Spatial Distribution Of Population Research Articles

Spatial distribution characteristics and influencing factors of milk tea stores in Wuhan based on sDNA and OPGD models.

Milk tea stores have rapidly expanded in Wuhan due to residents' increased consumption demand. Therefore, studying the spatial distribution and influencing factors of stores is important for optimizing their layout and promoting economic development. Using milk-tea store data from Amap City's Point of Interest function and road network data from Baidu HeatMap, we analyzed the spatial distribution characteristics of stores within the third ring road of Wuhan City using ArcGIS. We then examined the influencing factors by combining spatial design network analysis, optimal parameters-based geographical detection, and location-based service big data. Our results revealed the following: (1) The spatial distribution of stores was concentrated in areas with high closeness and betweenness centrality, forming a multi-core "northwest-southeast" distribution pattern with significant spatial positive correlation. (2) The stores' spatial pattern was influenced by the road network betweenness and the presence of office buildings, shopping malls, shopping centers, and tourism resources. The road network betweenness had the greatest impact on the stores' spatial distribution, while the kernel density of betweenness presented a "one major and multiple sub-core" structure consistent with that of the stores' spatial distribution. The kernel density of closeness and betweenness regulated the formation of the stores' core area and multiple sub-core areas, respectively, and both factors governed the stores' spatial distribution, which was characterized by a "widely-scattered and sporadically-clustered" pattern. (3) The stores' distribution was closely associated with the spatial and dynamic population distribution at different times of the day. By demonstrating the big data for the spatial distribution and driving factors of milk tea stores at the urban regional scale, we fill the research gap on the spatial distribution of milk tea stores at the meso-scale. Our results offer insights into the future urban planning of milk tea stores amid the current milk tea craze.

Spatial distribution and clustering properties of the young stellar populations in the Carina Nebula complex and Car OB1

Aims. We use Gaia DR3 astrometry and photometry to analyze the spatial distribution of the young stellar populations and stellar clusters and to search for new OB star candidates in the Carina Nebula complex and the full extent (∼5°, corresponding to ∼200 pc) of the Car OB1 association. Methods. We first performed a new census of high-mass stars in Car OB1 and compiled a comprehensive catalog of 517 stars with known spectral types (128 O-type, WR, and supergiant stars, and 389 B-type stars) that have Gaia DR3 parallaxes consistent with membership in the association. We applied the clustering algorithm DBSCAN on the Gaia DR3 data of the region to find stellar clusters, determine their distances and kinematics, and estimate ages. We also used Gaia astrometry and the additional astrophysical_parameters table to perform a spatially unbiased search for further high-mass members of Car OB1 over the full area of the association. Results. Our DBSCAN analysis finds 15 stellar clusters and groups in Car OB1, four of which were not known before. Most clusters (80%) show signs of expansion or contraction, four of them with a ≥2σ significance. We find a global expansion of the Car OB1 association with a velocity of vout = 5.25 ± 0.02 km s−1. A kinematic traceback of the high-mass stars shows that the spatial extent of the association was at a minimum 3–4 Myr ago. Using astrophysical parameters by Gaia DR3, we identified 15 new O-type and 589 new B-type star candidates in Car OB1. The majority (≳54%) of the high-mass stars constitute a non-clustered distributed stellar population. Based on our sample of high-mass stars, we estimate a total stellar population of at least ∼8 × 104 stars in Car OB1. Conclusions. Our study is the first systematic astrometric analysis that covers the full spatial extent of the Car OB1 association, and it therefore substantially increases the knowledge of the distributed stellar population and spatial evolution of the entire association. Our results suggest suggests Car OB1 to be the most massive known star-forming complex in our Galaxy.

