Trade Area Analysis - Thiessen Polygons

Authors: Safwan Rahman, Chris Fealy


Introduction


A trade area analysis is an analysis performed to understand the trade zones of a particular business. There are a number of methods in performing a trade area analysis, while this project focused on a Thiessen Polygons approach.

Essentially, a trade are will show some set of information within a particular boundary, designed and manipulated by the author. Information presented in these zones could be ones such as household income levels, education levels of the households within the zone, etc.

With the Thiessen polygons approach, we took each retail store and created a larger boarder around it, signifying its "trade zone". Thiessen polygons are defined by the midway point between one retail store to another. To understand further about Thiessen polygons, click here.


Study Area


The study area focused on this project is the Greater Vancouver Area, BC in Canada.

Methodology

1. The first step in conducting a trade area analysis is identifying a retail store. I will leave it unnamed in this post however and refer it now on as "retail store". All the retail stores were first identified in the Greater Vancouver Area. This can be done in several methods. We were given the points, however, a simple way to tackle this is by conducting your own research. Go to the retail store's website and look up all the retail stores in your study area. You can then digitize this in your own means. I recommend Google Maps and then convert to KML (tutorial coming soon). 
2. For the next step, we found boundary information (census tracts) through the Statistics Canada geo-database. Each census tract already had the demographic information needed. In this project we were concerned with 
1. Average Household Income
2. Population Age 15-19
3. Population Age 20-24
4. University degrees attained
3. Now we take each retail store and apply Thiessen polygons around then. This was done using the Thiessen Polygons tool in ESRI ArcMap. Each thiessen polygon now overlaps census tract boundaries. Therefore, the census tracts were divided within each Thiessen polygon. Some census tracts were present in two different Thiessen polygons, thus, we took the percentage of its presence in one. The demographic information in the census tracts were now totaled into each Thiessen polygon, except for Average Household Income, which was averaged in each polygon. 
4. Using our cartographic skills, we created a color coded map to display all the demographic information. 

Results & Analysis

Below are the maps created for each of the variables. 

From all the preceding maps, the most marketable zones are circled in yellow. These zones show optimal locations to focus on for any kind of marketing purpose. For example, the degrees attained map shown above highlights the S. Kensington area. If your target customers are ones that should have high levels of education, this zone circled above works well as it is in a central location, close to high traffic retail centers (Tier 1 and 2 malls, meaning over 100 shops), and are on major roadways. 

To conclude, this trade area analysis is just an example of how businesses should leverage GIS technology in finding optimal marketing locations. There are many  other methods in conducting a trade area analyses which I shall post in the near future.