Spatial Analysis Research Articles

Determinants of human-elephant conflict in tropical forest of South India

Abstract Human-elephant conflict (HEC) is a global conservation issue that undermines the conservation of an endangered species. HEC and the factors influencing crop-raiding behaviour were studied in the tropical forests of south India from Nov 2006 to Mar 2008. Crop depredation, protection methods, and land use changes were studied at Mudumalai Tiger Reserve (TR). A total of 297 incidents of crop damage were recorded, with peaks in the raiding during the monsoon season. Spatial analysis of HEC showed peripheral farms are more vulnerable (1.51%) than village centers (0.18%). Temporally, cropping patterns have changed from food crops to commercial crops. The elephant-damaged area was positively correlated with the effective perimeter (pe-rimeter with crops), total cultivated area, and area of degradation around the village. Logistic regression of crop raiding incidence with independent predictors showed that the incidence of raiding was 95% greater in moist deciduous forests than in thorny forests. Palatable crops were likely to be raided more frequently (391 times). Crops with protection methods were 22 times less likely to be targets of raids. In the areas adjacent to the Mudumalai TR, crop species, improvements in protection, and land use change reduced the HEC. Relocating villages from the core reserve and existing corridors in the eastern region is essential for conserving elephants in the Nilgiris, Western Ghats landscape.

Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.

Spatial distribution characteristics and influencing factors of youth soccer specialty schools in China

Youth soccer specialty schools are the primary project to strengthen the foundation of soccer talents, improve the level of soccer development, and achieve the dream of Chinese soccer. Taking 32,775 soccer specialty schools in China as the research sample, ArcGIS spatial analysis technology is used to explore the spatial distribution characteristics of soccer specialty schools and their influencing factors. The analysis shows that the overall distribution of the country forms five quantity concentration area and eight high-density core areas, with a high degree of spatial concentration and significant differences between the East and West. The factors influencing soccer specialty schools include nine factors in four dimensions: historical development, geographical environment, socio-economic, and educational resources. It was found that geographic environment and socio-economy provide the foundation and background for developing educational resources. However, the direct role of educational resources is more significant in a specific educational program, such as soccer specialty schools, followed by socio-economy and geographic environment. This study comprehensively analyzes and interprets soccer specialty schools’ spatial distribution characteristics and influencing factors. These results enrich the research on the spatial layout of sports, providing necessary theoretical support and practical references for the layout and high-quality development of related industries in China and other countries or regions.

The historical evolution and social transformation of settlements in the mountainous regions of western Guangdong: a case study of Xinyi County, China

The formation of China’s territory has a long historical process. Prior research has highlighted the diverse historical development patterns across different regions. To delve deeper into the distinctiveness among regions and their nuanced disparities, the research centers on the settlements within Xinyi County, situated in the mountainous terrain of western Guangdong Province, China. The current study employs the research method of “Backward Observation” by conducting a literature review and utilizing ArcGIS for spatial analysis to investigate the spatial distribution and evolution process of regional settlements in western Guangdong. The results suggest a predominant involvement of the state in propelling the advancement of the indigenous community within Xinyi County, a progression primarily fulfilled by the conclusion of the late Qing and initial Republican eras, guided by the persistent localization and gentrification of migrating populations. Xinyi’s advancement pattern and speed exhibited notable distinctions from neighboring areas within Guangdong Province. This investigation additionally delves into G. William Skinner’s inquiries regarding the exceptional attributes and evolutionary patterns of localities, aligning with the South China School’s viewpoint on the varied nature of regional advancement procedures and cadences. This research on Xinyi’s historical evolution is importance in coordinating the preservation and advancement of historical towns, addressing urban-rural development disparities, and formulating future development strategies.

Neighborhood as Theater: Building a Goffmanian Framework for Comparative Urban Ethnography

AbstractWhile the history of urban sociology features myriad detailed descriptions of neighborhood life reflecting various theoretical, political, and practical purposes, the field lacks general templates for conducting in-depth neighborhood research and comparison. In this paper, as a remedy, we propose a Goffmanian framework for qualitative neighborhood description and analysis which we call "neighborhood as theater." While revisiting previous ethnographic research of two neighborhoods in Hollywood, Los Angeles, conducted in the late 1990s, we argue that local social life operates along three main dramaturgical axes—sets, characters, and performances—each divided into several subcategories. These three focal points reveal observable, yet easily overlooked, key elements of neighborhood life besides illuminating broader, structural aspects and connections. We argue that conceptualizing neighborhoods “as theater" offers a general template for ethnographic urban research that can advance comparative and critical spatial analysis while remaining grounded in meaning-centered, embodied experiences of mundane local life.

