Resources
Peer Reviewed Publication Sources
- Worldpop - publications
- Flowminder - publications
- Facebook Data for Good
- Data-Intensive Development Lab - publications
- BayesPop Project - papers
- DHS Spatial Analysis Reports
Data
- Development Data Exchanges
- Remotely sensed environmental data
- Political boundaries and administrative subdivisions
- Population estimates
- WorldPop - Population
- Center for International Earth Science Information Network / Facebook Data for Good - High Resolution Settlement Layer
- Socioeconomic Data and Applications Center (SEDAC) - Gridded Population of the World (GPW), v4
- Oak Ridge National Laboratory (ORNL) - LandScan Global
- POPGRID Data Collaborative
- Demographic estimates
- Primary census and household survey data
- Built environment including settlements, facilities and infrastructures
- Population movement data
Topics
-
Data Science to understand Human Development as a complex system
- Don’t forget people in the use of big data for development (Joshua Blumenstock)
- The Best Stats You've Never Seen (Hans Rosling, Gapminder)
- Development as Freedom (Amartya Sen, 1999)
- Development and Complexity (Owen Barder, 2012)
- Big Data, New Epistemologies and Paradigm Shifts (Rob Kitchin)
- Scale (Geoff West, 2018)
-
Population and development data descriptions
- WorldPop, open data for spatial demography (Tatem)
- High resolution global gridded data for use in population studies (Lloyd, Sorichetta & Tatem)
- Spatially disaggregated population estimates in the absence of national population and housing census data (Wardrop et al.)
- Mapping road network communities for guiding disease surveillance and control strategies (Strano, Viana, Sorichetta & Tatem)
- A spatial database of health facilities managed by the public health sector in sub Saharan Africa (Maina et al.)
Methods: application
- Dasymmetric Allocation
- Random Forest Model
- Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data (Stevens, Gaughan, Linard & Tatem)
- Examining the correlates and drivers of human population distributions across low- and middle-income countries (Nieves et al.)
- Gridded Population Maps Informed by Different Built Settlement Products (Reed et al.)
- Random Forest Model
- Land Use Classification
- Random Forest Model
- Spatial Interpolation
- Hierarchical Bayesian Model
- Guidance for Use of The DHS Program Modeled Map Surfaces
- Creating Spatial Interpolation Surfaces with DHS Data
- Fine resolution mapping of population age-structures for health and development applications (Alegana et al.)
- High Resolution Age-Structured Mapping of Childhood Vaccination Coverage in Low and Middle Income Countries (Utazi et al.)
- Mapping vaccination coverage to explore the effects of delivery mechanisms and inform vaccination strategies (Utazi et al.)
- Spline Interpolation
- Comparison of Spatial Interpolation Methods to Create High-Resolution Poverty Maps for Low- And Middle-Income Countries (Wong, Brady, Campbell & Benova)
- Artificial Neural Networks
- Hierarchical Bayesian Model
- Spatial Interaction Processes
- Gravity Type Models
- The Use of Census Migration Data to Approximate Human Movement Patterns across Temporal Scales (Wesolowski et al.)
- Modeling internal migration flows in sub-Saharan Africa using census microdata (Garcia, Pindolia, Lopiano & Tatem)
- Mapping internal connectivity through human migration in malaria endemic countries (Sorichetta et al.)
- Exploring the use of mobile phone data for national migration statistics (Lai et al.)
- Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings (Kraemer et al.)
- Impedance Model
- Gravity Type Models