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Register a new userHigh-resolution human mobility data reveal race and wealth disparities in disaster evacuation patterns
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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.
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Non-pharmacologic interventions (NPIs) are one method to mitigate the spread and effects of the COVID-19 pandemic in the United States. NPIs promote protective actions to reduce exposure risk and can reduce mobility patterns within communities. Growing research literature suggests that socially vulnerable populations are disproportionately impacted with higher infection and higher fatality rates of COVID-19, though there is limited understanding of the underlying mechanisms to this health disparity. Thus, the research examines two distinct and complimentary datasets at a granular scale for five urban locations. Through statistical and spatial analyses, the research extensively investigates the exposure risk reduction of socially vulnerable populations due to NPIs. The mobility dataset tracks population movement across ZIP codes; it is used for an origin-destination network analysis. The population activity dataset is based on the number of visits from census block groups (CBG) to points of interest (POIs), such as grocery stores, restaurants, education centers, and medical facilities; it is used for network analysis of population-facilities interactions. The mobility dataset showed that, after the implementation of NPIs, socially vulnerable populations engaged in increased mobility in the form of inflow between ZIP code areas. Similarly, population activity analysis showed an increased exposure risk for socially vulnerable populations based on a greater number of inflow visits of CBGs to POIs, which increases the risk of contact at POIs, and a greater number of outflow visits from POIs to home CBGs, which increases risk of transmission within CBGs. These findings can assist emergency planners and public health officials in comprehending how different groups are able to implement protective actions and can inform more equitable and data-driven NPI policies for future epidemics.
Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.
Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of the population size at each location. However, when this information is not readily available, their applicability is rather limited. As mobile phones are ubiquitous, our aim is to investigate if mobility patterns can be inferred from aggregated mobile phone call data alone. Using data released by Orange for Ivory Coast, we show that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance. We argue that the strength of the model comes from directly incorporating the social dimension of mobility. Furthermore, as only aggregated call data is required, the model helps to avoid potential privacy problems.
The current study uses a network analysis approach to explore the STEM pathways that students take through their final year of high school in Aotearoa New Zealand. By accessing individual-level microdata from New Zealands Integrated Data Infrastructure, we are able to create a co-enrolment network comprised of all STEM assessment standards taken by students in New Zealand between 2010 and 2016. We explore the structure of this co-enrolment network though use of community detection and a novel measure of entropy. We then investigate how network structure differs across sub-populations based on students sex, ethnicity, and the socio-economic-status (SES) of the high school they attended. Results show the structure of the STEM co-enrolment network differs across these sub-populations, and also changes over time. We find that, while female students were more likely to have been enrolled in life science standards, they were less well represented in physics, calculus, and vocational (e.g., agriculture, practical technology) standards. Our results also show that the enrolment patterns of the Maori and Pacific Islands sub-populations had higher levels of entropy, an observation that may be explained by fewer enrolments in key science and mathematics standards. Through further investigation of this disparity, we find that ethnic group differences in entropy are moderated by high school SES, such that the difference in entropy between Maori and Pacific Islands students, and European and Asian students is even greater. We discuss these findings in the context of the New Zealand education system and policy changes that occurred between 2010 and 2016.
Given the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. While the accessibility of location-enabled devices as well as large-scale datasets of human activities, has fueled significant advances in our understanding, there is little agreement on the linkage between socioeconomic status and its influence on movement patterns, in particular, the role of inequality. Here, we analyze a heavily aggregated and anonymized summary of global mobility and investigate the relationships between socioeconomic status and mobility across a hundred cities in the US and Brazil. We uncover two types of relationships, finding either a clear connection or little-to-no interdependencies. The former tend to be characterized by low levels of public transportation usage, inequitable access to basic amenities and services, and segregated clusters of communities in terms of income, with the latter class showing the opposite trends. Our findings provide useful lessons in designing urban habitats that serve the larger interests of all inhabitants irrespective of their economic status.
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