most citedNeural Embeddings of Urban Big Data Reveal Emergent Structures in Cities

9 citations · 25 across the 5 of their papers we have counts for

collaborators

5 papers

physics.soc-ph20219 cited

Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities

Chao Fan, Yang Yang, Ali Mostafavi

In this study, we propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas and their interactions captured by hum…

cs.SI20214 cited

Unraveling the Temporal Importance of Community-scale Human Activity Features for Rapid Assessment of Flood Impacts

Faxi Yuan, Yang Yang, Qingchun Li +1

The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, s…

physics.soc-ph20214 cited

Unraveling the Dynamic Importance of County-level Features in Trajectory of COVID-19

Qingchun Li, Yang Yang, Wangqiu Wang +6

The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2,787 counties…

physics.soc-ph20203 cited

Early Indicators of COVID-19 Spread Risk Using Digital Trace Data of Population Activities

Xinyu Gao, Chao Fan, Yang Yang +4

The spread of pandemics such as COVID-19 is strongly linked to human activities. The objective of this paper is to specify and examine early indicators of disease spread risk in ci…

physics.soc-ph20205 cited

Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States

Chao Fan, Sanghyeon Lee, Yang Yang +3

The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the p…