Abstract The study introduces FloodGenome, an interpretable machine learning model, to assess flood risk disposition in urban areas by analyzing hydrological, topographic, and built-environment features their interactions. Utilizing data from the U.S. National Flood Insurance Program (2003-2023) across four metropolitan areas, it employs k-means clustering a random forest model classify predict property levels. model's effectiveness is proven different highlighting importance of factors like...

Abstract Lifestyle recovery captures the collective effects of population activities as well restoration infrastructure and business services. This study uses a novel approach to leverage privacy-enhanced location intelligence data, which is anonymized aggregated, characterize distinctive lifestyle patterns unveil trajectories after 2017 Hurricane Harvey in Harris County, Texas (USA). The analysis integrates multiple data sources record number visits from home census block groups (CBGs)...

Standard environmental hazard exposure assessment methods have been primarily based on residential places, neglecting individuals' exposures due to activities outside home neighborhood and underestimating peoples' overall exposures. To address this limitation, study proposes a novel mobility-based index for the evaluation. Using large-scale human mobility data, we quantify extent of population dwell time in high places 239 US counties three hazards. We explore how extends reach hazards leads...

Understanding the fundamental characteristics that shape inherent flood risk disposition of urban areas is critical for integrated design strategies reduction. Flood specifies an and event-independent magnitude property measures extent to which are susceptible damage if exposed a weather hazard. This study presents FloodGenome as interpretable machine learning model evaluation various hydrological, topographic, built-environment features their interactions in areas. Using claims data from...

Abstract Understanding the determinants underlying variations in urban health status is important for informing design and planning, as well public policies. Multiple heterogeneous features could modulate prevalence of diseases across different neighborhoods cities cities. This study examines related to socio-demographics, population activity, mobility, built environment their non-linear interactions examine intra- inter-city disparity four disease types: obesity, diabetes, cancer, heart...

Abstract Extreme weather poses significant threats to air transportation systems, causing flight rerouting and cancellations, as well passenger travel delays. With the growing frequency of extreme hazards, it is essential understand extent which disruptions in flights subsequent cancellations impact This study focuses on quantifying impacts a recent event (2022 Winter Storm Elliott) U.S. system by investigating delays measured based dwell time at airports using privacy-preserving...

Motivation: Enhancing the propagation efficiency of shear waves in deep tissues, improving sensitivity motion-sensitive sequences to small wave displacements, optimizing inversion algorithms can enhance quality MRE images and increase reliability results. Gradient strengt is one important factors affecting quality. Goal(s): The aim this study investigate role gradient field strength whole-brain images. Approach: Keeping other parameters constant, varying yields parameter maps that are...

Evaluating human exposure to environmental hazards is crucial for identifying susceptible communities and devising targeted health policies. Standard hazard assessment methods have been primarily based on place of residence, an approach which neglect individuals exposures due the daily life activities mobility outside home neighborhood. To address this limitation, study proposes a novel mobility-based index evaluation. Using large-scale fine-grained data, we quantify extent population dwell...

This study investigates the interplay among social demographics, built environment characteristics, and environmental hazard exposure features in determining community level cancer prevalence. Utilizing data from five Metropolitan Statistical Areas United States: Chicago, Dallas, Houston, Los Angeles, New York, implemented an XGBoost machine learning model to predict extent of prevalence evaluate importance different features. Our demonstrates reliable performance, with results indicating...

Abstract A human-centric perspective on flood exposure assessment of flood-hazard vulnerability transcends the conventional focus spatial by quantifying effect daily human activities in flood-prone areas. Using a novel index to quantify time individuals spend areas, this method characterizes latent exposure. Calculations rely millions fine-resolution location-based data points collected anonymously from smartphones opted-in users. comparative analysis multiple U.S. metropolitan cities based...

Abstract Understanding the determinants underlying variations in urban health status is important for informing design and planning, as well public policies. Multiple heterogeneous features could modulate prevalence of diseases across different neighborhoods cities cities. This study examines related to socio-demographics, population activity, mobility, built environment their non-linear interactions examine intra- inter-city disparity four disease types: obesity, diabetes, cancer, heart...

Understanding the determinants underlying variations in urban health status is important for informing design and planning, as well public policies. Multiple heterogeneous features could modulate prevalence of diseases across different neighborhoods cities cities. This study examines related to socio-demographics, population activity, mobility, built environment their non-linear interactions examine intra- inter-city disparity four disease types: obesity, diabetes, cancer, heart disease....