Spatial Distribution Of Population Research Articles

Population analysis using GIS software tools A case study of Chengdu

Population analysis using GIS software tools A case study of Chengdu

Abstract PO2-25-05: Spatial multiplexing of protein biomarkers for immune cell profiling of the tumor microenvironment with ChipCytometry

Abstract Understanding the spatial distribution of key immune cell populations is critical in advancing our understanding of cancer and the development of novel therapeutics. Historically, the spatial analysis of the tumor microenvironment (TME) has been limited to relatively low-plex immunohistochemical (IHC) or immunofluorescent (IF) assays, which were inadequate for deep immune cell profiling of the TME. Here, we present the analysis of fresh-frozen human breast tissue using spatial multiplexing technology called ChipCytometry, which combines iterative immuno-fluorescent staining using open-source antibodies for quantitative measure of virtually unlimited protein biomarkers on the same sample and high dynamic range imaging to facilitate quantitative phenotyping with single-cell resolution. In addition to traditional pathology review of the high-plex dataset, subsequent image analysis can be done using open-source or commercial software for advanced spatial analyses, including cluster analysis, neighborhood analysis and advanced data visualization. The results show precise expression levels for each of the protein biomarkers in the assay for each individual cell within the sample while maintaining spatial information about each cell. Dozens of immune cell subtypes were identified and quantified based on protein expression profiles. Spatial analysis of the samples revealed quantifiable heterogeneity of immune cell infiltration within the tumor samples, demonstrating the utility of the ChipCytometry platform for in-depth immune profiling in clinical samples. The ChipCytometry platform enables simultaneous detection of multiple protein biomarkers on a single tissue section for deep immune cell profiling in the TME. Combined with the single-cell spatial information, such datasets provide an opportunity for the discovery of new complex multiplexed biomarker signatures to inform therapeutic development and personalized medicine. Citation Format: Benton Berigan. Spatial multiplexing of protein biomarkers for immune cell profiling of the tumor microenvironment with ChipCytometry [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-25-05.

Biogeographical and seasonal distribution of pteropod populations in the western and central Mediterranean Sea inferred from sediment traps

Pteropods are a group of cosmopolitan holoplanktic gastropods that produce an aragonite shell and play an important role in both marine ecosystems and geochemical cycles. In addition to being affected by anthropogenic impacts that include warming and changes in carbonate system parameters, the Mediterranean Sea is considered to be understudied concerning pteropods dynamics and abundances. This work aims to document the modern spatial and temporal distributions of pteropods populations in the Northwestern and Central Mediterranean Sea (Gulf of Lions and Strait of Sicily), respectively. We present data from two sediment-trap records that cover the timeframe between early 1996 and early 2004 for the Gulf of Lions and late 2013 to late 2014 for the Sicily Strait. A total of 843 pteropod shells and 18 different species were identified. Limacina inflata, Creseis virgula and Creseis clava were the most abundant species in the Gulf of Lions, while in the Sicily Strait, C. conica replaced C. clava as the most abundant species. These taxons represented around 70% of the total individuals identified in both sites. Overall, our results suggest a greater pteropod abundance in the Gulf of Lions than in the Sicily Strait, most likely due to enhanced food conditions. In the Gulf of Lions, maximum fluxes occurred in autumn (32.5% of the annual pteropod fluxes registered in October), while in the Sicily Strait peak fluxes occurred in winter (30.5% of the annual pteropod fluxes registered in January). Comparison of temporal changes pteropod fluxes with satellite sea surface temperature (SST), and chlorophyll-a concentration suggest a possible positive effect of high algal accumulation and cool water conditions in the Strait of Sicily on the main pteropod groups. In turn, no clear relationships between pteropod groups, SST and chlorophyll-a were identified in the Gulf of Lions, highlighting the effect of salinity and carbonate system parameters. Overall, and despite the limitations associated with the use of sediment traps for pteropod population monitoring, the consistency of our results with the literature supports the use of sediment traps as useful tools for documenting the diversity and temporal distribution of pteropods.

