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Abstract: Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

Multipath delay is a significant error in GNSS precise positioning and affected by the station surroundings. This error cannot be effectively eliminated by observation difference techniques and must be carefully calibrated. Although the spatiotemporal correlation feature of multipath plays an important role in static multipath mitigation, this feature lacks research and needs to be thoroughly investigated. Therefore, the local filtering (LF) approach based on least square collocation (LSC) and moving average (MA) methods is proposed and investigated. The space correlation feature is characterized by covariance function or correlation function, and the residuals with small correlations are neglected while only that with relative larger correlations are used in multipath modelling and calibration. The experiments with short baseline MAT1_MATE and KERG_KRGG for both GPS and Galileo multipath calibration show that, the LF approach based on LSC, simple MA and weighted MA methods achieve better performance in multipath mitigation over short and long-time spans compared with conventional grid approach. For example, the GPS double difference (DD) residual variance reduction rates of baseline MAT1_MATE for the calibration of the next day with LF approach based on the three methods reach73.2%, 72.1% and 70.2%, respectively, which are obviously larger than 63.7% of the grid approach. The improvement reaches maximum 16.4% in the case of baseline KERG_KRGG for Galileo and the average value is around 11.5% i.e. the relatively numbers of the two values reach 29.9% and 20.2%, respectively. [ABSTRACT FROM AUTHOR]

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments. [ABSTRACT FROM AUTHOR]

Local terrain or microsite conditions influence the development of trees, particularly at early ages. These conditions might be described by edaphic or topographic variables. We mapped soil and topographic variables from four even-aged and even-spaced cork oak plantations located in two climatically distinct Portuguese regions. The major goal of this research was to understand the relation between soil and topographic fine-scale conditions and tree growth expressed by diameter without cork annual growth (idu). The methodology consisted in (1) analysing the spatial variability and autocorrelation of idu; (2) modelling idu with ordinary least squares (OLS) regressions; (3) comparing with spatial modelling of idu, incorporating spatial autocorrelation. The driest stands A and B, exhibited weaker spatial autocorrelation, distributed in smaller clusters (R2 < 0.03, OLS models), while stands C (R2 = 0.18, OLS models) and D (R2 = 0.11, OLS models) showed higher predictive capacity. Spatial models increased R2 scores, keeping most variables from OLS models and accounting for spatial autocorrelation. A + B + C + D OLS model obtained an R2 = 0.34 and respective spatial model R2 = 0.58. Apparent electrical conductivity at 0.5 (ECa0.5) and 1 m of soil depth, slope, elevation and topography position index were included as predictors (OLS), but only ECa0.5, slope and elevation were selected in the spatial model. Models were fitted using average to high productivity stands and should be used cautiously outside this range. Local terrain conditions determine the growth of young cork oak trees. Mapping soil and topographic variables before establishing new plantations may identify limiting microsite conditions where using cork oak species is not suitable due to low growth rates expectations. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies the effects of spatial correlation between the structural components on probabilistic seismic performance of steel moment resisting frames, using extended incremental dynamic analysis (IDA) method to consider the effects of aleatoric uncertainties caused by ground motion records. In this probabilistic assessment, a combination of Latin hypercube sampling (LHS) and Cholesky decomposition (CD) methods are utilized to take the epistemic uncertainties of the numerical model into account. In case the target correlation matrix is not positive definite, two powerful optimization algorithms are employed to find the closest positive definite correlation matrix. [ABSTRACT FROM AUTHOR]

Based on the data from 30 province - level divisions in China, this study used the coupling coordination degree and spatial autocorrelation model to evaluate the coupling and coordinated development level and spatial correlation between commercial health insurance and rural revitalization in China. The result showed that the comprehensive development level of commercial health insurance and rural revitalization in China showed distinctive regional differences, with the level of the coupling coordination of the two increasing steadily, featuring a "polarization equilibrium" among regions. The coupling coordination between regions had significant spatial spillover effects. Therefore, each region should formulate commercial health insurance development plans tailored to local conditions, implementing the coupling and coordinated development strategy for commercial health insurance and rural revitalization, thus giving full play to the regional spatial spillover effect. [ABSTRACT FROM AUTHOR]

With the increasing demand for electric vehicle public charging infrastructure (EVPCI), optimizing the charging network to ensure equal access is crucial to promote the sustainable development of the electric vehicle market and clean energy. Due to limited urban land space and the large-scale expansion of charging infrastructure, determining where to begin optimization is the first step in improving its layout. This paper uses a multidimensional assessment framework to identify spatial disparities in the distribution of EVPCI in Nanjing Central Districts, China. We construct a scientific evaluation system of the public charging infrastructure (PCI) layout from four spatial indicators: accessibility, availability, convenience, and affordability. Through univariate and bivariate local indicators of spatial autocorrelation (LISA), the spatial agglomeration pattern of the EVPCI service level and its spatial correlation with social factors are revealed. The results of this study not only identify areas in Nanjing where the distribution of PCI is uneven and where there is a shortage but also identify areas down to the community level where there are signs of potential wastage of PCI resources. The results demonstrate that (1) urban planners and policymakers need to expand the focus of PCI construction from the main city to the three sub-cities; (2) it is necessary to increase the deployment of PCI in Nanjing's old residential communities; and (3) the expansion of PCI in Nanjing must be incremental and optimized in terms of allocation, or else it should be reduced and recycled in areas where there are signs of resource wastage. This study provides targeted and implementable deployment strategies for the optimization of the spatial layout of EVPCI. [ABSTRACT FROM AUTHOR]

