COVID-19

New APP and human mobility data released: ODT Flow Explorer

Prediction and control of the spread of infectious diseases such as COVID-19 benefits greatly from our growing computing capacity to quantify fine-scale human movement. In response to the soaring needs of human mobility data during the COVID-19 pandemic, we extracted the worldwide daily population flows from billions of geotagged tweets and SafeGraph data, and developed an interactive geospatial web portal, called ODT (Origin-Destination-Time) Flow Explorer (http://gis.cas.sc.edu/GeoAnalytics/od.html), that allows researchers to query, aggregate, visualize, and download daily human movement data at various geographic scales. This article provides an overview of how we extracted of population movement from Twitter and SafeGraph data and demonstrates how the ODT Flow Explorer can be used to query, visualize, and download human mobility data.


Time-series clustering for home dwell time during COVID-19: what can we learn from it?

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph, and further assess the statistical significance of sixteen demographic/socioeconomic variables from five major categories. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures. Policymakers need to carefully evaluate the inevitable trade-off among different groups, making sure the outcomes of their policies reflect interests of the socially disadvantaged groups.

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Understanding the Disparity in HIV Service Interruption in the Outbreak of COVID-19 in South Carolina

To examine HIV service interruptions during the COIVD-19 outbreak in South Carolina (SC) and identify geospatial and socioeconomic correlates of such interruptions, we collected qualitative, geospatial, and quantitative data from 27 Ryan White HIV clinics in SC in March, 2020. HIV service interruptions were categorized (none, minimal, partial, and complete interruption) and analyzed for geospatial heterogeneity. Nearly 56% of the HIV clinics were partially interrupted and 26% were completely closed. Geospatial heterogeneity of service interruption existed but did not exactly overlap with the geospatial pattern of COVID-19 outbreak. The percentage of uninsured in the service catchment areas was significantly correlated with HIV service interruption (F = 3.987, P = .02). This mixed-method study demonstrated the disparity of HIV service interruptions in the COVID-19 in SC and suggested a contribution of existing socioeconomic gaps to this disparity. These findings may inform the resources allocation and future strategies to respond to public health emergencies.

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Analyzing the the characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google community mobility reports, 3) mobility data from Descartes Labs, and 4) Twitter mobility calculated via weighted distance. We find statistically significant positive correlations in the  between either two data sources, revealing their general similarity, albeit with varying Pearson’s  coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The positive correlation between RI and income at the county level is significant in all mobility datasets, suggesting that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. To our best knowledge, this is the first study that cross-compares multi-source mobility datasets. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.

We invite submissions to the Special Issue “GIScience for Risk Management in Big Data Era”
by ISPRS International Journal of Geo-Information

This Special Issue aims to capture recent efforts and advancements in harnessing the power of GIScience for risk management in the big data era.

The first group of possible topics is to inspire potential authors to deal with basic and new trends related to the big data era. The contribution of novel approaches to spatial data collection (social networks, sensors, citizen science, VGI, etc.), disaster big data processing and sharing, real-time data-centric intelligence based on sensors, harmonization of heterogeneous data into a single structure, cybersecurity of geographical information systems and others, is welcomed, along with analyses and commentary.

The second thematic block will cover cartography and GIS theories such as mobile disaster cartography, concepts, ontologization and standardization, cross-cultural aspects of disaster cartography, investigation of the psychological condition of end-users given by their personal character and situation, and the psychological condition of rescued persons are offered together with questions that are still open on the mapping methodologies and technologies for EW&CM from children and senior perspectives.

The third group of topics aims to address mapping and visualization techniques. Dynamic and real-time cartographic visualization concepts and techniques for enhanced operational activities for selected EW, DRM, and DRR purposes are highlighted. Included in the same group are both virtual environments for EW, DRM, and DRR as well as 3D analysis and visualization of disaster events.

The last group of topics is devoted to services and applications, and may include analyses and descriptions of location-based services for emergencies (web services, etc.), multimodal emergency positioning, mapping based on social big data, internet of things for solutions and visualizations, and disaster chain modeling.

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Human Mobility, Policy, and COVID-19: A Preliminary Analysis of South Carolina

Using geotagged Twitter data as the mobility data source and South Carolina as the case study, we present some preliminary findings and visualizations on population flows and human mobility changes during the pandemic at state level and county level. The potential associations between human mobility, state policies, and COVID-19 cases are also examined.

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How our collective efforts of fighting COVID-19 are reflected on maps?

The whole world is now fighting the coronavirus (COVID-19). Social/physical distancing and limiting travel are effective approaches to contain the virus. Everyone’s effort counts. By analyzing world population flows through the lens of geotagged Twitter data during the COVID-19 Pandemic, this article (story map) showcases how our collective efforts of fighting the virus are reflected on maps and how big social media data can be used for such analyses.

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Another interactive web app for aggregated population flows and statistics is being developed and tested.
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