For my final project in GEOG 352 (GNSS and Remote Sensing) I collaborated with a team to develop a spatial analysis of vending machine beverage availability and accessibility across the East Quad at Texas A&M University. The project focused on a common but often overlooked campus problem: students and faculty frequently rely on vending machines for quick beverages, yet availability and variety vary widely by location, leading to frustration and inefficiency. Our goal was to determine which vending machines were most likely to have specific beverages in stock at any given time and how accessible those machines were within a defined walking distance.
The project began with structured field data collection using ArcGIS Online and ESRI’s Field Maps app. Our team divided campus buildings within approximately 700–800 feet of the Eller Oceanography and Meteorology Building, and over the course of three days, we recorded the precise locations of vending machines along with detailed inventory data for each beverage type.
Location data were captured as point features, while beverage stock and machine characteristics were recorded and processed collaboratively using Google Sheets.
Once data collection was complete, we aggregated and analyzed multi-day inventory observations to determine average availability for each drink across machines. This allowed us to identify spatial patterns in beverage variety, stock consistency, and overall machine usefulness. The resulting ArcGIS Online map integrated location data, attribute tables, and visual symbology to communicate where users were most likely to find high-variety machines, caffeinated beverages, or consistently understocked locations.
Key findings from the analysis included:
Langford Architecture Building (A) consistently offered the widest variety of beverages and the highest overall stock, making it the most reliable vending location on the East Quad.
The Computing Services Annex provided the closest vending option to the Eller Building (excluding in-building machines) with a moderate but dependable selection.
The Commons Dining Hall showed a consistent lack of beverage availability relative to other locations.
Across all buildings, Pepsi was the most common drink, while Mountain Dew Baja Blast was the least common.
Several buildings lacked vending machines entirely, and some machines did not display internal stock, introducing both accessibility and usability challenges for users
The project also surfaced important methodological limitations. Because most vending machines were located indoors and surrounded by dense building infrastructure, GPS accuracy was occasionally reduced, affecting point precision. Additionally, data from certain locations, such as Langford Architecture Building (A), which houses multiple machines in close proximity, introduced slight sampling bias that we explicitly acknowledged in our discussion and conclusion.
Beyond the immediate results, the project demonstrated the practical value of GIS for everyday decision-making. Our analysis could be used to inform students where to find their preferred beverages more reliably, while also providing facilities management or vendors with insight into restocking priorities and machine placement optimization.
This project demonstrates my ability to:
Collect and manage real-world spatial data using mobile GIS tools and collaborative workflows.
Integrate field-collected GNSS point data with tabular inventory data for spatial analysis.
Translate everyday problems into structured geospatial questions with actionable insights.
Communicate findings through clear visual mapping, statistical summaries, and written interpretation.
The final product functions as both an academic GIS exercise and a practical, user-focused mapping tool, highlighting how geospatial analysis can improve accessibility, efficiency, and user experience in everyday campus environments.