Spatial Analysis Techniques Research Articles

Demographic Change and Regional Specialisation: The Case of Greek NUTS II Regions

This paper investigates the impact of demographic changes on regional specialisation across Greek NUTS2 regions from 2001 to 2021. It explores the relationship between age-specific variations in the economically active population and gross value added (GVA) across different economic sectors. Using demographic and GVA data alongside spatial analysis techniques such as spatial regression models, median Local Moran’s I, and location quotient, the study identifies spatial autocorrelation patterns. Findings reveal strong correlations between the economically active population and GVA, highlighting demographic factors’ crucial role in regional economic performance. Over the period 2001 to 2021, the 15–24 and 25–34 age groups generally experienced declining growth rates in total, male, and female economically active populations, while the 45–54 and 55–64 age groups saw increases. Regions adjacent to those with higher economically active populations showed high–high clustering. Especially, Attiki and Kentriki Makedonia, the two metropolitan regions of Greece, influence neighbouring regions through economic spillover effects, fostering economic sector clustering and emphasising regional specialisation. These findings highlight the complex interplay between demographic shifts and regional economic outcomes, noting spatial disparities and the need for targeted policy interventions. The study provides empirical evidence demonstrating how demographic changes influence regional economic development across different regions.

Whole-process survey of a landslide based on UAV tilt photography and 3D reconstruction and spatial analysis techniques

Whole-process survey of a landslide based on UAV tilt photography and 3D reconstruction and spatial analysis techniques

Inequalities in emergency service accessibility: spatial analysis of urban infrastructure

PurposeThis study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.Design/methodology/approachThe research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.FindingsThis study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.Originality/valueThis research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.

Explaining Environmental Distribution of Aedes albopictus using Machine Learning

Abstract. The Aedes albopictus mosquito, known for its role in transmitting diseases such as dengue fever, Zika virus, and chikungunya, poses a significant public health threat globally. Understanding its distribution patterns is crucial for effective disease surveillance and control. This study employs machine learning techniques, specifically MaxEnt modeling, to elucidate the relationship between environmental factors and the distribution of Aedes albopictus. Using presence-only data and a suite of environmental variables, we trained MaxEnt models to predict the potential distribution of Aedes albopictus across a geographical region. The models were validated using independent datasets and evaluated for their predictive accuracy and robustness. Our results reveal significant associations between Aedes albopictus presence and environmental factors such as temperature related variables. Furthermore, we employed spatial analysis techniques to identify areas at high risk of Aedes albopictus presence, aiding in targeted vector control strategies and disease prevention efforts. MaxEnt models demonstrated high predictive performance, effectively capturing the complex relationships between environmental variables and mosquito distribution in Nepal, India and Myanmar, along with Spain and Italy. By integrating machine learning algorithms with environmental data, this study provides valuable insights into the ecological drivers of Aedes albopictus distribution, enhancing our ability to mitigate the risk of mosquito-borne diseases in affected regions.

A geographic perspective of the association between physical activity and cardiovascular health: A need for community-level intervention

Cardiovascular diseases (CVDs) are a major cause of death globally, and minority communities are at higher risk of chronic health outcomes. A combination of lifestyle, including physical activity (PA), good nutrition, and reduced stress, can improve life expectancy. This study aimed to analyze the association between CVDs and PA among the adult population (N = 3,956) based on the World Health Organization (WHO) STEPwise Approach to Surveillance (STEPS) survey in Afghanistan. Descriptive statistics, logistic regression, and spatial analytical techniques were used to analyze the data. Based on the WHO STEPS data, the computed prevalence of CVDs, obesity, hypertension, diabetes, and high cholesterol were 7.41%, 45.57%, 34.06%, 9.51%, and 12.16%, respectively. Multivariate logistic analysis indicated that moderate work-related PA was associated with higher odds of CVDs and high cholesterol while inversely associated with obesity. Moderate leisure-related PA was positively associated with obesity. Vigorous leisure-related PA was associated with lower risks of CVDs, obesity, and high cholesterol but had a positive association with hypertension. Spatial analysis revealed a CVD hotspot in the southern region and the risk factors clustered in the northern region. These findings offer valuable insights for community and public health practitioners to design targeted interventions for reducing the burden of CVDs and risk factors in communities in developing countries.

