County-level GIS and Python analysis of health-facility availability, hospital-bed capacity, and population pressure in Kenya.
| Planning question | Which counties may face the greatest pressure on available health services? |
| Study area | Kenya, analyzed at county level |
| Tools | ArcMap 10.8, Python, pandas, matplotlib, Excel |
| Data period | 2019 population and 2020 health-facility data |
| Outputs | Four thematic maps, four charts, cleaned tables, Python scripts, and a PDF report |
Facility totals alone do not show whether health-service capacity is adequate for the population served. This project combines county population, facility, and hospital-bed data to create screening indicators that can help identify counties requiring closer assessment.
- Reviewed Kenya county boundaries, health-facility records, and population data.
- Cleaned and standardized the facility and population tables with Python.
- Summarized facilities, operational facilities, and hospital beds by county.
- Calculated population per health facility and population per hospital bed.
- Joined the indicators to county boundaries in ArcMap.
- Classified county-level health-access priority and produced maps and charts.
- Documented assumptions, limitations, and potential uses in a final report.
| Population per health facility | Total health facilities |
|---|---|
 |
 |
| Total hospital beds | Health-access priority |
|---|---|
 |
|
- Population pressure on health facilities varies substantially between counties.
- Bungoma, Narok, Mandera, Busia, Kakamega, Wajir, Kisii, Trans Nzoia, Vihiga, and Kwale appeared among the counties with the highest population per facility in this dataset.
- Facility count, population pressure, and bed capacity provide different views of access and should be interpreted together.
- The results are best used to prioritize further investigation, not as a standalone measure of health-care quality.
- Final thematic maps
- Python data-cleaning and analysis scripts
- Charts
- Processed analysis tables
- Final report
- Methods and supporting documentation
- Kenya administrative boundaries from the Humanitarian Data Exchange (HDX)
- Kenya Master Health Facility List, 2020, from OpenAfrica
- Kenya Census Population, 2019, from KNBS via HDX
Source notes and field definitions are documented in the repository's metadata folder.
- The facility data is from 2020 and the population data is from the 2019 census.
- County averages can hide important inequalities within counties.
- The source facility table did not provide usable coordinates for point-level travel analysis.
- Population per facility is a planning indicator; it does not measure travel time, staffing, medicines, affordability, service quality, or current operating conditions.
- Priority classes indicate where further assessment may be useful. They are not a clinical or operational judgment.
The repository also includes a simulated household survey and statistical-analysis plan showing how primary data could supplement the GIS findings. It includes questionnaire, quality-control, supervision, SPSS-entry, cleaning, and analysis guidance. No real household survey data was collected.
├── 01_Data_Raw Source datasets
├── 02_Data_Processed Cleaned and joined analysis tables
├── 03_Documentation Workflow and survey-planning documents
├── 04_Metadata Data dictionaries and source notes
├── 05_Maps Final map exports
├── 06_Output Additional analytical outputs
├── 07_Scripts Python cleaning, analysis, and chart scripts
├── 08_Report Final written report
└── 09_Charts Exported charts
Vivian Mbachi — GIS & Data Analyst, Nairobi, Kenya
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