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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.

China’s economic reforms and unprecedented growth have generated many fascinating issues for scholarly research. An understanding of urbanization and land use change in China is required for appropriate strategies and policies to facilitate future sustainable development. This paper reviews the literature on urbanization, land use and sustainable development in China with a focus on land use change. We argue that land use and environmental research are embedded in the complex economic-geographical processes and multiple trajectories of development and urbanization in China. This paper highlights the important role of space–time modeling in a multi-disciplinary setting in the study of urbanization, land use and sustainable development. It also points out potential areas for future research.

Clarifying the impact mechanisms of landscape patterns on ecosystem services is highly important for effective ecosystem protection, policymaking, and landscape planning. However, previous literature lacks knowledge about the impact mechanisms of landscape patterns on ecosystem services from a spatial perspective. Thus, this study measured landscape patterns and the ecosystem services value (ESV) using a series of landscape pattern metrics and an improved benefit transfer method based on land-use data from 2015. It explores the impact mechanisms of the landscape pattern metrics on the ESV using the ordinary least-squares method and spatial regression models in the middle reaches of the Yangtze River Urban Agglomerations (MRYRUA), China. We found that forestland was the main landscape type in the MRYRUA, followed by cultivated land, and the fragmentation degree of cultivated land was significantly higher than that of forestland. The findings demonstrate that landscape pattern metrics had a significant impact on ecosystem services, but could vary greatly. Moreover, ecosystem services in the MRYRUA exhibited significant spatial spillover effects and cross-regional collaborative governance was an effective means of landscape planning. This paper acts as a scientific reference and effective guidance for landscape planning and regional ecosystem conservation in MRYRUA and other similarly fast-growing urban agglomerations.

Social media data are increasingly being used for enhancing situational awareness and assisting disaster management. We analyzed the wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness. Findings show that social media data can characterize the wildfire across space and over time, and thus are applicable to provide useful information on disaster situations. Second, people have strong geographical awareness during wildfire hazards and are interested in communicating situational updates related to wildfire damage (e.g., containment percentage and burned acres), wildfire response (e.g., evacuation), and appreciation to firefighters. Third, news media and local authorities are opinion leaders and play a dominant role in the wildfire retweet network.

This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.

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.

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

The impact of climate extremes upon human settlements is expected to accelerate. There are distinct global trends for a continued rise in urban dwellers and associated infrastructure. This growth is occurring amidst the increasing risk of extreme heat, rainfall, and flooding. Therefore, it is critical that the urban development and architectural communities recognize climate impacts are expected to be experienced globally, but the cities and urban regions they help create are far more vulnerable to these extremes than nonurban regions. Designing resilient human settlements responding to climate change needs an integrated framework. The critical elements at play are climate extremes, economic growth, human mobility, and livability. Heightened public awareness of extreme weather crises and demands for a more moral climate landscape has promoted the discussion of urban climate change ethics. With the growing urgency for considering environmental justice, we need to consider a transparent, data-driven geospatial design approach that strives to balance environmental justice, climate, and economic development needs. Communities can greatly manage their vulnerabilities under climate extremes and enhance their resilience through appropriate design and planning towards long-term stability. A holistic picture of urban climate science is thus needed to be adopted by urban designers and planners as a principle to guide urban development strategy and environmental regulation in the context of a growingly interdependent world.

The number of urban vitality assessment studies is rising continuously, owing to the emergence of geographic data. The current literature focuses primarily on evaluation, rather than implementation, of urban vitality. Hence, a scoping review and research agenda are needed for urban vitality research to be more practical. This study aims to fill the research gap by exploring the content and methods of vitality assessment that can make urban vitality research more compatible with policy, planning, and design practice. We chose the neighborhood scale, which is the most practical initiative unit for vitality enhancement. We discovered that the gaps between the current research and practice primarily lie in the diversity of research subjects, the authenticity and comprehensiveness of vitality measurement, and the multi-domain of impact factor analysis. On this basis, we classified the following expandable aspects: (1) multi-type, multi-dimensional, multi-temporal, and implementation-adaptive vitality evaluation; (2) methods reflecting high-quality social interactions and the perceptions of vulnerable groups; (3) how design and urban management impacts vitality; and (4) the synergistic effects of multiple indicators on vitality. Overall, the research content and methodology presented in this paper can help neighborhood-scale vitality assessment to provide more meaningful insights for policy makers and practitioners.

Globally, the urban heat island (UHI) effect is a major problem which leads to urban residents suffering from adverse urban ecological environments and serious health risks. Understanding the impacts of urban landscape features on the thermal environment has been an important focus across various fields of research. The purpose of this study is to analyze the impacts of urban heat source–sink landscape patterns on urban heat islands, using the fast-growing Zhengzhou City in central China as the case study. Landsat data (captured in 1996, 2006, and 2014), various geospatial approaches, and correlation analysis were applied to facilitate the analysis. Based on the contributions of the urban landscape to land surface temperature (LST), we empirically identified the heat sources and heat sinks. Then, the composition and configurations of heat source and sink landscapes were estimated by a series of spatial metrics at the landscape and class levels. The results showed that the overall mean land surface temperature (LST) in the study area increased by 2.72 °C from 1996 to 2014. This observed increasing trend in overall mean LST is consistent with the process of rapid urbanization in the study area, which was evidenced by the dramatic increase in impervious surfaces and the substantial loss in vegetation cover. Generally, as observed, landscape composition has a stronger influence on LST than does landscape configuration. For heat sources, the proportion, size, aggregation, and density of patches have positive effects on LST, while adjusting the spatial distribution and abundance of urban landscape are effective ways to control the UHI effects. In contrast, the percentage, size, density, and aggregation of heat sink patches have negative effects on LST. Additionally, the effects of increasing total patch edges and shape complexity should be considered when mitigating the UHI effect. These findings are beneficial for furthering our understanding of how urban landscape patterns affect UHI, and they can help optimize urban landscape patterns to alleviate the UHI effect and enhance sustainable development in the study area.