Mathematical Modeling of Host-Parasitoid Dynamics for Sustainable Pest Management

Aims: The management of agricultural pest populations represents a critical challenge in sustainable crop production, particularly for challenging species like the tomato fruit borer, Helicoverpa armigera. The research work aimed in developing and analysing a novel mathematical model describing the interaction between the tomato fruit borer and its egg parasitoid, Trichogramma chilonis. The main objective of this study to analyze the stability of the proposed system of differential equations. Model Formulation: The model incorporates stage-structured population dynamics and impulsive control measures, providing insights into biological pest control strategies. In this model the tomato borer is represented by the egg and larval stages, and the parasitoid is considered in terms of the parasitized eggs. This model assumes uniform spatial distribution of both host and parasitoid populations, constant environmental conditions affecting parameters like reproduction and mortality rates, and idealized parasitoid release conditions where the released amount p remains consistent across interventions. Methodology: We used existing secondary data from research articles' instead of conducting primary field experiments for parameter identification for this mathematical model. The use of secondary data from peer-reviewed literature not only provides validated parameter values that have been rigorously tested and verified by multiple researchers, but also allows for the incorporation of findings from diverse geographical locations and climatic conditions. Results: Stability analysis identified three equilibrium points: the trivial, host-only E0,coexistence equilibria E1, and E2. The coexistence equilibirium E2 found to be locally stable under optimal parasitoid release intervals and quantities. The equilibrium point E2= (9.14,0.02,5.71) represents a coexistence state in the host-parasitoid dynamics, where the tomato fruit borer's egg density stabilizes at 9.14, the density of parasitized eggs at 0.02, and the larval density stabilizes at 5.71. This equilibrium is analyzed through its associated eigenvalues. This stability provides a reliable basis for implementing successful biological pest control strategies in tomato cultivation, ensuring a resilient and sustainable ecosystem.

Modelling the evolution of the Galactic disc scale height traced by open clusters

Context. The scale height (SH) of the spatial distribution of open clusters (OCs) in the Milky Way exhibits a well-known increase with age that is usually interpreted as evidence for dynamical heating of the disc or for the disc having been thicker in the past. Aims. We address the increase in the SH with age of the OC population from a different angle. We propose that the apparent thickening of the disc can be largely explained as a consequence of a stronger disruption of OCs near the Galactic plane by encounters with giant molecular clouds (GMCs). Methods. We present a computational model that forms OCs with initial masses and follows their orbits, while subjecting them to different disruption mechanisms. To set up the model and infer its parameters, we used and analysed a Gaia-based OC catalogue. We investigate both the spatial and age distributions of the OC population and discuss the sample completeness. The simulation results are compared to the observations. Results. Consistent with previous studies, the observations reveal that the SH of the spatial distribution of OCs increases with age. We find that it is likely that the OC sample is incomplete even for the solar neighbourhood. The simulations successfully reproduce the SH evolution and the total number of OCs that survive with age up to 1 Gyr. For older OCs, the model-predicted SH starts deviating from observations, although it remains within the uncertainties of the observations. This can be related to the effects of incompleteness and/or simplifications in the model. Conclusions. The OC encounters with GMCs effectively explain the SH evolution of the OC population. An interesting result is that the average time for an object with a Sun-like orbit to encounter a GMC is approximately 700 Myr, aligning well with previous estimates for the Sun obtained through different methods.

Reduced avoidance behaviour in Daphnia magna due to agrochemical-induced vulnerability.

Reduced avoidance behaviour in Daphnia magna due to agrochemical-induced vulnerability.

Population: It’s Spatial Dimensions and Attributes in Ranchi District

Population geography is an important branch of Human Geography that focuses on the study of Human population, its density, growth, composition and distribution. The study of population distribution refers to the statistical characteristics of a population including age, gender, race ethnicity, income, education and geographic location. Analyzing demographic distribution is crucial for understanding the social dynamics, economic trends and public policy implications. This policy can inform businesses, governments and organizations about the need and preferences of different population segments. The spatial distribution of population across geographic areas exhibits certain patterns which are of primary interest to both the demographers and geographers. This paper attempts to analyze the regional disparity of population at block-level in the most populous district of Jharkhand. Ranchi district is contributed by combination of various factors that has developed it into the most populous district of Jharkhand using the concentration index component. Apart from the trends in population growth, various methods have been adopted to gain a better understanding of the spatial dimension of the population.

