Spatial Analysis Model Research Articles

Geo-spatial analysis of high-risk fertility behaviors and child stunting in Ethiopia

BackgroundThe process of childbirth involves significant risks, particularly when certain high-risk fertility behaviors (HRFBs) are observed. HRFB of birth includes maternal age below 18 years or above 34 years at the time of childbirth, having a child born after a short birth interval (24 months), and having a high parity (more than three children). The majority of child stunting cases were linked to high-risk reproductive practices. Stunted children are those whose height-for-age Z-score is less than –2SD (standard deviation) from the median of a reference population. This study aimed to investigate the spatial association between HRFB of birth and stunting in under-five children across Ethiopia’s administrative zones.MethodThis study used cross-sectional data from the latest Mini Ethiopian Demographic and Health 2019 Survey, which included a weighted sample of 4,969 under-five children from 64 administrative zones. Spatial model analysis, specifically the spatial Durbin model, was used to examine the association between HRFB of birth and stunting in children. ArcGIS 10.8 was used for mapping and SAS 9.4 was used for model analysis.ResultsThe average mean proportion of HRFB of birth to the rate of stunting in children at the zonal level in Ethiopia was observed to be 58 and 36%, respectively, across Ethiopian zones. Children whose mothers displayed HRFB of birth and who were stunted were 24% at all times. The median value of HRFB of birth and stunting were 0.61 and 0.36, respectively. The average vegetable index and the livestock index across Ethiopian zones showed spatial variations of 0.57 and 0.12, respectively. In the selected spatial Durbin model, the deviance value was very small, indicating that the model fit the data well.ConclusionThe study found a high prevalence and significant spatial variation in both HRFB of birth and stunting across the Ethiopian zones. The spatial distribution of both HRFB of birth and stunting were found to be significantly clustered in the administrative zones of Ethiopia. These results emphasize the need for targeted interventions to address HRFB and stunting, supporting Ethiopia in achieving its Sustainable Development Goals.

Ecological Network Construction Based on Ecosystem Services and Landscape Pattern Indices in the Tabu River Basin

Unsustainable human activities threaten the ecological security of arid and semi-arid watersheds. However, improving the connectivity of ecological networks can enhance ecosystem services and preserve biodiversity in these fragile environments. In this study, we aimed to identify ecological sources of the Tabu River Basin by quantifying five key ecosystem services and using a morphological spatial pattern analysis model. We further optimized resistance surfaces using landscape pattern indices, constructed an ecological network, and provided recommendations for sustainable management. The results obtained were as follows: (1) Ecosystem services in the Tabu River Basin exhibit clear spatial heterogeneity, with significant increases in water yield, food supply, and carbon sequestration over the past two decades. (2) Landscape pattern indices, such as the aggregation index, patch cohesion index, and Shannon’s diversity index, showed declines, indicating heightened landscape fragmentation and reduced connectivity. (3) Over the study period, the number of ecological sources increased from 6 to 17, although the total area of these sources decreased. The number of corridors expanded from 9 to 36, with a total length increase of 362.47 km, whereas pinch points increased from 27 to 40. (4) After optimization, one additional corridor and 24 new pinch points were identified, alongside a marked reduction in resistance in the northern and central-western regions. These results provide a robust scientific basis for optimizing land-use planning and reinforcing ecological security in the Tabu River Basin.

Spatiotemporal Evolution Patterns and Influencing Factors of Rural Shrinkage Under Rapid Urbanization: A Case Study of Zhejiang Province, China

Under the context of urban–rural mobility driven by rapid urbanization, rural shrinkage is particularly pronounced in the developed regions of China, yet studies exploring its patterns and influencing factors are scarce. This paper uses Zhejiang Province as a case study to investigate the spatiotemporal evolution and influencing factors of rural shrinkage from 2000 to 2020. By employing spatial autocorrelation analysis and spatial econometric modeling, the study investigates both population and spatial shrinkage. The findings reveal the following: (1) a marked spatiotemporal imbalance exists in population and spatial dynamics in Zhejiang’s rural areas, where the interplay between population and spatial shrinkage contributes to regional disparities. (2) From 2000 to 2020, population shrinkage shifted from a global shrinkage to a more localized decline in the northeast and southwest, while spatial shrinkage shifted from a global non-shrinkage to a focus on central and southwestern areas. (3) Urbanization significantly negatively impacts rural shrinkage, while the spatial spillover effect of urbanization is notably positive. In contrast, factors related to natural conditions, location, and agricultural investment show significant positive effects but also notable negative spatial spillover effects. This study aims to provide a comprehensive analytical framework for understanding rural shrinkage and offers valuable insights for rural development, infrastructure planning, and spatial governance.