Before the anticipated Nankai Trough earthquake, the effects of multiple cascading hazards on bridge performance and the associated functionalities of road networks must be assessed to develop recovery strategies. This paper presents a framework for assessing the probabilistic connectivity of road networks exposed to both ground motion and tsunami, considering the spatial correlations among hazard intensities. To evaluate the joint probabilities of bridge states (i.e., passable and impassable states) and the probabilistic connectivity of road networks, the total probability theorem was used to integrate spatially correlated seismic and tsunami hazard assessments into the fragility estimates that consider the cascading effects of ground motion and tsunami-induced damages to bridges. For illustrative purposes, the probabilistic connectivity of hypothetical road networks in Japan that could be affected by both ground motion and tsunami during the anticipated Nankai Trough earthquake was assessed using the proposed method. The results showed that seismic retrofitting of bridges located along the route where the probability of impassable states due to tsunami is very low can enhance the probabilistic connectivity of entire road networks. [ABSTRACT FROM AUTHOR]

In the information age, digital information technology has gradually become a new quality of productivity to improve international competitiveness, and the status and importance of the pseudo human settlements(PHS) constructed by digital and information has been increasing in the human settlements(HS) system. In this context, exploring the coupling coordination of the internal systems of the PHS provides a theoretical basis for promoting the comprehensive improvement of the quality of the HS in the urban agglomerations, provides a theoretical reference for rationally interpreting the new type of human-land relationship in the information age, and provide a new perspective for the study of the human settlements geography. Currently, research on PHS is in its nascent stages, therefore, we construct a theoretical framework for the coupling coordination of the "three states" of the HS, takes the internal system of PHS as the entry point, empirically analyze the spatiotemporal characteristics of the PHS coupling coordination degree within urban agglomerations in eastern China and the driving factors by using the coupling coordination degree, spatial autocorrelation, the center of gravity and the ellipse of the standard deviation, and geo-detector models. Research shows: (1) Time course: exhibits an upward "inverted L" trend. (2) Spatial pattern: exhibits a general spatial pattern of "high in the north and low in the south". (3) Spatial correlation: shows a spatially positively correlated clustering trend. (4) Spatial evolution: demonstrates a decentralized pattern of migration from the northeast to the southwest, indicating that the coupling coordination grows faster in the southwest than in the northeast. (5) Driving factor: the development of coupling coordination degree of PHS results from multiple factors and systems. This research provides theoretical support for promoting the comprehensive improvement of the quality of PHS in the urban agglomerations in eastern China, and offers scientific reference for the construction of PHS in other regions of China. [ABSTRACT FROM AUTHOR]

In this research, the spatial correlation between the variables representing Bouguer gravity anomaly and seismicity in Iran is evaluated. For this purpose, the gravity anomalies and seismicity data of this region have been analyzed statistically as well as geostatistically, for the period 1975-2021. Based on the findings of this study, it can be concluded that the significant correlation observed between the variables of gravity anomaly variations and the seismicity-related variables, especially the variables related to the frequency of earthquake occurrences, suggests that the gravity anomaly variations can be considered as an affective factor in seismic activity of this region. The Iranian Plateau is one of the most seismically active regions on the Earth because of its geologic and tectonic setting. This plateau is marked by high topography relief and also by great changes in gravitational and isostatic anomalies across it. Many researchers have studied the variations of gravitational anomalies across Iran, and some have pointed to the relationships between these anomalies and seismicity in this region. The Bouguer gravity anomaly is obtained by making the necessary corrections to measurements taken directly from the ground stations, and well reflects the deep density variations in the crust. This anomaly can also clearly show changes in crustal thickness in different regions, such that areas with high crustal thicknesses show negative anomalies and areas with low crustal thicknesses show positive anomalies. In this study, at first, the study region was divided into rectangles with dimensions of 0.5 by 0.5 geographical degrees and then the variables related to the seismicity and gravity anomalies were calculated and computed for each cell. Pearson correlation coefficients between these variables were computed and validated using statistical software Minitab (ver. 16.2.2). Also, maps representing the spatial distribution pattern of these variables were prepared. The remarkable similarity between the spatial patterns of variations of these variables indicates a strong correlation between the Bouguer gravity anomaly and seismicity in this region. The Pearson correlation coefficient values calculated between the variables also confirm this correlation. These values indicate that both variables of average Bouguer anomaly and the range of variations of this anomaly show a significant positive correlation with the seismicity-related variables. This degree of correlation is stronger for the variable of the Bouguer anomaly variation and moreover, this variable is more correlated with the seismicity variables associated with the frequency of earthquakes. In the next step, variograms were prepared. The results obtained show that among the seismicity-related variables of the region, the b seismicity parameter (from the Gutenberg-Richter relation) has more spatial variability and show high spatial autocorrelation up to long distances. On the other hand, the other variables related to earthquake frequency and magnitude of earthquakes show less spatial autocorrelation. The variograms provided for the two variables representing the bouguer anomaly also show remarkable similarity to the seismicityrelated diagrams. This similarity is more pronounced for the variable of the Bouguer anomaly variation. The remarkable similarities of the variograms, along with the similarities of the spatial distribution maps of these variables, may indicate a close relationship between these two series of variables. Finally, it can be concluded that gravitational forces, especially forces caused by isostatic imbalances, can play an important role in the process of earthquake occurrences in Iran. [ABSTRACT FROM AUTHOR]

