Spatial Distribution Of Population Density Research Articles

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

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.

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.

Mixed Land Use as an Intrinsic Feature of Sprawl: A Short-Term Analysis of Settlement Growth and Population Distribution Using European Urban Atlas

This study investigates the land-use/population mix over time as the base to derive an indicator of urban sprawl. Land-use individual patches (provided by Urban Atlas, hereafter UA, with a detailed spatial geometry at 1:10,000 scale) were associated with the total (resident) population based on official statistics (census enumeration districts and other public data sources), providing a comprehensive mapping of the spatial distribution of population density by land-use class in a representative case study for the Mediterranean region (metropolitan Athens, Greece). Data analysis adopted a mix of statistical techniques, such as descriptive statistics, non-parametric curve interpolation (smoothing splines), and exploratory multivariate statistics, namely hierarchical clustering, non-metric multi-dimensional scaling and confirmative factor analysis. The results of this study indicate a non-linear gradient of density decline from downtown (dominated by compact settlements) to peripheral locations (dominated by natural land). Population density in agricultural land was locally high and increasing over time; this result suggests how mixed land use may be the base of intense sprawl in large metropolitan regions. The methodology implemented in this study can be generalized over the whole sample of European cities included in Urban Atlas, providing a semi-automatic assessment of exurban development and population re-distribution over larger metropolitan regions.

Modeling the dynamics and spatial heterogeneity of city growth

We propose a systems model for urban population growth dynamics, disaggregated at the county scale, to explicitly acknowledge inter and intra-city movements. Spatial and temporal heterogeneity of cities are well captured by the model parameters estimated from empirical data for 2005–2019 domestic migration in the U.S. for 46 large cities. Model parameters are narrowly dispersed over time, and migration flows are well-reproduced using time-averaged values. The spatial distribution of population density within cities can be approximated by negative exponential functions, with exponents varying among cities, but invariant over the period considered. The analysis of the rank-shift dynamics for the 3100+ counties shows that the most and least dense counties have the lowest probability of shifting ranks, as expected for ‘closed’ systems. Using synthetic rank lists of different lengths, we find that counties shift ranks gradually via diffusive dynamics, similar to other complex systems.

Spiral waves in population density distributions of invasive pests in warm-temperate deciduous forest ecosystems

Globally, forests are increasingly being affected by invasive alien pests, some of which have caused wholesale destruction of trees. Recent research suggests that, in the United States, large-scale forests have been invaded by an astonishing number (> 450) of tree-feeding pests. Invasive alien pests always induce cascading ecological effects, leading to harmful socioeconomic consequences; however, the prevention and control of invasive alien pests in forests have been facing significant challenges worldwide. Here, we studied theoretically the control of invasive nonnative pests in warm-temperate deciduous forest ecosystems by their natural enemies (biological control), and controlled them to an extent that ecosystems can bear. We obtained a novel type of spiral wave in the spatial distribution of population density, and also observed a transition phenomenon between spiral wave and anti-spiral wave, which is explained by a mathematical analysis. Specifically, the formation of this type of spiral wave is attributed to the directional transformation of states between the period-5 orbits under the impact of diffusion transport. And the transition phenomenon is caused by changes of relative positions of the period-5 orbits in phase space. Moreover, we also found a novel type of instability of spiral wave, which includes a birth-growth-death process of spiral fragments. The findings provide possible regularities in the process of biological control of invasive alien pests in the complex and changeable forest ecosystems, which may guide actual biological control projects.

