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20202023
most citedCommunity-scale Big Data Reveals Disparate Impacts of the Texas Winter Storm of 2021 and its Managed Power Outage

115 citations · 374 across the 61 of their papers we have counts for

collaborators
Showing 2021Show all

16 papers · 1 filter

physics.soc-ph2021

Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness

Faxi Yuan, Chao Fan, Hamed Farahmand +7

Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demo…

cs.LG2021

A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features

Hamed Farahmand, Yuanchang Xu, Ali Mostafavi

The objective of this study is to develop and test a novel structured deep-learning modeling framework for urban flood nowcasting by integrating physics-based and human-sensed feat…

physics.soc-ph2021★ 1 cited

Revealing the Global Linguistic and Geographical Disparities of Public Awareness to Covid-19 Outbreak through Social Media

Binbin Lin, Lei Zou, Nick Duffield +7

The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the o…

physics.soc-ph2021★ 9 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…

physics.soc-ph2021★ 6 cited

Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data

Faxi Yuan, William Mobley, Hamed Farahmand +5

The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Predictive flood…

physics.soc-ph2021★ 6 cited

Limitations of gravity models in predicting fine-scale spatial-temporal urban mobility networks

Chiawei Hsu, Chao Fan, Ali Mostafavi

This study identifies the limitations and underlying characteristics of urban mobility networks that influence the performance of the gravity model. The gravity model is a widely-u…