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Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.

Major disasters such as extreme weather events can magnify and exacerbate pre-existing social disparities, with disadvantaged populations bearing disproportionate costs. Despite the implications for equity and emergency planning, we lack a quantitative understanding of how these social fault lines translate to different behaviours in large-scale emergency contexts. Here we investigate this problem in the context of Hurricane Harvey, using over 30 million anonymized GPS records from over 150,000 opted-in users in the Greater Houston Area to quantify patterns of disaster-inflicted relocation activities before, during, and after the shock. We show that evacuation distance is highly homogenous across individuals from different types of neighbourhoods classified by race and wealth, obeying a truncated power-law distribution. Yet here the similarities end: we find that both race and wealth strongly impact evacuation patterns, with disadvantaged minority populations less likely to evacuate than wealthier white residents. Finally, there are considerable discrepancies in terms of departure and return times by race and wealth, with strong social cohesion among evacuees from advantaged neighbourhoods in their destination choices. These empirical findings bring new insights into mobility and evacuations, providing policy recommendations for residents, decision-makers, and disaster managers alike.

Transportation Network Company (TNC) services have become a prominent factor in urban transportation in recent years, and there is an ongoing debate regarding their relationship with public transit. While many argue that TNCs draw passengers away from public transportation, others believe the two modes complement each other. However, due to the inadequate sample size of rider surveys as primary data sources, our understanding of how riders choose between these two modalities remains limited. This study uses nine months of trip planning data generated by the Transit App, which captures how travelers engage with multiple options in real time, including TNC and public transit services. We extract measures from Transit describing the travel options and the habits of each individual user for sessions in which the user “tapped” on one of these two modes, indicating consideration of it as an option. Machine learning models predict the likelihood of a rider tapping TNC based on features of the available public transit options and other contextual factors (e.g., time of day, weather conditions). The models find that these taps are driven by factors that highlight the convenience of TNC, such as the waiting time, walking distance, and the number of transfers for public transportation trips. We also find that the majority of TNC trips tapped by app users combine the two modes when using the Transit App, with TNC acting as a connection to or from public transit. These results provide detailed additional evidence for current arguments for both competition and complementarity between TNC and public transit from a population that uses an app to navigate public transit.

There is growing evidence that natural disasters take an increasing toll on the global economy, with a predicted future rise in frequency and intensity under the climate change challenge. In addition to the escalating economic losses, extreme shocks such as pandemics and natural hazards can substantially disrupt human lives, especially in urban areas. Previous studies have attempted to capture, quantify and rationalize such perturbation effects as well as human responses. However, there is still a lack of quantitative understanding of how issues like data quality, pre-existing social and physical disparities, mobility properties, and evolving intensities of extreme shocks can be translated or linked to different emergency behaviors in large-scale emergency contexts. This study aims to advance the understanding of urban resilience with large-scale mobility data and makes three contributions to overcome existing limitations: Firstly, a mobility data quality assurance framework is developed to discover data anomalies issues and three aspects that could contribute to the injustice are identified and examined: representativeness, quality, and precision. Real-world mobility data throughout Hurricane Harvey is used to reveal persistent disparity of representativeness and significant drops of overall data precision throughout the event, which implies that the data biases could be reinforced and perpetuated during extreme shocks, and specific mitigation tasks should be employed. Secondly, I conduct a multi-scale study of disaster-induced evacuations of Hurricane Harvey. By analyzing three-month detailed human mobility data before, during, and after Hurricane Harvey, I report both universality and heterogeneity in multi-dimensional evacuation patterns. The research effort further reveals social disparities in the interplay of risk and resilience. Lastly, under the current challenge of COVID-19, dynamic daily high-resolution mobility networks are constructed and analyzed using methods originated from the network percolation theory. The results demonstrate a large-scale cluster structure with evolving patterns and enable identifying a small, manageable set of recurrent critical links located across the United States, serving as valves connecting divisions and regions. The overall findings provide new insights into understanding the urban social and physical disparities in resilience and managing the connectivity of mobility networks during unprecedented external shocks.

Transportation Network Company (TNC) services have become a prominent factor in urban transportation in recent years, and there is an ongoing debate regarding their relationship with public transit. While many argue that TNCs draw passengers away from public transportation, others believe the two modes complement each other. However, due to the inadequate sample size of rider surveys as primary data sources, our understanding of how riders choose between these two modalities remains limited. This study uses nine months of trip planning data generated by the Transit App, which captures how travelers engage with multiple options in real time, including TNC and public transit services. We extract measures from Transit describing the travel options and the habits of each individual user for sessions in which the user “tapped” on one of these two modes, indicating consideration of it as an option. Machine learning models predict the likelihood of a rider tapping TNC based on features of the available public transit options and other contextual factors (e.g., time of day, weather conditions). The models find that these taps are driven by factors that highlight the convenience of TNC, such as the waiting time, walking distance, and the number of transfers for public transportation trips. We also find that the majority of TNC trips tapped by app users combine the two modes when using the Transit App, with TNC acting as a connection to or from public transit. These results provide detailed additional evidence for current arguments for both competition and complementarity between TNC and public transit from a population that uses an app to navigate public transit.

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate its bound percolation by removing the weakly connected edges. The mobility network becomes vulnerable and prone to reach its criticality and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.

Natural disasters can significantly disrupt human mobility in urban areas. Studies have attempted to understand and quantify such disruptions using crowdsourced mobility data sets. However, limited research has studied the justice issues of mobility data in the context of natural disasters. The lack of research leaves us without an empirical foundation to quantify and control the possible biases in the data. This study, using 2017 Hurricane Harvey as a case study, explores three aspects of mobility data that could potentially cause injustice: representativeness, quality, and precision. We find representativeness being a major factor contributing to mobility data injustice. There is a persistent disparity of representativeness across neighborhoods of different socioeconomic characteristics before, during, and after the hurricane's landfall. Additionally, we observed significant drops of data precision during the hurricane, adding uncertainty to locate people and understand their movements during extreme weather events. The findings highlight the necessity in understanding and controlling the possible bias of mobility data as well as developing practical tools through data justice lenses in collecting and analyzing data during disasters.

Major disasters such as extreme weather events can magnify and exacerbate pre-existing social disparities, with disadvantaged populations bearing disproportionate costs. Despite the implications for equity and emergency planning, we lack a quantitative understanding of how these social fault lines translate to different behaviors in large-scale emergency contexts. Here we investigate this problem in the context of Hurricane Harvey, using over 30 million anonymized GPS records from over 150,000 opted-in users in the Greater Houston Area to quantify patterns of disaster-inflicted relocation activities before, during, and after the shock. We show that evacuation distance is highly homogenous across individuals from different types of neighborhoods classified by race and wealth, obeying a truncated power-law distribution. Yet here the similarities end: we find that both race and wealth strongly impact evacuation patterns, with disadvantaged minority populations less likely to evacuate than wealthier white residents. Finally, there are considerable discrepancies in terms of departure and return times by race and wealth, with strong social cohesion among evacuees from advantaged neighborhoods in their destination choices. These empirical findings bring new insights into mobility and evacuations, providing policy recommendations for residents, decision makers, and disaster managers alike.