Insights from a population grid of South Africa: An applied spatial satellite data analysis

The present study explores the reliability and accuracy of various spatial mapping methodologies in estimating and presenting the spatial characteristic and dynamics (location, distribution, density, and size) of the population in South Africa. As a basic underlying concept, the study first explores spatial heterogeneity, that is, that every location is related to every other location, and those nearby are related stronger. This study, therefore, illustrates the spatial relationships between locations and the spatial pattern of the population in South Africa. Analyzing the spatial images determines the extent of such influence and the nature of the spatial patterns. To this end, a granular gridded population dataset was derived using satellite image data, and the NASA’s Socioeconomic Data and Applications Center gridded population of the world version 4 population images and datasets were used. Several spatial data models and geostatistical applications were applied to study the spatial characteristics and dynamics of the population of South Africa from 2000 to 2020. Spatial analysis was performed using R-Studio, QGIS, and GeoDa. Among others, the results point to the fact that the South African population is very densely located that population density decreases marginally outward and suggests that the underlying process for the population distribution is stationary. This study proposes that it is indeed possible to reliably and accurately estimate and present gridded population images and datasets using spatial and geostatistical methodologies.

Learning from Users: Everyday Playful Interactions to Support Architectural Spatial Changes

While there's growing interest in eliciting situated playful interactions with technologies in different contexts, how these interactions might shape everyday spaces still needs to be fully explored. To address this gap, this article aims to guide the architectural spatial changes by exploring everyday playful interactions of technology-adopted users in domestic spaces. We present our contributions in a two-fold study: First, through an extensive diary study involving 13 technology-adopted residents in gated communities, where distinct boundaries offer increased opportunities for playful interactions, we identified four playful themes: (1) creating and expanding play-spaces, (2) balancing play and comfort, (3) intertwining imagination with spatial experience, and (4) gamifying household interactions. Secondly, by building on these themes, we outline three design implications to inform architectural design processes, aiming to translate everyday playful interactions into tangible spatial changes. These implications include adapting shape-changing and wearable technologies for playful flexibility, embedding new forms of communications within infrastructures, and turning homes into interactive entities with multi-sensory technologies. While these findings provide a starting point for exploring new architectural design possibilities in similar environments, further research with architects, policymakers, and design researchers in the Human-Computer Interaction (HCI) and Human-Building Interaction (HBI) fields is essential to actualizing these changes.

Spatial and Temporal Change Analysis of Urban Built-Up Area via Nighttime Lighting Data—A Case Study with Yunnan and Guizhou Provinces

As urbanization accelerates, characteristics of urban spatial expansion play a significant role in the future utilization of land resources, the protection of the ecological environment, and the coordinated development of population and land. In this study, Yunnan and Guizhou provinces were selected as the study area, and the 2013–2021 National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data were utilized for spatial and temporal change analysis of urban built-up areas. Firstly, the built-up areas in Yunnan and Guizhou provinces were extracted through ENUI (Enhanced Nighttime Lighting Urban Index) indices, and then the urban expansion speed and urban center of gravity migration were constructed and used to explore and analyze the spatial and temporal change and expansion characteristics of built-up areas in Yunnan and Guizhou provinces. The results showed the following. (1) Due to the complementarity between data types, such as NTL, EVI, NDBI, and NDWI, ENUI has better performance in expressing urban characteristics. (2) Influenced by national and local policies, such as “One Belt, One Road”, transportation infrastructure construction, geographic location, the historical background, and other factors, the urban expansion rate of Yunnan and Guizhou provinces in general showed a continuous advancement from 2013 to 2021, and there were three years in which the expansion rate was positive. (3) The center of gravity migration distance of most cities in Guizhou Province shows a trend of increasing and then decreasing, while the center of gravity migration distance in Yunnan Province shows a trend of continuous decrease in general. From the perspective of migration direction, Guizhou Province has the largest number of migrations to the northeast, while Yunnan Province has the largest number of migrations to the southeast. (4) Influenced by policy, economy, population, geography, and other factors, urban compactness in Yunnan and Guizhou provinces continued to grow from 2013 to 2021. The results of this study can help us better understand urbanization in western China, reveal the urban expansion patterns and spatial characteristics of Yunnan and Guizhou provinces, and provide valuable references for development planning and policymaking in Yunnan and Guizhou provinces.

