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The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has created complex problems that require organizations to collaborate within and across the sector line. Social media data can provide insights into how nonprofits interact for the pandemic response from both social network and geographical perspectives. This study innovatively investigated the connection and interaction patterns among 74 National Voluntary Organizations Active in Disaster (NVOAD) nonprofits and three government agencies based on structural analyses and content analyses of their Twitter communications during the long-term global COVID-19 pandemic. The daily tweeting quantities of all nonprofits were generally consistent with the pandemic severity in the United States before July 2020 and remained stable afterward. Nonprofits' tweets can reflect their purposes of sharing information, building communities, and taking actions for disaster response. Government agencies played leadership roles in providing COVID-19 guidelines and information. Human services, International and Foreign Affairs, and Public and Societal Benefit nonprofits, especially American Red Cross played central roles in the nonprofit communication network. Possible explanations include the following: (1) Geographically, connections and interactions among nonprofits are more likely to happen within the same city or in neighboring states. (2) Both mission homophily and heterophily contribute to connections and interactions among nonprofits, depending on their subsectors. The findings not only help the public better understand how nonprofits are collaboratively fighting the pandemic, but also provide guidance for nonprofits to plan for better interactions and communications in future disaster response. [ABSTRACT FROM AUTHOR]

Massive Geo-tagged social media data provide new opportunities for disaster risk assessment, prevention, and management. This article presents a proof of concept for assessing wildfire risk using Geo-tagged social media data, by taking wildfire risk as a function of wildfire hazard and social–ecological vulnerability. The case study of the United States shows that the regions with the highest wildfire hazard are concentrated in the Western, while the most vulnerable areas are mainly distributed in the Eastern, the Western Coast, and the Southern parts of the nation. Areas with high wildfire risk are mainly located in the Northwestern and Southeastern United States. It shows that the wildfire risk level has significant linear relationship with population density. Massive and vulnerable population might result in significant increase in wildfire risk perception. We conclude that Geo-tagged social media data have great potential in disaster risk studies. [ABSTRACT FROM AUTHOR]

Restrictions in the USA on registered sex offenders ( RSOs) are examined from the spatial aspects. The long history of various restrictions imposed by government, particularly local ones, is covered in the introduction. Spatial aspects, such as delineation of zones from which certain activities or certain people are excluded is the focus. Then the nature of restrictions on RSOs is considered at the state, county and municipal level. Typical of restrictions are that RSOs are prohibited from moving into residence within a prescribed distance of certain features in a community. The distances are typically 1,000 feet but are quite variable. Typical proscribed venues are schools, parks and day care centers, but there can be many others such as bus stops. Spatial aspects of these restrictions, such as how offender locations are geocoded and represented and how proscribed venues are delineated is analyzed. Specific details and theoretical concerns related to the many problematic issues with RSO restrictions is presented. In particular questions of their constitutionality and efficacy are raised. The paper concludes with a discussion of the implications of RSO restrictions for the discipline of geography in general and for the evolution of increasingly precise methods of spatial analysis in particular. [ABSTRACT FROM AUTHOR]

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The development of exploratory spatial data analysis methods is an active research domain in the field of geographic information science (GIS). At the same time, the coupled space-time attributes of economic phenomena are difficult to be represented and examined. Both GIS and economic geography are faced with the challenges of dealing with the temporal dynamics of geographic processes and spatial dynamics of economic development across scales and dimensions. This paper thus suggests a novel way to generalize the characteristics and the structure of space-time data sets, using regional economic data as the example. Accordingly, a reasonable number of general questions (data analysis tasks) can be abstracted. Then, tools (methods) may be suggested on that basis. The cross-fertilization between exploratory spatial data analysis (ESDA) and spatial economics is also identified and illustrated by the capabilities of these components, which have uncovered some interesting patterns and trends in the spatial income data of China and the United States. Through exploratory analysis of economic data, the detection of rich details of underlying geographical and temporal processes would be the first step toward such cross-fertilization. In addition, this exploratory analytical framework can be applied to other data sets that are also measured for areal units at multiple points in time. [ABSTRACT FROM AUTHOR]

Drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug-abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug-abuse risk behavior, given tweets. This is because: (1) tweets usually are noisy and sparse and (2) the availability of labeled data is limited. To address these challenging problems, we propose a deep self-taught learning system to detect and monitor drug-abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) to improve the classification performance and (ii) to capture the evolving picture of drug abuse on online social media. Our extensive experiments have been conducted on three million drug-abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug-abuse risk behaviors. [ABSTRACT FROM AUTHOR]

Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10% penetration in the US population) and Facebook's social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: (1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and (2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions., (© 2021. The Author(s).)

We describe the use of network modeling to capture the shifting spatiotemporal nature of the COVID-19 pandemic. The most common approach to tracking COVID-19 cases over time and space is to examine a series of maps that provide snapshots of the pandemic. A series of snapshots can convey the spatial nature of cases but often rely on subjective interpretation to assess how the pandemic is shifting in severity through time and space. We present a novel application of network optimization to a standard series of snapshots to better reveal how the spatial centres of the pandemic shifted spatially over time in the mainland United States under a mix of interventions. We find a global spatial shifting pattern with stable pandemic centres and both local and long-range interactions. Metrics derived from the daily nature of spatial shifts are introduced to help evaluate the pandemic situation at regional scales. We also highlight the value of reviewing pandemics through local spatial shifts to uncover dynamic relationships among and within regions, such as spillover and concentration among states. This new way of examining the COVID-19 pandemic in terms of network-based spatial shifts offers new story lines in understanding how the pandemic spread in geography.