Journal: Scientific Data

Article Title: Mapping internal connectivity through human migration in malaria endemic countries

doi: 10.1038/sdata.2016.66

Figure Lengend Snippet: Summary information about the edited IPUMSI 5-year internal migration microdata and the administrative unit datasets used to estimate the 5-year (2005–2010) internal human migration flows for every malaria endemic country

Article Snippet: It consists of 1) a Microsoft Visual Studio 2010 user interface allowing users to upload the IPUMSI census microdata to a PostgreSQL database; 2) example SQL queries that were used to match the spatial detail of the IPUMSI migration data to spatial detail of the corresponding administrative unit dataset and to identify internal migrants within the IPUMSI census samples 3) an ArcToolbox geoprocessing tool that assigns a unique ID to each administrative unit and calculates the corresponding total population and proportion of urban population; 4) a Python /ArcPy script that creates two tables, one containing spatial contiguity information between each pair of administrative units (‘contiguity.csv’) and another one containing the ISO country code, the continent in which the country is located, the distance between each pair of administrative units, their total population, proportion of urban population, surface area, and the geographic coordinates (GCS WGS84) of their centroid (‘distance.csv’); 5) two R scripts, one for the IPUMSI countries used to query the IPUMSI migration microdata loaded in the PostgreSQL database, calculate the response variable, and join the query result with the two output tables of the python script, and another one for the non-IPUMSI countries used just to join together the two output tables of the python script; and 6) an R script that performs the model selection and estimates the 5-year (2005–2010) internal human migration flows between subnational administrative units.

Techniques: Migration