From Chaos to Fractals: A Review of Mathematical Modeling of Population Dynamics

The dynamic changes in population size are a core issue in ecological research and are crucial for understanding ecosystem stability and biodiversity. Chaos theory and fractal theory offer new perspectives for exploring population dynamics. Through mathematical modeling and computational methods, the complexity and intrinsic patterns of population changes can be revealed. This paper reviews the application of chaos theory and fractal theory in the study of population dynamics. The research covers the role of chaos theory in revealing the nonlinear characteristics of population dynamics and sensitivity to initial conditions, as well as the ability of fractal theory to describe population spatial distribution patterns and scale effects. Special attention is given to the application of the fractal model (_p_ model) proposed by Su et al. in analyzing species abundance distributions (SAD) within communities, and the potential value of chaos and fractal theories in practical ecological conservation and management. This paper finds that chaos theory and fractal theory provide powerful analytical tools for studying population dynamics, helping to understand the complexity and unpredictability of population size changes. The application of these theories not only enhances the understanding of population spatial structure and distribution patterns but also provides a scientific basis for biodiversity conservation and ecosystem management. Future research may further deepen the application of these theories, develop new mathematical models and computational methods to better predict and manage population dynamics. Through interdisciplinary collaboration, these theories are expected to play a greater role in ecological conservation and resource management

Percolation analysis of spatiotemporal distribution of population in Seoul and Helsinki.

Spatiotemporal distribution of urban population is crucial to understand the structure and dynamics of cities. Most studies, however, have focused on the microscopic structure of cities such as their few most crowded areas. In this work, we investigate the macroscopic structure of cities such as their clusters of highly populated areas. To do this, we analyze the spatial distribution of urban population and its intraday dynamics in Seoul and Helsinki with a percolation framework. We observe that the growth patterns of the largest clusters in the real and randomly shuffled population data are significantly different, and highly populated areas during the daytime are denser and form larger clusters than highly populated areas during the nighttime. An analysis of the cluster-size distributions at percolation criticality shows that their power-law exponents during the daytime are lower than those during the nighttime, indicating that the spatial distributions of urban population during daytime and nighttime fall into different universality classes. Finally measuring the area-perimeter fractal dimension of the collection of clusters demonstrates that the fractal dimensions during the daytime are higher than those during the nighttime, indicating that the perimeters of clusters during the daytime are rougher than those during the nighttime. Our findings suggest that even the same city can have qualitatively different spatial distributions of population over time, and propose a way to quantitatively compare the macrostructure of cities based on population distribution data.

Decoding the interaction mediators from landscape-induced spatial patterns.

Interactions between organisms are mediated by an intricate network of physico-chemical substances and other organisms. Understanding the dynamics of mediators and how they shape the population spatial distribution is key to predict ecological outcomes and how they would be transformed by changes in environmental constraints. However, due to the inherent complexity involved, this task is often unfeasible, from the empirical and theoretical perspectives. In this paper, we make progress in addressing this central issue, creating a bridge that provides a two-way connection between the features of the ensemble of underlying mediators and the wrinkles in the population density induced by a landscape defect (or spatial perturbation). The bridge is constructed by applying the Feynman-Vernon decomposition, which disentangles the influences among the focal population and the mediators in a compact way. This is achieved through an interaction kernel, which effectively incorporates the mediators' degrees of freedom, explaining the emergence of nonlocal influence between individuals, an ad hoc assumption in modeling population dynamics. Concrete examples are worked out and reveal the complexity behind a possible top-down inference procedure.

