DOI: 10.5061/dryad.fn2z34tt6
title: Burkina Faso mobility data with some noise

description: Human mobility is a core component of human behavior and its
 quantification is critical for understanding its impact on infectious
 disease transmission, traffic forecasting, access to resources and care,
 intervention strategies, and migratory flows. When mobility data are
 limited, spatial interaction models have been widely used to estimate
 human travel, but have not been extensively validated in low- and
 middle-income settings. Geographic, sociodemographic, and infrastructure
 differences may impact the ability for models to capture these patterns,
 particularly in rural settings. Here, we analyzed mobility patterns
 inferred from mobile phone data in four Sub-Saharan African countries to
 investigate the ability for variants on gravity and radiation models to
 estimate travel. Adjusting the gravity model such that parameters were fit
 to different trip types, including travel between more or less populated
 areas and/or different regions, improved model fit in all four countries.
 This suggests that alternative models may be more useful in these settings
 and better able to capture the range of mobility patterns observed.
description: Administrative names and codes were assigned based on the
 shapefiles available at https://www.diva-gis.org/gdata. 
 Average trips and variance were determined by aggregated call
 data records (CDRs). Anonymized CDRs were provided by the mobile phone
 provider. Briefly, CDRs were first aggregated to tower locations and then
 to districts for each country. A similar method described by Zu
 Erbach-Schoenberg et al. (Popul. Health Metr. 2016) was used to assign
 cell towers to districts. Briefly, if a cell tower’s coverage zone fell
 squarely within one district, all CDRs associated with that tower were
 assigned to that district. If the coverage zone spanned more than one
 district, the number of CDRs assigned to each district was split according
 to the area of overlap between the coverage zone and districts. We only
 considered travel that crossed district boundaries, not local movement
 within the district. The average number of total monthly trips taken
 between each origin and destination was calculated. Since our data
 agreement does not allow for sharing the raw data, we are posting the
 aggregated trips that were scaled by with some multiplicative noise
 (normal distribution with mean = 1, variance = 0.5). 
 The trip distance was calculated as the haversine distance
 between centroids of districts. WorldPop population
 data for each country were analyzed as people per pixel for each district
 (https://www.worldpop.org/). WorldPop gridded building
 pattern datasets were used to categorize grid cells of each district as
 urban or rural as described elsewhere (Dooley, C. A., Boo, G., Leasure,
 D.R. and Tatem, 2020) using QGIS v3.6. Districts with more or less than
 50% urban grid cells were categorized as urban and rural,
 respectively. Statistical and spatial analysis was done
 in R v3.6.3.
description: This dataset is called by the script "Modeling Rural
 Mobility in Burkina Faso.Rmd", which is available on
 Github: https://github.com/hrmeredith12/Rural-mobility-models.git