Population distribution characteristics and its relationship with natural factors in karst mountainous areas of Northwest Guangxi

The smallest administrative unit of the sixth national census-township (town) is selected as the basic unit, the population spatial distribution characteristics at the township (town) level in karst mountainous areas of northwest Guangxi are analyzed by using Lorenz curve and spatial correlation analysis method, and the influence intensity of natural factors on regional population spatial distribution is detected by using geographic detector method. The results show that: 1. the spatial distribution of population at the township (town) level has the characteristics of imbalance, showing generally significant positive correlation and certain aggregation; 2. there are significant differences in the impact of the spatial distribution of various natural factors on the population distribution. For the towns without karst distribution in the northwest and central south of the study area, the population density increases with the increase of factors conducive to human residence, but the average population density is only 79 people / km2. In the towns with karst distribution in the East and south, the spatial distribution of population density and natural factors is not a simple increase or decrease relationship, but fluctuates with the change of karst distribution area. 3. The factor detection results of the geographic detector show that the altitude has the greatest impact on the spatial distribution of population. The interactive detection results show that the impact intensity of any two natural factors after superposition and interaction presents nonlinear enhancement and two factor enhancement. It can be seen that the karst mountain area in northwest Guangxi is similar to other areas. Altitude is one of the main factors affecting the spatial distribution of population, but the river network density and unique geological landform of karst mountain area have a strong catalytic effect on the spatial distribution of population. The superposition and interaction with other factors can further strengthen the impact on population distribution.

Evaluating the urban structure of Al-Kut city according to sustainability

The research attempts to assess the urban structure of Kut city - as a sample of Iraqi cities - according to the requirements of sustainability and the extent of their consistency or their distance from the requirements of achieving sustainable urban development. By studying the main urban components of Kut city, analyzing the spatial distribution of land uses, the nature of the neighbourhood, and the spatial distribution of population densities in it, road network and spatial distribution of urban land prices. The research concludes that the study area’s urban spatial structure is ineffective and has many defects. The city is spread over a large area, and the population densities are distributed unorganized. Most of the population densities are far from the centre area; this leads to long, random and erratic trips. Traffic problems in the city are exacerbated by the absence of a hierarchy of roads and weak links between parts of the city as a result of the spread of the city between the two banks of the river and the lack of adequate bridges.

Research on the Evolution of Population Distribution and Influencing Factors in Xi’an During the COVID-19 Epidemic Control Period: Based on a Perspective of Multi-source Spatio-Temporal Big Data

Under the “ultra-conventional” control measures of the COVID-19 period, urban population distribution is different from usual time. Studying its evolution laws has a certain reference effect for the judgment of urban population aggregations, the division of precise control zoning, and the differentiated management of places during the COVID-19 control period. Based on the data of Baidu heat maps and points of interest (POIs), this paper uses three models of the population density index (PDI), the exploratory spatial data analysis (ESDA) and the geological detector (Geodetector) to analyze the characteristics and the influencing factors of the evolution of population distribution of Xi’an in three stages which are closed control stage, unsealed control stage and slack control stage. The results show that: in the three stages, 1) The value of the PDI and the range of the PDI change in Xi’an continue to increase. The single-day PDI curve shows “low-high-low” changing characteristics from morning to night in and within the third ring zone, and “high-low-high” in the suburb. And, by comparison, it is found that the social vitality of the first and second ring zone is more strongly impacted because of COVID-19 control measures. 2) The overall population density in Xi’an is gradually increasing, this is represented by gradual increase of the very high- and the high-density areas, and continuous decrease of the low- and the very low-density areas. And the centripetal distribution of population density, which is high inside the city and low outside the city, is becoming more and more obvious. 3) The spatial distribution of population density represents obvious high-value clusters or low-value clusters in the three stages, ESDA shows a circle structure of inner heat and outer cold, and this trend continues to be reinforced. 4) The intensity order of influencing factors of the 7 types of facilities on population distribution is: residential communities > catering facilities > living service facilities > healthy facilities > commercial facilities > office places > green spaces and squares, and the influencing factor intensities show a continuous increasing or a continuous decreasing process in the three stages.

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach.