Carbon emission reduction effects of optimizing the urban spatial structure of counties around large cities: From the perspective of population spatial distribution

Carbon emission reduction effects of optimizing the urban spatial structure of counties around large cities: From the perspective of population spatial distribution

Dynamics of Population, Agricultural Land, and Vegetated Areas in the Central to Eastern Mountain and Hill Districts of Nepal

Depopulation and agricultural land abandonment are the common occurrences in rural mountainous regions worldwide. In Nepal increased wilderness is evident due to the conversion of cultivated land into forest, shrubs, and grassland. This study focuses on the central and eastern mountainous and Hill districts; where most districts have experienced negative population growth in the past two decades. Land use/ land cover maps were obtained from Lands at 8 image classification while population was collected from the National Statistical Office of Nepal. The Digital Elevation Model (DEM) was downloaded from the Shuttle Radar Topography Mission (SRTM) and topographic maps were also collected from the Survey Department of Nepal. A reality check Approach was adopted for ground verification. Maps of population, land use/ land cover, cultivated land, vegetated area, elevation, slope gradient, and slope aspect were developed. Based on these maps, changes in the spatial distribution of population, land use/ land cover, cultivated land, and vegetated area were identified and calculated. This study has revealed a significant decrease in population in higher altitudes and hill slope areas. This has resulted in large parts of cultivated lands being abandoned in this region and most excessive abandonment with the most extensive abandonment occurring in areas with marginal slop land. These lands are now converting into forests, shrubs, and grassland. It is concluded that agricultural land abandonment in the study area should not be considered solely based on physical marginality and accessibility but rather through a holistic approach that accounts for complex variability in these factors.

Understanding the coupled relationship between regional longevity and physical geographical environment in Hechi, Guangxi, China

Healthy ageing plays an important role in ageing societies in many countries, and centenarians are a sign of longevity. Longevity and its determinants have become issues of global concern and also a focus of research. Although many disciplines have conducted out a series of studies on longevity phenomena, few studies have systematically considered the impact of geographical environmental factors. Guangxi is one of the provinces with the highest centenarian ratio in mainland China, while Hechi City has the highest ratio of centenarians in Guangxi Province, and contains the world-famous longevity belt. This study took Hechi City, “the world’s longevity city”, as a model research area. First, the basic characteristics of the geographical environmental factors in Hechi City were analysed, and then geographical information system techniques and eXtreme Gradient Boosting machine learning methods were applied to systematically analyse the coupling relationship between the spatial distribution of the longevity population and the geographical environmental factors. In addition, the geographical environmental factors closely related to longevity were filtered and investigated. It was found that the centenarian is distributed along a belt-like pattern. It is mostly located in rocky mountainous areas with low elevations, small to medium undulations, medium slopes and brown calcareous soils, but is rarely found near the non-ferrous mining industry. This work provides a scientific basis for identifying the mechanisms that contribute to regional longevity and theoretical references for improving the health level of the elderly population according to local conditions.

GEOGRAPHIC DISTRIBUTION OF SENIOR SUBSIDIZED HOUSING IN NEW JERSEY: GEOGRAPHIC INFORMATION SYSTEMS APPROACH

Abstract Subsidized senior housing (SSH) offers a solution for low-income older individuals, enabling them to maintain economic stability and continue living independently. There has been limited investigation into the geographical distribution of SSH and its association with neighborhood disadvantage. This study seeks to address this gap by (1) describing the availability of SSH in New Jersey and (2) exploring the extent to which housing availability is linked to neighborhood disadvantage. SSH-availability was quantified by the number of SSH units within each census block group. Neighborhood disadvantage was measured using the area deprivation index (ADI). The eligible population was defined as residents aged 62 or older, living below the poverty line, and allocating 30% or more of their income to housing costs within the census block group. A Geographic Information System (GIS) was employed to visually analyze the spatial distribution of SSH, ADI scores, and eligible populations across New Jersey. Poisson regression was utilized to assess the relationship between SSH availability and neighborhood disadvantage, adjusting for the number of eligible residents. The results revealed a significant association between higher SSH availability and higher ADI scores after accounting for the number of eligible residents (p<.001). This suggests that SSH tend to be located in areas experiencing greater economic deprivation. These findings underscore the need for governmental intervention to mitigate geographic disparities in SSH provision for low-income older individuals. Additionally, further investigation is warranted to examine the accessibility of social and health-related resources surrounding SSH, which could support residents aging in place.

