Filling the Gap: Selecting a Location for a New Walkable Facility

By Hany A. Hassaballa

The decision to build a new public facility requires close evaluation of all contexts and factors related to the potential new location. One of the main issues that impacts the decision “to build, or not to build” is the supply and demand of the service provided by the new facility. This article addresses the development of a GIS geo-processing tool that can rank available plots based on their ability to fill the gap between the supply and demand of a desired service location. The article begins with a discussion of basic concepts (such as walking distance, population demand, and catchment area) and concludes with an explanation of methodology for ranking available plots. 
Calculating Walking Distance
For purposes of this article, targeted services are those that can be accessed through walking. Accessing these services is recurrent as much as multiple times daily - such as mosques (five times/day) or schools (daily basis). Reasonable walking distance is defined as 5 to 15 minutes (NJTransit, 1994). The average human walking speed is 3 miles/hour (Olson, 2010) - this makes a facility accessible by walking 0.25 to 0.75 miles. This article does not take into consideration routable network, but assumes that all areas are accessed equally. The result of walking distance is a circle with the radius depending on the time spent walking.
Population Demand
The population demand depends on the type of service provided. This article focuses on calculating the demand of a Muslim population for religious facilities (mosques) as an example of services that require short walking distance. In this case, there is a repetitive demand of five times a day; with this demand increasing to include weekly prayer on Fridays. The best way to represent the population is by calculating the number of Muslims living in each building and illustrating that number as a point feature class with Muslim population attributes.
There are other factors that impact service demands other than population number (such as accessibility), but these factors are outside the scope of this article.
Calculating Service Capacity
Each facility (mosque) has a different capacity to provide acceptable service level. Mosque capacity depends mainly on the square area of the mosque (praying area). Each prayer module (person) requires an area of 24 inches by 48 inches (The American Institute of Architects, 2007). That equals 1152 square inches of prayer area, or an area between 0.75 and 1.5 m2 required for each person to pray. For this article, an average area of 1 m2 per person was assumed. 
Calculating the Catchment Area Based on Capacity and Population
The concept of catchment area is used in all market analysis. In a commercial sense, a catchment area can be defined as the geographical zone, which contains the regular clients of a defined commercial center.  Mosque catchment area is the walking distance radius that surrounds the mosque. In some cases the capacity of the mosque is not large enough to accommodate all of the population within walking distance. This implies that there are actually two factors contributing to the catchment area of the mosque:
  1. Walking distance
  2. Population surrounding mosque

Based on these factors, two identical capacity mosques can have different catchment areas due to differential in population sizes surrounding each mosque. It is anticipated that the mosque surrounded by a larger density population will have a smaller catchment area as the mosque would reach maximum capacity before reaching the maximum walking distance leaving some population within its virtual catchment (walking distance) area with no service. A mosque located in a less dense area will have a larger catchment area given the same capacity.

Based on the above factors, a Python geo-processing tool was developed to calculate the catchment area of all existing mosques. The geo-processing tool calculates the catchment area (buffer) in an iterative manner, starting with the maximum walking distance (1200m) and a loop to compare the population within this walking distance and the capacity of each mosque. Once capacity is reached the service areacapa radius is set for each mosque.  Mosques with large capacity that exceed the surrounding population are set to the maximum walking distance while mosques with small capacity in very dense areas are given a minimum service area radius of 50m. The following image illustrates a typical result of the iterative geo-processing tool:
Ranking Available Plots Based on the Supply-Demand Gap
After determining the existing service areas (radiuses), available plots surrounding the mosques can be ranked based on the gap between the supply (existing mosque service area population) and demand (population that does not have mosque within walking distance reach or over filled mosques). 
A Python geo-processing tool is used to calculate the gap and rank the available plots. The tool first creates a buffer equivalent to the service area around the available (proposed) plots, and then clips (erases) these buffers using the existing mosque service areas. What remains from the plot’s service area represents the gap between the supply and demand.
Population is calculated based on what is left from the proposed plots service area. Each proposed plot is assigned the number of population that can be served in the event a facility (mosque) is built on that plot. Plots with higher populations - considered high demand plots, are given higher ranking (priority). It is to be noted that many other factors can contribute to the decision of ranking the plots, however these factors fall outside the scope of this article. 
  1. Aspelin, Karen, 2005. Establishing Pedestrian Walking Speeds. 
  2. NJTransit. 1994. Planning for Transit-Friendly Land Use A Handbook for New Jersey Communities.
  3. Olson, John, 2010. The Five Minute Walk: Calibrated to the Pedestrian.
  4. The American Institute of Architects, 2007. “Architectural Graphic Standards, 11th Edition”, 
  5. Transport Canada, 2010. Regional and Small Airports Study

Published Monday, June 17th, 2013

Written by Hany A. Hassaballa

Published in

Location Intelligence

If you liked this article subscribe to our bimonthly newsletter...stay informed on the latest geospatial technology

Sign up

© 2017 Directions Media. All Rights Reserved.