To estimate the connectivity of a road network, it is crucial to evaluate the correlation of hazard intensities among individual bridge locations since the probability of multiple bridges being damaged simultaneously depends on the degree of this correlation. However, research on connectivity assessment of bridge networks considering spatial correlations associated with flood intensities is scarce in the literature. When quantifying the spatial correlation of flood intensities, modeling based on the stream distance rather than the Euclidean distance is required, taking into account that river flow is restricted only within the stream network. To achieve this purpose, a novel methodology is proposed to evaluate the spatial correlation of a stream network based on a geostatistical linear model and stream network covariance models. In addition, this study considers the spatial correlation of seismic hazard intensity. With the proposed method, it is possible to identify which bridges play an important role in ensuring the connectivity of the road network under multiple hazards, i.e. flood and seismic. As an illustrative example, the proposed method is applied to a hypothetical bridge network in Kumamoto Prefecture, Japan. The results demonstrate that improved network connectivity can be achieved by implementing a relevant retrofitting strategy for important bridges. [ABSTRACT FROM AUTHOR]

Many post‐processing methods improve forecasts at individual locations but remove their correlation structure. However, this information is essential for forecasting larger‐scale events, such as the total precipitation amount over areas like river catchments, which are relevant for weather warnings and flood predictions. We propose a method to reintroduce spatial correlation into a post‐processed forecast using an R‐vine copula fitted to historical observations. The method rearranges predictions at individual locations and ensures that they still exhibit the post‐processed marginal distributions. It works similarly to well‐known approaches, like the "Schaake shuffle" and "ensemble copula coupling." However, compared to these methods, which rely on a ranking with no ties at each considered location in their source for spatial correlation, the copula serves as a measure of how well a given arrangement compares with the observed historical distribution. Therefore, no close relationship is required between the post‐processed marginal distributions and the spatial correlation source. This is advantageous for post‐processed seamless forecasts in two ways. First, meteorological parameters such as the precipitation amount, whose distribution has an atom at zero, have rankings with ties. Second, seamless forecasts represent an optimal combination of their input forecasts and may spatially shifted from them at scales larger than the areas considered herein, leading to non‐reasonable spatial correlation sources for the well‐known methods. Our results indicate that the calibration of the combination model carries over to the output of the proposed model, that is, the evaluation of area predictions shows a similar improvement in forecast quality as the predictions for individual locations. Additionally, the spatial correlation of the forecast is evaluated with the help of object‐based metrics, for which the proposed model also shows an improvement compared to both input forecasts. [ABSTRACT FROM AUTHOR]

This study examines spatial disparities and associations between the social deprivation index (SDI) and Type 2 Diabetes, Dementia, and Heart Failure in Italy's Lazio Region. The primary goal is to assess how social deprivation impacts health inequalities by analysing SDI-disease correlations. This retrospective study uses 2020 socioeconomic data and 2021 epidemiological indicators in Lazio Region, Italy. The SDI, constructed following established guidelines, measures social deprivation. Statistical tools, including regression models, Moran's I test, and LISA techniques, are used to analyse spatial patterns. Utilizing a retrospective approach, we merge 2020 socioeconomic and 2021 epidemiological data for analysis. The SDI is computed using established methods. Spatial disparities are explored through regression models, Moran's I test, and LISA techniques. The study reveals significant disparities in disease incidence. District V in Rome exhibits high Type 2 Diabetes (113.75/1000) and Heart Failure (37.98/1000) rates, while Marcetelli has elevated Dementia incidence (19.74). Southern municipalities face high unemployment (up to 25%), whereas bordering areas have higher education levels (30–60%). Disease hotspots emerge in Rome and centre-north municipalities, aligning with social deprivation patterns. Regression models confirm the link between disease incidence and socioeconomic indicators. SDI ranges from − 1.31 to + 10.01. This study underscores a correlation between social deprivation and disease incidence. Further national-level research is essential to deepen our understanding of how social deprivation influences health outcomes, with potential implications for addressing health disparities both regionally and nationally. [ABSTRACT FROM AUTHOR]

Based on the reconstructed MODIS data and ECMWF reanalysis data from 2003 to 2021, spatial correlations between chlorophyll a (Chl a) and sea surface temperature (SST), photosynthetically available radiation (PAR), aerosol optical thickness (AOT), and wind speed (WS) in the Bohai Sea were analyzed from the perspective of time domain and frequency domain. Results indicate that the frequency-domain analysis was more conducive to revealing the correlations between Chl a and environmental factors. The spatial pattern of time-domain correlations was similar to the isobaths of the Bohai Sea, which was positive in shallow waters and negative in deep waters for SST, PAR, and AOT, and was reversed for WS. Frequency-domain correlations were obtained by performing Fourier Transform and were higher than correlations in time domain. The spatial distributions indicated that the effects of SST and PAR on Chl a were greater than AOT and WS in the Bohai Sea. Additionally, cross-spectrum analysis was applied to explore the response relationships. A depth-dependent pattern was shown in correlations and time lags, indicating that the influential mechanism of environmental factors on Chl-a concentration is related to seawater depth. [ABSTRACT FROM AUTHOR]