Machine learning-based urban noise appropriateness evaluation method and driving factor analysis

The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City. The findings are as follows: 1.Machine learning algorithms can be effectively used for urban noise evaluation. The optimized model accurately reflects the noise suitability levels in Nanchang City. 2.The feature importance ranking reveals that population spatial distribution has the most significant impact on urban noise, followed by distance to water bodies and road network density. These three features significantly influence the assessment of urban noise suitability and should be prioritized in noise control measures. 3.The weakly suitable noise areas in Nanchang’s central urban region are primarily concentrated on the east bank of the Ganjiang River, making this a key area for noise management. Overall, the Unsuitable, Slightly suitable, Moderately suitable, Relatively suitable, and Height suitable areas constitute 9.38%, 16.03%, 28.02%, 33.31%, and 13.25% of the central urban area, respectively. 4.The SHAP model identifies the top three features in terms of importance, showing that different values of feature variables have varying impacts on noise suitability. This study employs data mining concepts and machine learning techniques to provide an accurate and objective assessment of urban noise levels. The results offer scientific decision-making support for urban spatial planning and noise mitigation measures, ensuring the healthy and sustainable development of the urban environment.

Global stability and asymptotic profiles of a partially degenerate reaction diffusion Cholera model with asymptomatic individuals

Abstract Considering the prevalence of asymptomatic individuals during the spread of disease, this article develops a model of degenerate reaction diffusion Cholera with asymptomatic individuals. First, the well-posedness of model is studied, including the global existence of solutions and the existence of attractor. Second, the basic reproduction number ℛ 0 {{\mathcal{ {\mathcal R} }}}_{0} is defined to determine whether the disease is vanishing or persistent. In particular, we also analyze the asymptotic behavior of the endemic steady state when the diffusion rate of susceptible or asymptomatic individuals tends to 0 or infinity. Finally, by fitting the theoretical results with some numerical simulations, we find that the spatial distribution of disease and local epidemic risk are less affected by the mobility of susceptible populations, whereas the mobility of asymptomatic or symptomatic populations significantly affects the spatial and temporal distribution of infected populations. In addition, we found that the proportion of asymptomatic individuals to infected individuals is also a key factor in disease epidemics, and how to quickly diagnose asymptomatic individuals for disease control and prevention should be of a particular concern.

Environmental niche, spatial distribution and size structure of shelf-dwelling populations of the gorgonian Eunicella cavolini (Koch, 1887) in NW Mediterranean

Environmental niche, spatial distribution and size structure of shelf-dwelling populations of the gorgonian Eunicella cavolini (Koch, 1887) in NW Mediterranean

GIS-Based Spatial Analysis and Strategic Placement of Fine Dust Alert Systems for Vulnerable Populations in Gangseo District

Air pollution, particularly fine particulate matter (PM), poses significant health risks to vulnerable populations such as children, older adults, and individuals with chronic illnesses. Understanding the spatial distribution of these populations and their access to air quality information is crucial for effective interventions. In urban areas like Gangseo District, the distribution of essential facilities and accessibility varies greatly. While studies have highlighted the health impacts of PM, research on optimizing air quality monitoring for at-risk groups remains limited. This study aims to identify optimal locations for air quality monitoring by analyzing the spatial distribution of vulnerable populations and facility accessibility. Using Geographic Information Systems (GIS) and isochrone maps, we identified areas with high concentrations of vulnerable groups and poor access to healthcare facilities. Our findings revealed significant disparities in access to air quality information, with some high-risk areas underserved by current monitoring systems. This study integrated demographic data and spatial analysis to propose strategic monitoring placements. The methodology can be applied to other urban settings and offers a framework for improving air quality management. This study underscores the importance of targeted air quality monitoring to protect vulnerable populations and suggests practical steps for policymakers to enhance public health.

Evaluating spatial coupling between employment, population density, and metro stations in Shenzhen: A network accessibility perspective

Evaluating spatial coupling between employment, population density, and metro stations in Shenzhen: A network accessibility perspective

Benthic foraminifera as bio-indicators of natural and anthropogenic conditions in Roscoff Aber Bay (Brittany, France).