Impacts of environments on school myopia by spatial analysis techniques in Wuhan

The prevalence of myopia in China has increased significantly in recent years, and the age of onset has become younger. Previous studies have indicated that various environmental factors can influence the development of school myopia. However, the environmental impacts on school myopia remains to be investigated. Discoveries in this field may contribute to better urban planning. This study involved 7,610 students (aged 6–12 years, 4084 boys and 3526 girls) from six primary schools in Wuhan, China. We evaluated the associations between school myopia and the environment by analyzing the geographical distribution of myopic children. We utilized the spatial statistical analysis model. The Normalized Difference Vegetation Index (NDVI) risk coefficient for a 5,000-m radius around target schools was 0.379 (p = 0.008), while the NDVI risk coefficient for a 100-m radius around target schools was 0.241 (p = 0.047). The sports area risk coefficient for a 5,000-m radius around target schools was 0.234 (p = 0.016). We found that the specific buffers of NDVI and sports area around schools were associated with the prevalence of school myopia in schools, which worth further research to guide future initiatives on school myopia from an environmental perspective.

Neighborhoods, Networks, and HIV Care Among Men Who Have Sex With Men: Proposal for a Longitudinal Study.

The majority of people living with HIV in the United States are men who have sex with men (MSM), with race- and ethnicity-based disparities in HIV rates and care continuum. In order to uncover the neighborhood- and network-involved pathways that produce HIV care outcome disparities, systematic, theory-based investigation of the specific and intersecting neighborhood and social network characteristics that relate to the HIV care continuum must be engaged. Using socioecological and intersectional conceptual frameworks, we aim to identify individual-, neighborhood-, and network-level characteristics associated with HIV care continuum outcomes (viral suppression, retention in care, and antiretroviral adherence) among MSM living with HIV in New York City. In the longitudinal cohort study, we assess 3 neighborhoods of potential influence (residential, social, and health care access activity spaces) using Google Earth. We investigate the influence of neighborhood composition (eg, concentrated poverty and racial segregation) and four neighborhood-level characteristics domains: (1) community violence, physical disorder, and social disorganization (eg, crime rates and housing vacancy); (2) alcohol and other drug use; (3) social norms (eg, homophobia and HIV stigma); and (4) community resources (eg, social services and public transit access). We test theoretical pathways of influence, including stress or coping, stigma or resilience, and access to resources, across the different neighborhoods in which MSM live, socialize, and receive HIV care. At each visit, we locate each participant's reported activity spaces (ie, neighborhoods of potential influence) and collect individual-level data on relevant covariates (including perceptions of or exposure to neighborhoods) and social network inventory data on the composition, social support, and perceived social norms. The outcomes are HIV viral suppression, retention in care, and antiretroviral adherence. These data are combined with an existing, extensive geospatial database of relevant area characteristics. Spatial analysis and multilevel modeling are used to test the main theory-driven hypotheses and capture independent neighborhood-level and network-level effects and changes over time. The study began enrollment in March 2019 and concluded visits in December 2023, with a total of 327 participants enrolled. The median age was 44.1 (SD 11.5) years. Almost all participants self-identified as cisgender men (n=313, 98.1%) and as gay, homosexual, or bisexual (n=301, 94.4%). Overall, 192 (60.1%) participants identified as non-Hispanic Black, and 81 (25.3%) identified as Hispanic. Most (n=201, 63%) reported at least occasional difficulty in meeting basic needs (eg, rent and food) in the past 6 months. The mean number of years living with HIV was 15.4 (SD 10.1). This study will have direct implications for the design of multilevel interventions, addressing factors at the neighborhood, network, and individual levels. Results may inform urban planning and program design to improve HIV care outcomes for MSM, particularly for Black and Latino MSM living in urban areas. PRR1-10.2196/64358.

