Population Density Distribution Research Articles

Spatio-temporal patterns and population exposure risks of urban heat island in megacity Shanghai, China

Accompanied by global warming and rapid urbanization, the urban heat island (UHI) effect has been intensifying, which leads to an increased risk of population heat exposure, especially in mega-cities. However, there are fewer studies on the identification of UHI ranges and the associated population heat exposure risks, particularly on the fine scale. Therefore, this study took Shanghai as a case study and explored the spatio-temporal evolution characteristics of the UHI ranges and population heat exposure risks, based on five-period MODIS land surface temperature (LST) product, population density distribution data, and land use and cover change data from 2000 to 2020, combined with the temperature attenuation mutation (TAM) method, breakpoint regression model, population heat exposure relative risk assessment method, and spatial autocorrelation analysis. The results showed that: (1) From 2000 to 2020, the UHI range in Shanghai was continuously expanding, and the intensity of the surface UHI was gradually increasing. The overall pattern was characterized by a stable large heat island in the central urban area and new small heat islands that continued to emerge in the peripheral suburbs. (2) The population heat exposure risks in Shanghai intensified from 2000 to 2020, with an increasing number of people exposed to high-intensity UHI ranges, doubling the population coverage in these areas. By the end of 2020, the population exposed to UHI ranges accounted for approximately 81 % of the total population of Shanghai. (3) The spatial pattern of population heat exposure risks from 2000 to 2020 was generally similar. Population heat exposure regions of high risk level were primarily concentrated in the central urban areas, showing typical spatial clustering characteristics, while population heat exposure regions of low risk level were mainly dispersed in the peripheral suburbs, and showed a gradual transformation trend into population heat exposure regions of high risk level. Our findings can provide scientific basis and decision support for precise and refined urban heat risk control and planning management.

The equity implications of TOD in Curitiba

Transit Oriented Development (TOD) projects are being increasingly adopted worldwide as a way to promote the coordination between transport and land-use planning. However, little attention has been given to how TOD projects are associated with socioeconomic and spatial inequalities and its effects on people’s access to economic activities and public services, particularly in the Global South. In this paper we analyze how socio-spatial inequalities have been shaped by transport and land-use planning in Curitiba (Brazil), a city internationally recognized for its TOD planning based on Bus Rapid Transit (BRT). We examine how the spatial organization of the BRT system is associated with the distribution of population densities, socioeconomic groups, and real-estate values and its implications in terms of inequalities of access to employment opportunities and health services. The results show that Curitiba's TOD has had limited influence on population densities, but contributed for the unequal distribution of its benefits, privileging high-income classes and premium real-estate along its main BRT corridors. These effects contribute to push low-income communities to peripheral urban areas with limited accessibility benefits from the transit system. Our findings suggest that Curitiba’s success story should be seen as a cautionary tale about the consequences of TOD planning, which perpetuate the spatial concentration of resources and reinforce inequalities of access to opportunities. The broader lesson of this study is that TOD planning must be constantly evaluated by its socials and environmental impacts, and be guided by mixed housing, and social inclusion to avoid potential consequences in terms of segregation and peripheralization of poorer communities.

Prediction of viral spillover risk based on the mass action principle

Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic virus diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both virus diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic virus diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring.

Analyzing Role of FSI in City Sprawl- Case Study: Pune City

Pune is the second-largest city in the state of Maharashtra after Mumbai, and is an important city in terms of its economic and industrial growth. Over the years Pune Municipal Corporation area has grown in concentric rings. Future growth of PMC will be mainly governed by existing transport corridors, existing and future industrial developments in and around PMC and the expansion of central business district (CBD). The aim of the research is to analyze role of FSI in city sprawl supported with a case study of Pune City. The objectives of the research are- to assess existing and proposed policies/projects undertaken in Pune City for last two decades, to understand population growth trend and its distribution within and around the city, to analyze factors contributing to intensification and spatial distribution of population density, to examine the effects of increased population density on the existing infrastructure facilities in the urban settlements and lastly to propose strategies or proposals for the selected study area. The methodology adopted for the study initiates with assessing the data collected from secondary sources. The increase in population and built-up area attributed to various factors are studied using satellite images and GIS techniques. Population and built-up density can be linked up together to understand how the former growth affects the latter. The distribution and pattern of population density helps to provide useful information in provision of necessary service infrastructure. Finally, different approaches of accommodating the future population are studied and proposed in the study. Keywords Urban sprawl; Density; Spatial Distribution; Accessibility; Urban Growth