The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-à-vis other states in India. The study uses Pearson’s correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

Pattern Dynamics of a Diffusive Toxin Producing Phytoplankton–Zooplankton Model with Three-Dimensional Patch

In this paper, the Turing pattern formation in a diffusive toxin producing phytoplankton–zooplankton model with three-dimensional patch is investigated. Firstly, the conditions for the existence of Hopf bifurcation and stationary pattern are obtained based on the linear stability analysis. Then, the amplitude equations are derived by using nonlinear analysis and standard multiple-time-scale analysis. This study shows that there exists a pattern transition from spot pattern to stripe pattern with the decrease of the amount of toxicity, and the death rate of zooplankton also has a significant effect on the pattern structure. The obtained results suggest that the toxicity of phytoplankton and the death rate of zooplankton both play vital roles in the spatial distribution of population density.

Urbanization patterns and poverty reduction: A new perspective to explore the countries along the Belt and Road

Urbanization and poverty reduction are two vital aspects for global sustainable and healthy development. In the future, the countries along the Belt and Road will play a more important role in global urbanization and poverty reduction. Therefore, this paper aims at elaborating the characteristics and patterns of urbanization and poverty reduction in these countries using data from 1996 to 2016. The results indicate that the average urbanization level is 48% in 2016 and the average annual growth rate of these countries is 0.61 percentage points between 1996 and 2016. In addition, West Asian and Middle Eastern countries have a higher level of urbanization, while ultra-high-speed and high-speed development countries are primarily Southeast Asian countries and China. Furthermore, these countries differentiate significantly in terms of spatial distribution of population density. Regions with a population density of 0–1 person per square kilometer account for 33% of the total area and 0.09% of the total population, while regions with a population density of over 1000 people per square kilometer account for 1.45% of the total area and 39.56% of the total population. Moreover, using mathematical morphology methods, we identify the 10 regions with the largest urban land areas among these countries, and seven regions are located in China and Southeast Asia. Finally, the population living in extreme poverty in these countries dropped from 1.17 billion to 355.6 million during the 20-year period, and several countries, such as China, Laos, Vietnam, Indonesia, Bangladesh, and Mongolia, have made tremendous progress both in urbanization and poverty reduction. These findings are beneficial for comprehending urban development and poverty reduction in the countries along the Belt and Road as well as for providing references for regional cooperation.

GIS based research on the spatial distribution of population density in illegal buildings in Shenzhen City

GIS based research on the spatial distribution of population density in illegal buildings in Shenzhen City

Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

An integrated optimization model was developed for the spatial distribution of agricultural crops in order to efficiently utilize agricultural water and land resources simultaneously. The model is based on the spatial distribution of crop suitability, spatial distribution of population density, and agricultural land use data. Multi-source remote sensing data are combined with constraints of optimal crop area, which are obtained from agricultural cropping pattern optimization model. Using the middle reaches of the Heihe River basin as an example, the spatial distribution of maize and wheat were optimized by minimizing cross-entropy between crop distribution probabilities and desired but unknown distribution probabilities. Results showed that the area of maize should increase and the area of wheat should decrease in the study area compared with the situation in 2013. The comprehensive suitable area distribution of maize is approximately in accordance with the distribution in the present situation; however, the comprehensive suitable area distribution of wheat is not consistent with the distribution in the present situation. Through optimization, the high proportion of maize and wheat area was more concentrated than before. The maize area with more than 80% allocation concentrates on the south of the study area, and the wheat area with more than 30% allocation concentrates on the central part of the study area. The outcome of this study provides a scientific basis for farmers to select crops that are suitable in a particular area.

Increase of Elderly Population in the Rainstorm Hazard Areas of China.