Identification of key areas for Territorial Ecological Restoration: focusing on ecological security and restoration potential

Implementing Territorial Ecological Restoration is a crucial approach to achieving ecological stability and regional sustainable development, and it also serves as an important measure for promoting the coordinated development of socioeconomic and ecological environments in the Yellow River Basin. However, effective identification of key areas for Territorial Ecological Restoration remains challenging in planning ecological restoration projects. This study focuses on ecological security and restoration potential, taking Linxia Prefecture in the ecologically fragile area of the upper Yellow River as an example. By evaluating six types of ecosystem services in this area, utilizing morphological spatial pattern analysis models and circuit theory, we identified the structural elements of ecological security patterns and assessed the potential for ecological restoration, identifying key areas for regional ecological restoration.The findings indicated that (1) 13 ecological patches were identified in total, predominantly distributed in the southwest and central regions of Linxia Prefecture. 25 ecological corridors were identified, demonstrating a high level of spatial coherence in terms of significance and connectivity, establishing a closely integrated ecological security network primarily in the southwest of Linxia Prefecture. (2) Thirteen ecological pinch points and seventeen ecological barrier points have been identified, concentrated in the central and southwestern regions of Linxia Prefecture. (3) Based on the characteristics and urgency of key areas for Territorial Ecological Restoration, they are classified and graded as “point-line-surface” and “primary-very important-important.” The primary restoration areas of the point type are mainly located in the central part of Linxia Prefecture, while the primary restoration areas of the line and plane types are mainly in the southwestern part of Linxia Prefecture. This paper emphasizing a holistic approach that prioritizes ecosystem integrity and social support to guide targeted restoration strategies across various ecological features, ultimately aiming for sustainable regional development and maximizing restoration benefits in ecologically fragile areas.

Spatial analysis and soft computational modeling for hazard assessment of potential toxic elements in potable groundwater

Swiftly increasing population and industrial developments of urban areas has accelerated the worsening of the water quality in recent years. Groundwater samples from different locations of the Doon valley, Garhwal Himalaya were analyzed to measure concentrations of six potential toxic elements (PTEs) viz. chromium (Cr), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), and lead (Pb) using Inductively Coupled Plasma Mass Spectrometer (ICP-MS) with the aim to study the spatial distribution and associated hazards. In addition, machine learning algorithms have been used for prediction of water quality and identification of influencing PTEs. The results inferred that the mean values (in the units of µg L−1) of analyzed PTEs were observed in the order of Mo (1.066) > Ni (0.744) > Pb (0.337) > As (0.186) > Cr (0.180) > Cd (0.026). The levels and computed risks of PTEs were found below the safe limits. The radial basis function neural network (RBF-NN) algorithms showed high level of accuracy in the predictions of heavy metal pollution index (HPI), heavy metal evaluation index (HEI), non-carcinogenic (N-CR) and carcinogenic (CR) parameters with determination coefficient values ranged from 0.912 to 0.976. However, the modified heavy metal pollution index (m-HPI) and contamination index (CI) predictions showed comparatively lower coefficient values as 0.753 and 0.657, respectively. The multilayer perceptron neural network (MLP-NN) demonstrated fluctuation in precision with determination coefficient between 0.167 and 0.954 for the prediction of computed indices (HPI, HEI, CI, m-HPI). In contrast, the proficiency in forecasting of non-carcinogenic and carcinogenic hazards for both sub-groups showcased coefficient values ranged from 0.887 to 0.995. As compared to each other, the radial basis function (RBF) model indicated closer alignments between predicted and actual values for pollution indices, while multilayer perceptron (MLP) model portrayed greater precision in prediction of health risk indices.

Spatial analysis and sequential equation modeling (SEM) of genetic diversity in date palm (Phoenix dactylifera L.)

Spatial analysis and sequential equation modeling (SEM) of genetic diversity in date palm (Phoenix dactylifera L.)

Construction, assessment, and protection of green infrastructure networks from a dynamic perspective: A case study of Dalian City, Liaoning Province, China

The green infrastructure (GI) network, an important nature-based solution (NBS) strategy, is pivotal for sustainable urban development. However, current research perspectives focus on constructing a static GI network, and research on evaluating and protecting GI networks in the context of spatiotemporal changes has been limited. This research aims to comprehensively characterise the spatio-temporal changes in the GI network and protect its efficiency. We selected Dalian City as a case study and applied the Future Land-use Simulation (FLUS) model to predict its land use for 2030 and 2040. Based on predicted and historical land-use data, the 1990–2040 GI network of Dalian City was constructed utilising the Integrated valuation of ecosystem services and trade-offs (InVEST), morphological spatial pattern analysis (MSPA) and minimum cumulative resistance (MCR) model, while its spatiotemporal changes were evaluated. The results revealed that the hub area exhibited positive growth over time, whereas the link lengths showed an opposite trend. Furthermore, the overall position of the GI network shifted toward the southwest. The flatness indicator detected that the direction of the GI network shape gradually became less pronounced. Consequently, the structure of the GI network shifted from multi-centre to mono-centre. Additionally, priority protection for hubs is mainly in the northeast, while links are primarily in the coastal regions. The urgency of protection for both increases over time. This research developed a dynamic GI network construction and assessment method, providing a scientific basis and reference for future rational GI network planning, ecological protection planning, and related land policy formulation.

