Spatial Autocorrelation Theory Research Articles

Spatial Prediction and Influencing Factors Analysis of Soil Salinization in Coastal Area Based on MGWR

Quantitative analysis of the spatial non-stationary characteristics of soil salinization influencing factors and the prediction of its spatial distribution are of great significance for the rational use of coastal saline soil resources and the formulation of local prevention and control measures. In this study, the Hekou District of Dongying City, Shandong Province, was used as the study area, and the descriptive statistics of soil salinization status were conducted using classical statistical methods. Spatial autocorrelation theory was used to explore the characteristics of global and local spatial structure of soil salinization in the study area. Influential factors related to soil salinity were selected, and multivariate linear regression （MLR）, geographically weighted regression （GWR）, and multi-scale geographically weighted regression （MGWR） methods were used to model and predict the spatial distribution of soil salinity in the study area and to analyze the spatial heterogeneity of the effects of different influencing factors on soil salinity. The results showed that： ① The mean value of soil salinity in the study area was 5.84 g·kg-1, indicating severe salinization, with a global Moran's I index of 0.19 （P<0.00） and obvious spatial aggregation characteristics. ② Among the three models, the MGWR model had the highest modeling accuracy. Compared with that of the MLR model, the Radj2 of GWR and MGWR improved by 0.05 and 0.07, respectively, and the RSS decreased by 210.13 and 179.95, respectively. ③ The results of MGWR regression showed that the spatial distribution of soil salinity appeared to be mainly affected by the middle soil salinity, soil clay content, and vegetation cover from the mean values of standardized regression coefficients of different influencing factors. Different influencing factors had significant spatial non-stationary characteristics on soil salinization. ④ The results of the spatial distribution prediction of soil salinity in MGWR showed that the areas of high soil salinity （≥6 g·kg-1） were mainly distributed in the northern part of the study area, with an overall spatial trend of decreasing from the coast to the interior. The results of the study can be used as a reference for the analysis and predictive mapping of factors affecting soil salinization in the county and on a larger scale using MGWR.

Analysis of Spatio-Temporal Changes and Driving Factors of Wetland Ecosystem Health Based on the AHP-SOM-DPSR Model—A Case Study of Wetlands in the Qin-Mang River

Wetland ecosystems are vital for ecological security. However, rapid industrialization and urbanization have led to regional degradation, particularly in areas like the Qin-mang River Basin, a pivotal site in China’s ecological security strategy. Urgent measures are required to safeguard the health of its wetland ecosystems. This study employs the hierarchical analysis method-neural network-driver-pressure-state-response (AHP-SOM-DPSR) model to evaluate the ecological health of wetland ecosystems in the Qin-mang River Basin over a thirty-year period, from 1992 to 2022. Initially, the Drive-Pressure-State-Response (DPSR) model establishes a comprehensive indicator system encompassing 19 indicators spanning driving forces, pressures, states, and responses. Subsequently, a hybrid approach combining Analytic Hierarchy Process (AHP) and Self-Organizing Map (SOM) determines indicator weights to evaluate spatiotemporal ecosystem changes over the past three decades. Additionally, spatial autocorrelation theory analyzes ecosystem health in the study area. Finally, Pearson correlation coefficient analysis examines the driving factors influencing ecosystem health and their impacts. Results indicate: (1) Ecosystem health has deteriorated gradually from 1992 to 2022, underscoring the imperative for enhanced wetland management in the Qin-mang River Basin. (2) High spatial autocorrelation areas, primarily in the central-southern and northern regions, highlight priority zones for wetland ecosystem management. (3) Urbanization levels, average temperature, and total population significantly impact wetland ecosystem health in the Qin-mang River Basin. These findings offer valuable scientific insights for guiding ecological management and conservation efforts in the region.

Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.

Decoupling effect and spatial-temporal characteristics of carbon emissions from construction industry in China

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of “double carbon target”. The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is “high in the east and low in the west, high in the south and low in the north,” showing obviously regional differences.