In light of global warming, increased extreme precipitation events have enlarged the population exposed to floods to some extent. Extreme precipitation risk assessments are of great significance in China and allow for the response to climate change and mitigation of risks to the population. China is one of the countries most influenced by climate change and has unique national population conditions. The influence of extreme precipitation depends on the degree of exposure and vulnerability of the population. Accurate assessments of the population exposed to rising rainstorm trends are crucial to mapping extreme precipitation risks. Studying the population exposed to rainstorm hazard areas (RSHA) at the microscale is extremely urgent, due to the local characteristics of extreme precipitation events and regional diversity of the population. The spatial distribution of population density was mapped based on the national population census data from China in 1990, 2000 and 2010. RSHA were also identified using precipitation data from 1975 to 2015 in China, and the rainstorm tendency values were mapped using GIS in this paper. The spatial characteristics of the rainstorm tendencies were then analyzed. Finally, changes in the population in the RSHA are discussed. The results show that the extreme precipitation trends are increasing in southeastern China. From 1990 to 2010, the population in RSHA increased by 110 million, at a rate of 14.6%. The elderly in the region increased by 38 million at a rate of 86.4%. Studying the size of the population exposed to rainstorm hazards at the county scale can provide scientific evidence for developing disaster prevention and mitigation strategies from the bottom up.

Concentration profile of endemic equilibrium of a reaction–diffusion–advection SIS epidemic model

Cui and Lou (J Differ Equ 261:3305–3343, 2016) proposed a reaction–diffusion–advection SIS epidemic model in heterogeneous environments, and derived interesting results on the stability of the DFE (disease-free equilibrium) and the existence of EE (endemic equilibrium) under various conditions. In this paper, we are interested in the asymptotic profile of the EE (when it exists) in the three cases: (i) large advection; (ii) small diffusion of the susceptible population; (iii) small diffusion of the infected population. We prove that in case (i), the density of both the susceptible and infected populations concentrates only at the downstream behaving like a delta function; in case (ii), the density of the susceptible concentrates only at the downstream behaving like a delta function and the density of the infected vanishes on the entire habitat, and in case (iii), the density of the susceptible is positive while the density of the infected vanishes on the entire habitat. Our results show that in case (ii) and case (iii), the asymptotic profile is essentially different from that in the situation where no advection is present. As a consequence, we can conclude that the impact of advection on the spatial distribution of population densities is significant.

Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities

Abstract Understanding the consequences of spatial structure on ecological dynamics is a central theme in ecology. Recently, research has recognised the relevance of river and river‐analogue network structures, because these systems are not only highly diverse but also rapidly changing due to habitat modifications or species invasions. Much of the previous work on ecological and evolutionary dynamics in metapopulations and metacommunities in dendritic river networks has been either using comparative approaches or was purely theoretical. However, the use of microcosm experiments provides the unique opportunity to study large‐scale questions in a causal and experimental framework. We conducted replicated microcosm experiments, in which we manipulated the spatially explicit network configuration of a landscape and addressed how linear versus dendritic connectivity affects population dynamics, specifically the spatial distribution of population densities, and movement behaviour of the protist model organism Tetrahymena pyriformis. We tracked population densities and individual‐level movement behaviour of thousands of individuals over time. At the end of the experiment, we found more variable population densities between patches in dendritic networks compared to linear networks, as predicted by theory. Specifically, in dendritic networks, population densities were higher at nodes that connected to headwaters compared to the headwaters themselves and to more central nodes in the network. These differences follow theoretical predictions and emerged from the different network topologies per se. These differences in population densities emerged despite weakly density‐dependent movement. We show that differences in network structure alone can cause characteristic spatial variation in population densities. While such differences have been postulated by theoretical work and are the underlying precondition for differential dispersal evolution in heterogeneous networks, our results may be the first experimental demonstration thereof. Furthermore, these population‐level dynamics may affect extinction risks and can upscale to previously shown metacommunity level diversity dynamics. Given that many species in natural river systems exhibit strong spatiotemporal patterns in population densities, our work suggests that abundance patterns should not only be addressed from a local environmental perspective, but may be the outcome of processes that are inherently driven by the respective habitat network structure.