Spatially Explicit Impact of Land Use Changes in the Bay Area on Anthropogenic Phosphorus Emissions and Freshwater Eutrophication Potential.

Land use changes significantly impact anthropogenic phosphorus (P) emissions, their migration to a water environment, and the formation of freshwater eutrophication potential (FEP), yet the spatiotemporally heterogeneous relationships at the regional scale have been less explored. This study combines land use classification, P-flow modeling, spatial analysis, and cause-effect chain modeling to assess P emissions and P-induced FEP at a fine spatial resolution in Guangdong-Hong Kong-Macao Greater Bay Area and reveals their dynamic responses to land use changes. We find that land conversion from cultivated land to impervious land corresponded to an increase in P emissions of 4.1, 1.8, and 0.5 Gg during 2000-2005, 2005-2010, and 2010-2015 periods, respectively, revealing its dominant but weakening role in the intensification of P emissions especially in less-developed cities. Expansion of aquacultural land gradually became the primary contributor to the increase in both the amount and intensity of P emissions. Land conversions from cultivated land to impervious land and from natural water bodies to aquacultural land led to 35.9% and 25.3% of the increase in FEP, respectively. Our study identifies hotspots for mitigating the environmental pressure from P emissions and provides tailored land management strategies at specific regional development stages and within sensitive areas.

Distribution characteristics and development layout of rural tourism resources based on LSTM in-depth learning in the context of common prosperity

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:150%"><span style="font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman","serif"">The development and layout of rural tourism resources is the core issue supporting the development and planning of tourism destinations. Currently, it mainly relies on the experience and judgment of planners, with strong subjectivity and lack of objective information technology support. Therefore, in the context of common prosperity, a method for distribution characteristics and development layout of rural tourism resource based on LSTM deep learning is proposed. After distinguishing various rural tourism resources using the classification method of rural tourism resources based on LSTM in-depth learning, the spatial distribution characteristics of various tourism resources are estimated using the analysis method for spatial distribution characteristics of tourism resources based on kernel density estimation; After identifying the current status of tourism resource functional areas through a method of tourism resource development and layout based on a grid spatial analysis model combined with the distribution characteristics information mastered, and from the perspective of tourists, it can optimize the layout mode of functional areas to maximize the benefits of rural tourism resources on the basis of satisfying tourists' preferences. After testing, this method can utilize intelligent information technology to optimize the development and layout of rural tourism resources.</span></span></span></span></span></span></p>

An attention-based hybrid model for spatial and temporal sentiment analysis of COVID-19 related tweets in the contiguous United States

ABSTRACT Understanding the sentiments of social media posts can help health authorities respond to disease outbreaks, through a proxy measure of fear, confidence, and community compliance. Sentiment analysis identifies a pattern of emotion through the written word and assigns a positive, neutral, or negative value to it. As of February 2023, there were 677.7 million confirmed cases of coronavirus disease (COVID-19) and more than 6.7 million confirmed deaths. In this paper, around 170,000 COVID-19-related tweets were collected between September 2020 and January 2021 in the contiguous United States. Data preprocessing and exploratory investigation were completed for analysis of the collected dataset. Further, a novel and unified architecture called attention-based one-dimensional convolution with bidirectional long short-term memory layers (CNN-BiLSTM-ATT) is proposed to classify people’s sentiments as positive, neutral, and negative based on COVID-19-related tweets. In the CNN-BiLSTM-ATT model, the CNN layer can extract the low-level semantic features from textual data, and the BiLSTM layer can extract both the previous and future contextual representations. The attention module can improve the information focus from the outputted layer of the BiLSTM. The proposed method can extract both the local phrase representations and the global feature of sentences. Numerical experiments were conducted on COVID-19-related tweets using the proposed method and other baseline models to compare their performances. Our experimental results demonstrate that the CNN-BiLSTM-ATT model achieves an average accuracy of 95.16% and a macro-average F1-score of 95.12%, which outperforms the baseline models.

Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis

Cooking with traditional fuels can lead to severe health issues caused by household air pollution, and can also affect gender equality and drive environmental degradation. In Nepal, despite government efforts to promote electric cooking, more than half of the population still uses traditional fuels, with electric cooking adoption remaining below 1%. Several of the barriers to and enablers of clean cooking vary geographically; however, few studies have considered spatial explicit information in planning national-scale transitions to clean cooking. In this study we provide a spatially explicit roadmap to estimate the required investments and benefits gained from the transition across Nepal. This study uses geospatial modelling methods to evaluate strategies to achieve the Government of Nepal's vision for a national-scale transition to clean cooking. We integrate the open-source clean cooking geospatial assessment tool OnStove and a spatial multicriteria analysis model. With OnStove, we evaluate which cooking technologies and fuels maximise the net benefits of a clean-cooking transition across each km2 of the region. With the multicriteria analysis, we weigh stakeholder preferences and prioritise areas of action where policy should be implemented. We used the most up-to-date geospatial data to the year 2023, such as the High Resolution Settlement Layer, Open Street Maps' road networks, the Global Human Settlement Layer, NASA/USGS forest cover maps, and Facebook's Relative Wealth Index, among others. We also relied on data from the Nepal Oil Corporation, the Nepal Electricity Agency, the Central Bureau of Statistic's 2021 national census, and the Alternative Energy Promotion Center. We evaluate four scenarios capturing advances on clean cooking policy up to the year 2022, current market inefficiencies, and the potential effects of new policies for clean-cooking transition in Nepal. Our results show that transitional and clean cooking technologies provide higher net benefits than traditional options everywhere across Nepal in all scenarios. Our net-benefit analysis shows that around 9563 deaths could be averted yearly if benefits and externalities were perceived and valued correctly. Furthermore, substantial benefits could be achieved in regard to greenhouse gas emissions avoidance, time saved, and health-cost reductions. Our results also show that the current subsidy strategy from the Government of Nepal is well aligned with the benefits achieved under a cost-benefit analysis. In this context, electric cooking can bring the highest benefits to the largest part of the population. The analysis showed how high subsidies for liquefied petroleum gas in Nepal can present trade-offs with energy security and independence, and how this could be avoided by transferring part of the subsidy to cover differentiated electric cooking tariffs. Accounting for stakeholder preferences and sociodemographic and geographical differences to prioritise areas of focus can balance affordability constraints and target the most vulnerable people first, thus achieving integrated and inclusive planning. Using spatially explicit modelling approaches to evaluate strategies for a clean cooking transition can provide more nuanced results that have not been possible before. This approach can enable data-driven and integrated planning to help to understand which locations of a study area should be prioritised for policy application. Integrated planning can help to reduce affordability constraints on the population and design strategies for a sustainable and inclusive transition. These strategies allow financial institutions, donors, impact investors, development organisations, and government agencies to use their resources, funds, and assistance to create a large impact. Clean Cooking Alliance.

The Garden Vegetation Coverage Monitoring and Spatial Analysis Model based on Remote Sensing Technology

A correlation study was conducted between SPOTVEGNDVI monthly data and rainfall data in Yulin, Yan ’an, Xi ’an and Ankang of Shaanxi Province from 2015 to 2022. The ecological effects of urban vegetation cover were studied using the revised standardized Vegetation Index (NDVIC). It is found that NDVIC has a strong correlation with precipitation, and its correlation increases with the increase of altitude. Vegetation cover in the Ankang area increased significantly from 2016 to 2022. The net primary productivity index of urban green space increased significantly in October 2016-2022, which is an objective reflection of the effectiveness of urban green construction. This study can lay a theoretical foundation for the objective quantitative assessment of the ecological effects of urban vegetation cover.

Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities.

The role of solar district heat in the energy transition of the German heating sector

This study explores the integration of solar district heating (SDH) into German heating networks, focusing on the energy transition in the heating sector. Utilizing energyPRO software, 8410 scenario variations were analysed, considering collector areas ranging from 1000 to 200 000 m2 and thermal storage capacities from 0 to 100 000 m³. The research identifies the most cost-effective SDH system designs, with solar fractions between 11 % and 18 % and collector area/storage ratios between 4 and 6.7, resulting in levelized costs of heat (LCOH) between 45 and 64 €/MWh. The techno-economic potential of SDH in Germany is estimated at 17.6 TWh by using a spatial analysis model. The paper presents sensitivity analyses and preliminary design equations, contributing to a comprehensive understanding of SDH's role in decarbonizing the heating sector. By integrating spatial data analysis with energy system modeling, the study offers a novel methodological approach, providing insights into the technical and economic feasibility of SDH systems in Northern Germany and similar climatic regions. The findings aim to assist policymakers and decision-makers in evaluating the costs and dimensions of SDH systems based on local heat demands and influencing economic and technical factors.