Living benthic foraminifera, known as environmental bio-indicators of both natural and anthropogenic conditions in marine environments, were investigated in the coastal environment of Roscoff Aber Bay (Brittany, France). Eight sampling sites subject to natural variations (freshwater inputs, tides) and/or anthropogenic impacts (pollution, eutrophication) were studied over four seasons in 2021-2022 (November, February, May, August). We sought to understand the spatial distribution of foraminiferal populations within and between sampling sites over the different seasons and to identify sensitive species and those tolerant to anthropogenic impacts. To this end, sedimentary and biogeochemical characteristics of the sediments were examined by measuring grain size, temperature, oxygen, salinity, pH, environmental pigment concentration (chl a and phaeopigments), total organic carbon (TOC), isotopic ratios of carbon (δ13C), nitrogen (δ15N) and sulfide (δ34S), and chl a fluorescence. Considering these parameters as potential driving factors, four environments were distinguished among the sampling sites: open water, terrestrial, oligotrophic and eutrophic. These showed an increasing gradient of organic supply as well as very different microbial activities, highlighted by carbon and sulfide isotopic ratios. Foraminiferal population study revealed the dominant species characterising these main environments. The lowest abundance but highest diversity of foraminifera was found in the harbour site, associated with the dominance of Haynesina germanica, suggesting this species is tolerant to eutrophic environments and anthropogenic impacts. Open water was dominated by Ammonia beccarii and Elphidium crispum, while Quinqueloculina seminula was the most abundant species in the site with the greatest terrestrial influence. Interestingly, the observed organic enrichment of the harbour due to anthropogenic activities (fisheries, waste deposits, etc.) does not seem to significantly affect foraminiferal diversity. Overall, the benthic foraminiferal species in Roscoff Aber Bay appear to be an excellent proxy for marine environmental conditions under various natural and anthropogenic influences.

Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models

It is becoming increasingly important for wildlife managers and conservation ecologists to understand which resources are selected or avoided by an animal and how to best predict future spatial distributions of animal populations in the long term. However, inferring the patterns of space use by animals is a challenging multiscale inference problem, and formal uncertainty quantification of parameter estimates is an essential component of models that provide useful predictions across scales. In this study, we develop an approximate Bayesian inference framework for step selection models of animal movement which quantifies the uncertainty in estimates of resource selection and avoidance parameters within the Bayesian paradigm. The framework allows joint inference of movement and resource selection parameters of animals and is multiscale in that parameters inferred from fine scale movement steps scale to produce predictions of long-term patterns of space use. Our analysis focuses on simulated movement data in which we test the performance of our framework by altering movement parameters in the data-generating process. In our simulations, individuals respond to two environmental covariates and we employ all combinations of positive and negative selection coefficients corresponding to attraction to an environmental feature and avoidance of an environmental feature, respectively. In all scenarios, we recover the movement parameters used for the simulation of synthetic movement data using variational inference, an approximate Bayesian method, allowing us to formally quantify the uncertainty associated with each parameter for varying data set sizes. Our framework successfully recovered all combinations of movement parameters of the simulated data and accurately captured their posterior distributions given the available data suggesting that the framework is reliable and suitable for inferring how animals select resources and move on a landscape.Notably, our analysis shows that even for reasonably large data sets (circa 10,000 observations) there can still be considerable uncertainty associated with resource selection parameters which can in turn lead to inaccurate predictions of long term space use if not properly incorporated into the modelling approach. To further illustrate the utility of our approach, we also present a case study of its application to an example data set consisting of GPS locations of a fisher (Martes pennanti). Our approach will be of interest to ecologists looking to address conservation questions such as when and where animals are likely to spend most of their time. Furthermore, the approach could be used to predict new suitable areas for conservation based on how GPS collared animals use or avoid resources while including uncertainty around the predictions, thereby helping to make informed management decisions.