Identification of bundles and driving factors of ecosystem services at multiple scales in the eastern China region

Intensive human activities have caused serious damage to the natural environment and threatened ecosystem services, while the relationships between ecosystem services at different spatial scales vary to some extent. Therefore, identifying different patterns, evolutionary trends, and influencing factors of ecosystem services at different scales is more helpful in providing scientific decision-making for sustainable ecosystem management. Taking eastern China as the research area, with the help of a Gaussian mixture model, redundancy analysis and geographically weighted regression models, the spatiotemporal pattern evolution of ecosystem services from multiple scale perspectives were conducted, and the interrelationships between ecosystem services and the driving factors were explored. It expanded the knowledge of ecosystem services, the relationships between ecosystem services, and how the socio-ecological drivers of ecosystems vary at different spatial scales, and enabled more precise manipulation of socio-ecological drivers through different driving models. The average value of ecosystem services in the southern region was 0.672, which was better than that in the northern region, which was 0.353. Ecosystem services were lower in regions with a wide distribution of arable land and high population density and economic development. The relationship between ecosystem services was more manifested as synergistic relationship. As the scale expanded, the synergies and trade-offs between most ESs were also stronger and the average increase was 18.42 %. Especially, the relationships between some services developed from synergistic relationships at small scales to trade-offs at the county level. From the perspective of ecosystem service bundle, the overall ecosystem environment of the research area has been greatly improved, with enhanced supply function and weakened vulnerability. The area of ecosystem provisioning bundles increased by an average of 10.31 % as the scale increased, while the ecosystem regulation bundle decreased by 7.33 %. By comparison, the non-spatially driven model had better accuracy at the scale of 1 km2 and 100 km2, while the spatially driven model had higher accuracy at the county scale. Besides, normalized difference vegetation index occupied an absolute dominant position, and the natural elements acted more at the micro-scale, while the socio-economic elements were more influential at the macro-scale. These results provided a basis for subsequent research on multi-scale ecosystem control and ecological compensation strategies, and showed that natural and social factors were scale-dependent, that it was necessary to study ecosystem services, and emphasized the importance of developing appropriate policies at particular scales.

Spatial analysis and risk factors of dengue hemorrhagic fever in Yogyakarta city, 2017-2018

Background: Dengue hemorrhagic fever (DHF) is a public health problem with high morbidity and mortality rates. These diseases are caused by the bite of the Aedes aegypti mosquito. Yogyakarta City is one of the endemic areas of DHF in Indonesia. This study aims to analyze the spatial distribution of DHF incidents and determine the risk factors associated with DHF incidence in Yogyakarta in 2017-2018. Methods: This was a descriptive quantitative with an ecological study design and using secondary data. Data was analyzed using statistical analysis and spatial analysis. Results: Spatial analysis shows that the DHF distribution does not follow the distribution of population density, house density, and larva-free rate. We found the population density (p value= 0.010, r = -0.661), house density (p value= 0.059, r = -0.516), and larva-free rate (p value= 0.907, r= -0.034). Conclusions: Spatially, there is no relationship between population density, house density, and larva-free rate to the DHF incidence in the City of Yogyakarta in 2017-2018. There is a relationship between population density and the incidence of DHF. In contrast, house density and larva-free rate have no association with the incidence of DHF in Yogyakarta in 2017-2018. The higher the population density, house density, and larva-free rate, the tendency for the incidence to decrease.

