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The aim of the paper is to indicate which determinants had the major impact on the spatial diffusion of the Bollywood cinema in two aspects: the first appearance of this cinema in different countries and the number of films distributed there. The distance between the country and Mumbai and the size of the diaspora were taken as key determinants. The concept of Hägerstrand was adopted as the spatial diffusion model. The procedure involved gathering data for 5,832 Hindi movies produced in Mumbai distributed in cinemas in 76 countries from 1970 to 2010. The hypotheses were verified and it was proved that the spatial diffusion of the popular hindi cinema was influenced by a number of social, cultural and political determinants, of which the size of the Indian diaspora was the most important. The paper can be a reference to the discussion about diaspora identity and the intersection of cultures.

Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. Conclusions Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries’ settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

How populations distribute in both space and time is one of the key issues in ecological systems, which can characterize the relationship between populations, space–time structure and evolution law. Consequently, pattern dynamics in ecosystems has been widely investigated including their causes and ecological functions. In order to systematically understand the interactions in ecosystems, we summarize the related results in pattern formation of ecological systems. Based on mathematical modeling and analysis, we show the mechanisms of different patterns including feedback, scale-dependent, phase separation, nonlocal effects, time delay and spatial heterogeneity. This work offers assistance for better understanding the complexity of ecosystems and provides new insights for self-organizations evolution and ecosystem protection. We hope that our results may be applied in other related fields such as epidemiology, medical science, atmospheric science and so on.

The theory of the principal eigenvalue is developed for an elliptic eigenvalue problem associated with a linear parabolic cooperative system with some zero diffusion coefficients. Then the basic reproduction number and its computation formulae are established for reaction-diffusion epidemic models with compartmental structure. These theoretical results are applied to a spatial model of rabies to study the influence of spatial heterogeneity and population mobility on disease transmission. [PUBLICATION ABSTRACT]

Dengue fever occurs throughout mainland China, except in the Tibet Autonomous Region. During 2005–2020, there were 12,701 imported cases and 81,653 indigenous cases recorded. The indigenous cases were mainly clustered in Guangdong (74.0%) and Yunnan provinces (13.7%). Indigenous dengue fever is a seasonal illness in mainland China, manifesting predominantly in summer and autumn. Indigenous dengue fever cases tend to peak every 5years and have shown a substantial increase during the period 2005–2020. During the study period, indigenous dengue fever occurred more than ten times in each of the seven counties of Guangdong Province. Indigenous dengue fever has spread from low to high latitudes; that is, from the southwestern, southern, and southeastern areas to the central and northern regions, and from border ports and cities to rural areas. Aedes aegypti has become widespread in Yunnan Province but has diminished in Guangxi, Guangdong, and Hainan provinces in recent years. Aedes albopictus is distributed throughout mainland China, spanning 25 provinces and municipalities. To maintain effective public health prevention and control, it is important to monitor dengue occurrence, provide dengue classification guidance, and ensure sustainable vector management of Aedes.

COVID-19 hit Italy in February 2020 after its outbreak in China at the beginning of January. Why was Italy first among the Western countries? What are the conditions that made Italy more vulnerable and the first target of this disease? What characteristics and diffusion patterns could be highlighted and hypothesized from its outbreak to the end of March 2020, after containment measures, including a national lockdown, were introduced? In this paper, we try to provide some answers to these questions, analyzing the issue from medical, geographical and planning points of view. With reference to the Italian case, we observed the phenomenon in terms of the spatial diffusion process and by observing the relation between the epidemic and various environmental elements. In particular, we started from a hypothesis of the comparable economic, geographical, climatic and environmental conditions of the areas of Wuhan (in the Hubei Province in China, where the epidemic broke out) and the Po Valley area (in Italy) where most cases and deaths were registered. Via an ecological approach, we compared the spatial distribution and pattern of COVID-19-related mortality in Italy with several geographical, environmental and socio-economic variables at a Provincial level, analyzing them by means of spatial analytical techniques such as LISA (Local Indicators of Spatial Association). Possible evidence arose relating to COVID-19 cases and Nitrogen-related pollutants and land take, particularly in the Po Valley area.

In just a few weeks, COVID-19 has become a global crisis and there is no longer any question of it being a major pandemic. The spread of the disease and the speed of transmission need to be squared with the forms and characteristics of economic globalization, disparities in development between the world’s different regions and the highly divergent degree of their interconnectedness. Combining a geographic approach based on mapping the global spread of the virus with the collection of data and socio-economic variables, we drew up an OLS model to identify the impact of certain socio-economic factors on the number of cases observed worldwide. Globalization and the geography of economic relations were the main drivers of the spatial structuring and speed of the international spread of the COVID-19.

We characterize the shape of spatial externalities in a continuous time and space differential game with transboundary pollution. We posit a realistic spatiotemporal law of motion for pollution (diffusion and advection), and tackle spatiotemporal non-cooperative (and cooperative) differential games. Precisely, we consider a circle partitioned into several states where a local authority decides autonomously about its investment, production and depollution strategies over time knowing that investment/production generates pollution, and pollution is transboundary. The time horizon is infinite. We allow for a rich set of geographic heterogeneities across states. We solve analytically the induced non-cooperative differential game and characterize its long-term spatial distributions. In particular, we prove that there exist a Perfect Markov Equilibrium, unique among the class of the affine feedbacks. We further provide with a full exploration of the free riding problem and the associated border effect.

Gelugpa is the most influential extant religious sect of Tibetan Buddhism, which is the spiritual prop for Tibetans, with thousands of monasteries and followers in Tibetan areas of China. Studies on the spatial diffusion processes of Gelugpa can not only reveal its historical geographical development but also lay the foundation for anticipating its future development trend. However, existing studies on Gelugpa lack geographical perspective, making it difficult to explore the spatial characteristics. Furthermore, the prevailing macro-perspective overlooks spatiotemporal heterogeneity in diffusion processes. Therefore, taking monastery as the carrier, this study establishes a multi-level diffusion model to reconstruct the diffusion networks of Gelugpa monasteries, as well as a framework to explore the detailed features in the spatial diffusion processes of Gelugpa in Tibetan areas of China based on a geodatabase of Gelugpa monastery. The results show that the multi-level diffusion model has a considerable applicability in the reconstruction of the diffusion networks of Gelugpa monasteries. Gelugpa monasteries in the Three Tibetan Inhabited Areas present disparate spatial diffusion processes with diverse diffusion bases, speeds, stages, as well as diffusion regions and centers. A powerful single-center diffusion-centered Gandan Monastery was rapidly formed in U-Tsang. Kham experienced a slower and more varied spatial diffusion process with multiple diffusion systems far apart from each other. The spatial diffusion process of Amdo was the most complex, with the highest diffusion intensity. Amdo possessed the most influential diffusion centers, with different diffusion shapes and diffusion ranges crossing and overlapping with each other. Multiple natural and human factors may contribute to the formation of Gelugpa monasteries. This study contributes to the understanding of the geography of Gelugpa and provides reference to studies on religion diffusion.

Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.