Evolution of Land Use Landscape Patterns in Karst Watersheds of Guizhou Plateau and Its Ecological Security Evaluation

Landscape ecological safety is of great significance in maintaining ecological balance, ecological protection, economic development, and promoting the sustainable use of regional land resources. This study collects three-phase remote sensing (RS) image data of 2000, 2010, and 2020 to elucidate the spatial and temporal changes in land use of the Guizhou Plateau Karst Watershed. We construct a landscape ecological security index using the ArcGIS and landscape pattern index method. With the spatial autocorrelation theory, we analyze the evolution of watershed landscape patterns and changes in characteristics of ecological security. The results show that the cultivated land is being converted to construction land in the watershed from 2000 to 2020. The percentage of the patch area of the constructed land is increasing. However, the patch cohesion of the cultivated land, grassland, and watershed is decreasing. The ecological safety of the Nanming River Basin landscape is in a positive trend, clustering in the central urban areas. The growth rate of the landscape ecological safety index increased by 5.80% from 2000 to 2020, and the aggregation of the ecological safety index was dominated by high–high aggregation and low–low aggregation, which was spatially positively correlated with the spatial aggregation effect. The findings provide a scientific reference for managing ecological balance and optimizing the land resource allocation in karst watersheds.

An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.

Spatial-Temporal Change Characteristic Analysis and Environmental Risk Evaluation of Pesticide Application in Anhui Province

The excessive application of pesticides causes the increase in pesticide residues, and pesticide pollution presents a point-line-plane 3D space pollution trend. This paper takes pesticide application intensity (PAI for short) as the object, adopts spatial autocorrelation theory to analyze the spatial-temporal change characteristics of PAI in Anhui Province from 2003 to 2020, and constructs the environmental risk index method to evaluate pesticide environmental risk, aiming to provide a reference for macro control pesticide dosage, zoning guidance, and agricultural sustainable development in Anhui Province. The following results were obtained: From 2003 to 2020, the PAI in Anhui Province showed a spatial-temporal change process of first increasing and then decreasing, as well as first clustering and then random distribution. Before 2013, the significant hot spots and cold spots of PAI in Anhui Province were located in southern and northern Anhui, respectively. In addition, the scope of both experienced a process of expanding and then contracting. In recent years, there have been no significant hot and cold areas of PAI in Anhui Province. The overall trend of pesticide environmental risk in the provincial cities in Anhui Province decreased. In 2020, pesticide environmental risk in various provincial cities is dominated by medium- and low-risk levels, which are still higher than in 2003. High-risk and extremely high-risk levels are mainly concentrated in Huainan and Suzhou in northern Anhui and Chizhou and Huangshan in southern Anhui.

海南岛东北部土地利用与生态系统服务价值空间自相关格局分析

PDF HTML阅读 XML下载 导出引用 引用提醒 海南岛东北部土地利用与生态系统服务价值空间自相关格局分析 DOI: 10.5846/stxb201803090468 作者: 作者单位: 海南省林业科学研究所,海南省林业科学研究所,海南省林业科学研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 海南省财政科技计划资助（SQ2017JSKF0035，KYYS-2018-32） Spatial autocorrelation pattern analysis of land use and the value of ecosystem services in northeast Hainan island Author: Affiliation: Hainan Forestry Institute,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:土地利用/覆被变化是全球环境变化的重要原因，对生态系统服务价值空间分布格局有直接影响。以海南岛东北部为研究对象，基于遥感影像数据，应用空间自相关理论定量探讨了土地利用与生态系统服务价值的空间自相关格局和分布特征，并运用双变量空间自相关分析两者之间的空间响应规律。结果表明：2016年海南岛东北部生态系统服务功能总价值为132.09亿元，其中林地构成生态系统服务功能价值的主体，而湿地的单位生态系统服务价值最高。土地利用类型具有显著的空间自相关性（P < 0.05），不同土地利用类型所表现的空间聚集或异常的区域及范围明显不同，以建设用地和林地的空间聚集性最强。研究区生态系统服务价值在空间上呈显著的正相关性（P < 0.05），高值区明显聚集于海岸带、东寨港和清澜港红树林一带，低值区集中在海口城区、文昌北部农耕区域；土地利用程度与生态系统服务价值呈空间负相关关系（P < 0.05），且具有明显的空间溢出效应。根据研究结论建议继续实施生态修复与保护政策，优化土地利用结构，提升生态核心的服务价值，实现区域可持续发展和维护生态安全。 Abstract:Land use and land cover change are important factors for global environmental change, and they also directly affect the spatial distribution patterns of the values on ecosystem services. This study used remote sensing image data and applied spatial autocorrelation theory to quantify the spatial autocorrelation patterns and distribution characteristics of land use and the value of ecosystem services in northeastern Hainan island. Additionally, we used a bivariate spatial autocorrelation to analyze the spatial response law. The results showed that the total value of the ecosystem services function in northeastern Hainan island was 13.209 billion yuan in 2016, in which forests had the largest contribution, while as for the unit ecosystem service value, the contribution of wetlands was the highest. Land use types had a significant spatial autocorrelation (P < 0.05). The area and scope of spatial aggregation and the anomaly represented by different land use types were significantly different. The ecosystem service value of the study area had a significant positive correlation in space (P < 0.05). The high value area was in the coastal zone and mangrove area in Dongzhaigang and Qinglangang. The low value area was in Haikou city and the farming area in northern Wenchang. The land use degree was negatively correlated with the value of ecosystem services (P < 0.05), and there was a clear spatial spillover effect. According to the study results, we suggest that ecological restoration and protection policies be implemented, and there should be optimization of land use structure, enhancement of the service value of the ecological core, and regional sustainable development to maintain ecological security. 参考文献 相似文献 引证文献

