A look into the data and research we are sharing so others can explore, question, and build on it.
This page opens up how we are using NGO Finder data to examine municipality-level service coverage. We are sharing the current analysis, comparisons, and methodology in the hope that researchers, partners, and practitioners will find it useful and take it further.
Search the shared municipality dataset by municipality name, official code, or province. Municipality data will appear here as imports are loaded.
Workflow
Explore map data
Scan the municipality map built from the data currently loaded in NGO Finder.
Workflow
Compare municipalities
Line up places side by side to compare the service records currently mapped in NGO Finder.
Workflow
Download data
Export our current municipality dataset as CSV for analysis and reporting.
Workflow
Read methods
Review the assumptions, matching logic, and data limits behind the current model.
Workflow
Ask a question
Search the research knowledge base by methods, legal process, support, and source.
Research paths
Move between national context, evidence, our municipal data, and place pages
Use these pages together: start with the national baseline, move into the knowledge base, then inspect ranked municipalities and open local profiles.
National GBV baseline
Read the national context layer that explains how the research should be interpreted.
Research knowledge base
Browse source-linked answers on methods, pathways, legal process, and interpretation.
Our municipal data
Filter the municipality data currently loaded in NGO Finder before opening detail pages.
Municipality directory
Browse all municipality research profiles directly when you already know the places you want to inspect.
Shared map view
Open the choropleth built from the data we currently have
The interactive municipality map now lives on its own page so this overview can stay lighter and easier to browse. It reflects the municipality-level service data currently loaded into NGO Finder, not a complete picture of every service on the ground.
Top 20 municipalities in our current dataset by population
Compare the largest municipalities in the NGO Finder dataset and see how many NGOs, police stations, and hospitals or clinics are currently mapped per 10,000 people.
Ways to explore it
Explore our data, analysis, and context
Use the map, our municipal data, and methodology together: the map helps spot patterns, the data view helps compare municipalities, and the detail pages make it easier to inspect the underlying local picture.
Map Of Our Current Data
Open the dedicated choropleth map built from the municipality service data we currently have.
Open map →
Our Municipal Data
Compare the municipality data currently loaded in NGO Finder using service counts and services per 10,000 people.
Open rankings →
Research Methodology
Review the data sources, caveats, assumptions, and limits behind the current analysis.
Read methodology →
Compare Municipalities
Build a side-by-side municipality set for service counts, per-10k metrics, mapped cities, and caveats.
Start comparison →
National GBV Baseline
Read national prevalence, help-seeking, disability, and methodology context from the HSRC study.
Open context layer →
Research Knowledge Base
Search source-linked answers by methods, service pathways, legal process, support, and interpretation.
Open knowledge base →
Methodology and Limits
How to read and reuse this shared work
This page combines municipality population data currently loaded in NGO Finder with our mapped service database. At the moment, the app is using municipality population figures stored as 2022 population data, together with municipality boundaries imported from a 2018 boundary file. Service density is shown as the number of NGOs, police stations, and hospitals or clinics per 10,000 residents so others can understand both the usefulness and the limits of the current dataset.
Methodology
- Population is assigned at municipality level and used to calculate services per 10,000 people.
- Service counts come from the NGO Finder database, grouped into NGOs, police stations, and hospitals or clinics.
- Municipality matching depends on city-to-municipality mappings, so results are only as strong as those mappings.
- The choropleth map is intended as a directional overview of the data currently loaded into NGO Finder, not a complete census of services.
Challenges and Limits
- Coverage may be incomplete because some services may be missing from the database, newly opened, duplicated, or already closed.
- Population changes over time, so current on-the-ground demand may differ from the population figures used here.
- This version does not measure service quality, capacity, staffing, operating hours, accessibility, or specialization.
- A large hospital and a small hospital are currently counted equally, which can flatten important differences in real service availability.
- People often travel across municipal boundaries for care, but this view mostly attributes services to the municipality they are mapped into.
What Researchers Should Keep In Mind
- Treat these results as a screening layer for hypothesis generation, not as a final measure of need or service adequacy.
- Low service density may reflect true undersupply, but it can also reflect data gaps, boundary effects, or outdated records.
- Comparisons are more meaningful when combined with local knowledge, field verification, deprivation indicators, transport access, and population change over time.
- Future improvements should include service quality weighting, recency checks, closure detection, and stronger validation against external administrative datasets.
Phase 2
Next research directions
The next phase should move beyond simple counts and make the research more useful for policy work, service planning, and stronger data interpretation.
Quality and Capacity Weighting
Weight services by size, staffing, specialization, operating hours, and actual service capacity instead of counting every facility equally.
Stronger Demand Signals
Add deprivation, transport access, rurality, safety, and population change so service need can be compared against service supply.
Data Freshness and Validation
Track closures, new openings, verification dates, and external reference datasets to improve confidence in the research outputs.