Sign In / Sign Out
Navigation for Entire University
- ASU Home
- My ASU
- Colleges and Schools
- Map and Locations
Avipsa completed her Bachelors degree in Computer Science from University of Calcutta and then moved on to complete her masters degree in Geoinformatics from Wilhelms Westfaelische Universitaet in Muenster. Her research primarily focuses on developing computational & statistical methods to solve a number of spatial-temporal problems - identifying trends in active transportation (eg: bicycling) patterns in urban areas from crowdsourced data, understanding movement behavior in terrestrial animals (grizzly bears) and performing image segmentation by integrating multiple sensor modalities (LiDAR, Thermal Imagery) for terrestrial object detection. In her spare time she likes to travel, read and learn about different regional cultures across the world. She is also interested in photography, music and dance.
PhD Geography (Computational Spatial Science), Arizona State University (Fall 2017 onwards)
MS in Geoinformatics, University of Muenster, Germany (2017)
BS in Computer Science, University of Calcutta, India (2010)
Avipsa's research interest lies in developing data driven solutions using big data to answer questions for a wide range of topics including active transportation, human mobility patterns and public health. She uses machine learning and statistical tools to develop generalizable frameworks that can help in making informed decision about transportation planning, infrastructure management and public health by policymakers.
She has also collaborated with National Labs on several other research problems involving built environment monitoring from LIDAR data, assessing the resilience of transportation networks in the face of natural hazards, monitoring and characterizing the spread of COVID-19 using social vulnerability indicators & human mobility patterns. Some of her earlier work has also been in collaboration with the industry where she has developed an open source crime density mapping tool from real time data using Python.
The tools she develops as part of her research are highly robust and generalizable - they can be used to answer different research questions base don availability of data for multiple study areas. My research is highly interdsciplnary and the models I develop have direct and indirect policy implications.
Spatial Analysis and Research Center (SPARC)
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).https://doi.org/10.1016/j.trc.2021.102981
5. Roy A , Kar B. "Characterizing the Spread of COVID-19 from Human Mobility Patterns and SocioDemographic Indicators", ARIC'20, ACM SIGSPATIAL. https://doi.org/10.1145/3423455.3430303 (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. https://doi.org/10.32866/001c.14520.(2020)
3. Roy A,Nelson TA.,Fotheringham AS, Winters M. 2019. "Correcting Bias in Crowdsourced Data to Map Bicycle Ridership of All Bicyclists." Urban Science. https://doi.org/10.3390/urbansci3020062 (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. https://doi.org/10.1145/3356395.3365546 (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. https://doi.org/10.1145/3055601.3055603 (2017)