Using GIS to Rank Public Facility Sites by Supply–Demand Gap

Selecting a site for a new public facility is not merely a land-use decision; it is a spatial optimization problem. Planners must assess how effectively a proposed location balances supply and demand within a defined geographic context. One of the most influential variables in such decisions is whether the facility can adequately serve the surrounding population within a reasonable walking distance.

This article outlines the development of a GIS geo-processing tool designed to rank available plots based on their ability to reduce gaps between existing service supply and unmet demand. The methodology integrates walking distance assumptions, population distribution, facility capacity, and catchment modeling into a structured spatial workflow.

Walking Distance as a Service Constraint

The analysis focuses on facilities accessed primarily by walking—services that are frequent and routine, such as schools or mosques. For recurrent services, acceptable walking time typically ranges between five and fifteen minutes. With an average walking speed of approximately three miles per hour, this translates to a practical access radius of roughly 0.25 to 0.75 miles.

For modeling purposes, the approach assumes uniform accessibility across space rather than applying a routable network. Walking distance is therefore represented as a circular buffer, with radius determined by the time threshold selected. While network-based analysis could refine realism, the circular model simplifies iterative computation.

Estimating Population Demand

Demand depends directly on the type of service provided. In this case, the example considers religious facilities serving a Muslim population. Demand is characterized by repeated daily attendance, increasing weekly for congregational prayer.

To spatially represent demand, population is allocated at the building level. Each building is assigned a Muslim population count and represented as a point feature class with relevant attributes. This approach allows granular demand estimation rather than relying solely on aggregated census tracts.

Other influences—such as transportation barriers or socioeconomic variables—may affect demand but fall outside the defined analytical scope.

Defining Service Capacity

Facility capacity varies depending on usable space. For mosques, the determining factor is the prayer area. Architectural standards estimate that each individual requires approximately 24 by 48 inches of space—equivalent to about one square meter per person for analytical simplicity.

By dividing total prayer area by per-person spatial requirements, a maximum capacity value is calculated for each facility. This capacity becomes the limiting threshold within catchment modeling.

Catchment Area as a Function of Capacity and Density

In market analysis, a catchment area represents the zone from which clients are drawn. For walking-based services, this zone is initially defined by maximum walking distance. However, capacity introduces a second constraint.

If surrounding population density is high, a facility may reach full capacity before serving everyone within walking distance. In such cases, the effective service radius contracts. Conversely, in low-density environments, a facility may not reach capacity even at maximum walking distance, resulting in a larger catchment.

Thus, two identical facilities can exhibit different effective service radii solely due to differences in surrounding population density.

Iterative Geo-Processing Workflow

To calculate catchment areas dynamically, a Python-based geo-processing tool was developed. The tool operates iteratively:

• Begin with the maximum walking distance (e.g., 1200 meters).
• Calculate total population within the buffer.
• Compare this value to facility capacity.
• If population exceeds capacity, reduce the radius incrementally until capacity aligns with demand.
• Assign the resulting radius as the effective service area.

Facilities in low-density regions retain the maximum radius. Those in dense areas may be constrained to minimal service radii, in some cases as small as 50 meters.

The outcome is a set of differentiated catchment zones reflecting both spatial accessibility and capacity limitations.

Ranking Proposed Plots by Supply–Demand Gap

Once existing service areas are established, candidate plots can be evaluated. The objective is to identify locations that most effectively address unmet demand.

The ranking tool performs the following steps:

• Create a service buffer around each proposed plot using the same walking-distance logic.
• Erase areas already covered by existing facilities.
• Calculate remaining population within the uncovered portion of the buffer.
• Assign each plot a demand score based on population that would be newly served.
• Plots with higher unserved population values represent stronger candidates, as they reduce the supply–demand imbalance more significantly.

While other planning factors—such as zoning, cost, infrastructure availability, and accessibility—may influence final decisions, the ranking system isolates the spatial service gap as a quantifiable metric.

Conclusion

The integration of walking distance, building-level population modeling, facility capacity, and iterative buffer adjustment demonstrates how GIS can move beyond visualization into decision-support modeling. By explicitly measuring service gaps, planners can prioritize sites that maximize community benefit.

Spatial analysis transforms what might otherwise be subjective site selection into a structured, reproducible methodology—grounded in measurable supply and demand relationships.