Temporal trends, spatial and spatiotemporal clusters of cervical cancer mortality in Brazil from 2000 to 2021

Cervical cancer, despite being preventable through primary and secondary prevention strategies, remains one of the leading causes of morbidity and mortality among women in Brazil. This study aimed to analyze the temporal, spatial, and space-time patterns of cervical cancer mortality in Brazil. An ecological study was conducted using temporal, spatial, and space-time analysis techniques, using death certificates with cervical cancer as the underlying cause or associated condition among females in Brazil from 2000 to 2021. Death certificate and population data were provided by the Department of Health Informatics of the Unified Health System (DATASUS) and the Brazilian Institute of Geography and Statistics (IBGE), respectively. A total of 123,306 deaths associated with cervical cancer among women were registered during the study period. A rising trend in mortality was detected since 2014 onwards, after 14 years of decline. Particularly, an increase in mortality was observed among the younger age groups, and in the North and Northeast regions regardless of age. Heterogeneity in the spatial distribution of cervical cancer mortality was observed, with high mortality clusters around the country, but mostly concentrated in the North and Northeast regions. These findings suggest a need and an opportunity to develop efficient and effective health policies targeting those regions and groups of women at higher risk which in turn will allow for fast and significant reductions in cervical cancer mortality in Brazil.

Distribution and determinants of early marriage and motherhood: a multilevel and geospatial analysis of 707 districts in India

BackgroundEarly marriage and motherhood have long been prevalent in India, with 44.5% of women aged 20–24 reporting marriage before 18 in NFHS 3 (2005–2006), dropping to 26.8% in NFHS 4 (2015–2016). Early motherhood has also seen a significant decline, with first births by age 18 decreasing from 34.3% in NFHS I to 8.2% in NFHS V. Despite these improvements, significant regional disparities persist due to social, normative, and legal factors. This study investigates the spatial heterogeneity of early marriage and motherhood across Indian districts, offering a multilevel analysis that reveals critical local variations often obscured at broader levels. Understanding these patterns is crucial for targeted policy interventions and addressing the root causes of early marriage and motherhood.MethodsUtilizing data from the fifth round of National Family Health Survey, this study employs multilevel logistic regression and geospatial analysis to assess the determinants and spatial distribution of early marriage and early motherhood among ever-married women in India. The analysis incorporates individual, household, and community-level variables, complemented by spatial analysis techniques, including Empirical Bayes Bivariate Moran’s I values and LISA cluster maps, to identify regional patterns and hotspots.ResultsThis study revealed that educational attainment emerged as a critical determinant, with uneducated women significantly more likely to marry early. Socioeconomic factors, such as poverty and limited mass media exposure, also heightened the risk of early marriage and motherhood. Caste and religion were significantly associated with these events, with marginalized groups facing higher prevalence. Spatial analysis revealed significant geographic disparities, with central and eastern regions showing higher concentrations of early marriage and motherhood. District-level characteristics and the influence of neighboring districts were also significant, highlighting the importance of localized interventions.ConclusionsThe findings underscore the critical role of education, economic empowerment, and media literacy in mitigating early marriage and motherhood risks. The study calls for multi-sectoral interventions in geographical hotspots to break the cycle of early family formation and promote reproductive health. Policies enhancing educational opportunities, addressing economic disadvantages, and considering district-specific factors are essential. Comprehensive strategies are necessary to empower women, foster reproductive health, and address the multifaceted nature of early marriage and motherhood in India.

Spatial variations in health service utilization among migrant population: a perspective on health equity.