Benthic foraminiferal patchiness – revisited

Abstract. Many benthic organisms show aggregated distribution patterns due to the spatial heterogeneity of niches or food availability. In particular, high-abundance patches of benthic foraminifera have been reported that extend from centimetres to metres in diameter in salt marshes or shallow waters. The dimensions of spatial variations of shelf or deep-sea foraminiferal abundances have not yet been identified. Therefore, we studied the distribution of Globobulimina turgida dwelling in the 0–3 cm surface sediment at 118 m water depth in the Alsbäck Deep, Gullmar Fjord, Sweden. Standing stock data from 58 randomly replicated samples depicted a log-normal distribution of G. turgida with weak evidence for an aggregated distribution on a decimetre scale. A model simulation with different patch sizes, outlines, and impedances yielded no significant correlation with the observed variability of G. turgida standing stocks. Instead, a perfect match with a random log-normal distribution of population densities was obtained. The data–model comparison revealed that foraminiferal populations in the Gullmar Fjord were not moulded by any underlying spatial structure beyond 10 cm diameter. Log-normal population densities also characterise data from contiguous, gridded, or random sample replicates reported in the literature. Here, a centimetre-scale heterogeneity was found and interpreted to be a result of asexual reproduction events and restricted mobility of juveniles. Standing stocks of G. turgida from the Alsbäck Deep temporal data series from 1994 to 2021 showed two distinct cohorts of samples of either high or low densities. These cohorts are considered to represent two distinct ecological settings: hypoxic and well-ventilated conditions in the Gullmar Fjord. Environmental forcing is therefore considered to impact the population structure of benthic foraminifera rather than their reproduction dynamics.

Spatiotemporal Characteristics of Ozone Pollution and Resultant Increased Human Health Risks in Central China

The spatiotemporal characteristics of ozone pollution and increased human health risks in Central China were investigated using a long time series of ozone concentrations from 2014 to 2020. We found a gradual increase in ozone pollution, with the highest concentrations observed in the northeastern region. The spatial distribution of population density showed distinct patterns, with the northeastern and east-central regions coinciding with areas of high ozone concentrations. The study found an overall increasing trend in MDA8 ozone concentrations, with a regional average increase of 3.5 (μg m−3) per year, corresponding to a 4.4% annual increase. We observed a significant clustering of areas at a higher risk of premature mortality associated with long-term ozone exposure, particularly in the northeastern region. Estimated premature mortality due to ozone pollution in Central China between 2014 and 2020 shows an increasing trend from 2014 to 2019 and a decreasing trend in 2020 due to the occurrence of extreme ozone pollution and the subsequent recovery of ozone concentrations after the closures due to COVID-19. Premature mortality due to ozone exposure is affected by both ozone levels and the exposed population, with high correlation coefficients exceeding 0.95. The high total population (more than 220 million per year) and increasing ozone levels exacerbate the problem of premature mortality due to ozone pollution. This study improves our understanding of the impact of ozone pollution on human health and emphasizes the dynamic nature of ozone pollution and its impacts on human health over time. It underscores the need for further study and comprehensive action to mitigate these health risks.

Transition from MAR to EIR of deuterium plasma detachment in NAGDIS-II

Volumetric plasma recombination in a detached deuterium (D) plasma was experimentally studied in the linear plasma device NAGDIS-II. The radial distributions of the electron temperature (T e ) and the electron density (n e ) were measured by upstream and downstream laser Thomson scattering (LTS) systems and D-atom excited states of the Balmer series (n = 3–7) were measured by optical emission spectroscopy (OES). The results indicated that the electron static pressure, particle flux and heat flux present a two-step reduction with an increasing neutral pressure, suggesting that molecule activated recombination (MAR) and electron–ion recombination (EIR) were activated, respectively. The difference in the distributions of the atomic state population densities was observed to be a feature of the transition from the MAR-dominant phase to the EIR-dominant phase. The transition between the two phases had a distinct boundary at T e ∼0.7 eV, which was considered a transition point between the phases. The ratios of the atomic state population densities showed different features in the two phases, and are expected to be useful in characterizing the phase and degree of plasma detachment.

Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities

BackgroundMany interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively.MethodsThis observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O’ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence.ResultsIn total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran’s I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana.ConclusionsOur findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.

