Spatio-Temporal Data Science (STANCE) Lab

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Avipsa Roy

Assistant Professor in Urban Planning and Public Policy, University of California Irvine

I am an Assistant Professor in the Department of Urban Planning and Public Policy at UCI’s School of Social Ecology, and lead the STANCE Lab. I have a Ph.D. in Computational Spatial Science from Arizona State University and a Masters’ in Geoinformatics from the University of Muenster in Germany.

My research is highly interdisciplinary and involves developing methods in urban analytics and geospatial data science to answer overarching questions in public health, environmental science and social equity. Recent projects have focused on 1) utilizing bias-corrected crowdsourced big data to understand active transportation usage patterns in urban areas, 2) how big data can be used for spatio-temporal change detection, 3) how to inform policymakers about travel behavior from raw GPS and accelerometer data by classifying travel modes , 4) how human mobility patterns and social vulnerability indicators determine COVID-19 risk and 5) image segmentation from LiDAR and thermal imagery for built environment monitoring. I have also worked on developing an open-source Python framework for in-database spatial analytics ibmdbpy-spatial. My other projects are also available via GitHub.

I have presented my research at several national and international academic conferences including the American Association of Geographers annual meeting as well as the American Geophysical Union annual meeting. Apart from these I’ve also presented at Scipy, ACM SIGSPATIAL and Symposium on Data Science and Statistics organized by the American Statistical Association and an invited presentation at the NaTMEC. My work has been covered by ASU Solutions, and .

I have worked at various private organizations including IBM, Accenture, and Cognizant as well as government agencies like the Los Alamos National Laboratory and Oak Ridge National Laboratory. My research and projects are available on Google ScholarResearchGate.


We are always looking for talented graduate students at the master’s and PhD levels whose interests align with my own. I particularly seek students with strong coding and data science skills who can hit the ground running in the STANCE Lab. However these skills are not prerequisites. Drop me an email at along with your CV if you are interested.

You can view/download my full CV here.

Peer-Reviewed Publications:

6. Nelson TA, Roy A, Ferster CJ, Fischer J, Brum-Bastos VDB, Winters M and Laberee K.“Generalized Model for Mapping Bicycle Ridership with Crowdsourced Data”, In Transportation Research Board Part C: Emerging Technologies (2021). 

5. Roy A , Kar B. “Characterizing the Spread of COVID-19 from Human Mobility Patterns and SocioDemographic Indicators”, ARIC’20, ACM SIGSPATIAL. (2020)

4. Roy A, Fuller D, Stanley K, Nelson TA. 2020. “Classifying Transport Mode from Global Positioning Systems and Accelerometer Data: A Machine Learning Approach.” Transport Findings.

3. Roy A,Nelson TA.,Fotheringham AS, Winters M. 2019. “Correcting Bias in Crowdsourced Data to Map Bicycle Ridership of All Bicyclists.” Urban Science. (2019)

2. Roy, A, Fouche E, Rodriguez RA, Moehler G. “In-Database Geospatial Analytics using Python”, In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC’19),Illinois, Chicago, USA. (2019)

1. Roy, A, Pebesma E. “A Machine Learning Approach to Demographic Prediction using Geohashes”. Avipsa Roy and Edzer Pebesma. 2017, In Proceedings of the 2nd International Workshop on Social Sensing(SocialSens’17). ACM, New York, NY, USA, 15-20. (2017)

Media/Press Releases

  1. Machine Learning model to identify neighborhoods most at risk for COVID-19, ASU Now, November 2020.
  2. Using AI to build healthy and safe transportation in citiesASU Now, September 2020.
  3. Artificial Intelligence for Healthy Transportation, May 2020.
  4. Using Python to Study Patterns in Bicycle Related Incidents from Crowdsourced, November 2019.
  5. Correcting bias in crowdsourced fitness app data to map bicycling ridership, ASU SGSUP News, July 2019. 

Research Outcomes