Information use shapes the dynamics of range expansions into environmental gradients

AbstractAimGlobally, the geographical distributions of species are dynamic and strongly influenced by dispersal. At the same time, range dynamics feed back and may select for increased dispersal at expanding range fronts. This interplay between macroecological and evolutionary dynamics happens almost universally across environmental gradients and such gradients can have a direct impact on the fitness of organisms due to the match or mismatch between an individual's environmental optimum and the current conditions along the gradient. Importantly, gradients also provide individuals with information on environmental changes because dispersing individuals may sense whether environmental conditions improve or deteriorate. However, the ability of organisms to use this information on the environment and to subsequently adjust dispersal decisions plastically, that is, deciding to further disperse into the gradient or not, has been largely ignored and the macroecological consequences remain unclear. We here aim to demonstrate the impact of informed dispersal on the eco‐evolutionary dynamics of ranges.LocationLaboratory and theoretical.MethodsWe used individual‐based simulations and controlled experiments in replicated microcosm landscapes. Range expansions of the protist model organism Tetrahymena were tracked using video recording and analysis.ResultsWe show that information on environmental gradients had a severe impact on range dynamics and inverted the spatial distribution of population densities in comparison with controls where this information was not provided. Additionally, the use of information on gradients prevented evolutionary changes in dispersal rates and an acceleration of range expansion.Main conclusionsWe demonstrate the strong impact of informed dispersal and subsequent behavioural changes on range dynamics in environmental gradients. More generally, our findings highlight the importance of informed dispersal for spatial ecological and evolutionary dynamics.

Dispersal and the Movius Line: Testing the effect of dispersal on population density through simulation

Abstract It has been proposed that a strong relationship exists between the population size and density of Pleistocene hominins and their competence in making stone tools. Here we focus on the first ‘Out of Africa’ dispersal, 1.8 Ma ago, and the idea that it might have featured lower population density and the fragmentation of hominin groups in areas furthest away from the point of origin. As a result, these distant populations in Central and East Asia and Europe would not be able to sustain sophisticated technological knowledge and reverted to a pattern of simpler stone-knapping techniques. This process could have led to the establishment of the ‘Movius Line’ and other long-lasting continental-scale patterns in the spatial distribution of Lower Palaeolithic stone technology. Here we report on a simulation developed to evaluate if, and under what conditions, the early ‘Out of Africa’ dispersal could lead to such a demographic pattern. The model comprises a dynamic environmental reconstruction of Old World vegetation in the timeframe 2.5–0.25 Ma coupled with a standard biological model of population growth and dispersal. The spatial distribution of population density is recorded over the course of the simulation. We demonstrate that, under a wide sweep of both environmental and behavioural parameter values, and across a range of scenarios that vary the role of disease and the availability of alternative crossing points between Africa, Europe and Asia, the demographic consequence of dispersal is not a gradual attenuation of the population size away from the point of origin but a pattern of ecologically driven local variation in population density. The methodology presented opens a new route to understand the phenomenon of the Movius Line and other large-scale spatio-temporal patterns in the archaeological record and provides a new insight into the debate on the relationship between demographics and cultural complexity. This study also highlights the potential of simulation studies for testing complex conceptual models and the importance of building reference frameworks based on known proxies in order to achieve more rigorous model development in Palaeolithic archaeology and beyond.

Spatial Distribution of Human Population in France: Exploring the Modifiable Areal Unit Problem Using Multifractal Analysis

Case studies in geography are strongly dependent on the size of the spatial units used for the analysis. This has been expressed as the Modifiable Areal Unit Problem (MAUP): whatever the phenomenon under consideration, it is impossible to identify a single spatial partition that would be most appropriate to analyze it. In this respect, multifractal analysis may be an interesting tool for geographers. It integrates not just a series of nested spatial resolutions, as fractal analysis does, but also a series of points of view about the quantity of information contained in each spatial unit. In this article, we first expose the mathematical bases of multifractal analysis and we describe how it applies to geographical analyses. We insist on the mathematical notion of dimension, which allows us to describe how multifractal parameters can be used to quantify the MAUP. Then, we use the method to characterize the spatial distribution of population density in France. The main result is a typology map of population density that uses the MAUP as a descriptive tool. This map allows the joint identification of several phenomena: the main cities, the rural settlement patterns, and several types of periurban settlement patterns.