Study on the influencing factors of the evolution of space pattern based on principal component analysis in Duolun County

In order to explore the influencing factors of spatial and temporal evolution of production-living-ecological space in Beijing Tianjin sandstorm source area, the remote sensing images, natural environment and socio-economic data of Duolun County in Inner Mongolia from 2000 to 2020 were selected, and the spatial auto-correlation model and principal component analysis model were used to analyze the spatial pattern evolution and influencing factors of production-living-ecological space. The results show that: (1) the function of production space decreases slightly, and the degree of spatial agglomeration decreases; (2) The function of living space rose slightly, and its spatial agglomeration degree showed an upward trend; (3) The ecological spatial function showed a slow upward trend, and its spatial agglomeration degree increased; (4) The spatial pattern of production-living-ecological space is characterized by “high in the southwest and low in the northeast”; (5) Precipitation has the greatest impact on the spatial evolution of the production-living-ecological space. The distance from the main residential areas, per capita GDP, the distance from the main roads and the distance from the main waters have strong explanatory power on the spatial evolution of the production-living-ecological space.

Phân tích mối tương quan về du lịch của tỉnh Bến Tre với khu vực lân cận dựa vào mô hình không gian

The article employs a spatial analysis model using two methods, Average Nearest Neighbour (ANN) and Kernel density, to identify correlations in tourism development in Ben Tre province compared to neighboring areas. The study focuses on surveying five types of tourist attractions: natural ecology, history and culture, modern entertainment, community, and spirituality. The results indicate the distribution trends of these types of attractions, serving as a basis for formulating resource exploitation strategies and developing various forms of tourism in Ben Tre province to create differentiation and enhance competitiveness in the region.

Intoxication in the city: Investigating spatial patterns and determinants of drugs and alcohol-related illegal activities in India's geostrategic corridor

Illicit drug and alcohol-related activities pose significant challenges to metropolitan centres globally, impacting public safety and socio-economic stability. This research analyzes the spatial and temporal patterns of drugs and alcohol-related illegal activities (DARIA) in Siliguri, known as the "Gateway of North-East India." The primary objectives are to explore DARIA patterns, identify clusters and concentrated areas, and investigate contributing factors. Using GIS-based spatial analysis and geographical models, we studied incidents recorded over three years from 2021 to 2023. Advanced spatial statistical approaches were applied to identify specific clusters of DARIA occurrences. The pure temporal analysis highlighted periods of increased illegal activity. Various geographical models, including ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR), were used to examine the spatial association. The MGWR model, with an explanatory power of R2 = 0.816, provided a detailed understanding of spatial relationships and variations in DARIA. Our findings indicate specific zones with frequent DARIA occurrences, mainly near major transportation hubs, commercial points, and areas with significant mobility. Key exploratory factors such as illiteracy rate, population density, proximity to playgrounds and slums were highly associated with DARIA incidents. These findings contribute to developing safer, more resilient, and more sustainable cities worldwide.

Regional coordinated development of low-carbon sports industry in China

This study develops a systematic modeling framework, comprising a prediction model, a super-SBM model, and a spatial autocorrelation analysis model, to explore the spatial–temporal evolution tendencies of development efficiency within China's 30 regions in the low-carbon sports industry from 2006 to 2025. This framework aims to provide theoretical insights for the formulation of more targeted policies. Based on the empirical findings, the main conclusions of this study are as follows: (1) The optimal buffer operator grey prediction model demonstrates the highest accuracy among the prediction models examined. (2) The development efficiency curves of the 30 regions exhibit a significant increasing trend from 2006 to 2021, with values generally peaking between 0.4 and 0.6. (3) Notably, the disparity in development efficiency between developed and less developed regions is expected to progressively widen. (4) The development efficiency of the low-carbon sports industry across the 30 regions typically displays high-high clustering and low-low clustering during China's four five-year plan periods. This underscores the importance and urgency of promoting regional coordinated development within the low-carbon sports industry.