How Esri Innovation Program Student of the Year Winners Demonstrate Geospatial Excellence

Each year, universities that are members of the Esri Innovation Program (EIP) nominate a Student of the Year who used Esri’s geospatial technology in an innovative way. For Yichen Yang from Yale University, that meant using geographic information system (GIS) tools to research heat in cities by observing sensor data collected by bicycle riders. For Luke Zaruba from the University of Minnesota, it meant determining exactly where and when people were using scooters in Minnesota to look at micromobility trends.

EIP universities advance geospatial education by teaching the latest technology supported by ArcGIS software, promoting GIS research, and fostering institutional cross collaboration.

In 2022, that student was Yang. The following year, it was Zaruba. These are their winning projects:

As a trained meteorologist and now a PhD candidate at the Yale School of the Environment, Yang has been passionate about environmental studies and is determined to help tackle climate-related issues.

"Being exposed to diverse environmental topics, I set this goal of using my meteorological knowledge to positively impact human welfare and help people face climate change," said Yang.

His EIP winning project, Innovative Data Acquisition Reveals Urban Thermal Extremes, used smart thermal sensors to collect street-level air temperature and humidity data from both private and public cyclists around the world, from New Haven, Connecticut, to San Francisco, California, and Hong Kong, China. The sensors were attached to bikes and used to trace and map heat levels as cyclists rode around their city, capturing air temperature, moisture content, and geographic coordinates.

Data was transferred to the cyclists' cell phones via Bluetooth and sent back to the research team. Gmail API for Python was used to extrapolate the data, which is then classified according to sensor name and shared in ArcGIS Online. By using a web-based mapping software like ArcGIS Online, Yang could also visualize the data as interactive dashboards and web maps.

Yang now researches how human activity impacts urban environments, contributing to the "Biking for Science and Health" project led by the Yale Center of Earth Observation. This project seeks to enhance the accuracy of information regarding atmospheric conditions and geographic spread in urban areas.

"ArcGIS software strengthened the validation process and provided opportunities to visualize and archive the new data, " said Yang. "Without this online system, it would be much more difficult for us to make our work seen by others."

Yang adds that ArcGIS Online seamlessly allowed bicyclists using non-GIS-based tools to upload their data to the cloud-based platform without confusion or conversion. Using GIS, remote sensing, neural network analysis, and meteorology, Yang produced land surface temperature information with high spatial and temporal accuracy. His study shows that urban warming can occur in extremes across a cityscape and varies based on human impact. On a hot summer day, warming ranges from 3 degrees in the atmosphere to 11 degrees on the land surface. The information is invaluable for city mitigation strategies.

Yang found the project to be a personally rewarding experience, especially when he observed the effects of his bike rides and was praised by other GIS professionals at the 2022 Esri User Conference.

"I was thrilled to be there and to discover that there are so many people who care about GIS, who see it as a powerful tool in research," said Yang. "It really feels good to be with like-minded people from around the world. It felt like another home."

Zaruba has long had a fascination for maps, travel, and "seeing how the world works." That interest only expanded after he took an undergraduate urban geography course. Now, as a graduate student earning a master's in GIS at the University of Minnesota, Zaruba combines GIS, science, and data science to solve complex spatial problems using advanced computational methods.

As a grad student, he came across Minneapolis's open data on electric scooter use, which was stored as tables with very little connection to where the trips were occurring. "After looking into it deeper, I realized that there wasn't any way to easily think about scooter trips spatially and analyze patterns across both time and space," he said.

This gap inspired Zaruba’s winning EIP project, Micromobility in Minneapolis, Using spatial data science to inspect urban mobility data, which studies scooter travel in Minneapolis. Urban planners and residents alike benefit from offering city-wide single-person transportation options such as bicycles, electric skateboards, or scooters. A micromobility transportation system can help lessen environmental damage, provide economical transportation, and alleviate traffic congestion.

Seeking to inform future decision-makers on how to best prepare the urban landscape for more forms of micromobility, Zaruba used spatial data science techniques to analyze Minneapolis scooter trip data. The analysis aimed to gain a better understanding of where and when users were traveling. “All of this data in its raw form wasn’t very useful for understanding the spatiotemporal dynamics of how people are using the scooters,” said Zaruba.

Zaruba starts the process by transforming the raw data tables containing trip records into spatial data by combining those records with road centerline data. In doing this, each trip now has a start and end location allowing for spatiotemporal analytics to be performed on the data. “This means we can examine the spatial patterns of where trips are starting and ending across time, whether that is through the year or throughout the hours of a day,” said Zaruba.

Next Zaruba took the collected data and cleaned it to transform it into a space-time cube—a visual that can be thought of as a giant cube consisting of many small cubes, with each cube representative of a specific area at a given time. By thinking of it this way, “each smaller cube counts for both the number of trips that started and ended within the spatial and temporal extent of that cube,” said Zaruba.

From the space-time cube, Zaruba created various visualizations to interpret the data including graphs, heat maps, and dot density maps. Next, Zaruba leveraged ArcGIS Pro to streamline multidimensional data, which enabled him to analyze both spatial and temporal attributes. Additionally, he developed time series clustering to find connections in the data.

Zaruba’s project demonstrated that seasonal variations, particularly in cities such as Minneapolis known for unpredictable climate, were a major factor in scooter use. Moreover, he pinpointed why the university differed from the rest of Minneapolis and discovered that it was due to the timing of the academic calendar when students were on campus or had already departed from school.

Looking to the future, Zaruba hopes to continue his research in another transportation-related project: understanding the spatiotemporal patterns and dynamics of serious to fatal car accidents across Minnesota. “This project really sits at the intersection of GIS, data science, and data engineering. I am excited to continue working on it before I graduate with my master's degree,” said Zaruba.

As EIP universities strive to equip students for the future, student innovations such as Yang’s and Zaruba’s are essential to widening the scope of geospatial science through GIS and spatial technologies.