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An Open Data-Driven Approach to Optimising Healthcare Facility Locations Using Python
A tutorial in Python with an open data stack
This work was co-authored with Prof Joaquim Gromicho and Kai Kaiser. The author(s) are responsible for all errors and omissions.
According to research published in 2020 on global maps of travel time to healthcare facilities, 43.3% (3.16 billion people) cannot reach a healthcare facility on foot within one hour.
Accurately calculating travel time to healthcare facilities is fundamental in assessing healthcare accessibility, particularly in regions where barriers to access can significantly impact public health outcomes. These calculations are vital for resource allocation, healthcare utilization, equitable healthcare access, and strategic planning for future facilities. However, to calculate this, a lot of data crunching is needed, including the location of hospitals, population distribution, and travel time calculations based on road network data such as OpenStreetMaps or APIs such as Google or Mapbox.
Geographic variability, such as differing terrains, road conditions, and weather, also contributes to the calculation of travel times. The availability and type of transportation also restrict access to health facilities, with many rural areas lacking reliable public or personal transport options. Furthermore, the accuracy and availability of geocoded data of all the hospitals are often not available, particularly in developing countries, leading to less precise estimations of access.
This blog leverages the power of open-source data and tools to address the challenge of calculating physical access in countries and administrative regions, especially where population censuses are infrequent and road network and health facility data are not regularly updated.
This is demonstrated in Timor-Leste, where healthcare access is hindered by systemic inefficiencies and…
