91猎奇

This web map tool provides a snapshot on the location and sectoral typology of the high-tech zones in the U.S. at the most granular level.

This project assessed and visualized broadband availability vis-a-vis the location of those who are in need of broadband access in the Assembly District (AD) 30 communities. 

A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery. International Journal of Geographical Information Science.

The San Jos茅 Bike Equity Web Map (SJ-BE iMap) measures and maps the City of San Jos茅's best and worst regions for cycling through geospatial analyses and visualization

Alignment Score measures and maps the impact of a climate strategies between regions and their cities on vehicle trip reduction over time

As part of SPUR's 15-minute city projects, SAVI measured and mapped access and proximity (within one-half mile) to groceries, financial institutions, parks, community centers, childcare, and health facilities. They show that areas with high access to some services and amenities, say, financial institutions, can have low access to other services and amenities, in particular, groceries.

An empirical investigation into Affordable Transit-Oriented Development (A-TOD) in California; Applying classification and scoring systems:

This project evaluates California's affordable housing investments in Transit-Oriented Development, assessing transit quality and equity in High Quality Transit Areas.

Research and Development; the Bay鈥檚 Bike Equity iMap:

This project assesses bike equity in the Bay Area, supporting multimodal planning with Delphi-AHP indicators.

Parking Tools Performance Assessment; Empirical Analysis of Parkade and Parknav:

This project evaluates Parkade and Parknav's effectiveness in managing parking and enhancing user experience.

Building AI and Machine Learning Technologies for Enhancing Transportation SJ Station Area Safety: 

This project creates an AI-based crime prediction tool integrating crime data and transportation networks.

Yang, B., Liu, H., Kang, E., Shu, S., Xu, M., Wu, B., Beck, R., Hinkel, K., & Yu, B., (2021) 

Yang, B.; Hawthorne, T.L.; Hessing-Lewis, M.; Duffy, E.J.; Reshitnyk, L.Y.; Feinman, M.; Searson, H. (2020) 

Zandiatashbar, A., & Hamidi, S. (2022)

Zandiatashbar, A. (2021)

Rohrmeier, K. and Heher, J. (2021)

Albrecht, J., Petutschnig, A., Ramasubramanian, L., Resch, B., & Wright, A. (2021)

Petutschnig A, Albrecht J, Resch B, Ramasubramanian L, and A Wright (2022)

Hamerlinck, J. and Ramasubramanian, L. (2021)

Kovacs-Gy枚ri, A, Ristea, A, Havas, C, Mehaffy, M, Hochmair, H, Resch, B, Juhasz, L, Lehner, A, Ramasubramanian, L, and T Blaschke (2020)