Spatial heterogeneity in unintended pregnancy and its determinants in India

BackgroundUnderstanding the geographic variation of unintended pregnancy is crucial for informing tailored policies and programs to improve maternal and child health outcomes. Although spatial analyses of unintended pregnancy have been conducted in several developing countries, such research is lacking in India. This study addresses this gap by investigating the geographic distribution and determinants of unintended pregnancy in India.MethodsWe analysed data from the National Family Health Survey-5 encompassing 232,920 pregnancies occurring between 2014 and 2021 in India. We conducted a spatial analysis to investigate the distribution of unintended pregnancies at both state and district levels using choropleth maps. To assess spatial autocorrelation, Global Moran’s I statistic was employed. Cluster and outlier analysis techniques were then utilized to identify significant clusters of unintended pregnancies across India. Furthermore, we employed Spatial Lag Model (SLM) and Spatial Error Model (SEM) to investigate the factors influencing the occurrence of unintended pregnancies within districts.ResultsThe national rate of unintended pregnancy in India is approximately 9.1%, but this rate varies significantly between different states and districts of India. The rate exceeded 10% in the states situated in the northern plain such as Haryana, Delhi, Uttar Pradesh, Bihar, and West Bengal, as well as in the Himalayan states of Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. Moreover, within these states, numerous districts reported rates exceeding 15%. The results of Global Moran’s I indicated a statistically significant geographical clustering of unintended pregnancy rates at the district level, with a coefficient of 0.47 (p < 0.01). Cluster and outlier analysis further identified three major high-high clusters, predominantly located in the districts of Arunachal Pradesh, northern West Bengal, Bihar, western Uttar Pradesh, Haryana, Delhi, alongside a few smaller clusters in Odisha, Madhya Pradesh, Uttarakhand, and Himachal Pradesh. This geographic clustering of unintended pregnancy may be attributed to factors such as unmet needs for family planning, preferences for smaller family sizes, or the desire for male children. Results from the SEM underscored that parity and use of modern contraceptive were statistically significant predictors of unintended pregnancy at the district level.ConclusionOur analysis of comprehensive, nationally representative data from NFHS-5 in India reveals significant geographical disparities in unintended pregnancies, evident at both state and district levels. These findings underscore the critical importance of targeted policy interventions, particularly in geographical hotspots, to effectively reduce unintended pregnancy rates and can contribute significantly to improving reproductive health outcomes across the country.

Artistic Interventions in Urban Renewal: Exploring the Social Impact and Contribution of Public Art to Sustainable Urban Development Goals

The aim of this study was to explore the multiple roles of public art in urban renewal and sustainable development goals, combining qualitative and quantitative methods, with an emphasis on community engagement and the application of theoretical frameworks. Through a detailed analysis of public art projects in Teruel, Spain, and Chengdu, China, this study shows how public art plays an important role in enhancing cultural values, promoting social inclusion, and driving economic growth. Qualitative methods such as interviews and content analysis were used to gain in-depth insights into the views and experiences of artists, urban planners, policymakers, and community members. At the same time, quantitative methods such as questionnaires and spatial analysis were used to assess the specific impacts of public art on urban beautification, social inclusion, and economic growth.

Quantitative Analysis about the Spatial Heterogeneity of Water Conservation Services Function Using a Space–Time Cube Constructed Based on Ecosystem and Soil Types

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.

Spatial and Single-Cell Transcriptomics Reveals the Regional Division of the Spatial Structure of MASH Fibrosis.

