Scholar
Peng Luo
Google Scholar ID: Okwd550AAAAJ
MIT
Citations & Impact
Citations
584
H-index
13
i10-index
18
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Luo, P., Song, C., Li, H., Zhu, D., Duarte, F., 2025. Modeling shared micromobility as a label propagation process for detecting the overlapping communities. Computers, Environment and Urban Systems
- Luo, P., Li, Y., Li, Z., Song, Y. & Meng, L., 2025. Measuring univariate effects in the interaction of geographical patterns. International Journal of Geographical Information Science
- Luo, P., Chen, C., Gao, S., Zhang, X., Majok Chol, D., Yang, Z., & Meng, L., 2025. Understanding of the predictability and uncertainty in population distributions empowered by visual analytics. International Journal of Geographical Information Science, 1–31.
- Luo, P., Song, Y., Zhu, D., Cheng J. and Meng L. A Generalized Spatial Heterogeneity Model for Interpolation. 2022. International Journal of Geographical Information Science, 37(3), 634-659 (Top 10 read at 2023)
- Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y. and Meng, L., 2022. Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing, 185, pp.111-128
- Luo, P., Song, Y. and Wu, P., 2021. Spatial disparities in trade-offs: economic and environmental impacts of road infrastructure on continental level. GIScience and Remote Sensing, 58(5), pp.756-775.
- Luo, P., Zhang, X., Cheng, J. and Sun, Q., 2019. Modeling population density using a new index derived from multi-sensor image data. Remote Sensing, 11(22), p.2620.
Research Experience
- Postdoc at Senseable City Lab, MIT; Involved in the project Favelas 4D
Education
- Ph.D. from Technical University of Munich; Former visiting researcher at the University of Oxford
Background
- Research interests focus on using geographic domain knowledge for urban analysis, particularly considering the unique characteristics of geographic data, such as sparsity and bias. His research experience includes spatial prediction, human mobility analysis, and visual AI.
Co-authors