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Published on: February 25, 2013
The use of census migration data to approximate human movement patterns across temporal scales
Amy Wesolowski1, Caroline O Buckee, Deepa K Pindolia
1Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. awesolow@andrew.cmu.edu
Plos One
|January 18, 2013
Summary
Human population census data can approximate shorter-term human movement patterns, even in low-income countries. This study validates census migration data against mobile phone movement data in Kenya.
Area of Science:
- Mobility studies
- Demography
- Data science
Background:
- Human movement is crucial for economies, services, and disease spread but is poorly quantified.
- Census migration data captures long-term relocations but misses shorter-term movements.
- Existing census data may serve as a proxy for finer-scale human mobility patterns.
Purpose of the Study:
- To assess the utility of census migration data as a proxy for shorter-term human movement patterns.
- To compare census-derived migration data with mobile phone usage data in Kenya.
- To examine movement patterns across weekly, monthly, and annual timescales.
Main Methods:
- Utilized an extensive mobile phone usage dataset for Kenya in 2009.
- Extracted inter-county movements from mobile phone data across different timescales.
- Compared extracted movement data with national census data on change of residence.
Main Results:
- Strong correlations were found in the relative ordering of county-level movements (incoming, outgoing, between-county) between the two data sources.
- Trip duration distributions from census and mobile phone data showed similarity.
- A spatial interaction model revealed consistent relationships across various movement timescales.
Conclusions:
- Census migration data demonstrates significant relationships with fine-temporal scale movement patterns.
- Census data can effectively approximate certain features of human movement across multiple timescales.
- The utility of census-derived migration data can be extended to understand shorter-term mobility.
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