The impact of the spatial population distribution on economic growth: Evidence from the United States

AbstractWe introduce a new class of measures called spatial population concentration (SPC) designed to quantify how many people live on average within a given radius of every person within a geographic area. The distinguishing feature relative to existing measures is that ours are the first to satisfy scale invariance, replication invariance, sensitivity to transfers, aggregativity, sensitivity to neighborhoods, invariance to uninhabited regions, concurrently. We calculate the SPC measure at the US county level for various radii and identify that the strongest relationship with subsequent economic growth can be found for a 25‐km radius. Interacting SPC with diverse infrastructure metrics does not alter the optimal radius. This indicates that regional policies such as infrastructure projects which influence density should strategically target the 25‐km distance range to maximize the growth impact. We also show that the importance of SPC has declined over time.

Shapes of dark matter haloes with discrete globular cluster dynamics: The example of NGC 5128 (Centaurus A)

Within the Lambda cold dark matter (Lambda CDM) cosmology, dark matter haloes are expected to deviate from spherical symmetry. The particular shape of a galactic halo reflects the environment and mass assembly history of its host, as well as the nature of dark matter. Constraining halo shapes at large galactocentric distances is challenging because of the low density of luminous tracers. The well-studied massive early-type galaxy NGC 5128, also known as Centaurus A (CenA), has a large number of radial velocity measurements for globular clusters (GCs) and planetary nebulae (PNe) extending over a vast area of its extended low-surface-brightness stellar halo. In this work, we aim to determine the deviation from spherical symmetry of the dark matter halo of CenA at 5 using its GCs as kinematic tracers of the gravitational potential. We investigated the largest photometric catalogue of GC candidates in order to accurately characterise the spatial distribution of the relaxed population of GCs. To investigate the presence of non-relaxed structures in the kinematic catalogue of GCs, we used the relaxed point-symmetric velocity field as determined by the host's PNe population. We used anisotropic Jeans modelling under axisymmetric assumptions together with the Gaussian likelihood and GCs as discrete tracers. The gravitational potential is generated by flattened stellar and dark matter distributions. We leveraged the different orbital properties of the blue and red GCs ---such as rotation and velocity anisotropy--- to model both populations separately. By minimising $ we iteratively find the best-fit parameters. We find that the discrete kinematics of the GCs are consistent with being drawn from an underlying relaxed velocity field determined from PNe. The best-fit parameters of the gravitational potential recovered from the blue and red GCs separately agree well and we use them to compute the final results: $M_ $, and the flattening $q_ DM $. Both GC populations show mild rotation, with red having a slightly stronger rotational signature and radially biased orbits, and blue GCs preferring negative velocity anisotropy. An oblate or a spherical dark matter halo of NGC 5128 is strongly disfavoured by our modelling.

Partition refinement of WorldPop population spatial distribution data method: A case study of Zhuhai, China.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.

Abstract 5497: Towards a drag and drop spatial biology workflow: Introducing sample reinterrogation as a conduit for the progressive development and on-line analysis of high parameter spatial biology data

Abstract The potential for spatial biology in cancer research is understood and edified. With it we can understand the tumor microenvironment, unravel responsiveness to drug treatments and cell therapies, and ultimately stratify patients for targeted treatment and clinical trial enrollment. Yet, researchers exploring spatial biology may be discouraged by what can be a resource intensive exercise, and most importantly, a commitment of high value patient samples. Furthermore, the design of reagent panels used to study samples is not trivial, and a specimen’s unique biomarker expression profile may require adjustments. Here we present a novel solution that permits sample re-interrogation and in-flight adjustments of reagent panels in our spatial biology workflow. The CellScape™ platform for Precise Spatial Multiplexing paired with VistaPlex™ Assay Kits provide a ready-to-use cyclic multiplex immunofluorescence (IF) solution to interrogate fresh frozen and FFPE tissue samples. Tissues are mounted in enclosed fluidic chambers, while staining and imaging are conducted on the CellScape instrument. The experiment requires no additional autostainer equipment and is divided into separate staining and imaging rounds. The end user may therefore pause between rounds and conduct inter-round analyses, which can then guide decisions of whether and how to continue the experiment. To illustrate the benefit of this approach, we present a use case in which we applied VistaPlex antibody panels to reveal the spatial distribution of major immune cell populations. Inter-round analysis and conclusions were then drawn from this preliminary phenotype map before successive rounds of rationally planned staining were conducted to further deepen the depth and breadth of spatial phenotyping. The CellScape sample reinterrogation feature is a pragmatic spatial biology solution that optimizes the use of precious resources, whilst allowing researchers to generate informed decisions about antibody panel designs almost in real time. This enhanced utility may reduce the resistance to spatial biology among researchers working with samples that are limited or irreplaceable. Citation Format: Matthew H. Ingalls, Xenia Meshik, Charles E. Jackson, Kayla E. Cashion, Arne Christians, Anke Brix, Marcus Boettcher, J Spencer Schwarz, Oliver Braubach. Towards a drag and drop spatial biology workflow: Introducing sample reinterrogation as a conduit for the progressive development and on-line analysis of high parameter spatial biology data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5497.