Urban land is the primary location for manufacturing and services, facilitating the expansion and interconnectedness of economic activities and factor flows to shape various urban land-use performances (ULUP). Exploring these spatial linkages of urban land-use performance can play a crucial role in fostering cohesive urban development. Taking 109 prefecture-level cities as research samples, this paper explores the characteristics and influencing factors of the spatial network associated with ULUP in the Yangtze River Economic Belt through modified gravity model, network analysis and QAP analysis. The analysis shows that ULUP has shown an N-shaped trend over the past two decades. It has formed a network of correlations, with Chongqing, Shanghai, and Wuhan emerging as central nodes. Notably, the correlation predominantly occurs between geographically adjacent cities, with weaker links between the Yangtze River Economic Belt's upstream, midstream, and downstream regions. The network can be divided into four distinct types: main inflow plate, main outflow plate, agent plate, and bidirectional spillover plate. Geographic location is the most significant factor influencing network formation, followed by resource mismatch, economic development, and openness. The study provides theoretical guidance and empirical support for improving the utilisation of urban land and promoting coordinated development. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study conducted risk ranking for risk substances present in meat products, exploring the temporal and spatial distribution patterns of these risk items. The entropy weight method was used to calculate the risk values of different risk substances in circulation, production and catering links, and the risk substances were ranked by the 80-20 principle. The descriptive statistical method was used to explore the temporal and spatial distribution patterns of meat product production links. Through global and local correlation analysis, the characteristics of the spatial distribution of high-risk and relatively high-risk substances nationwide were studied. The high-risk substances in the production and circulation links were N-dimethylnitrosamine, benzo[a]pyrene, cadmium, peroxide value, new red, nitrite, arsenic, mercury, coliforms, and total bacteria number, while the relatively high-risk substances in the catering link was the total bacteria number. The highest meat product unqualified rate was 0.21% in 2017, followed by 0.19% in 2020. In various regions, the highest unqualified rate was in Ningxia, at 0.30%. For meat products from all provinces nationwide, the qualified rates for high and relatively high-risk substances only exhibited global spatial correlation for arsenic, allura red, and total bacterial count (P

Abstract In the information age, digital information technology has gradually become a new quality of productivity to improve international competitiveness, and the status and importance of the pseudo human settlements(PHS) constructed by digital and information has been increasing in the human settlements(HS) system. In this context, exploring the coupling coordination of the internal systems of the PHS provides a theoretical basis for promoting the comprehensive improvement of the quality of the HS in the urban agglomerations, provides a theoretical reference for rationally interpreting the new type of human-land relationship in the information age, and provide a new perspective for the study of the human settlements geography. Currently, research on PHS is in its nascent stages, therefore, we construct a theoretical framework for the coupling coordination of the “three states” of the HS, takes the internal system of PHS as the entry point, empirically analyze the spatiotemporal characteristics of the PHS coupling coordination degree within urban agglomerations in eastern China and the driving factors by using the coupling coordination degree, spatial autocorrelation, the center of gravity and the ellipse of the standard deviation, and geo-detector models. Research shows: (1) Time course: exhibits an upward “inverted L” trend. (2) Spatial pattern: exhibits a general spatial pattern of “high in the north and low in the south”. (3) Spatial correlation: shows a spatially positively correlated clustering trend. (4) Spatial evolution: demonstrates a decentralized pattern of migration from the northeast to the southwest, indicating that the coupling coordination grows faster in the southwest than in the northeast. (5) Driving factor: the development of coupling coordination degree of PHS results from multiple factors and systems. This research provides theoretical support for promoting the comprehensive improvement of the quality of PHS in the urban agglomerations in eastern China, and offers scientific reference for the construction of PHS in other regions of China.

As a typical religious cultural heritage, Tibetan Buddhist monasteries in the Hehuang region of northwest China have strong connections with surrounding villages that practice Tibetan Buddhism. This article investigates the spatial distribution correlation between the two entities using methods such as kernel density, spatial quantity relationship model, gravity center model, and qualitative description. It analyzes the relationship between Tibetan Buddhist monasteries and traditional Tibetan villages at three levels: macro, meso, and micro, exploring the spatial correlation and influence factors. The study results are as follows: Firstly, the spatial distribution of traditional villages and Tibetan Buddhist monasteries in the Hehuang region exhibits a positive correlation with a moderate spatial correlation. Secondly, the distribution of traditional villages and Tibetan Buddhist monasteries in the Hehuang region demonstrates a high spatial balance. Yet, there are instances where certain counties have an abundance of monasteries but lack traditional villages, particularly in Minhe County, Guide County, Hualong County, and Ledu County. Various factors such as economic, policy, and cultural integration contribute to this phenomenon. Thirdly, the location relationship between traditional villages and Tibetan Buddhist monasteries in the Hehuang region can be categorized into three types: Villages separated from Tibetan Buddhist monasteries, Tibetan Buddhist monasteries in villages, and Tibetan Buddhist monasteries near villages. Finally, it is proposed to protect traditional villages in the area based on the principle of protecting cultural diversity. To safeguard Chinese traditional villages in the future, it is essential to comprehensively analyze the spatial relationship between villages and religious architectural heritage, and prioritize the preservation of significant villages located within the influence zone of these religious architectural heritage by incorporating them into the list of protected traditional villages. This study aims to enhance theoretical research on traditional villages in ethnic minority regions and provide a scientific basis for the comprehensive protection and coordinated utilization of traditional villages and religious architectural heritage in the Hehuang region and other ethnic regions.

