Growing With Purpose: A GIS Model for Future GUTS Installations by Justin Myers

Growing With Purpose is the Directed Research project I completed while at the University of Toledo.

Downloads

• Poster (59 MB)
• Trifold Brochure (6.06 MB)

Contents

• Abstract
• Introduction
• Methods
• Conclusion
• Figures & Graphs

Abstract

Native plant gardens on the University of Toledo campus not only provide essential habitat for wildlife and pollinators but also provide ecological benefits while simultaneously offering significant aesthetic value. Historically however, garden placement has lacked a data-driven approach regarding visibility and engagement. This study utilizes Geographic Information Systems (GIS) to identify optimal locations for future GUTS (Greening UToledo through Service) installations by modeling foot traffic around campus. Since real-time pedestrian data was unavailable, building capacity was used as a proxy for foot traffic. Weighted values were applied to high-traffic areas such as parking lots, sports facilities, and buildings such as the Student Union & Carlson Library, while existing gardens were negatively weighted to prioritize underserved areas. The building capacity data provided by the College of Natural Sciences and Mathematics revealed three primary hotspot areas for potential development: Honors Village/Tucker Hall, Ritter/Stranahan, and McComas Village. These preliminary results demonstrate that a weighted GIS model can successfully identify high-impact locations, providing a framework that can be used for future GUTS gardens.

Introduction

Greening UToledo through Service (GUTS) aims to create native landscapes around the University of Toledo campus, increasing biodiversity and sustainability, through the help of undergraduate service learning volunteers. However, previous methods of determining placement of these installations were based on instinct rather than a data-driven approach. The purpose of this research is to use Geographic Information Systems (GIS) to identify new garden locations with the highest community impact. By using building capacity as a proxy for human density and applying a negative weight to existing gardens, this model prioritizes areas around campus that lack these native installations.

Methods

To find the best locations for new gardens, a spatial analysis was performed in ArcGIS Pro using the University of Toledo campus data. Because direct data on foot traffic was unavailable, building capacity was used as a proxy to estimate human density.

The model was built using the following logic:

• • Parking Lots: Weighted on a 1–5 scale based on the physical size of the lot.
• • Buildings without Capacity: For structures missing data, an estimated value between 0 and 5 was assigned.
• • Existing Gardens: To prevent new gardens from being clustered in the same areas, current garden locations were given a weight between -5 and 5 based on their distance from buildings, parking lots, and sports facilities.
• • Intelligent Averaging: All factors were combined into an Average_Weight field that was scaled 1–5. To account for missing building data, an “intelligent average” function was utilized to calculate priority based only on available valid inputs, preventing null values from skewing the results.

This data was then visualized as a Heat Map to identify the highest priority zones for native installations.

Conclusion

This research shows how GIS can be used to make sure new installations are placed where they are actually needed on the University of Toledo campus. Instead of just picking subjective spots, the model uses building and parking data to find the areas with the most people and the fewest existing native plants. By following this map, GUTS can be more efficient with its volunteers and resources. These results provide a clear plan for where to plant next to make the campus more biodiverse and make sure the student body actually sees and enjoys the new green spaces.

Figures & Graphs

Below are the figures and graphs produced from this research. Clicking any image will make it larger.