Population Spatial Patterns Research Articles

Integrated Water Management Under Different Water Rights Institutions and Population Patterns: Methodology and Application

AbstractWhile numerous studies have investigated demand management policies as a means of mitigating the impacts of climate change and population growth, little attention has been given to the interaction of spatial population patterns and water institutions that affect water shortages. In this article, we develop a methodology to evaluate how population location under alternative water institutions and climate scenarios impacts water demands, shortages, and derived economic values. We apply this methodology to the South Platte River Basin (SPRB) in Northeastern Colorado under three scenarios with ∼1,800 simulations. Results suggest that while water rights institutions have a negligible impact on total volumetric shortages relative to climate change, they have substantial distributional and economic implications. Results also suggest that continuous population growth in upstream cities yields the lowest water shortages if per capita use decreases with urbanization. However, if we assume that per capita demands do not decrease with population density, an equal distribution of population to upstream and downstream regions yields the lowest water shortage and highest economic value. These findings indicate the need that planning efforts must account for return flows and development patterns throughout a watershed in order to reduce water shortages and promote economic prosperity.

Away from the cities? A medium-to-long-term investigation of how the COVID-19 pandemic has changed population spatial patterns in Italy

Away from the cities? A medium-to-long-term investigation of how the COVID-19 pandemic has changed population spatial patterns in Italy

Population re-establishment and spatial dynamics of crowberry (Empetrum nigrum ssp. hermaphroditum), a foundation species in restored alpine ecosystems.

Many ecosystems are defined and shaped by one or a few common, foundation species. Even though such species hold a key role in the restoration of these ecosystems, the demographic processes involved in their re-establishment have rarely been studied. Foundation species' population dynamics, re-establishment history, and the abiotic and biotic factors that affect individual establishment at restored sites can be studied by addressing population spatial patterns and age structure. Such an approach to studying population dynamics is particularly relevant for long-lived species with low mortality, such as shrubs in alpine areas. We studied a population of the foundation species Empetrum nigrum ssp. hermaphroditum at an alpine spoil heap site and found evidence of population re-establishment starting within a decade after construction. High Empetrum densities close to the spoil heap edges indicated that short distances to seed sources in the surroundings had a strong positive effect on establishment of individuals. Empetrum individuals were significantly clustered, which indicated intraspecific facilitation. As revealed by spatial analyses of recruits and older, established individuals, clustering developed gradually over time, which indicated a shift from no interaction to increased facilitation. We conclude that intraspecific facilitation promotes Empetrum reestablishment at the studied alpine spoil heap. Synthesis: We show that population spatial patterns and age structure can be successfully used to unveil the re-establishment history of a foundation species in a restoration context. Efficient seed dispersal and intraspecific facilitation seem to be important factors behind Empetrum's successful re-establishment at alpine spoil heaps. Identification of abiotic and biotic factors determining foundation species' establishment success at restored sites can support planning and improve success of restoration.

Multi-Scale Dynamics and Spatial Consistency of Economy and Population Based on NPP/VIIRS Nighttime Light Data and Population Imagery: A Case Study of the Yangtze River Delta

Population and economy are crucial factors contributing to regional disparities. Studying the patterns and relationships between these two elements is essential for promoting sustainable development in regions and cities. This study constructs multi-scale geographic concentration indices and inconsistency indices, utilizing NPP/VIIRS and LandScan data to quantitatively analyze the spatial pattern changes of population and economy in the Yangtze River Delta across various spatial scales, revealing the matching relationships between population and economic elements within cities. The results indicate that the economy in the Yangtze River Delta is spreading outward from the core areas, with the average population–nightlight inconsistency index decreasing from 1.57 to 1.33. This suggests that the imbalance between population and economy within the urban agglomeration is gradually improving, consistent with trends observed in statistical survey data. Within individual cities, there is a noticeable spatial mismatch between population and nightlight intensity, with the population primarily concentrated in urban core areas. As urban spaces expand, the areas where population concentration is significantly lower than nightlight concentration are gradually diminishing. By 2022, the land area where population and economic concentration are coordinated within the Yangtze River Delta urban areas increased from 9.13% to 16.24%. Population concentration in these coordinated regions rose from 11.33% to 16.33%, while nightlight concentration increased from 9.98% to 13.63%. The improved geographic concentration and inconsistency indices are effective indicators for multi-scale monitoring of population and economic spatial changes. The integration of NPP/VIIRS nighttime light data and LandScan data provides an effective method for uncovering different spatial patterns of population and socio-economic element aggregation in urban structures. This can offer insights for promoting sustainable regional and urban development.