Abstract 5389: Development of T-cell exclusion in melanoma

Abstract Immune checkpoint inhibitors (ICIs), which prevent immune cell exhaustion and boost an antitumor response, have shown remarkable treatment efficacy in patients with advanced melanoma. However, its efficacy is limited by the emergence of resistance associated with an immunosuppressive tumor microenvironment (TME). The contribution of abnormal tumor vasculature to limited immune cell infiltration into the tumor core has been reported but the mechanisms of its development are still elusive. We analyzed how tumor angiogenesis affects the immune cell exclusion at different time points of primary melanoma using syngeneic murine melanomas. The spatial distribution of various immune cell populations and vascular integrity were analyzed in whole tumor cross-sections via multiplex immunofluorescence staining. We found that the exclusion pattern of CD8+ T cells was less evident with a balanced distribution across the whole tumor regions at the early stage. However, as the tumor grows, there is a pronounced accumulation of CD8+ T cells at the tumor periphery with a conspicuous absence in the core. Tumor blood vessels were structurally uniform and functionally stable in the early developmental tumor but became unstable and leaky as the tumor progressed, which was associated with the development of CD8+ T-cell exclusion. To understand the mechanism underlying vascular regulation of immune exclusion during tumor progression, we will investigate the expression of genes related to tumor angiogenesis using spatial transcriptomic analysis and RNA sequencing of endothelial cells (CD45+CD31-) sorted from the murine melanomas. These results highlight the contribution of spatial tumor vascular destabilization to the T-cell exclusion development, implying the potential of targeting tumor vasculature to enhance immune activation and the therapeutic efficacy of ICI in melanoma. Citation Format: Ha-Ram Park, Hee Won Yang, Minah Kim. Development of T-cell exclusion in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5389.

Abstract 3894: The temporal influence of the tumor microenvironment in response to checkpoint blockade

Abstract Immune checkpoint inhibition (ICI) has produced a paradigm shift in treatment across a range of cancer subtypes. However, our understanding of why only certain patients respond, as well as our ability to predict this response, is still quite limited. This is exacerbated by the complexity of the tumor microenvironment (TME), which contains cancer cells, immune cells, and stromal cells. These differing cell types can have context-dependent roles in either promoting or inhibiting anti-tumor immune responses, making it challenging to identify features which reliably correlate with patient outcome. In addition, the roles of these diverse cell types are often influenced by their location within the TME, meaning that assays without spatial information cannot fully resolve this complexity. To address these gaps, we mapped the spatial distribution and phenotype of 21 cell populations across 117 patients with metastatic triple negative breast cancer that received nivolumab (anti-PD1) through the TONIC clinical trial (NCT02499367). We collected metastatic samples prior to and during treatment to understand how anti-PD1 reshaped the TME. We also collected archival material from the original primary tumor for each patient. We generated multiplexed imaging data from these samples, identified the location and phenotype of each cell, and then quantified the spatial distribution, diversity, and functional marker status of all cell populations in the TME. We detected numerous features indicative of a productive immune response that correlated positively with clinical benefit, whereas features indicative of a suppressive microenvironment correlated negatively with clinical benefit. We observed significant temporal effects, where many pre-treatment predictive features were not predictive on-treatment; similarly, many on-treatment predictive features had no predictive power prior to treatment. Intriguingly, we found features in the original primary tumors which predicted subsequent (metastatic) response to immunotherapy. We observed substantial differences in predictive accuracy of multivariate models based on timepoint, with on-treatment samples exhibiting the best performance and primary tumor samples exhibiting the worst. Of note, we observed substantially worse predictive performance when using bulk DNA sequencing data to predict outcome from the same patients, highlighting the importance of spatial information. Taken together, we show that the features associated with a productive anti-tumor immune response are temporally structured, and differ based on what phase of response is profiled. Furthermore, using the primary tumor to assess whether metastatic TNBC patients are good candidates for ICI may miss important predictive signals. These findings shed new light on the determinants of ICI outcome, and may shape the design of subsequent trials to better understand the temporal aspects of ICI. Citation Format: Noah F. Greenwald, Iris Nederlof, Hugo Horlings, Marleen Kok, Christina Curtis, Michael Angelo. The temporal influence of the tumor microenvironment in response to checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3894.