As health equity becomes a prioritized goal in global health policy, extensive research has revealed that socio-economic and geographical factors jointly exacerbate barriers to medical service access for both internal and international migrant populations, further accelerating existing health disparities. This study explores healthcare service utilization disparities among internal migrants in China, a population profoundly affected by the country's economic reforms and urbanization since the late 1970s. These transformations have led to significant migratory movements and subsequent healthcare challenges for these populations. Leveraging data from the 2017 China Migrant Dynamic Survey, comprising 169,989 samples across 28 provinces, we introduce a novel metric-the "No Treatment ratio" (NT-ratio). This ratio quantifies the proportion of migrants who, after falling ill, choose not to seek treatment relative to the total migrant population in a given province or region, serving as a critical measure of health risk. Building upon Anderson's Behavioral Model of Health Services Use, we adapted the model to better reflect the unique circumstances of migrant populations. The study employs spatial autocorrelation, hotspot analysis, and geodetector techniques to dissect the multifaceted factors influencing healthcare disparities. Our Findings reveal that the NT-ratio is significantly higher in eastern and northeastern China. Key factors influencing the NT-ratio include age, left-behind experiences, health education, and per capita medical resources. In response to these disparities, we recommend optimizing the distribution of medical resource, strengthening tiered diagnosis and treatment systems, and integrating health, education, and social security resources. These measures aim to improve healthcare utilization among migrant populations and reduce health inequities, aligning with global health objectives.

Spatial analysis and mapping of malaria endemic-prone areas using environmental risk factors and spatial multi criteria decision analysis in the Northern Zone of Plateau State, Nigeria.

Compressive mapping and in-depth analysis of malaria vulnerability risk foci are important tools for strategic planning, vector surveillance, implementing necessary interventions, and managing scarce resources to combat the daunting challenges of malaria infections in Nigeria. The study utilized environmental risk factors and spatial multi-criteria decision analysis techniques to analyze and map the spatial variations in malaria-endemic prone areas within the Northern Zone of Plateau State. Analysis of the criteria's weights of environmental risk factors and the stratification of the malaria risk vulnerability revealed that high-risk areas covered a total of 4017.25 Km2, while low-risk areas covered 1294.83Km2, having a close resemblance to the corresponding ground conditions. The results showed that a significant 60% of the study areas were characterized by high risk of malaria transmission, particularly in most settlements around the Jos North, Jos South and Barkin Ladi Local Government Areas. The malaria risk map's predictive abilities demonstrated a high level of accuracy, as indicated by the area under the curve score of 0.989. Furthermore, the risk map demonstrates a high-performance rate in identifying malaria hotspots, with a sensitivity of 98%, a proficiency rate of 54%, and a statistical significance confidence level ranging from 90% to 99% for identifying malaria hotspots and coldspots. The malaria risk analysis and mapping within the endemic prone areas of the Northern Zone of Plateau State not only equips policymakers with effective tools for identifying malaria foci but also offers a deeper comprehension of the appropriate intervention measures to be implemented.

Mapping development potential and priority zones for utility-scale photovoltaic on the Qinghai-Tibet Plateau

Sustainable renewable energy is a critical goal of the 2030 Sustainable Development Agenda. The scientific and rational development of solar power in the Qinghai-Tibet Plateau (QTP) is vital for China's carbon peak and carbon neutrality goals. However, more accurate, high spatial resolution assessments are needed to evaluate the utility-scale photovoltaic (PV) development potential on the QTP. To address this, we integrated geographical and technical potentials into a unified framework for assessing PV development potential. Using the spatial multi-criteria decision analysis technique, supported by the Bayesian Best-Worst method, we generated a 30-m suitability map for assessing geographical and technical potentials, along with priority zones. We found that approximately 35.22 % of the study area is suitable for utility-scale PV development, mainly concentrated in the Qaidam Basin in Qinghai and the northern Tibet Plateau in Xizang. The estimated PV technical potential for highly suitable and suitable classes is approximately 42.34 million GWh/a, which is equivalent to reduction of approximately 31.43 billion t/a of carbon emissions. We also formulated four priority development strategies using a spatially explicit model to meet the urgent 2030 target for utility-scale PV installations. Moreover, validation of existing PV projects and sensitivity analysis were conducted, confirming the accuracy and robustness of the results. This study provides practical guidance for informed decision-making by the government, investors, and stakeholders.