Electromobility Prospects in Greece by 2030: A Regional Perspective on Strategic Policy Analysis

Electromobility represents a strong option for reducing carbon emissions in the road transport sector. This study presents a methodology and a simulation tool that project the evolution of the market share of electric vehicles (EVs) in the new car market. The analysis adopts a stylized regional resolution, which accounts for attributes on the NUTS-2 level, such as the population density, GDP/capita, education levels, and current EV charger distribution, to simulate the uptake of BEVs in different regions. The methodology applies discrete choice modelling techniques, considering tangible and intangible factors, including purchasing and operation costs, an estimated cost for range anxiety and public charging, and a market maturity index. The analysis is based on four different scenarios, referring to the updated Greek National Energy Climate Plan. The results reveal that regions with a higher average income, GDP/capita, and population density show a higher uptake of EVs. Overall, the tool implements a method of simulating the market evolution of EVs up to 2030 in reference to regional parameters and, hence, highlights the regions that require the most attention in order to achieve national targets. The results can inform policymakers in developing tailored strategies and financial support to accelerate the adoption of BEVs, particularly in regions where their uptake prospects are lower.

Plant parasitic nematodes associated with oil palm trees in three regions of Ghana

Field surveys were conducted in three oil palm producing regions of Ghana to determine the frequency, population density and geographical distribution of plant parasitic nematodes associated with oil palm. Palms ranging from 4 to 12 years were surveyed during the peak periods of two successive cropping seasons of 2019 and 2020. A total of 64 soil and root samples were collected and plant parasitic nematodes extracted and identified from them. 716 mixed populations of 11 economically important plant-parasitic nematode genera were recovered and identified from both roots and soils during the period. These were Aphelenchus, Criconemoides, Helicotylenchus, Hemicriconemoides, Meloidogyne, Pratylenchus, Radopholus, Rotylenchus, Tylenchorynchus, Tylenchus and Xiphinema. Meloidogyne and Pratylenchus were the most frequently isolated and widely distributed geographically. Meloidogyne recovered from both soil and roots were 220 whilst Pratylenchus recovered were 129. These nematodes are very important pests of several crops, therefore, if not managed properly, could pose a threat to oil palm production in Ghana.

Evolution Characteristics and Causes—An Analysis of Urban Catering Cluster Spatial Structure

Studying the development characteristics of the urban catering industry holds significant importance for understanding the spatial patterns of cities. In this manuscript, according to the characteristics of the distribution of catering points and based on catering point of interest (POI) data of 106 cities in China in 2016 and 2022, we propose the Natural Nearest Neighbor Single Branch Model (NNSBM) to identify catering points by adaptive clustering, which improves the efficiency of identifying catering clusters. Subsequently, a catering spatial structure division model is constructed to classify the spatial structure of catering clusters into 3 major categories and 17 subcategories, and the evolution pattern of urban catering clusters is analyzed. In addition, based on the population density raster data, a bivariate spatial autocorrelation model is employed to analyze the complex relationship between the distribution of urban catering clusters and population density, revealing the distinctive characteristics of urban catering cluster evolution. The results showed that (1) In the initial stage of catering cluster formation, catering activities tend to gather first in a specific area of the city, giving rise to the main catering cluster. However, as the catering industry progresses, the phenomenon of “central fading” occurs within the main catering cluster. (2) The overall trend of the catering spatial structure of most cities showed an evolution toward low primacy–high concentration (Lp-Hc), and cities at different stages of catering capacity exhibited different evolution characteristics of catering clusters. (3) The influence of population density on catering distribution was staged, with a varying impact on cities with different types of catering spatial structures.