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

Effects of Landscape Heterogeneity and Disperser Movement on Seed Dispersal

The primacy of endozoochory for the maintenance and expansion of many woody plant populations is well known, but seed dispersal is not well understood for most species. This is especially true for rare species, where small population size and low fruit production can limit field- or observation-based experiments. Additionally, the effect of environmental heterogeneity on disperser movement is rarely investigated but has been shown to improve estimates of plant population spatial patterns and dynamics. We used simulation experiments to explore the effects of environmental heterogeneity and disperser movement on Lindera subcoriacea seed dispersal, a rare shrub from the southeastern United States with avian-dispersed seeds. Our experiments incorporated environmental heterogeneity and simulated disperser movement for five bird species, based on either landscape permeability or straight path rules. We anticipated that permeability-based movement would result in greater dispersal distances and seed dispersal effectiveness, which characterizes both quantity and quality. Generally, we did not find differences in seed dispersal between permeability and straight path experiments. However, we did find that permeability-based experiments had greater deposition into suitable habitat during flight (23 vs. 1%). These rare but longer distance depositions may be especially important for plants that are influenced by gap or interpopulation dynamics. We also found consistently greater dispersal into high quality habitats regardless of disperser species in permeability experiments, implying that incorporating species-specific assessments of landscape utilization (occupancy) could influence the effectiveness of seed dispersal. Our study suggests that including environmental heterogeneity in seed dispersal models can provide additional insights not provided by avian parameters (e.g., gut capacity, seed retention time, and flight speed) commonly used to inform dispersal models.

Spatiotemporal pattern evolution and influencing factors of population spatial distribution in Changsha-Zhuzhou-Xiangtan urban agglomeration, China

<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.</span></p>

Population Exposure Changes to Mean and Extreme Climate Events Over Pakistan and Associated Mechanisms.

The increasing prevalence of warmer trends and climate extremes exacerbate the population's exposure to urban settlements. This work investigated population exposure changes to mean and extreme climate events in different Agro-Ecological Zones (AEZs) of Pakistan and associated mechanisms (1979-2020). Spatiotemporal trends in mean and extreme temperatures revealed significant warming mainly over northern, northeastern, and southern AEZs. In contrast, mean-to-extreme precipitation changes showed non-uniform patterns with a significant increase in the northeast AEZs. Population exposure to mean (extreme) temperature and precipitation events increased two-fold during 2000-2020. The AEZs in urban settlements (i.e., Indus Delta, Northern Irrigated Plain, and Barani/Rainfall) show a maximum exposure to extreme temperatures of about 70-100×106 (person-days) in the reference period (1979-1999), which increases to 140-200×106 person-days in the recent period (2000-2020). In addition, the highest exposure to extreme precipitation days also increases to 40-200×106 person-days during 2000-2020 than 1979-1999 (20-100×106) person-days. Relative changes in exposure are large (60%-90%) for the AEZs across northeast Pakistan, justifying the spatial population patterns over these zones. Overall, the observed changes in exposure are primarily attributed to the climate effect (50%) over most AEZs except Northern Irrigated Plain for R10 and R20 events, where the interaction effect takes the lead. The population exposure rapidly increased over major AEZs of Pakistan, which could be more vulnerable to extreme events due to rapid urbanization and population growth in the near future.

Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous.

