Tanzania - Accessibility Indicators

This dataset provides insights into spatial accessibility to healthcare and
education services across Tanzania. It has been created using free and
open tools such as [openrouteservice](https://openrouteservice.org/) and open
data sources, primarily [OpenStreetMap](https://www.openstreetmap.org/) (OSM).


To assess accessibility to education and healthcare, we use travel-time 
isochrones—polygons representing areas reachable within a given time or distance by 
car. We overlay these isochrones with [WorldPop](https://www.worldpop.org/) population 
data, which provides 100m-resolution estimates. This allows us to calculate the 
population within time intervals from 10 to 120 minutes away from hospital services and 
distance intervals from 5 to 50 km away from schools. The unit of analysis is defined 
by [geoboundaries](https://www.geoboundaries.org/) country borders, and where available 
we also summarise results at finer administrative levels (ADM 1–4).


Data Structure:

- **name**: Region or country name.
- **iso**: ISO3 country code.
- **id**: Unique identifier for the administrative unit.
- **country**: ISO3 country code.
- **admin_level**: Administrative level of the unit.
- **category**: Service category — `education`, `hospitals` or
  `primary_healthcare`.
- **range_type**: Method used for the catchment zone — `distance` or `time`.
- **range**: Distance (in meters) or Time away (in seconds) from schools used
  to generate the polygon.
- **population**: Total population within the specified range.
- **school_age_population**: Number of school-age individuals within the range.
- **school_age_population_share**: Cumulative percentage of school-age
  population.
- **school_age_population_interval**: Incremental school-age population added
  in the current distance band.
- **school_age_population_interval_share**: Proportion of new school-age
  population in the current interval.
- **population_share**: Cumulative percentage of total population.
- **population_interval**: Incremental population added in the current distance
  band.
- **population_interval_share**: Share of the total population represented by
  the current interval.

This dataset is one of many [HeiGIT exports on HDX](https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology).
See the [HeiGIT](https://heigit.org/) website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out
to us and tell us about your research at
[communications@heigit.org](mailto:communications@heigit.org) – we would be
happy to amplify your work.

References:

- [Geldsetzer, P., Reinmuth, M., Ouma, P. O., Lautenbach, S. et al. (2020)](https://www.thelancet.com/journals/lanhl/article/PIIS2666-7568(20)30010-6/fulltext)
- [Petricola, S., Reinmuth, M., Lautenbach, S. et al. (2022)](https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-022-00315-2)
- [Klipper, I. G., Zipf, A., and Lautenbach, S. (2021)](https://agile-giss.copernicus.org/articles/2/4/2021/)
- [Ruiz Sánchez, R., Reinmuth, M., Albornoz, C., Lautenbach, S., and Zipf, A. (2025)](https://agile-giss.copernicus.org/articles/6/10/2025/)

Further Information:

- [Open Access Lens](https://giscience.github.io/open-access-lens/#/)

**Limitations**:

* **OSM Completeness**: This analysis relies on OpenStreetMap (OSM) data. While OSM is
the most complete open map of the world, data quality varies significantly by region.
In areas with unmapped roads or facilities, accessibility may be underestimated.

* **Population Estimates**: Population counts are derived from WorldPop top-down
estimates (constrained). These are statistical models based on census projections and
satellite imagery, not direct census counts, and may contain inaccuracies at the local
pixel level.

* **Travel Time Assumptions**: Isochrones are calculated using standard vehicle speeds
for different road types. These models do not account for real-time traffic, seasonal
weather conditions (e.g., flooding), or road surface degradation.

* **Boundary Precision**: Administrative boundaries are sourced from geoBoundaries.
These may differ slightly from official government demarcations or other schemas.

Tags: education, health facilities, transportation
Organization: HeiGIT (Heidelberg Institute for Geoinformation Technology)