Leveraging GIS & Geospatial Data in Monitoring and Evaluation Systems

Most M&E systems aggregate data by administrative unit — district, upazila, county. This obscures critical spatial patterns: a district may show 80% latrine coverage, but all functional latrines could be clustered around market towns, leaving remote hamlets unserved. Without a map, that inequity is invisible.

GIS closes this gap by attaching coordinates to every data point — every borehole, every school, every beneficiary household. When displayed on a map, patterns of inclusion and exclusion become immediately apparent, enabling targeted resource allocation.

Geo-tagged Data Collection: Using GPS-enabled mobile devices, enumerators capture latitude and longitude alongside survey responses. This ensures that every observation is spatially anchored and can be verified against satellite imagery.

Spatial Dashboards: Interactive maps replace static bar charts. Stakeholders can zoom from national overview to village-level detail, filter by indicator, and overlay multiple layers — water points against disease incidence, for example — to uncover correlations.

Buffer & Proximity Analysis: GIS can answer questions like "How many households are within 500 metres of a functional water point?" or "Which health facilities are more than 10 kilometres from the nearest referral hospital?" These analyses directly inform programme targeting.

Change Detection: By comparing satellite imagery over time, programmes can verify infrastructure construction (was the school actually built?), track environmental change (deforestation, flood extent), and validate self-reported data.

MEL360's GIS module was deployed for PKSF's Bangladesh Rural WASH for Human Capital Development project, covering 78 upazilas across four divisions. Every water point, latrine, and hygiene facility installed under the programme was geo-tagged at the point of construction.

A two-tier validation workflow required field engineers to capture GPS coordinates and photographs, which were then cross-referenced against satellite imagery by a central verification team. This eliminated ghost infrastructure — a persistent challenge in large-scale WASH programmes.

The spatial dashboard enabled PKSF's programme managers to identify "coverage deserts" — clusters of households more than 500 metres from any functional water point — and redirect resources in real time. Within the first six months, the programme reduced unserved clusters by 34%.

Data Quality: GPS accuracy varies by device and environment. Under dense tree canopy or in urban canyons, accuracy can drop to 15–20 metres. Define acceptable accuracy thresholds (typically ≤10 m for rural infrastructure) and build validation rules into your data collection forms.

Offline Capability: Many programme areas lack reliable connectivity. MEL360's mobile app caches map tiles and GPS data locally, syncing when connectivity is restored. Enumerators can navigate to assigned locations using offline maps.

Data Privacy: Geo-tagged beneficiary data is sensitive. Coordinates can identify individual households. MEL360 applies spatial obfuscation (random displacement within a defined radius) for beneficiary-level data while preserving aggregate spatial patterns.

Interoperability: GIS data should be exportable in standard formats (GeoJSON, Shapefile, KML) for integration with national spatial data infrastructures and donor GIS platforms.

You don't need a GIS specialist on staff to start leveraging spatial data. Begin with these steps:

1. Enable GPS capture in your existing mobile data collection forms. MEL360 makes this a single toggle per form field.

2. Start with one "quick win" — map all programme sites on a single interactive layer. Share the map with stakeholders. The visual impact alone will build buy-in for deeper GIS integration.

3. Overlay programme data with publicly available geospatial datasets — population density, road networks, climate zones — to enrich your analysis without additional data collection.

4. Define clear data governance policies for geospatial data, especially regarding beneficiary privacy and data sharing with third parties.