Abstract 434: Understanding metabolic heterogeneity of estrogen receptor positive metastatic breast tumors using spatial transcriptomics and geographic information systems approaches

Abstract The tumor microenvironment consists of numerous cell populations showing diverse metabolic preferences, causing metabolic heterogeneity and therapy resistance. Fulvestrant, a clinically approved selective estrogen receptor (ER) degrader, is the standard of care for ER-positive breast cancer, yet resistance has been reported among metastatic breast cancer patients. In our previous studies, we showed such resistance is attributable to metabolic heterogeneity in the tumor microenvironment. Despite metabolic heterogeneity posing a common challenge in cancer research, current methods, such as histology and Magnetic Resonance Imaging, cannot explore nutrient uptake at transcript-level resolution with spatial context, making it difficult to understand the heterogeneity that affects cellular growth and metabolism. Visium Spatial Transcriptomics, a method that shows regional gene expression, is emerging as a powerful spatial analysis tool, and we integrated this method with geographic information systems to investigate the tumor microenvironment heterogeneity and highlight the importance of cell-microenvironment interactions. We developed a computational pipeline to visualize hotspots of spatially relevant genes using breast cancer metastatic samples from xenograft models and human patients. This pipeline integrates Visium tools and geospatial methods for processing transcriptome profiling data. We analyzed the degree of local spatial association based on Local Moran’s I to estimate regions with significant co-localization of single and multiple gene expressions within a tumor. We then leveraged the co-localized areas in the process of biomarker identification and revealing biological pathways. We also used unsupervised clustering of the complete transcriptome profiles to test the resemblance between identified clusters and hotspots as well as any disruption in the spatial structure of clusters in response to therapy or metastasis. Gene set enrichment analysis of these clusters revealed several glucose or lipid metabolism pathways, and mapping of the genes corresponding to each pathway showed mutually exclusive gene expression patterns in the sample. We also observed minimal co-localization of two metabolic pathways but extensive co-localization between metabolic genes and ER target genes, suggesting distinct metabolic preferences and spatial distributions of endocrine therapy resistant cell populations. Altogether, our analysis pipeline established a computational framework for characterizing the link between tumor microenvironment, local metabolism, and changes in transcriptional response to external stimuli. Our findings support the development of strategies to target different endocrine resistant regions using a combination of ER and metabolic signaling inhibitors. Citation Format: Jin Young Yoo, Sabrina Akter, Nandana Varma, Atharva Jain, Aiman Soliman, Zeynep Madak Erdogan. Understanding metabolic heterogeneity of estrogen receptor positive metastatic breast tumors using spatial transcriptomics and geographic information systems approaches [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 434.

Consistent Construction of the Density Matrix from Surface Hopping Trajectories.