Differentiated strategies for synergistic mitigation of ammonia and methane emissions from agricultural cropping systems in China

Ammonia (NH3) and methane (CH4) emissions from agricultural cropping systems in China have substantially impacted environmental quality and climate change, which become the key sector for synergizing pollution control and carbon reduction. However, the experiences of synergistic NH3 and CH4 emission governance at fine scales in this sector are still lacking. Here, we estimated NH3 and CH4 emissions from agricultural cropping systems in China in 2019 and identified regional characteristics of these emissions using advanced spatial analysis techniques. Subsequently, we developed an innovative synergistic mitigation zoning identification method to establish targeted and spatially differentiated reduction measures, addressing a critical gap in mitigation strategies for agricultural cropping systems in China. The results showed that NH3 and CH4 emissions from the three most important crops were 3.04 Tg N yr-1 and 8.13 Tg C yr-1, respectively. NH3 and CH4 emissions exhibited the highest values in Eastern China, but the highest emission densities of NH3 (49.8 kg N ha-1 yr-1) and CH4 (394 kg C ha-1 yr-1) were observed in Northwest and South China, respectively. Further, bivariate local indicators of spatial association (LISA) maps revealed that the middle and lower reaches of the Yangtze River were hotspots for simultaneous high emissions of NH3 and CH4. Finally, based on the identified eight categories of zones, region-specific mitigation measures were proposed, including improved fertilization practices, irrigation methods, and straw management. These targeted, spatially differentiated mitigation strategies provide a reference framework and feasible approach for synergistic pollution control and carbon reduction in China's agricultural sector.

INTEGRATING LAND USE ANALYSIS WITH WATER DEMAND ESTIMATION: A CASE STUDY OF PUTRAJAYA, MALAYSIA

The challenges posed by population growth, urbanization, and changing land use patterns on sustainable water resource management are significant. This paper puts forth an integrated framework aimed at assessing future water demand in Putrajaya, Malaysia. The proposed framework combines population projections, estimations of water demand, and analyses of land use activities. Through an examination of demographic trends and land use patterns, the framework predicts population growth and identifies areas with high water demand. Daily water use patterns in homes and businesses (temporal analysis) inform the designing future water infrastructure, incorporating temporal aspects. Statistical and spatial analysis techniques are then utilized to merge these projections with water demand estimations to quantify water requirements in various zones and types of land use. This study has unveiled two daily peaks in water demand, which align with household schedules. Residential areas emerge as the primary consumers of water, displaying an evening peak distinct from the midday peak seen in businesses. The current water demand in Putrajaya is estimated at 94 million litres per day, with domestic usage surpassing non-domestic usage in a ratio of 3:2. Projections based on future land use plans foresee a 19% increase in demand, underscoring the urgency for proactive water management strategies. Spatial analysis has highlighted residential areas as the main users of water, with demand levels varying throughout the city. By comprehending these temporal and spatial patterns, water authorities can strategically target interventions, optimize infrastructure siting, and forecast future demand trends. These proactive measures are essential for securing a sustainable water future for Putrajaya.

SPATIAL ANALYSIS BETWEEN YOUTH AND ADULT EDUCATION AND LABOR MARKET REMUNERATION

This study analyzes the spatial distribution of migration from traditional high school to Youth and Adult Education (YAE) and its relationship with formal work remuneration. For this, the Exploratory Spatial Data Analysis (ESDA) technique was used through global and local analyses. The results of the global analysis indicate a negative spatial autocorrelation between migration from regular secondary education to YAE and formal work remuneration. The local analysis indicated that the Northeast had the highest proportion of municipalities in the cluster with high migration of individuals in the YAE and higher remuneration in the formal market, while the South region had the highest concentration of municipalities with a low proportion of municipalities in the cluster with low migration of individuals in the YAE and high remuneration in the formal job market. Therefore, it is concluded that the low quality of YAE teaching leads individuals to enter the formal job market with a lower pay.

The Spatial Variation of Soil Structure Fractal Derived from Particle Size Distributions at the Basin Scale

