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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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 tencategory 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 com- mercial 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.

Original languageEnglish
Title of host publicationProceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2016
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450345835
DOIs
StatePublished - Oct 31 2016
Event2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2016 - San Francisco, United States
Duration: Oct 31 2016 → …

Publication series

NameProceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2016

Conference

Conference2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2016
Country/TerritoryUnited States
CitySan Francisco
Period10/31/16 → …

Keywords

  • Big Data
  • Mobile phone data
  • Multi-category land use classiffication
  • Smart cities

Fingerprint

Dive into the research topics of 'Exploiting mobile phone data for multi-category land use classification in Africa'. Together they form a unique fingerprint.

Cite this