Proper construction of the density matrix based on surface hopping trajectories remains a difficult problem. Due to the well-known overcoherence in traditional surface hopping simulations, the electronic wave function cannot be used directly. In this work, we propose a consistent density matrix construction method, which takes the advantage of occupation of active states to rescale the coherence calculated by wave functions and ensures the intrinsic consistency of the density matrix. This new trajectory analysis method can be used for both Tully's fewest switches surface hopping (FSSH) and our recently proposed branching corrected surface hopping (BCSH). As benchmarked in both one- and two-dimensional standard scattering models, the new approach combined with BCSH trajectories achieves highly accurate time-dependent spatial distributions of adiabatic populations and coherence compared to exact quantum results.

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>

Prediction of the size and spatial distribution of free-roaming dog populations in urban areas of Nepal

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the ‘zero by 30′ global mission.

Multi-scale Driving Mechanism of Urbanization on PM2.5 Concentration in Urban Agglomeration in the Middle Reaches of the Yangtze River

Exploring the nonlinear effect of urbanization on PM2.5 concentration and its driving mechanism is crucial for controlling urban air pollution. Based on remote sensing data and statistical data from 2002 to 2020, spatial autocorrelation, systematic dynamic panel regression, and spatio-temporal geographical weighted regression models were used to analyze the spatio-temporal evolution of PM2.5 concentration in the urban agglomeration of the middle reaches of the Yangtze River and explore the driving mechanism of urbanization on PM2.5 concentration at different spatial scales. The results showed that:① PM2.5 concentration in the middle reaches of the Yangtze River showed an overall decreasing trend from 2002 to 2020, with a spatial distribution pattern of "high in the north and low in the south." ② Hot spot cities expanded towards the western part of the urban agglomeration, whereas cold spot cities showed enhanced spatial correlation. ③ The relationship between PM2.5 concentration and economic, land, and population urbanization followed N-shaped, U-shaped, and U-shaped curves, respectively. Secondary industry and energy consumption significantly promoted the change in PM2.5 concentration, and precipitation and vegetation helped mitigate PM2.5 pollution. ④ The overall driving effects of all urbanization factors in local areas were transformed, and the main areas of influence were concentrated in the southeast, northwest, and southwest of the study area. Considering the current urban development status and regional characteristics of the urban agglomeration in the middle reaches of the Yangtze River, promoting green industrial transformation, rational planning of urban spatial distribution and population distribution, and enhancing infrastructure construction will facilitate the coordinated development between urbanization and environmental protection.

Spatial population distribution dynamics of big cats and ungulates with seasonal and disturbance changes in temperate natural forest

Wildlife conservation and management in human-dominated landscapes are major concerns for wildlife ecologists and managers. The dynamics of human disturbance, combined with seasonal limitations in the availability of nutritious foods, may restrict wildlife population growth and recovery. However, understanding how large mammal species adjust their population distribution in forest habitats with seasonal changes in food and disturbances requires a deeper and more extensive analysis. In this study, we found that three ungulate species, roe deer (Capreolus pygargus), sika deer (Cervus nippon), and wild boar (Sus scrofa), employ robust, conservative, and flexible distribution strategies, respectively, to adapt to the effects of seasonal changes and human disturbances. Moreover, croplands, villages, and grazing have some negative effects on the distribution of roe deer and sika deer, while wild boar can be highly abundant near human land use. Additionally, roe deer, sika deer, and wild boar are also affected by the abundance of shrub species they consume. During the cold season, the populations of the Amur tiger (Panthera tigris altaica) and Amur leopard (P. pardus orientalis) were primarily located near roads and dense forests, respectively. In the warm season, the distribution of both big cats was influenced by prey abundance, and Amur tigers also avoided grazing livestock. Nevertheless, the negative effects of human land use on Amur tigers and wild boars increased during the warm season, which was attributed to more frequent human activities during that time. Consequently, it is crucial to implement season-specific habitat management, particularly by regulating human disturbances during the warm season, in order to promote the recovery and expansion of populations of big cats and ungulates.

Impacts of the Relocation Program on Native American Migration and Fertility

This paper estimates the migratory and fertility effects of the federal Relocation Program, which attempted to move Native American individuals to urban areas under the promises of financial assistance and job training. I find the Relocation Program increased the Native American population in the target cities by more than 100,000 people. I also find that second- and third-generation Native American women living in cities have a 50 percent lower fertility rate than those living in areas with historically large Native American populations. These findings indicate that this program meaningfully shifted the spatial distribution of the Native American population.

APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the population diversity. The performance of APSO-SL is evaluated on the CEC2013 and CEC2017 test suites, and one engineering problem. Experimental results show that APSO-SL has the best performance compared with other competitive PSO variants.

Population-land spatial coupling in urban non-core region and environmental pollution: evidence from urbanization in China

Urban non-core region land exploitation and population immigration toward urban non-core areas, two fundamentals of urbanization in China, challenge the environment; however, the pollution effect remains unclear. Using prefecture-level data in China between 2003 and 2020, our study investigates the pollution effect of population-land spatial coupling in the urban non-core region. The empirical results demonstrate that excessive urban non-core region land expansion alone negatively impacts the environment. Meanwhile, outward spatial population distribution in a town, primarily measured by the relative proportion of the urban non-core region population size, directly aggravates environmental pollution on the one hand and indirectly improves the environment by alleviating the adverse effect of land-led urbanization. These findings remain robust under various regional heterogeneity analyses and still stand regardless of the investigated pollutants. The practical implications suggest that breaking through the population-land spatial coupling when the urban core region is small is advisable; meanwhile, efforts in maintaining the coupling, especially for areas with sizeable peripheral land exploitation, should be held.

Measures of Urbanisation and Interrelationship between Factors Thereof in Tiruchirapalli District: A SEM Analysis

Urbanization is a complex socio-economic process that transforms the built environment, converting formerly rural into urban settlements, while also shifting the spatial distribution of population from rural to urban areas. Urbanization was an important driving force in migration and commuting, because urban areas offer many economic opportunities to rural people through better jobs, new skills and cultural changes. The specific objective formulated for the study are to find out the various measures of urbanisation in the study area and to develop a structural equation model for depicting the interrelationship between these factors of urbanisation. A multistage random sampling technique was adopted. The tools of analysis for the study are Principal Component Analysis (PCA), Confirmatory Factor Analysis (CFA) using Structural Equation Model (SEM). The results revealed that the variables in the study had effect on urbanisation and the recommended fitness models and reliability test were achieved the required level for the study. The study concluded that the urbanisation has influence on the factors such as cropping pattern, land use pattern, growing employment and rural-urban migration in the study area.

Modeling the Spatial Distribution of Population Based on Random Forest and Parameter Optimization Methods: A Case Study of Sichuan, China

Spatial population distribution data is the discretization of demographic data into spatial grids, which has vital reference significance for disaster emergency response, disaster assessment, emergency rescue resource allocation, and post-disaster reconstruction. The random forest (RF) model, as a prominent method for modeling the spatial distribution of population, has been studied by many scholars, both domestically and abroad. Specifically, research has focused on aspects such as multi-source data fusion, feature selection, and data accuracy evaluation within the modeling process. However, discussions about parameter optimization methods during the modeling process and the impact of different optimization methods on modeling accuracy are relatively limited. In light of the above circumstances, this paper employs the RF model to conduct research on population spatialization with multi-source spatial information data. The study primarily explores the differences in model parameter optimization achieved through random search algorithms, grid search algorithms, genetic algorithms, simulated annealing algorithms, Bayesian optimization based on Gaussian process algorithms, and Bayesian optimization based on gradient boosting regression tree algorithms. Additionally, the study investigates the influence of different optimization algorithms on the accuracy of population spatialization modeling. Subsequently, the model with the highest accuracy is selected as the prediction model for population spatialization. Based on this model, a spatial population distribution dataset of Sichuan Province at a 1 km resolution is generated. Finally, the population dataset created in this paper is compared and validated with open datasets such as GPW, LandScan, and WorldPop. Experimental results indicate that the spatial population distribution dataset produced by the Bayesian optimization-based random forest model proposed in this paper exhibits a higher fitting accuracy with real data. The Coefficient of Determination (R2) is 0.6628, the Mean Absolute Error (MAE) is 12,459, and the Root Mean Squared Error (RMSE) is 25,037. Compared to publicly available international datasets, the dataset generated in this paper more accurately represents the spatial distribution of the population.