In connectivity models, land cover types are assigned cost values characterizing their resistance to species movements. Landscape genetic methods infer these values from the relationship between genetic differentiation and cost distances. The spatial heterogeneity of population sizes, and consequently genetic drift, is rarely included in this inference although it influences genetic differentiation. Similarly, migration rates and population spatial distributions potentially influence this inference. Here, we assessed the reliability of cost value inference under several migration rates, population spatial patterns and degrees of population size heterogeneity. Additionally, we assessed whether considering intra-population variables, here using gravity models, improved the inference when drift is spatially heterogeneous. We simulated several gene flow intensities between populations with varying local sizes and spatial distributions. We then fit gravity models of genetic distances as a function of (i) the 'true' cost distances driving simulations or alternative cost distances, and (ii) intra-population variables (population sizes, patch areas). We determined the conditions making the identification of the 'true' costs possible and assessed the contribution of intra-population variables to this objective. Overall, the inference ranked cost scenarios reliably in terms of similarity with the 'true' scenario (cost distance Mantel correlations), but this 'true' scenario rarely provided the best model goodness of fit. Ranking inaccuracies and failures to identify the 'true' scenario were more pronounced when migration was very restricted (<4 dispersal events/generation), population sizes were most heterogeneous and some populations were spatially aggregated. In these situations, considering intra-population variables helps identify cost scenarios reliably, thereby improving cost value inference from genetic data.

Emergent multilevel selection in a simple spatial model of the evolution of altruism.

Theories on the evolutionary origins of altruistic behavior have a long history and have become a canonical part of the theory of evolution. Nevertheless, the mechanisms that allow altruism to appear and persist are still incompletely understood. It is well known, however, that the spatial structure of populations is an important determinant. In both theoretical and experimental studies, much attention has been devoted to populations that are subdivided into discrete groups. Such studies typically imposed the structure and dynamics of the groups by hand. Here, we instead present a simple individual-based model in which altruistic organisms spontaneously self-organize into spatially separated colonies that themselves reproduce by binary fission and hence behave as Darwinian entities in their own right. Using software to automatically track the rise and fall of colonies, we are able to apply formal theory on multilevel selection and thus quantify the within- and among-group dynamics. This reveals that individual colonies inevitably succumb to defectors in a within-colony “tragedy of the commons”. Even so, altruism persists in the population because more altruistic colonies reproduce more frequently and drive less altruistic ones to extinction. Evidently, the colonies promote the selection of altruism but in turn depend on altruism for their existence; the selection of altruism hence involves a kind of evolutionary bootstrapping. The emergence of the colonies also depends crucially on the length scales of motility, altruism, and competition. This reconfirms the general relevance of these scales for social evolution, but also stresses that their impact can only be understood fully in the light of the emergent eco-evolutionary spatial patterns. The results also suggest that emergent spatial population patterns can function as a starting point for transitions of individuality.

Demographic Spatialization Simulation under the Active “Organic Decentralization Population” Policy

A matter of considerable concern is managing rapid population growth in a growing megacity. After years of endeavor, the “decentralize and population cap” policy has finally been implemented and has achieved some success in Beijing, China. Before applying what has been learnt from this experience to other places, two questions remain to be addressed: “Can urbanization result in land-population harmony under the double effects of accessible guiding plans and invisible push-pull forces?” and “What will be the likely geo-simulation of population density resulting from a city decentralization process?” Under the guidance of “orderly city development” theory, our research (1) simulated the effects of the “organic population decentralization” policy on future population density dynamics; (2) proposed a new framework that coupled models of Verhulst logistic differential population and Cellular Auto-Markov; and (3) analyzed the steering effect of the policy toward a spatial population distribution that could be described as “spread through decentralization.” The results showed that Beijing is currently at the beginning of the “suburbanization” stage. This study can help geographers obtain an innovative method that couples the existing spatial population patterns and the potential population size, which is beneficial for urban planners in determining the spatial structure of a relative equilibrium status for urban development.

Spatial distribution pattern and intraspecific association of dominant species Quercus aliena var. acu-tiserrata in Qinling Mountains, China

