TY - GEN
T1 - Exploiting mobile phone data for multi-category land use classification in Africa
AU - Mao, Huina
AU - Thakur, Gautam
AU - Bhaduri, Budhendra
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - 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.
AB - 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.
KW - Big Data
KW - Mobile phone data
KW - Multi-category land use classiffication
KW - Smart cities
UR - http://www.scopus.com/inward/record.url?scp=85002323811&partnerID=8YFLogxK
U2 - 10.1145/3007540.3007549
DO - 10.1145/3007540.3007549
M3 - Conference contribution
AN - SCOPUS:85002323811
T3 - Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2016
BT - Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2016
PB - Association for Computing Machinery, Inc
T2 - 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2016
Y2 - 31 October 2016
ER -