To elucidate the regional distribution of metabolic dysfunction-associated steatohepatitis (MASH) fibrosis within the liver and to identify potential therapeutic targets for MASH fibrosis. Liver sections from healthy controls, patients with simple steatosis and MASH patients were analysed using spatial transcriptomics integrated with single-cell RNA-seq. Spatial transcriptomics analysis of liver tissues revealed that the fibrotic region (Cluster 9) was primarily distributed in lobules, with some fibrosis also found in the surrounding area. Integration of the single-cell-sequencing data set (GSE189175) showed a greater proportion of inflammatory cells (Kupffer cells and T cells) and myofibroblasts in MASH. Six genes, showing high- or low-specific expression in Cluster 9, namely, ADAMTSL2, PTGDS, S100A6, PPP1R1A, ASS1 and G6PC, were identified in combination with pathology. The average expression levels of ADAMTSL2, PTGDS and S100A6 on the pathological HE staining map were positively correlated with the increase in the degree of fibrosis and aligned strongly with the distribution of fibrosis. ADAMTSL2+ myofibroblasts play a role in TNF signalling pathways and in the production of ECM structural components. Pseudotime analysis indicated that in the early stages of MASH, infiltration by T cells and Kupffer cells triggers a significant inflammatory response. Subsequently, this inflammation leads to the activation of hepatic stellate cells (HSCs), transforming them into myofibroblasts and promoting the development of liver fibrosis. This study is the first to characterise lineage-specific changes in gene expression, subpopulation composition, and pseudotime analysis in MASH fibrosis and reveals potential therapeutic targets for this condition.

Konsep Tata Ruang Goa Gajah dan Kolam Patirthan Berdasarkan Arsitektur Tradisional Bali

The aim is to find out the application of the spatial layout of Goa Gajah and the Patirthan pond based on traditional Balinese architecture and to find out the relationship between Goa Gajah and the Patirthan pond reflected in the function of the building. The research method is qualitative with inductive reasoning. Data collection was carried out using literature studies, observations, interviews and documentation studies. The collected data was processed using spatial character analysis, contextual analysis and qualitative analysis supported by spatial theory of traditional Balinese architecture and architectural function theory. The results of the research concluded that Goa Gajah and the Patirthan pond were identified as being based on traditional Balinese architectural spatial concepts, namely; Tri Hita Karana; Tri Angga and Tri Loka; and orientations. Analysis of the relationship between Goa Gajah and the Patirthan pond also implies a philosophical relationship between mountains and the sea. The activities at Goa Gajah and the Patirthan pond clarify the relationship between buildings, and represent the building as a social environment for ascetics.

How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection

Deep Neural Networks (DNNs) have garnered significant attention across various research domains due to their impressive performance, particularly Convolutional Neural Networks (CNNs), known for their exceptional accuracy in image processing tasks. However, the opaque nature of DNNs has raised concerns about their trustworthiness, as users often cannot understand how the model arrives at its predictions or decisions. This lack of transparency is particularly problematic in critical fields such as healthcare, finance, and law, where the stakes are high. Consequently, there has been a surge in the development of explanation methods for DNNs. Typically, the effectiveness of these methods is assessed subjectively via human observation on the heatmaps or attribution maps generated by eXplanation AI (XAI) methods. In this paper, a novel GeoStatistics Explainable Artificial Intelligence (GSEAI) framework is proposed, which integrates spatial pattern analysis from Geostatistics with XAI algorithms to assess and compare XAI understandability. Global and local Moran’s I indices, commonly used to assess the spatial autocorrelation of geographic data, assist in comprehending the spatial distribution patterns of attribution maps produced by the XAI method, through measuring the levels of aggregation or dispersion. Interpreting and analyzing attribution maps by Moran’s I scattergram and LISA clustering maps provide an accurate global objective quantitative assessment of the spatial distribution of feature attribution and achieves a more understandable local interpretation. In this paper, we conduct experiments on aircraft detection in SAR images based on the widely used YOLOv5 network, and evaluate four mainstream XAI methods quantitatively and qualitatively. By using GSEAI to perform explanation analysis of the given DNN, we could gain more insights about the behavior of the network, to enhance the trustworthiness of DNN applications. To the best of our knowledge, this is the first time XAI has been integrated with geostatistical algorithms in SAR domain knowledge, which expands the analytical approaches of XAI and also promotes the development of XAI within SAR image analytics.

Remarks on Selected Morphological Aspects of Cancer Neuroscience: A Microscopic Photo Review.

This short review and pictorial essay presents a morphological insight into cancer neuroscience, which is a complex and dynamic area of the pathobiology of tumors. We discuss the different methods and issues connected with structural research on tumor innervation, interactions between neoplastic cells and the nervous system, and dysregulated neural influence on cancer phenotypes. Perineural invasion (PNI), the most-visible cancer-nerve relation, is briefly presented, focusing on its pathophysiology and structural diversity as well as its clinical significance. The morphological approach to cancer neurobiology further includes the analysis of neural density/axonogenesis, neural network topographic distribution, and composition of fiber types and size. Next, the diverse range of neurotransmitters and neuropeptides and the neuroendocrine differentiation of cancer cells are reviewed. Another morphological area of cancer neuroscience is spatial or quantitative neural-related marker expression analysis through different detection, description, and visualization methods, also on experimental animal or cellular models. Morphological studies with systematic methodologies provide a necessary insight into the structure and function of the multifaceted tumor neural microenvironment and in context of possible new therapeutic neural-based oncological solutions.

