Burkina Faso mobility data with some noise

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.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.gitAdministrative 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