Spatial epidemiology often involves the analysis of spatial count data with an unusually high proportion of zero observations. While Bayesian hierarchical models perform very well for zero‐inflated data in many situations, a smooth response surface is usually required for the Bayesian methods to converge. However, for infectious disease data with excessive zeros, a Wombling issue with large spatial variation could make the Bayesian methods infeasible. To address this issue, we develop estimating equations associated with disease mapping by including over‐dispersion and spatial noises in a spatial zero‐inflated Poisson model. Asymptotic properties are derived for the parameter estimates. Simulations and data analysis are used to assess and illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Probabilistic seismic performance assessment method for buildings offers a valuable approach to simulate the broader regional impacts: economic losses, downtime, and casualties. A crucial aspect of this process entails accounting for the spatial correlation of building performances, aiming for an accurate estimation of the probability of extreme regional losses, such as the simultaneous collapse of buildings with similar structural characteristics. In this study, a correlation model based on a Gaussian random field is employed, and several key challenges associated with its application are addressed. In addition, efficiency of five different methods of selecting station records from the same earthquake scenario is compared. The minimum number of earthquake records necessary to achieve a stable correlation result is determined. Additionally, spatial correlations derived from different history earthquake events are compared. By addressing these critical issues, this research contributes to refining the reliability of probabilistic methods for regional resilience assessment.

Rice cropped policy has been constantly changing, affecting the value of irrigated land. This study aims to examine distribution patterns of irrigated land value from a new perspective of uncertainties in single- and double-cropped rice policies in China. The real options approach was employed to calculate irrigated land value under policy uncertainty, and Moran's index was applied to identify the spatial distribution characteristics of irrigated land value at the provincial level. The empirical results were as follows: significantly positive spatial correlations existed between the land value under two cropped rice policies. The northeast region of China had a low-value clustering effect based on geographical characteristics. In contrast, the southern provinces of China had a high-value clustering effect based on the criteria of administrative regions. This study considered policy uncertainty when evaluating the economic effect on irrigated land when revealing spatial correlation in land value. We provide a theoretical and empirical basis for the formulation of cropped rice policies.

Typhoons are among the most significant natural disasters affecting the eastern and southern coastal regions of China, inflicting substantial annual damage on both coastal and inland areas. Since the initiation of the reform and opening-up policy, the socioeconomic development of the coastal regions of China has been swift, leading to increased exposure to typhoons. In the context of global climate change, typhoons are expected to increase in frequency and intensity in China. Therefore, researching on the spatiotemporal pattern characteristics of typhoons impacting China is of critical importance for understanding the impact patterns and risk changes of typhoon disasters, as well as for formulating policies on disaster response, prevention, and mitigation. This study aims to provide valuable insights into the formulation of such policies. Based on these objectives, this study utilized a comprehensive dataset, including county-level socioeconomic and disaster statistics, historical typhoon wind and rainfall data, and high-precision topographic data. Using county-level administrative regions as spatial units, this study employed various methods, such as time-series statistical analysis, gravity model, geographical detector, spatial correlation analysis, and geographically weighted regression, to analyze the spatiotemporal distribution patterns and influencing factors of typhoon disaster conditions in China from 1978 to 2020. The findings of this study are as follows: (1) The number of deaths and missing persons, quantity of damaged housing, death, and missing rate per million people, and proportion of direct economic loss to GDP caused by typhoon disasters have all shown a declining trend, indicating significant achievements in disaster prevention and mitigation efforts. (2) The center of gravity of typhoon disaster-related losses has shifted southward, corresponding with the economic development of coastal regions, demonstrating a reduced disaster impact in coastal areas and an increased impact in inland areas. (3) Wind and rain induced by typhoons are the primary driving factors of disaster conditions, and topographical factors are also drivers of casualties and crop loss. (4) The two major regions, Zhejiang-Northern Fujian and Western Guangdong-Eastern Guangxi, exhibit significant characteristics of disaster condition agglomeration, closely related to typhoon activity patterns and levels of economic development. (5) There is a negative correlation between the gross local product and disaster conditions in some areas, reflecting the role of socioeconomic development in enhancing the capacity for disaster prevention and mitigation.

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction. [ABSTRACT FROM AUTHOR]

Advanced manufacturing agglomeration (AMA) plays a significant role in supporting regional development, but few studies have examined it on a regional geographic space. In this paper, the relationship between AMA and regional economic‒innovative‒social coupling coordination development (EISCCD) was studied spatially at a fine-grained scale (500 m × 500 m), taking Liangjiang New Area (LJNA), China, as an example. The results showed that (1) the spatial patterns of AMA and EISCCD in LJNA were similar; (2) among the four different combinations, the economic‒social combination had the highest degree of coupling coordination and a more balanced spatial distribution; (3) AMA and the four coupling coordination combinations all showed significant positive relationships, with innovation playing a prominent mediating role; and (4) there was evident spatial heterogeneity in the relationship between AMA and ECICCD. These results demonstrate the possibility of building suburban subcenters with advanced manufacturing. These findings are beneficial for urban and regional planners to monitor the development status at different locations within cities, enabling precise construction and management of various facilities, enhancing land utilization efficiency, and aiding in the realization of a business-friendly and livable urban environment. [ABSTRACT FROM AUTHOR]

