou, ChinaSustainability10.3390/su1508650515:8(6505)Online publication date: 11-Apr-2023
https://doi.org/10.3390/su15086505
Okmi MPor LAng TKu C(2023)Mobile Phone Data: A Survey of Techniques, Features, and ApplicationsSensors10.3390/s2302090823:2(908)Online publication date: 12-Jan-2023
https://doi.org/10.3390/s23020908
Li JXie XZhao BXiao XQiao JRen W(2021)Identification of Urban Functional Area by Using Multisource Geographic DataComplexity10.1155/2021/88752762021Online publication date: 1-Jan-2021
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Published In

UrbanGIS '16: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

October 2016

67 pages

ISBN:9781450345835

DOI:10.1145/3007540

Conference Chair:
Huy T. Vo

© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

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SIGSPATIAL'16

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31, 2016

California, Burlingame

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Cited By

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Wang JGao CWang MZhang Y(2023)Identification of Urban Functional Areas and Urban Spatial Structure Analysis by Fusing Multi-Source Data Features: A Case Study of Zhengzhou, ChinaSustainability10.3390/su1508650515:8(6505)Online publication date: 11-Apr-2023
https://doi.org/10.3390/su15086505
Okmi MPor LAng TKu C(2023)Mobile Phone Data: A Survey of Techniques, Features, and ApplicationsSensors10.3390/s2302090823:2(908)Online publication date: 12-Jan-2023
https://doi.org/10.3390/s23020908
Li JXie XZhao BXiao XQiao JRen W(2021)Identification of Urban Functional Area by Using Multisource Geographic DataComplexity10.1155/2021/88752762021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8875276
Moustaka VVakali AAnthopoulos L(2018)A Systematic Review for Smart City Data AnalyticsACM Computing Surveys10.1145/323956651:5(1-41)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3239566
Deng XNewsam S(2017)Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land UseProceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics10.1145/3152178.3152182(1-8)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3152178.3152182

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