The accurate characterization of soil structure is fundamental to groundwater science, environmental ecology, and Earth systems science. To address the challenge of quantifying the high spatial variability of large-scale soil structures, this study used a laser particle size analyzer to measure the distribution of soil particle size in 207 samples from ten profiles across the Daqing and Ziya River basins in the North China Plain. The quantified soil structure, expressed as soil fractal dimension D, was derived using monofractal theory. Various spatial analysis techniques, including Moran’s I index, correlation analysis heat maps, the Kolmogorov–Smirnov one-sample test, and geostatistical semivariogram function, were jointly applied to investigate the spatial variability of soil structural fractals across different depths in the piedmont plain–coastal areas of the two river basins. The results indicate the following: (1) Quantitative analysis confirms that under the influence of piedmont alluvial and fluvial dynamics, soil D values homogenize from the piedmont to the coastal areas, with decreasing particle size differences closer to the coast. However, the spatial variability of the soil structural fractals in the Ziya River Basin was greater than that in the Daqing River Basin. (2) The combined effects of climate change, regional differences, and human activity led to greater spatial variability in the soil structural fractals in the Ziya River Basin than in the Daqing River Basin. The correlation between D values and burial depth was strongest in the Xianxian profile (−0.78), whereas the spatial correlation was strongest in the Hengshui and Dacheng profiles (−0.47). (3) The greatest spatial variability in soil D values occurred at depths of 1–2 m, with a coefficient of variation of 23.595%, which was significantly higher than those at depths of 0–1 (14.569%) and 2–3 m (16.284%).

School Closures and the Spatial Ecology of Education Access in 10 U.S. Cities

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.

Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data.

The agglomeration and dispersion of tourist attractions in space greatly affect the development of regional tourism resources and the consumption choice of tourism market. At present, the research on the spatial distribution characteristics of tourist attractions and their influencing factors mainly adopts induction and investigation, and there is a lack of effective statistical models for the research on the spatial distribution of tourist attractions and their influencing factors in some historical and cultural ancient cities. This paper uses Internet technology to obtain the spatial distribution data of tourist attractions in Shaoxing city, and uses mean nearest neighbor analysis, nuclear density analysis, imbalance index analysis, standard deviation ellipse and other spatial statistical analysis techniques and geographical detector methods to study the spatial distribution characteristics and influencing factors of tourist attractions in Shaoxing City. This paper studied the distribution characteristics of tourist attractions in Shaoxing city, such as spatial aggregation, distribution equilibrium and spatial orientation, and applied geographical detector to study the influencing factors of the spatial distribution of scenic spots. It was concluded that the spatial distribution pattern of scenic spots was affected by various factors such as natural environment, social environment and economic environment. The explanatory power of two-factor interaction is obviously stronger than that of single factor. The research results provide scientific basis for the planning, layout and development of tourist attractions in Shaoxing and its similar cities, and then promote the high-quality development of tourism in Shaoxing and its similar historical and cultural ancient cities.

Spatial Analysis of the Effect of Aspergillus spp Fungus on the Soil of the Plateau (Najaf - Karbala)

This study investigated the spatial and temporal variation of Aspergillus spp fungus in the soil of the Najaf-Karbala plateau, covering Najaf district center and Al-Haidariya district. Soil samples were collected from 36 sites over four seasons and analyzed microbiologically to identify types and quantities of Aspergillus spp fungus. Spatial analysis techniques, including GIS and spatial autocorrelation, were used to examine the distribution of Aspergillus spp fungus across the study area. The results showed significant spatial and temporal variation in the occurrence of Aspergillus spp fungus, with highest concentrations found in autumn and spring seasons. The study highlights the environmental hazard posed by Aspergillus spp fungus in the Najaf-Karbala plateau soil, with potential health risks for humans, animals, and plants. Further research is needed to understand the sources and drivers of Aspergillus spp fungus presence and to develop strategies for mitigating its environmental and health impacts.

Spatial disparities in cause-specific mortality in Ukraine: A district-level analysis, 2006–19

Turbulent socio-economic development, recent political challenges, and remarkable regional diversity with deep historical roots make Ukraine an important case study for understanding mortality trends in Eastern Europe. In this paper, we provide the first comprehensive, spatially detailed analysis of cause-specific mortality trends and patterns in Ukraine, focusing on the period 2006–19. We rely on official mortality data and use various demographic and spatial analysis techniques. Our results suggest a notable attenuation of the long-standing West–East and West–South–East mortality gradients. Cardiovascular mortality at older ages largely explains the gap between the vanguard (lowest mortality) and laggard (highest mortality) areas, especially for females and in the most recent period. By contrast, the impact of mortality from external causes has greatly diminished over time. Hotspot analyses reveal strong and persistent clustering of mortality from suicide, HIV, and lung cancer. Further research should focus on in-depth assessment of the mechanisms causing the observed patterns.