Spatial variations and predictors of overweight/obesity among under-five children in Ethiopia: A geographically weighted regression analysis of the 2019 Ethiopian Mini Demographic and Health Survey.

Overweight/ obesity among under-five children is an emerging public health issue of the twenty-first century. Due to the quick nutritional and epidemiological change, non-communicable diseases, premature death, disability, and reproductive disorders have grown in low-income countries. Besides, little attention has been given. Therefore, we aimed to explore spatial variations and predictors of overweight/obesity among under-five children in Ethiopia using a geospatial technique. A total weighted sample of 3,609 under-five children was included in the study. A cross-sectional study was conducted using a nationally representative sample of the 2019 Ethiopia Mini Demographic and Health Survey data set. ArcGIS version 10.8 was used to explore the spatial variation of obesity. SaTScan version 9.6 software was used to analyze the spatial cluster detection of overweight/obesity. Ordinary least square and geographically weighted regression analysis were employed to assess the association between an outcome variable and explanatory variables. A p-value of less than 0.05 was used to declare it statistically significant. The spatial distribution of overweight/obesity among under-five children in Ethiopia was clustered (Global Moran's I = 0.27, p-value<0.001). The significant hot spot areas or higher rates of childhood obesity, were found in Southern Amhara, Northwest Somalia, Border of Harari, central Addis Ababa, Eastern SNNPR, and Northwestern Oromia region. In spatial SaT Scan analysis, 79 significant clusters of overweight/obesity were detected. The primary clusters were located in SNNPR, Oromia, and Addis Ababa (RR = 1.48, LLR = 31.40, P-value < 0.001). In the geographically weighted regression analysis, urban residence, cesarean section, rich households, and female children were statistically significant predictors. Overweight or obesity among under-five children show spatial variations across Ethiopian regions. GWR analysis identifies cesarean section, wealth index, urban residence, and child sex as significant predictors. The Ministry of Health and Ethiopian Public Health Institute should target regions with these contributing predictors, promoting localized physical education, health education campaigns, and ongoing community monitoring to encourage active lifestyles and reduce sedentary behaviors among children.

GEOGRAPHICALLY WEIGHTED MACHINE LEARNING MODEL FOR ADDRESSING SPATIAL HETEROGENEITY OF PUBLIC HEALTH DEVELOPMENT INDEX IN JAVA ISLAND

Random Forest (RF) machine learning models have emerged as a prominent algorithm, addressing problems arising from the sole use of decision trees, such as overfitting and instability. However, conventional RF has global coverage that may need to capture spatial variations better. Based on the analysis of the level of public health development, the relationship between the level of health development and risk factors can vary spatially. We use a modified RF algorithm called Geographically Weighted Random Forest (GW-RF) to address this challenge. GW-RF, as a tree-based non-parametric machine learning model, can help explore and visualize relationships between the Public Health Development Index (PHDI) as response variables and factors that are indicators at the district level. GW-RF output is compared with global output, which is RF in 2018 using the percentage of the population with access to clean/decent water (X1), consumption of eggs and milk per capita per week (X2), number of healthcare facilities per 1000 people (X3), number of doctors per 1000 people (X4), pure participation rate ratio female/male (X5), percentage of households that have hand washing facilities with soap and water (X6) as independent variables. Our results show that the non-parametric GW-RF model shows high potential for explaining spatial heterogeneity and predicting PHDI versus a global model when including six major risk factors. However, some of these predictions mean little. Findings of spatial heterogeneity using GW-RF show the need to consider local factors in approaches to increasing PHDI values. Spatial analysis of PHDI provides valuable information for determining geographic targets for areas whose PHDI values need to be improved.