The real-time sharing of traffic data can offer improved services to users and timely respond to environmental changes. However, this data often involves individuals' sensitive information, raising substantial privacy concerns. It is imperative to find ways to protect the privacy of the shared traffic data while maintaining its ongoing data utility. In this paper, a Differential Privacy-based scheme with Spatial Correlation for Real-time traffic data (named as DP-SCR) is proposed. DP-SCR not only ensures the high data utility of shared traffic data, but also provides strong privacy protection. Specifically, DP-SCR is designed to adhere to w-event ε -differential privacy, ensuring a high level of privacy protection. Subsequently, a novel adaptive allocation based on spatial correlation prediction is proposed to optimize the privacy budget allocation in differential privacy. In addition, a feasible dynamic clustering algorithm is developed to minimize the relative perturbation error, which further improves the quality of shared data. Finally, the analyses demonstrate that DP-SCR provides w-event privacy for the shared data of each section, and the spatial correlation is a more pronounced characteristic of the traffic data than other characteristics. Meanwhile, experiments conducted on real-world data show that the MAR and MER of the predicted data in DP-SCR are smaller than those in other baseline DP-based schemes. It indicates that the DP-SCR scheme proposed in this paper can provide more accurate shared data. [ABSTRACT FROM AUTHOR]

The agglomeration bonus (AB) has been advocated as an incentive mechanism to boost spatially coordinated conservation efforts, where such coordination is thought to be beneficial to achieving biodiversity or other ecological outcomes. Specifically, an AB is paid to individual landholders if their conserved habitats are spatially connected to the conserved habitats of adjacent neighbours. This paper employs a series of controlled lab experiments with agriculture students to investigate the performance of AB in budget‐constrained discriminatory‐price auctions across different landscape types. We focus on the spatial correlation of opportunity costs and environmental benefits as one potentially important aspect of the landscape. We set up a stylised agricultural landscape where the conservation agency aims to connect fragmented wildlife habitats by incentivising farmers to enrol land in a conservation programme. We investigate the effects of an AB in landscapes where opportunity costs and environmental benefits are uncorrelated, negatively correlated or positively correlated over space. We found that the benefits of an AB in improving landscape‐scale environmental outcomes were significant in the positive correlation landscape. However, the AB resulted in worse outcomes in the uncorrelated and negative landscapes. [ABSTRACT FROM AUTHOR]

The studies on balancing high-quality basic education development and controlling the increasing housing prices for sustainable development and social equity in big cities have drawn a huge academic attention. Taking Guangzhou's sub-districts as study areas, this research investigates the relationships of spatial distribution between housing prices and the high-quality schools with different grades and stages through examining point of interest (POI) data. The results show that: ① The public fiscal investment, educational reforms, enrollment changes, population numbers, applicant poll, as well as the local history, spatial planning and its functions are key factors that affect the spatial relationships between high-quality schools and housing prices. ② From the perspective of educational stages, high-quality elementary schools contribute the greatest correlation with housing prices, while sequentially the kindergartens, secondary schools and high schools are less related. ③ The general spatial correlation presents a "core-periphery" differentiation between the inner city and the suburban area from high to low in the municipality of Guangzhou; while in sub-districts level, the higher the density of high-quality schools, the more costly the housing it is. ④ There is a strong correlation relationship between the density of high-quality public schools and the high housing prices in city core, and there is a high agglomeration of high-quality private schools in the suburbs, especially the private kindergartens at the municipal level, contributes the greatest correlation with housing prices, while there is no correlation between the high quality schools and housing prices in the outskirts. The research calls for a reform to decrease the strong correlation between the high quality basic education and housing prices through rational planning by locating more better schools to suburban areas to change the imbalance of basic education distribution in Chinese cities. [ABSTRACT FROM AUTHOR]

Elevated duct (EleD) is an abnormal atmospheric refraction structure with a suspended trapped layer. The precise and highly resolved elevated duct‐height‐based data (EleDH) is crucial for radio communication systems, especially in electromagnetic wave path loss prediction and EleDH‐producing systems. However, producing high‐resolution EleDH is challenging because of the massive details in the EleDH data. Direct and high‐time refinement procedures mostly lead to unrealistic outcomes. The study provides a Dense‐Linear convolutional neural network (DLCNN)‐based EleDH refinement technique based on the development of statistical downscaling and super‐resolution technologies. Additionally, the stack approach is used, and the refining order is taken into consideration to ensure precision in high‐time refinement and provide reliable outcomes. To demonstrate the strength of DLCNN in capturing complex internal characteristics of EleDH, a new EleD data set is first funded, which only contains the duct height. From this data set, we use the duct height as the core refinement of the EleD's trapped layer and the thickness of the trapped layer to ensure reliable duct height. Seven super‐resolution models are utilized for fair comparisons. The experimental results prove that the DLCNN has the highest refinement performance; also, it obtained excellent generalization capacity, where the minimum and maximum obtained Accuracy(20%), MAE, and RMSE were 85.22% ∓ 88.30%, 36.09 ∓ 45.97 and 8.68 ∓ 10.14, respectively. High‐resolution EleDH improves path loss prediction, where the minimum and maximum obtained bias were 2.37 ∓ 9.51 dB. Key Points: We propose the elevated duct height refinement model Dense‐Linear convolutional neural network (DLCNN), which can capture the high frequency detail and locate high‐frequency regionThis research demonstrated that the prediction of electromagnetic wave propagation path loss benefits from elevated duct‐height‐based data refinement through DLCNNThe study avoided unreliable results by refining the top and bottom of the elevated duct's trapped layer successively [ABSTRACT FROM AUTHOR]