Determinants of the embodied CO2 transfers through electricity trade within China

There is unequal spatial distribution of resource endowment, population density, industrial structure, and economic development with diverse differences in labor, energy, and capital productivities in China. However, previous studies paid little attention to the determinants of CO2 transfers embodied in electricity trade. In this study, we use both the absolute and comparative advantage theories to reveal the determinants of embodied CO2 transfers through electricity trade within China. Results show that China's electricity sector has higher labor productivity but lower asset efficiency and energy productivity than that of mining and manufacturing sectors. The large-scale electricity trade alleviates the shortage of electricity supply in developed regions by outsourcing to the less-developed regions, reduces the unequal spatial distribution of coal and natural gas reserves, and changes CO2 flow embodied in power grid. Econometric analysis shows that coal reserve contributes to the increase of embodied CO2 emission, while natural gas reduces the embodied CO2 emission. The regional differences in the opportunity cost of labor productivity of non-electricity sector are the dominant factor of the embodied CO2 transfers through electricity trade within China, while asset efficiency and energy productivity are not significant in the regressions. Our findings could provide details about China's power grid expansion when confronting climate mitigation in the future.

Sustainable Urban Distribution of Educational Institutions and Population Density in Baghdad City Using Remote Sensing Techniques

Urban planning include the creation of strategies as well as the management of metro regions, municipalities, and cities. In this study, the importance of applications of remote sensing and GIS in urban planning will be studied. The distribution of educational destitution cases in cities will be considered. A study area (Baghdad city) will be adopted, and the spatial analysis of the distribution will be according to population densities. In this study, the focus was on the importance of the sustainable distribution of urban educational institutions and the spatial appropriateness of this distribution according to the study areas and the available information. Distribution maps were prepared according to the densities for the number of schools and population densities, and tables were also prepared for the age density of students as well as the preparations of teachers in these areas that were adopted in this study. The data collection was based on the GIS atlas. The spatial analysis will be done to illustrate and measure the correction coefficient between the numbers of schools teaches centers in the adopted area. The results showed that some areas of Karkh and Rusafa have sufficient numbers of schools, while others suffer from a lack of such schools. These areas were mapped based on spatial analysis data, and a proposed map was drawn for educational institutions such as schools of all kinds (secondary, middle, and primary). The proposed school distribution map will be done according to population density. After stowing and performing a spatial analysis of the distribution of education institutions in the study area and collecting data on population densities, we suggest a new spatial distribution by adding the numbers of other schools that fit with the need, population density, and number of students.

Spatial Accessibility of Urban Emergency Shelters Based on Ga2SFCA and Its Improved Method: A Case Study of Kunming, China

Emergency shelters are important public facilities for urban disaster prevention and mitigation. Measuring their spatial accessibility provides a valuable reference for urban modernization and a balanced layout of emergency shelters. In previous studies, the Gaussian two-step floating catchment area (Ga2SFCA) method has the shortcoming of ignoring the effect of a fixed search radius on accessibility. In this study, an improved Ga2SFCA method was proposed to evaluate emergency shelter accessibility in the main urban area of Kunming, China. The parameters of the serviceability of the shelters and the degree of population aggregation were introduced to calculate their effect on the search radius. The traditional Ga2SFCA method and the improved method were compared and analyzed. The results showed that the improved method is more reasonable and realistic in the search radius of densely populated areas and areas lacking shelters, thus providing highly objective and accurate accessibility to emergency shelters. Further, the spatial distribution of the emergency shelters in the main urban area of Kunming was unbalanced and did not match the population density distribution. The accessibility of shelters in parts of the northwestern, eastern, and central parts of the city was low. The method presented in this study could be used to provide reference and support for the government and planners to reasonably plan shelter facilities and promote equal access for all residents to shelter resources.

TaskPOI Priority-Based Energy Balanced Multi-UAVs Cooperative Trajectory Planning Algorithm in 6G Networks