Spatial and Temporal Evolution and Conservation Significance of Traditional Villages in the Yangtze River Basin, China

Traditionally, “ancient villages” are communities that date back to a previous era, possess abundant traditional resources, and hold significant historical, social, cultural, ecological, and economic significance. This study examines the geographical and chronological development of 2957 traditional communities in the Yangtze River Basin and extensively employs GIS spatial analysis, geostatistical analysis, and historical literature review to study the evolution of the locations of traditional settlements under various factors. The study’s findings revealed that: (1) From a geographical standpoint, the traditional villages in the study area present a distribution state of “two cores, small aggregation, and many dispersions”. The two core clusters are located at the intersection of Hunan, Guizhou, and Chongqing provinces, where the natural geographical advantages attracted people from different dynasties to settle here. (2) From a temporal perspective, the quantity of traditional villages shows a growing trend, and the distribution center of each dynasty shows an east–west direction. Among them, the shift from the Song to the Yuan dynasties marked the period of greatest change in village distribution, which originated from the Song Dynasty’s quick economic growth. In addition, the village distribution was clustered in the eastern part of the country, adapting to the needs of commercial trade. When the Song Dynasty fell, the society was in turmoil, and the villages were transferred to the central region to avoid the war. The Yuan Dynasty also laid down the fundamental distribution pattern of conventional villages. (3) From the standpoint of choosing a village location, most of them are distributed in mountainous, hilly, and plain regions; the gentle slopes of 0°~15° are favored by people. Sufficient water sources, abundant precipitation, and a suitable climate are also necessary choices. (4) From the perspective of driving factors, traditional village distribution in the basin is impacted by historical, cultural, social, and ecological factors. Included among these, traditional village distribution is inversely connected with socio-economic variables. Under the interaction of two factors, the distribution will be more affected. To summarize, the complex human geographic factors together create the conventional village dispersal pattern, and examining these elements holds substantial practical importance for comprehending, safeguarding, and preserving traditional villages.

Assessment of Ventilation Potential and Construction of Wind Corridors in Chengdu City Based on Multi-Source Data and Multi-Model Analysis

The establishment of urban ventilation corridors (UVCs) aims to mitigate the urban heat island effect. While most studies focus on the construction and assessment of the environmental benefit of UVCs, they often overlook the analysis of UVCs’ topological features. This research integrates multi-source data including 3D urban buildings, historical meteorological observations, high-resolution remote sensing, and land use planning, combined with multiple models, including geographic information system spatial analysis, circuit theory, and complex networks. Based on an assessment of urban ventilation potential, the circuit model was applied to extract UVCs aligned with the prevailing wind direction for both summer and winter seasons. Complex network modeling was employed to analyze the topological features of the ventilation network. From the analytical results, a multi-level wind corridor system for Chengdu was quantitatively developed. The results indicate that the city’s overall ventilation resistance is high, with notable spatial clustering, and the southeastern region faces substantial ventilation obstructions. A total of 143 critical ventilation nodes were identified, with the number of air inlets and outlets in summer being significantly fewer than in winter. However, the cooling effect of ventilation corridors in the prevailing summer wind direction is superior to that in winter. The ventilation network comprises 16 communities with distinct ventilation characteristics, exhibiting moderate connectivity, lacking small-world properties, and showing congestion and instability.

Leveraging explainable machine learning for enhanced management of lake water quality

Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km2) subtropical lake as an example (Lake Okeechobee, Florida, USA), this study aims to (1) predict key water quality parameters using machine learning (ML) algorithms based on easily measurable variables, (2) identify spatial patterns of these parameters, and (3) determine environmental drivers influencing turbidity levels. The study employs four ML algorithms—Extreme Gradient Boosting (XGB), Light Gradient-Boosting Machine (LGBM), Support Vector Regression (SVR), and Random Forests (RFs)—to predict total phosphorus (TP), total nitrogen (TN), nitrate + nitrite (NOx-N), and turbidity, via station-specific and lake-wide modeling approaches. The station-specific models uncover spatial patterns, while the lake-wide models support operational decision-making. Results indicated that lake stage (water level), water temperature, and, most notably, turbidity were the main nutrient predictors, with XGB demonstrating superior prediction performance. Spatial analysis using K-means clustering identified three distinct lake regions based on nutrient levels and turbidity. Due to its importance, SHapley Additive exPlanations (SHAP) were employed to identify and quantify environmental factors affecting turbidity. Inflows and lake stage were found as primary drivers of turbidity near lake inlets, while wind speed and air temperature affected turbidity in the middle of the lake. This research advances the understanding of lake water quality dynamics, emphasizing the importance of frequent monitoring of turbidity and its environmental drivers for enhanced management and future mitigation efforts.