Consider a fixed number of clustered areas identified by their geographical coordinates that are monitored for the occurrences of an event such as a pandemic, epidemic, or migration. Data collected on units at all areas include covariates and environmental factors. We apply a probit transformation to the time to event and embed an isotropic spatial correlation function into our models for better modeling as compared to existing methodologies that use frailty or copula. Composite likelihood technique is employed for the construction of a multivariate Gaussian random field that preserves the spatial correlation function. The data are analyzed using counting process and geostatistical formulation that led to a class of weighted pairwise semiparametric estimating functions. The estimators of model parameters are shown to be consistent and asymptotically normally distributed under infill-type asymptotic spatial statistics. Detailed small sample numerical studies that are in agreement with theoretical results are provided. The foregoing procedures are applied to the leukemia survival data in Northeast England. A comparison to existing methodologies provides improvement. [ABSTRACT FROM AUTHOR]

The Li 3 LaMg 2 TiO 7 :Mn4+ fluorescent ceramics were synthesized via the high-temperature solid-phase method and characterized using XRD analysis and spectral properties evaluation. Under the excitation of a 375 nm LED, the red emission at 711 nm was detected. Interestingly, the quantum efficiency of this red light is closely correlated with the spatial distribution of the ceramics. The quantum efficiency of the ceramic interior reached 23.6 %, while on the outer upper surface, it significantly soared to 93.1 %, representing an almost fourfold disparity. Furthermore, the luminous efficiency is shape-dependent, with a cylinder exhibits higher efficiency than sphere or cuboid. For samples of the same shape, the upper surface efficiency surpasses that of the side and bottom surfaces. Based on an analysis of crystal structure and energy level diagrams, we attribute this phenomenon to two factors: firstly, ceramic shape-induced distortion of crystal lattice squeezes numerous electrons on outer surfaces into adjacent energy wells resulting in a Q-switching effect; secondly, the temperature gradient between the upper and lower regions induces convective airflow, leading to variations in electron distribution across the upper and lower surfaces, thereby resulting in disparate luminous efficiencies. This spatially dependent luminescence phenomenon holds potential for exploring high-efficiency fluorescent materials. [ABSTRACT FROM AUTHOR]

Village classification is an important foundation for preparing village planning and formulating development strategies. This study takes a typical plateau mountain area-Yulong County, Yunnan Province as the research object. First, the analytical hierarchy process is used to build a “three evaluations” (ecological sensitivity, development potential, and construction suitability)indicator system. Second, spatial correlation analysis of the “three evaluations” was carried out to obtain the initial village classification results. Finally, the state space method was used to calculate the village bearing capacity, which could be used to correct the initial village classification. Thus, the final village classification could be obtained. The results show that a significant plateau mountain space distribution of heterogeneity was shown in the villages of the research area, and the spatial autocorrelation coefficients of the “three evaluations” were 0.343, 0.253, and 0.110, respectively; more than 60% of the villages were in a bearing state and the higher bearing capacity was concentrated in the southeast, northwest, and central regions of the research area. After the comprehensive carrying capacity was calculated and corrected, the special protection category accounted for 23%, the suburban integration category accounted for 4%, the relocation and withdrawal category accounted for 15%, the agglomeration and promotion category accounted for 40%, and the temporary unclear category accounted for 18%. For different types of villages, the layout optimization strategy should be proposed according to the carrying capacity calculation results. The results of this research can provide a reference and basis for the formulation of township(town)-level territorial spatial planning and the implementation of “multi-planning” practical village planning. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) enable the determination of station displacements, which are essential to understanding geophysical processes and establishing terrestrial reference frames. Unfortunately, GNSS station position time series exhibit spatially and temporally correlated noise, hindering their contribution to geophysical and geodetic applications. While temporal correlations are commonly accounted for, a strategy for modeling spatial correlations is still lacking. Therefore, this study proposes a diagnosis of the spatial correlations of the white and flicker noise components of GNSS position time series, using the global Nevada Geodetic Laboratory dataset. This analysis reveals different spatial correlation patterns for white and flicker noise and the superposition of three distinct spatial correlation regimes (large-scale, short-scale and station-specific), providing insight into the noise sources. We show, in particular, that about 70% of flicker noise corresponds to large-scale variations possibly attributable to orbit modeling errors. We also evidence an increase in the spatial correlations of white noise at distances below 50 km, most pronounced in the vertical component, where 50% of the white noise appears to be driven by short-scale effects—possibly tropospheric delay mismodeling. [ABSTRACT FROM AUTHOR]

