Turning Geospatial Data into Planning Decisions: Evaluating a Participatory Accessibility Model for Urban Drinking Water

UBDC's Oluwatimilehin Adenike Shonowo has been conducting research into these multidimensional access challenges.

Introduction

Urban planning increasingly relies on geospatial data to identify underserved populations and prioritise infrastructure investment. Spatial models can map where services are located, estimate ravel time, and generate deprivation surfaces that appear precise and objective. But producing a map is not the same as understanding access.

In rapidly urbanising cities across Sub-Saharan Africa, proximity to a waterpoint does not automatically guarantee adequate service. Infrastructure functionality, drinkability, waiting time, affordability, and seasonal reliability all shape lived access conditions. Accessibility is multidimensional.

This research asks a critical question:

When we model drinking water accessibility using advanced spatial methods, how well do those outputs reflect lived realities?

To answer this, a participatory accessibility modelling framework was implemented and quantitatively evaluated in two large Nigerian cities: Kano and Lagos.

Modelling Accessibility

Spatial accessibility models estimate the potential interaction between population demand and service supply. One widely used method is the Enhanced Two-Step Floating Catchment Area (E2SFCA) approach. It integrates population demand, service supply, and network-based traveltime with distance decay.

The model generates continuous accessibility scores under defined behavioural and infrastructural assumptions. It is widely used in health service planning and increasingly applied to infrastructure contexts. However, accessibility models remain structural abstractions. They capture physical reachability under specific parameters but do not automatically capture lived experience.

To strengthen interpretation, this study explicitly evaluates model outputs against participatory validation data collected at grid level.

Conceptual Framework

Drinking water accessibility was conceptualised as a spatial interaction between 100m population grid cells and qualified infrastructure supply.

Unlike simple proximity models, service quality was incorporated into the supply term. Waterpoints were classified into three service categories: Optimal water (functional, improved, drinkable), Moderate water (functional but not meeting all optimal criteria), and Limited water(non-drinkable sources).

These categories were operationalised using weighted supply coefficients (1.0, 0.5, 0.2 respectively). Accessibility therefore reflects both spatial reachability and relative service adequacy.

However, affordability magnitude, queueing duration, volumetric capacity, and seasonal variability were not explicitly modelled due to data constraints. The model estimates physical accessibility under network-constrained walking conditions, not full multidimensional service adequacy.

Travel-Time and Threshold Assumptions

Accessibility was estimated using pedestrian network travel times derived from OpenRouteService and OpenStreetMap data. A walking speed of 5 km/hour was assumed.

A 60-minute catchment threshold was adopted. While international guidelines often recommend 30 minutes as a benchmark for acceptable access, empirical evidence from the case study areas indicates that households frequently travel up to an hour to collect water in underserved neighbourhoods. The 60-minute threshold therefore reflects observed coping realities rather than ideal standards.

Accessibility scores were standardised within each city and classified into three deprivation levels: Low, Medium, and High.

Evaluating Model Performance

Rather than stopping at map production, grid-level model classifications were compared directly with participatory validation data aggregated at the same spatial resolution.

Performance was assessed using overall accuracy, class-specific recall, and macro-averaged F1 scores. This allows examination not only of whether the model aligns with community-reported conditions, but how it behaves across deprivation levels.

Data Insights

Comparing model outputs with participatory validation data revealed an important pattern: the accessibility model did not behave uniformly across contexts.

In Kano, the model showed a strong tendency to classify large areas as severely deprived. While it was highly sensitive to zones experiencing clear structural scarcity, it struggled to distinguish between moderate and lower levels of deprivation. In practice, this means the model amplified signals of severe need but compressed variation elsewhere. If used without validation, such behaviour could lead to over-prioritisation of some areas while overlooking gradations in service conditions.

Lagos displayed a contrasting pattern. Here, the model performed more evenly across intermediate levels of deprivation but failed to consistently identify areas experiencing the most severe access constraints. Rather than inflating extreme deprivation, the model diluted it. For planners, this creates a different but equally important risk: genuinely constrained communities may appear less urgent than they are.

These opposing patterns reveal a critical insight. The same modelling framework, applied consistently across two cities, produced different structural biases. The misalignment between model estimates and lived access conditions was not random. It was shaped by local infrastructural, spatial, and service realities.

This reinforces a broader lesson. Network-based physical accessibility, even when weighted for service quality, does not fully capture lived water access conditions. Models estimate structural reachability. Communities experience access as a combination of travel time, reliability, functionality, affordability, and everyday coping strategies.

Participatory validation did not invalidate the model. Instead, it exposed where and how the model’s assumptions diverged from ground realities. That divergence differed between cities, highlighting the importance of contextual interpretation rather than methodological uniformity.

For infrastructure planning, the implication is clear. Accessibility models are powerful tools for identifying spatial patterns of potential service imbalance. But they should not be treated as self-validating decision engines. Without empirical grounding, model outputs may introduce systematic bias into targeting strategies.

The value of this work lies not in rejecting spatial modelling, but in strengthening it. By integrating community-grounded validation, planners can interpret accessibility surfaces more cautiously, adjust classification thresholds where necessary, and avoid unintended distortions in resource allocation.

Turning geospatial data into planning decisions requires more than computation. It requires context, evaluation, and humility in interpretation.

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