Eighty percent of people coping with extreme poverty reside in rural areas, where basic transportation access is often lacking. This limits their ability to reach critical destinations such as hospitals, schools, and markets. Although governments and the development sector recognize the importance of rural infrastructure, data inequity remains a critical barrier, with even the most fundamental data needed to identify and address transport barriers frequently unavailable. This lack of data presents a major roadblock to scaled planning and investment.

Bridges to Prosperity (B2P) is a nonprofit organization that partners with governments and remote communities to create access to essential health care, education, and economic opportunities by facilitating the construction of trail bridges and other rural transportation infrastructure. A key aspect of understanding the impact of this work is accurately estimating the number of people affected by new infrastructure. These estimates drive investment in the rural access sector and inform stakeholders where infrastructure is needed. However, B2P faces challenges in quantifying impacted populations due to the rural, data-scarce environments it works in and the lack of fixed catchment areas for trail bridges. B2P’s ongoing efforts to measure impacted populations include developing a randomized control trial (RCT), conducting pre-construction needs assessments in nearby villages, extracting estimates from the WorldPop gridded population dataset, and placing cameras to capture volume and direction of traffic across bridges.

This presentation introduces B2P’s open geospatial programming methodology for estimating population impact at scale. Using open-source Python GIS packages such as GeoPandas and Rasterio and piloting GeoJupyter in JupyterLab, B2P compared its community-level needs assessment data with the 2022 Rwandan Census and global gridded population datasets, including WorldPop, GPW, GRUMP, LandScan, and GHS-POP. These comparisons have enabled B2P to confidently generate population estimates based on catchment areas defined in collaboration with the RCT research team. These estimates also feed into more complex impact models built from open data which paint a picture of where rural transportation solutions are most needed and how communities stand to benefit from trail bridges in the future.