by Jack Forsyth
Geovis Project Assignment @RyersonGeo, SA8905, Fall 2020
Project Link: https://gis.jackforsyth.com/
Who shops at which store? Answers to this fundamentally geographic question often use a wide variety of models and data to understand consumer decision making to help locate new stores, target advertisements, and forecast sales. Understanding store trade areas, or where a store’s customers come from, plays an important role in this kind of retail analysis. The Trade Area Models web app lets users dip their toes into the world of retail geography in a dynamic, interactive fashion to learn about buffers, Voronoi polygons, and the Huff Model, some of the models that can underlie trade area modeling.
The web app features a tutorial that walks new users through the basics of trade area modeling and the app itself. Step by step, it introduces some of the underlying concepts in retail geography, and requires users to interact with the app to relocate a store and resize the square footage of another, giving them an introduction to the key interactions that they can use later when interacting with the models directly.
The web app is designed to have a map dominate the screen. On the left of the browser window, users have a control panel where they can learn about the models displayed on the map, add and remove stores, and adjust model parameters where appropriate. As parameters are changed, users receive instant feedback on the map that displays the result of their parameter changes. This quick feedback loop is intended to encourage playful and exploratory interactions that are not available in desktop GIS software. At the top of the screen, users can navigate between tabs to see different trade area models, and they are also provided with an option to return to the tutorial, or read more about the web app in the About tab.
One of the key limitations in the app is a lack of specificity in models. Buffer sizes and store square footage areas are abstracted out of the app for simplicity, but this results in a lack of quantitative feedback. The Huff Model also uses Euclidean distance rather than drive time which ignores the road network and alternative means of transit like subway or foot traffic. The Huff Model also uses census tract centroids, which can lead to counter intuitive results in large census tracts. The sales forecasting aspect of the Huff Model tab makes large assumptions on the amount of many spent by each household on goods, and is impacted by edge effects of both stores and customers that may fall outside of the Toronto CMA. The drive time buffers also fully rely on the road network (rather than incorporating transit) and are limited by an upper bounded travel time of 60 minutes from the Mapbox Isochrone API.
The application in its current form is useful for spurring interest and discussion around trade area modeling, but should be more analytical to be useful for genuine analysis. A future iteration should remove the abstractions of buffer sizes and square footage estimates to allow an experienced user to directly enter exact values into the models. Further, more demographic data to support the Huff Model, and parameter defaults for specific industries would help users more quickly create meaningful models. Applying demographic filters to the sales forecasting would allow, for example, a store that sells baby apparel to more appropriately identify areas where there are more new families. Another useful addition to the app would be integration of real estate data to show retail space that is actually available for lease in the city so that users can pick their candidate store locations in a more meaningful way.
The Trade Area Models web app gives experienced and inexperienced analysts alike the opportunity to learn more about retail geography. While more analytical components have been abstracted out of the app in favour of simplicity, users can not only learn about buffers, Voronoi polygons, and the Huff Model, but interact with them directly and see how changes in store location and model parameters affect the retail landscape of Toronto.