Application of sandwich spatial estimation method in cancer mapping: A case study for breast cancer mortality in the Chinese mainland, 2005.

High-accuracy spatial distribution estimation is crucial for cancer prevention and control. Due to their complicated pathogenic factors, the distributions of many cancers' mortalities appear blocky, and spatial heterogeneity is common. However, most of the commonly used cancer mapping methods are based on spatial autocorrelation theory. Sandwich estimation is a new method based on spatial heterogeneity theory. A modified sandwich estimation method suitable for the estimation of cancer mortality distribution is proposed in this study. The variances of cancer mortality data are used to fuse sandwich estimation results from various auxiliary variables, the feasibility of which in estimating cancer mortality distributions is explained theoretically. The breast cancer (BC) mortality of the Chinese mainland in 2005 was taken as a case, and the accuracy of the modified sandwich estimation method was compared with that of the Hierarchical Bayesian (HB), the Co-Kriging (CK) and the Ordinary Kriging (OK) methods. The accuracy of the modified sandwich estimation method was better than the HB, the CK and the OK methods, and the estimation result from the modified sandwich estimation method was more likely to be acceptable. Therefore, this study represents an attempt to apply the sandwich estimation method to the estimation of cancer mortality distributions with strong spatial heterogeneity, which holds great potential for further application.

Accurate detection of user interest data in cloud computing environment

The accurate detection of user interest data in cloud computing environment can improve the quality of data management. For the accurate detection of user data, we need the adaptive train for the spatial data clustering process obtaining the data clustering objective function, and then complete the accurate detection of a characteristic data. This paper proposed the method of user interest data detection in cloud computing environment based on spatial autocorrelation and differential evolution theory. The method used spatial autocorrelation theory of neighborhood object to obtain the distance between outlier spatial data and its neighborhood spatial data, clustering all data for obtaining the data mean reference point, fitting the generated data mean reference point. The higher-order cumulant feature of data row was extracted. We used the differential evolution theory for the adaptive training on the clustering process of spatial data, the data clustering objective function was obtained. On this basis, we complete the user interest data detection. Experimental results show that the proposed method can accurately detect the user interest data in data space, and the false alarm rate of the proposed method is well below the traditional method. In the case of the same amount of data, the running time of the proposed method is lower than the traditional method. The proposed method has high detection accuracy and greatly improves the quality of data management in the cloud computing environment.

A new methodology of spatial cross-correlation analysis.

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.

Influence Models of Urban Road Network Operation Performance Base on Spatial Autocorrelation Theory

In order to better identify the spatial influence between adjacent parts of road networks, the paper introduces the spatial autocorrelation theory in evaluating the operation performance of urban road networks. The research proposes several spatial correlation validation indicators to verify the spatial characteristics among the road networks. Based on the analysis of spatial characteristics, the relationship between operation performance and influencing factors under the impact of spatial effect is examined. Furthermore, a spatial autocorrelation based influence models at three traffic flow levels is developed by using the data from a partial urban road network in Beijing. The model analysis shows that the spatial autocorrelation model is more effective in analyzing the urban road network operation performance under the influence of various factors. This model will be beneficial in identifying traffic network problems and improving traffic operations of the urban road network.

