Urban Analytics, Spatial Data Science, Urban Science, Urban Sustainability


Xiaojiang is a tenure-track assistant professor at Department of Geography and Urban Studies, Temple University. He was a Postdoctoral Fellow at MIT Senseable City Lab. His research focuses on developing and applying geospatial analyses and data-driven approaches in the domain of urban studies. He has proposed to use Google Street View for urban environmental studies and developed the Treepedia project, which aims to map street greenery for cities around the world. He is also working on using human trace data to study human activities and investigate the connection between urban environments and human activities. He has been selected as the 50 Rising Stars in Geospatial World in 2021. He has been awarded the Global Young Scientist Award at World Geospatial Developers Conference. 

His research interests include Urban Science, Spatial Data Science, data-driven urban environment modeling, mobility and travel behavior profiling, urban computing and spatial analyses, and remote sensing. His research aims to provide a better understanding of human and urban environment systems and explore using digital technologies to benefit human society. His work has been featured in popular media outlets, including TIME, Wall Street Journal, Scientific American, The Guardian, Forbes, Atlantic-Citylab, Associated Press, and MIT News.


Selected Publications

  • Xiaojiang Li & Wang, G. (2021). GPU parallel computing for mapping urban outdoor heat exposure. Theoretical and Applied Climatology, 1-11.
  • Xiaojiang Li & Wang, G. (2021). Examining runner's outdoor heat exposure using urban microclimate modeling and GPS trajectory mining. Computers, Environment and Urban Systems, 89, 101678.
  • Xiaojiang Li (2021). Investigating the spatial distribution of resident’s outdoor heat exposure across neighborhoods of Philadelphia, Pennsylvania using urban microclimate modeling. Sustainable Cities and Society, 103066.
  • Sevtsuk, Andres, Rounaq Basu, Xiaojiang Li, and Raul Kalvo. 2021, "A big data approach to understanding pedestrian route choice preferences: Evidence from San Francisco." Travel Behaviour and Society 25 (2021): 41-51.
  • Xiaojiang Li, (2020). Examining the spatial distribution and temporal change of the green view index in New York City using Google Street View images and deep learning. Environment and Planning B: Urban Analytics and City Science, 2399808320962511.
  • Xiaojiang Li, Bill Cai, Jinhua Zhao, Carlo Ratti, (2019), A novel method for predicting and mapping the presence of sun glare using Google Street View, Transportation Research, C, Emerging Technologies, 106, 132-144.
  • Xiaojiang Li., & Ratti, C. (2019). Mapping the spatio-temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model. Landscape and urban planning191, 103387.
  • Xiaojiang Li, Carlo Ratti, Estimating the shade provision of street greenery in Boston by combining remote sensing data and Google Street View panoramas, Urban Forestry and Urban Greening, 2018.
  • Xiaojiang Li, Carlo Ratti, Ian Seiferling, 2018, Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View, Landscape and Urban Planning, 169, 81-91.
  • Xiaojiang Li, Paolo Santi, Theodore Courtney, Carlo Ratti, Investigating the association between streetscape built environment and human walking activities using human trace data, Transaction in GIS, 1–16.
  • Xiaojiang Li, Zhang, C., Li, W., Ricard, R., Meng, Q., Zhang, W. Assessing street-level urban greenery using Google Street View and a modified green view index, Urban Forestry and Urban Greening, 2015, 14(3).

Courses Taught

  • GUS 8061 Big Spatial Data Science
  • GUS 5073 Geovisualization
  • GUS 5062 Fundamental of GIS