Skip to main content
Menu Search & Directory
Close

SLU Innovation Challenge

Hosted by the Department of Innovation and partners, an innovative technology challenge is held each year at Saint Louis University. The challenge:

  • Encourages accessible applications of innovative technologies that advance the University's strategic plan.
  • Fosters student curiosity and ingenuity.
  • Recognizes and awards pioneering ideas
  • Creates collaborative student and faculty cohorts around shared interests.

2021 Innovation Challenge

The 2021 challenge stuck with the Geospatial theme and challenged participants to apply open-source or open-access geospatial tools or open geospatial data to address a real-world problem of their choosing. Supported by SLU’s Geospatial Institute and industry partners Amazon Web Services and Esri, teams developed innovative projects on topics ranging from Latino voting patterns and mapping COVID-19 mortality to remote sensing of violent conflicts. Teams who presented during the Geospatial Innovation Challenge Webinar received a cash prize.

Watch the 2021 Innovation Challenge Webinar.

2021 Teams

Innovative Prototype for Mapping COVID-19 Cases/Deaths

Team Members

  • Ruaa Al Juboori, MBChB, MPH, Ph.D. student
  • Ucheoma Nwaozuru, Ph.D., Post Doctoral Fellow

Project Summary

As of May 6, 2021, the United States reported a total of 32.3 million cases of COVID-19 and over 570,000 deaths since the beginning of the pandemic. Reducing COVID-19 morbidity and mortality will require equitable distribution of vaccination and other prevention services. Yet, the national focus is on the big metropolitan regions with limited representation of rural counties. Many of these counties have larger minority populations. Some of these counties are showing spikes in the number of COVID-19 cases/deaths. Therefore, providing a balanced and nuanced analysis of the urban vs rural areas using Rural-Urban Continuum Codes (RUCC) will provide a more comprehensive picture of rural counties with the majority of Native American, Latino and Black populations.

This is a prototype/proof of concept to visualize health outcomes, particularly COVID-19 cases/deaths based to highlight rurality. We undertook an exploratory approach with no underlying theoretical framework, to provide a typology to understand if rurality matters in health outcomes. In the case of COVID-19 cases/deaths, information on the influence of location can inform tailored distribution strategies for COVID-19 vaccines and for other health outcome measures.

Beyond the Latino Vote: A Geo-Spatial Visualization Tool to Analyze Socio-Spatial Variation in Latino Voting Patterns

Team Members

  • Laura Brugger, M.A., Ph.D. student
  • Pedro Spindler-Ruiz, MPP, Ph.D. student

Project Summary

Latino voters have often been portrayed as a monolithic group, even though studies have shown that Latino voting patterns are not uniform and instead vary based on key socio-spatial characteristics such as education, class, region and country of origin, among many others. This inaccurate portrayal of a Latino voting group presents challenges for protecting voting rights among a growing electorate.

Through this project, we sought to gain a better understanding of variation in how Latinos voted in the 2020 presidential election through the creation of an open-source, interactive GIS visualization tool and by estimating socio-demographic characteristics at the precinct-level. We plan to use this tool to inform strategies that address the ecological fallacy and strengthen the Voting Rights Act for Latinos in the United States.

See the project's story map

If a bomb goes off in the forest, and no one is around to hear it…: Predicting Explosions in Remote Locations

Team Members

  • Caleb Buffa
  • Zach Phillips, Ph.D.

Project Summary

We can confirm the locations of explosions when there are people around, but what if nobody is around to witness an explosion, or worse: everyone in the explosion is killed and there is no reporting? We used the location of known explosions to create an app that estimates unknown, unconfirmed explosions. The app uses change detection statistics from known explosion events in the ACLED database to estimate the probability of an explosion in a user-selected location using Machine Learning and the Google Earth engine.

2020 Innovation Challenge

The 2020 challenge focused on applications of Geospatial Technology and Geospatial Information Systems (GIS). SLU recently founded the Geospatial Institute to accelerate research, training, and innovation in geospatial science and technology.

Through its partnerships with the National Geospatial Intelligence Agency (NGA) and industry leaders like Esri, Saint Louis University is uniquely positioned to advance our city's proud geospatial heritage that stretches back to the mapping of the Louisiana territory in 1806. This challenge honors that pioneering spirit and commitment to advancing the frontier of science and technology.

Successful teams received a cash prize and invitation to both present their project and participate in an upcoming geospatial-focused cohort of the SLUStart entrepreneurship program.

2020 Teams

Balloon Technology for Cheaper Cell Towers

Team Members

Project Summary

We sought to create a first-generation prototype of a cheaper alternative for cell towers using a balloon, mainly to be used during emergency situations when mainstream towers are down. We developed a battery-powered circuit to power and charge Arduino controlled RF receivers and transmitters to relay messages between first responders and victims of crisis.

We lifted the system into the air using a helium-filled balloon, with the payload beneath. Further improvements could be made by increasing structural integrity, using higher-end equipment, adding solar panels, and implementing a secure communication protocol.

Project Presentation

SLU 2020 Geospatial Innovation Challenge - Cheaper Alternative for Cell Towers Using Balloons 

Spatial Prediction of Freeway Accidents Using Machine Learning Technique

Team Members

Project Summary

 The proposed approach in this project was used to develop accident prediction models for a 4.91-mile segment of Interstate 270 in St. Louis, Missouri. The developed models will help the transportation agencies to proactively allocate their resources in anticipation of roadway accidents.

Random Forest, a Machine Learning Method built-in ArcGIS Pro was used to develop the prediction models. Overall, the performance of the Random Forest model's ability to estimate the location of traffic accidents was satisfactory and outperformed the performance measures reported for accident prediction outlined in the literature.

Project Presentation

SLU 2020 Geospatial Innovation Challenge - Public Space Management

Detection of Free Spots in Public Spaces

Team Members

  • Abhinav Kumar , Parks College of Engineering Aviation and Technology
  • Eric Suter , Parks College of Engineering Aviation and Technology

Project Summary

With neural networks and machine learning becoming increasingly accessible, the technology can be applied for every-day use in public spaces to openly share useful information.

For our project, a small, self-sufficient internet-of-things device with a camera can determine whether a seat in a public space is currently occupied or predict when it will become vacant. Combined with geospatial data such as a 3D-scan of the space the device is in, the collected information can effectively communicate to end-users the availability of a certain location.

Project Presentation

 SLU 2020 Geospatial Innovation Challenge - Public Space Management