To explore the spatial distribution and intraspecific correlation of Quercus aliena var. acutiserrata, a domi-nant tree species in a 25 hm2 plot of warm temperate deciduous broadleaved forest in Qinling Mountains, the pair-correlation function g(r) was used to study the spatial pattern and intraspecific association. The results showed that the diameter class structure of Q. aliena var. acutiserrata was bimodal, with a large proportion of young trees (1 cm≤DBH＜5 cm), indicating an increase population structure with good capability of regeneration. The abundance of middle trees (15 cm≤DBH＜25 cm) was slightly more than that of big trees (25 cm≤DBH＜35 cm) and old trees (DBH≥35 cm), but far less than that of young trees and small trees. The spatial distribution of Q. aliena var. acuteserrata was obviously altitude dependent, which mainly distributed in the middle and high altitude areas. Results of complete spatial randomness (CSR) model analysis showed that young trees, small trees, adult trees, big trees, and old trees were aggregated in the large scale (＜60 m). Heterogeneous Poisson (HP) model was used to eliminate habitat heterogeneity. The results of HP model showed that the individual aggregation degree of each diameter class decreased, indicating that the distribution was affected by habitat heterogeneity. At the small scale (＜40 m), spatial correlation was positively correlated between individuals with small diameter gap, whereas the spatial correlation was negative correlation and no correlation between individuals with large diameter gap. At large scale (>40 m), the spatial correlation was positively correlated between large-diameter individuals, but negatively correlated and unrelated between saplings and other diameter individuals. Our results indicated that biological cha-racteristics of Q. aliena var. acutiserrata and habitat heterogeneity were important drivers for the formation of population spatial pattern.

Species Diversity and Carbon Sequestration Oxygen Release Capacity of Dominant Communities in the Hancang River Basin, China

Plants play an important role in the storage of organic carbon as a carbon reservoir, mainly reflected in the absorption of carbon dioxide and the release of oxygen by plants through photosynthesis, which plays an important role in regulating regional carbon balance. This study aimed to explore the pattern of dominant vegetation species diversity in small basin ecosystems, analyze the photosynthetic carbon sequestration characteristics of dominant vegetation and its carbon sink potential, and use the Hancang River Basin as the research area to obtain species abundance data using the sampling method. The community characteristics were analyzed from the aspects of species richness, α diversity index, dominant population spatial pattern, and interspecific correlation. Fifteen typical plants were selected; the photosynthetic carbon sequestration capacity of the plants was measured and quantified, and the correlation of the nitrogen ecological measurement characteristics of the leaves were analyzed. The results showed that all 15 dominant vegetation types were clustered, and the aggregation intensity and interspecific correlation were closely related. Spearman’s analysis showed that the dominant vegetation community structure and diversity in the Hancang River Basin were unstable, and there may be strong interspecies competition and frequent species replacement in the future succession process. The daily trend of the net photosynthetic rate of dominant vegetation tended to be bimodal or nearly bimodal, and the peak generally appeared at 10:00 or 14:00. The leaf area index of the dominant species was greater than 3.0, of which the largest was Platanus acerifolia at 5.31. The smallest was Hemerocallis fulva (L.) L., which was 1.16. The average carbon sequestration per unit leaf area and unit land area of the dominant species was 10.02 g·m−2·d−1 and 42.35 g·m−2·d−1, respectively, and the average oxygen release was 7.29 g·m−2·d−1 and 30.8 g·m−2·d−1, respectively. The average maintenance breathing volume of the leaves was 2.94 μmol CO2 m−2s−1. Comprehensive analysis of the ecological restoration process of the Hancang River Basin should focus on Platanus acerifolia, Prunus serrulata var. lannesiana, Prunus cerasifera, Ligustrum lucidum, and other highly efficient carbon sequestration plants, to carry out rational planting and to build a multilayer composite configuration of forest shrub and grass models to improve the ecological problems of the basin.

Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways

Gridded population projections constitute an essential input for climate change impacts, adaptation, and vulnerability (IAV) assessments as they allow for exploring how future changes in the spatial distribution of population drive climate change impacts. We develop such spatial population projections, using a gravity-based modeling approach that accounts for rural-urban and inland-coastal migration as well as for spatial development patterns (i.e. urban sprawl). We calibrate the model (called CONCLUDE) to the socioeconomically diverse Mediterranean region, additionally considering differences in socioeconomic development in two geographical regions: the northern Mediterranean and the southern and eastern Mediterranean. We produce high-resolution population projections (approximately 1 km) for 2020–2100 that are consistent with the Shared Socioeconomic Pathways (SSPs), both in terms of qualitative narrative assumptions as well as national-level projections. We find that future spatial population patterns differ considerably under all SSPs, with four to eight times higher urban population densities and three to 16 times higher coastal populations in southern and eastern Mediterranean countries compared to northern Mediterranean countries in 2100. In the South and East, the highest urban density (8000 people km−2) and coastal population (107 million) are projected under SSP3, while in the North, the highest urban density (1500 people km−2) is projected under SSP1 and the highest coastal population (15.2 million) under SSP5. As these projections account for internal migration processes and spatial development patterns, they can provide new insights in a wide range of IAV assessments. Furthermore, CONCLUDE can be extended to other continental or global scales due to its modest data requirements based on freely available global datasets.

Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China

ABSTRACT High spatiotemporal population data are critical for a wide range of applications (e.g. urban planning and management, risk assessment, and epidemic control). However, such data are still not widely available due to the limited knowledge of complex human activities. Here we proposed a spatiotemporal downscaling framework for estimating hourly population dynamics in Beijing by integrating remote sensing and social sensing data. First, we generated two baseline maps of population during sleep and work times using a dasymetric method. Second, we generated urban functional zones using a random forest model and derived human activity patterns from social sensing data. Finally, we estimated the hourly population dynamics at a 500-meter resolution using a temporal downscaling method. Results show the significant spatial difference of the population over time, especially between working hours (9:00 − 18:00) and sleeping hours (after 0:00). The spatial pattern of population is more homogenous within the sixth ring area in Beijing during work time compared to sleep time when there are more clusters of high population. The comparison of spatiotemporal patterns with the referenced real-time heat maps from Baidu indicates that our population data are reliable. The framework presented in this paper is transferable in other regions. The resulting dataset of hourly population dynamics is of great help for governments of emergency responses as well as for studies about human risks to environmental issues.

Spatial Pattern of Population Ageing and Household Health Spending in India

Studies on the association between population ageing and health spending are inconclusive. Though per capita health spending has a strong age gradient, evidence from the developed countries attributes increasing health expenditure to high spending in terminal year of life. In India, though studies have examined the level, trends and determinants of health care spending, no attempt has been made on association of population ageing and per capita health spending across the districts of India. The objective of this paper was to examine the spatial dependencies and heterogeneities of population ageing and per capita health spending across 640 districts of India. Data from the Census of India (2011) and the consumption expenditure rounds of the National Sample Survey Organisation (NSSO) (2009–10 and 2011–2012) were used in the analysis. Univariate and bivariate LISA analysis, ordinary least square regression, and spatial error model were used to decipher the spatial pattern of population ageing and health spending in the districts of India. Results suggest that per capita health expenditure is positively associated with the proportion of the older adults (aged 60 years and above) in the districts of India. Districts with less than 5% older adults have an annual per capita health expenditure of ₹629 compared to ₹2432 in districts with older adults 12% or more. Districts of India also exhibit large spatial heterogeneity in household health spending. Bivariate Moran’s I statistic of proportion of older adults and per capita annual health expenditure was 0.48, suggesting spatially association of the share of the older adults and the per capita annual health expenditure in India. Results of the spatial error model confirmed that share of urban population, mean wealth score, and mean household size are significant predictors of per capita annual health expenditure in India. This study is important to monitor the progress of universal health coverage across the disadvantaged districts and to integrate age component in universal health coverage programmes.

Very high-resolution true color leaf-off imagery for mapping Taxus baccata L. and Ilex aquifolium L. understory population

According to the “Habitat” Directive 92/43/EEC, the conservation status of natural habitats depends on the occurrence of populations of their typical species. For some forest habitats, typical species do not occur as canopy dominant trees but are found in the understory. This is the case with the priority habitat ’‘Apennine beech forests with Taxus and Ilex’’ 9210*. Taxus baccata L. and Ilex aquifolium L. are evergreen tree species and occur as isolated trees or groups in the understory of beech dominated forests. Accordingly, the knowledge of the spatial pattern of populations of typical species is fundamental for habitat monitoring goals. In this perspective, this study aims to evaluate the potential of very high-resolution, true color, leaf-off imagery (pixel size = 0.11 m), supplied by Google Earth, for mapping these populations. Understory layer detection has been accomplished through an object-oriented approach, based on multiresolution segmentation. The classification was developed with thresholds based on spectral and geometric properties, as well as on textural and contextual information. The main critical issues are represented by site conditions, where shadowing can prevent crown detection. The thematic accuracy of the target species map resulted in a Producer Accuracy of 0.77 and a User Accuracy of 0.80. The proposed procedure offers a good methodological foundation with which to map the actual spatial extent of forest broadleaved deciduous habitat types, characterized by low abundance and patchily distributed populations of yew and holly.