In the emergency rescue scenario where the communication infrastructure is damaged, Unmanned Air Vehicle(UAV) networking communication, as a part of the 6G air-space-ground integrated network system, plays a huge role in the establishment of the emergency communication network. In this paper, we fully consider the distribution of rescue areas, the type of tasks and the characteristics of UAVs, and design the TaskPOI-based multi-UAVs collaborative trajectory planning algorithm(TMCTP) based on regional tasks, aiming to improve the completed efficiency of rescue tasks and the utilization rate of energy consumption in the UAV backbone network. Firstly, we filter the population-intensive points of interest(PiPOI) region based on the location heat map of population density distribution and use the K-means clustering algorithm to generate task points of interest(TaskPOI) and use the neural collaborative filtering algorithm to determine the task priority and different priorities correspond to different task types. Then, we design a single UAV flight model and a multi-UAVs cooperative flight model. At the same time, considering the flight energy consumption, communication energy consumption and data acquisition energy consumption of UAV, the energy consumption equilibrium distribution model is designed to ensure that multi-UAVs have low energy consumption and balance the residual energy distribution while completing various levels of tasks. Finally, based on UAV flight model, energy consumption equilibrium distribution model, and combined with task priority of TaskPOI, TMCTP algorithm for the trajectory planning of the UAV group is designed by using multi-agent reinforcement learning algorithm. The experimental results show that in the TMCTP algorithm, the success rate of the UAV arriving at the goal and the completed rate of the action are converged to 1, the times of collisions is converged to 0, and the average reward, the loss of algorithm are all converged to a good degree, and the PiPOI coverage rate in the area exceeds 60%. In terms of energy consumption, the difference between the energy consumption of each UAV is small, and compared with the average residual energy using GP3, the difference is not large, indicating that the algorithm can improve the PiPOI coverage of the entire rescue area without generating a large energy consumption. In this way, the UAV group after arriving at the goal will provide communication services for the rescue area and improve the connectivity and life cycle of the 6G air network.

Partially Centralized Model-Predictive Mean Field Games for controlling multi-agent systems

Fast, high-performance control of large Multi-Agent Systems (MASs) is a major challenge. We address this problem using Mean Field Games (MFGs), which deduce the macroscopic dynamics of the density distribution of the agent population from the microscopic dynamics of each agent. To control MASs using the MFG, two main problems need to be solved: preventing control performance degradation caused by distribution estimation errors and ensuring the scalability of communication. To overcome these issues, we develop a new control method called the Partially Centralized Model-Predictive MFG (PCMP-MFG). The proposed method solves the first issue by repeating distribution estimation and model-predictive control at each time; it solves the second by introducing broadcast control. Our numerical results show that the proposed PCMP-MFG method outperforms the conventional MFG-based method in a wide parameter range.

Quantifying the human cost of global warming

AbstractThe costs of climate change are often estimated in monetary terms, but this raises ethical issues. Here we express them in terms of numbers of people left outside the ‘human climate niche’—defined as the historically highly conserved distribution of relative human population density with respect to mean annual temperature. We show that climate change has already put ~9% of people (&gt;600 million) outside this niche. By end-of-century (2080–2100), current policies leading to around 2.7 °C global warming could leave one-third (22–39%) of people outside the niche. Reducing global warming from 2.7 to 1.5 °C results in a ~5-fold decrease in the population exposed to unprecedented heat (mean annual temperature ≥29 °C). The lifetime emissions of ~3.5 global average citizens today (or ~1.2 average US citizens) expose one future person to unprecedented heat by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

Level of fire danger of the local territory

The object of the study is the fire risk of the local area. The problem to be solved is to take into account most of the significant parameters in the territorial placement of fire-rescue units of different functional capacities. As part of the solution to this problem, a technique for assessing the fire risk of a large-scale local area has been developed. The methodology is focused on local territories of a large area with a low population density. A special feature of the proposed method is the differentiated fire risk assessment of each point of the surface plane. For such an assessment, the parameters that are decisive from the point of view of impact on the fire hazard are analyzed and structured. The specified factors include the spatial distribution of population density and buildings, the transport and communication network, the spatial distribution of the density and type of vegetation, and statistical data on landscape fires. The use of existing geo-informational resources in real time is foreseen. A new approach of ranking the fire risk of the elementary plane of the territory in accordance with the necessary number of resources of rescue units to ensure the appropriate level of safety is proposed. Neural network data processing methods were used to compare local area parameters with fire risk ranks. A neural network capable of comparing the fire risk of the territory with its parameters was obtained. The functionality of the developed methodology was tested and the fire risk levels of an arbitrary area were graded with an average degree of correlation of 0.97. The proposed method allows for assessment and correction of the state of provision of local territories with civil protection resources. The developed methodology is especially relevant when creating new fire and rescue units of territorial communities.