Study on the spatiotemporal distribution patterns and influencing factors of cultural heritage: a case study of Fujian Province

The spatiotemporal distribution characteristics of cultural heritage reveal the trajectory of human activity changes, and a deep analysis of its natural and cultural factors holds significant reference value for the overall conservation and management of cultural heritages. This study focuses on the cultural heritage at the provincial level and above in Fujian, utilizing GIS spatial analysis to explore the spatiotemporal evolution of cultural heritages and their natural and human influencing factors. The research findings are as follows: (1) The distribution of cultural heritage in Fujian exhibits a clustering pattern, with dense areas transitioning from the upstream regions of the prehistoric and pre-Qin periods to the eastern coastal areas gradually. (2) The Ming and Qing dynasties have the highest number of cultural heritages, with the type of heritage transitioning from ancient sites in the early periods to ancient architecture, and in modern times, mainly important historical sites and representative architectural heritages. (3) The overall centroid coordinates of cultural heritage reveal a shift from the northern part of Fujian to the eastern and southern parts. (4) Natural factors significantly influence the distribution of cultural heritage, with a higher concentration in plain and hilly areas, on slight slopes with gradients between 0.5° and 2.0°, and on the southern and southeastern slopes, especially within a 1-kilometer radius of rivers. (5) The creation of cultural heritage during historical periods is closely linked to the regional history, culture, political, and economic environments. The positive development of these socio-cultural factors has a promotional effect on the quantity of cultural heritage. This study demonstrates the utility and applicability of GIS spatial analysis techniques in cultural heritage research, providing a methodological framework that can be adapted and applied internationally. The findings offer insightful data that can inform targeted conservation and development strategies for cultural heritage, ensuring their effective preservation and sustainable management across different regions.

Spatial Heterogeneity and Key Influencing Factors of Soil Organic Carbon, Soil Total Nitrogen, and Carbon to Nitrogen Ratio in main plantations of Torreya grandis cv. Merrillii

Soil organic carbon （SOC） and soil total nitrogen （STN） serve as important indicators of the elemental balance within forest ecosystems reflecting soil fertility and quality. Accurate knowledge regarding the spatial variability of regional SOC, STN, and C∶N ratio and their influencing factors is of great significance for precise fertilization and soil health. In this study, a total of 117 topsoil samples （0-20 cm in depth） based on a 1 km×1 km grid were collected in the Torreya grandis cv. Merrillii plantation in Zhejiang Province. A combination of multi-dimensional statistical approaches （random forest model, structural equation model, redundancy analysis, and variation partitioning analysis） and diverse spatial analytical techniques （geostatistics, Moran's I index, etc.） were applied to reveal the spatial distributions and influencing factors of SOC, STN, and C∶N ratio in the Torreya. grandis cv. Merrillii region. The results showed that the average ω（SOC）, ω（STN）, and C∶N ratio were 17.63 g·kg-1, 1.48 g·kg-1, and 12.65, respectively, and their coefficients of variation were 68.08%, 67.41%, and 46.03%, respectively, indicating a moderate degree of variability. In general, the SOC, STN, and C∶N ratio of the Torreya grandis cv. Merrillii plantations were at an intermediate level in the national plantation. The semi-variance results showed that the nugget/sill values of SOC, STN, and C∶N ratio were 49.98%, 45.88%, and 49.93%, respectively, demonstrating a moderate level of spatial autocorrelation. The spatial distribution results showed that SOC, STN, and C∶N ratio decreased from northeast to southwest, with the majority of the region exhibiting above-medium fertility levels of SOC. The results of correlation analysis and redundancy analysis indicated that AN, AP, and AK were significantly correlated with both SOC, STN, and C∶N ratio （P<0.05）. The results of random forest, structural equation model, and variation partitioning analysis evidenced that the main influencing factors of SOC and STN were soil-available nutrients （AN, AP, and AK）. Therefore, our results could provide important insights for enhancing soil carbon and nitrogen pools in special plantations in Zhejiang Province, enhancing the capacity of plantations to adapt to regional climate change through ecological measures such as appropriate fertilization practices and strategic understory vegetation cultivation.