A multitude of studies have extensively examined strategies for achieving sustainable development in the real estate market. As the pivotal component of land economy, the real estate market plays a crucial role in ensuring its sound operation. However, it is currently undergoing significant adjustments and grappling with rampant speculative activities, resulting in an alarming bubble. By scrutinizing the speculative motivations of different entities, we present a novel perspective on mitigating speculation. Our analysis reveals that digital inclusive finance effectively curbs residents' and enterprises' speculative behavior, as evidenced by diminished prevention motivation and investment substitution motivation. Utilizing data from 280 cities, this study measures real estate market speculation by establishing a model that the volatility of the housing market turnover, as a proportion of GDP, deviates from the actual housing demand transactions based on economic fundamentals. Furthermore, it investigates the relationship between digital inclusive finance and real estate market speculation, along with its spatial effects. The findings indicate that digital inclusive finance significantly curbs real estate market speculation and has a negative spatial spillover effect. This research provides a novel model and perspective for exploring real estate market speculation while positively impacting sustainable development within the real estate market. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB), a crucial transportation corridor spanning China's east and west and linking coastal and inland regions, is not only pivotal in the nation's strategic development but also drives regional economic and social progress through its transportation industry. Despite rapid growth, the industry faces challenges such as low efficiency, resource supply–demand imbalances, and environmental issues. To advance green and sustainable progress, this study establishes a regional transportation green efficiency evaluation system. Using principal component analysis (PCA) to refine input data, the undesirable super-SBM model quantitatively assesses green transportation efficiency (GTE) in YREB provinces and cities, revealing regional disparities. The study also explores spatial correlations and distribution characteristics of GTE. Results indicate that ① YREB's GTE shows a U-shaped trend, with significant differences between upper, middle, and lower reaches, being stronger in the east and weaker in the west (lower > middle > upper reaches); ② GTE exhibits spatial correlation in YREB regions, with clear clustering; and ③ cold and hot spots of GTE in the middle reaches are relatively stable, with upstream areas generally cold or sub-cold, and hot spots mainly downstream. [ABSTRACT FROM AUTHOR]

Urbanization interacts with land use through resource consumption and space encroachment. Clarifying the spatial correlations of the interactive relationship between urbanization and land use, along with their spatiotemporal dynamics, is of vital importance for addressing the complex interplay between urban development and land resources and identifying regional differences. However, previous studies have not sufficiently explored these issues. Herein, we introduce a coupling coordination degree (CCD) model and present the results of exploratory spatiotemporal analyses involving in-depth investigation of the CCD between urbanization quality and land-use intensity in 290 Chinese cities. The results demonstrate that the CCD for most cities was at the transition-period or basic-coordination stage. The dynamics of the spatial correlation of the CCD was found to increase from the east to the central and western regions, but this was found to decline overall. The movement direction and spatial dependence of the local spatial structure of the CCD exerted a dominant synergistic effect. The transition of the spatial correlation was mainly Type I (stable local and neighboring morphology), showing strong transfer inertia, path dependence, and locking features. Dynamic transitions occurred more in central and eastern cities. The results suggest that more cross-city cooperation could contribute to moderate land-resource exploitation for high-quality urbanization. [ABSTRACT FROM AUTHOR]

The reliability assessment of pipeline networks under nature disasters helps to improve the disaster prevention capability and overall resilience of pipeline networks. For the earthquake hazard, the seismic damage of pipelines in the network is usually correlated due to the spatial correlation of critical parameters, including characteristics of pipeline, soil properties and ground motions. This paper proposes a framework to evaluate the seismic reliability of pipeline network under spatially correlated parameters based on the quasi-Monte Carlo (QMC) simulation. The influence of spatial correlated random variables on the seismic reliability of pipeline network is investigated with explicit consideration of the correlated random variables including the peak ground velocity, the wall thickness and the yield stress of pipe segment. The framework has been implemented in seismic reliability evaluation of two pipeline networks. The result shows that the QMC method has better simulation accuracy than the standard MC method under same sampling numbers. The spatial correlations have different influences on the seismic connectivity reliability of pipeline networks with different topological redundancy and parallelism of connective paths from sources to user nodes. For the pipeline network with greater redundancy and parallelism, the model without considering the spatial correlations overestimates the network connectivity reliability. [ABSTRACT FROM AUTHOR]

This paper conducts a brief survey of spatial unit roots within the context of spatial econometrics. We summarize important concepts and assumptions in this area and study the parameter space of the spatial autoregressive coefficient, which leads to the idea of spatial unit roots. Like the case in time series, the spatial unit roots lead to spurious regression because the system cannot achieve equilibrium. This phenomenon undermines the power of the usual Ordinary Least Squares (OLS) method, so various estimation methods such as Quasi-maximum Likelihood Estimate (QMLE), Two Stage Least Squares (2SLS), and Generalized Spatial Two Stage Least Squares (GS2SLS) are explored. This paper considers the assumptions needed to guarantee the identification and asymptotic properties of these methods. Because of the potential damage of spatial unit roots, we study some test procedures to detect them. Lastly, we offer insights into how to relax the compactness assumption to avoid spatial unit roots, as well as the relationship between spatial unit roots and other models, such as the Spatial Dynamic Panel Data (SDPD) model and Lévy–Brownian motion. [ABSTRACT FROM AUTHOR]

Digital finance (DF) has become the key to break through the energy dilemma. This paper measures green total factor productivity (GTFEE) of 30 provinces in China, and analyzes the spatial spillover effect and influence mechanism between DF and GTFEE. The research results demonstrate that the DF significantly improves the local GTFEE, and this promote effect is attributed to green technology innovation, industrial structure upgrading, and reasonable capital allocation. Interestingly, digital finance reduces GTFEE of adjacent areas under the different spatial matrix. DF plays a greater role in improving GTFEE of the developed eastern regions, while it plays a smaller role in the central and western regions. We also found that the higher degree of marketization and environmental regulation can increase the positive influences of DF on GTFEE. Therefore, we suggest that China should improve the construction of digital infrastructure, develop a differentiated digital financial system, and promote energy transformation. [ABSTRACT FROM AUTHOR]

From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China's transportation carbon emission intensity. This study found that China's transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: "two-way spillover", "net benefit", "broker", and "net spillover". The "net benefit" plate was mainly located in developed regions, and the "net spillover" plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity. [ABSTRACT FROM AUTHOR]