An explicit relationship between time-domain noise correlation and spatial autocorrelation (SPAC) results

SUMMARY The success of recent ambient noise tomographic studies is now understood to arise due to cross-correlation properties documented in the acoustics community since the 1950s. However, despite the fact that Aki’s 1957 spatial autocorrelation (SPAC) work yields identical analytical results to certain noise correlation results, the precise relationship between SPAC and time-domain cross-correlation remains not entirely transparent. Here, we present an explicit comparison of the two approaches and clarify that SPAC theory is indeed equivalent to the cross-correlation theory used for recent noise tomography studies. This equivalence allows theoretical work from each field to be applied to the other, and we illustrate a few examples of this.

New spatial econometric techniques and applications in regional science

The papers appearing in this special issue of Papers in Regional Science, which is devoted to spatial econometrics, come from the First International Conference of the Spatial Econometrics Association held in Cambridge (UK) 12-14 July 2008. This conference was the first official meeting of the new association, which was established in May 2006 in Rome and which has already attracted more than 150 members from around the world. At the Cambridge conference there were close to 120 delegates and more than 100 papers were presented. With regard to the eight papers appearing in this special issue, we would particularly like to thank the authors and the referees for their contribution to what we believe is an interesting and lively selection. Recent years have seen a real explosion in the application of spatial statistical models in all branches of social sciences and in particular in economics. Spatial econometrics models have been used to analyse different topics (see for example Anselin et al. 2004 for a review) and as a matter of fact spatial regression techniques are now becoming an established component in the applied econometrics toolbox, as witnessed by the increasing attention given to this topic in standard econometrics textbooks (Maddala 2001; Woolridge 2002; Gujarati 2003; Kennedy 2003; Baltagi 2008).

Consistency of the spatial autocorrelation method with seismic interferometry and its consequence

ABSTRACTWe have cross‐checked the conventional theory of the spatial autocorrelation method and the consequence of seismic interferometry: the retrieval of the elastodynamic Green's function. Their mutual consistency is almost complete. The basic formulas of the conventional spatial autocorrelation theory can be derived by an alternative approach based on the retrieval of the elastodynamic Green's function. The only discrepancy is found with the average of the complex coherence function over azimuth in a wavefield dependent on azimuth. It is hypothesized, in discussion, that this discrepancy is due to the way of representing the wavefield in the background theory of seismic interferometry that can produce only wavefields moderately dependent on azimuth and that the mentioned consequence of seismic interferometry can also only make sense in a wavefield moderately dependent on azimuth.Our field experiment with a wavefield dependent on azimuth showed that the consequence of seismic interferometry in the logical framework of the conventional spatial autocorrelation theory is appropriate under such degrees of approximation as the measure proposed in this study, i.e., the deviation of the total dispersion curves is between about 10 and 16 per cent at the maximum from those averaged over azimuth.The acceptance of the retrieval of Green's function gives a proper physical meaning to the complex coherence function: the real part of the elastodynamic Green's function normalized by its zero‐offset version. This makes it possible to take a deterministic approach rather than the statistical one on which the conventional spatial autocorrelation method is based and gives fruitful new aspects and perspectives. For example, the formula for the multi‐mode case is given and the possibility of exploration of two or three dimensional velocity structures is suggested.

Spatial autocorrelation and localization of urban development

A nonlinear analysis of urban evolution is made by using of spatial autocorrelation theory. A first-order nonlinear autoregression model based on Clark’s negative exponential model is proposed to show urban population density. The new method and model are applied to Hangzhou City, China, as an example. The average distance of population activities, the auto-correlation coefficient of urban population density, and the auto-regressive function values all show trends of gradual increase from 1964 to 2000, but there always is a sharp first-order cutoff in the partial autocorrelations. These results indicate that urban development is a process of localization. The discovery of urban locality is significant to improve the cellular-automata-based urban simulation of modeling spatial complexity.