Population based Air Pollution Exposure and its influence factors by Integrating Air Dispersion Modeling with GIS Spatial Analysis

Air pollution is a major environmental health problem. The study of interaction between air pollution and human will benefit to the human health and well-being of community. Both a model for assessing population relative risk of air pollution exposure (MAPRRAPE) and air pollution concentration methods were applied in a case study to determine the optimal method in evaluating risk of population exposure to Sulfur Dioxide (SO2). The framework for building the MAPRRAPE was described in detail. Then, the spatial patterns of population by demographic characteristics exposed to SO2 from industrial, vehicle, and the mixture of industrial and vehicle pollution sources, as well as an in-depth quantitative investigation using correlation analysis were studied for further source appointment. The results showed that the MAPRRAPE was more reliable than air pollution concentration model in determining population exposure risks by demographic characteristics. The high risk areas of whites exposed to SO2 were larger than blacks and the other races due to a large number of whites, and other age groups exposed to SO2 were larger than children and the old people. In addition, the correlation analyses showed that the relative risks of population by demographic characteristics exposed to SO2 had a more significant correlation with vehicle pollution source than industrial pollution source. The results of source appointment thus demonstrated that vehicle pollution source was the main pollution source. This study suggests that there is a clear need for the implementation of programs and services that will reduce population exposed to air pollution with focusing on densely populated areas for an ultimate improvement of community health status and the environmental conditions.

Evolution of Spatial Pattern and Influencing Factors of Population in Guangdong-Hong Kong-Macao Greater Bay Area

This paper takes the population of Guangdong-Hong Kong-Macao Greater Bay Area as the research object, and analyzes the evolution of population spatial pattern of Guangdong-Hong Kong-Macao Greater Bay area. According to the data, eleven areas of “two districts and nine cities” in Guangdong-Hong Kong-Macao Greater Bay area district are divided into three levels; the relative migration index is used to divide them into relative moving in areas and relative moving out areas, on the other hand, using the spatial panel data regression model to analyze the influencing factors of the evolution of the spatial pattern of population in Guangdong, Hong Kong and Macao. The main conclusions are as follows: 1) from 2005 to 2017, the spatial distribution of population in Guangdong-Hong Kong-Macao Greater Bay area shows a trend of continuous aggregation. The geographic concentration of population in Shenzhen, Dongguan, Hong Kong and Macao is relatively high. Guangzhou, Foshan, Shenzhen and Zhongshan belong to the region of relative migration. 2) This paper selects the quality of basic education, housing price, industrial structure, medical service level, traffic convenience and income level as independent variables, adds the spatial weight matrix, and uses the spatial error model to regress the geographic concentration of population. Main results: income level, industrial structure, medical condition and traffic convenience level all promote the population agglomeration of Guangdong-Hong Kong-Macao Greater Bay area.

Research on Population Spatialization Method Based on PMST-SRCNN

How to improve the accuracy of population spatialization by using downscaling technology has always been a difficult issue in academic research. The population spatialization model constructed from the global or local perspective alone has its own limitations that cannot capture the local and global characteristics of the population distribution. Based on the counties of Chongqing municipality in 2010, this paper uses the two steps of “removing-rough” rasterizationof partitioned multivariate statistical regression and the “getting-accuracy” of super-resolution convolutional neural network to construct a coupling model of population spatialization to complete global and local Feature learning and compare and analyze with other four schemes. The results show that the mean square error and root mean square error of the coupled model of partitioned multivariate statistical regression and super-resolution convolutional neural network are the lowest, especially in densely populated areas. Studies have shown that although super-resolution convolutional neural network has a good ability to downscale learning, it still does not reflect the heterogeneity of population spatial patterns well, and the coupling of multilevel global feature learning models and super-resolution convolutional neural network models can make up for this to a certain extent.