Local Spatial Autocorrelation Statistics Research Articles

Characterization of domain formation in complex membranes.

A widely known property of lipid membranes is their tendency to undergo a separation into disordered (Ld) and ordered (Lo) domains. This impacts the local structure of the membrane relevant for the physical (e.g., enhanced electroporation) and biological (e.g., protein sorting) significance of these regions. The increase in computing power, advancements in simulation software, and more detailed information about the composition of biological membranes shifts the study of these domains into the focus of classical molecular dynamicssimulations. In this chapter, we present a versatile yet robust analysis pipeline that can be easily implemented and adapted for a wide range of lipid compositions. It employs Gaussian-based Hidden Markov Modelsto predict the hidden order states of individual lipids by describing their structure through the area per lipid and the average SCC order parameters per acyl chain. Regions of the membrane with a high correlation between ordered lipids are identified by employing the Getis-Ord local spatial autocorrelation statistic on a Voronoi tessellation of the lipids. As an example, the approach is applied to two distinct systems at a coarse-grained resolution, demonstrating either a strong tendency towards phase separation (1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dilinoleoyl-sn-glycero-3-phosphocholine (DIPC), cholesterol) or a weak tendency toward phase separation (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine (PUPC), cholesterol). Explanations of the steps are complemented by coding examples written in Python, providing both a comprehensive understanding and practical guidance for a seamless integration of the workflow into individual projects.

Spatial analysis of the epidemiological risk of leprosy in the municipalities of Minas Gerais.

Leprosy remains a significant public health problem of high importance. This investigation aims to analyze the spatial distribution of the leprosy epidemiological risk in the municipalities of Minas Gerais. This ecological study was conducted with new leprosy cases diagnosed from 2004 to 2019 in the municipalities of the state of Minas Gerais. Based on the epidemiological indicators, a composite indicator called the leprosy epidemiological risk index was estimated, classifying municipalities as high, medium, low and very low risk. For the spatial analysis, the global and local spatial autocorrelation statistics were used to identify the spatial distribution of the leprosy epidemiological risk in the periods 2004-2011 and 2012-2019 and classified as High/High, Low/Low, High/Low and Low /High. Although leprosy is declining in the state of Minas Gerais, the Global Moran Index confirmed the spatial dependence between municipalities for the two analyzed periods, characterizing the formation of clusters. When performing the local spatial autocorrelation, it was found that the macroregions with the highest number of municipalities with high indices, surrounded by other municipalities with high indices (high-high), were Northwest, East, South East, North, and Northeast. The low risk macroregions were Southeast, Center, South-Center and South. Leprosy has a heterogeneous spatial pattern and remains concentrated in historically endemic areas of the state. It underscores the importance of intensifying actions to combat leprosy in these municipalities and macroregions. Promote improved access to health services and combat stigma and prejudice to eliminate leprosy as a public health problem.

Spatial analysis of 10-year predicted risk and incident cardiovascular atherosclerotic diseases in a swiss urban population

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Swiss National Science Foundation GlaxoSmithKline. Introduction Whether cardiovascular risk score geographically aggregates and informs on spatial development of atherosclerotic cardiovascular disease (ASCVD) remains unknown. Aims We first determined the spatial distribution of cardiovascular risk predicted in an urban population. Second, we compared risk maps with clustering of incident ASCVD over a 10-year follow-up. Sensitivity analyses were conducted to identify explanatory risk factors for both clusters of predicted risk and observed ASCVD. Methods Using data from a population-based cohort study, we computed the Systematic Coronary Risk Evaluation Score 2 (SCORE2 and SCORE2-OP) in participants free from ASCVD at baseline (2003-2006). Geographical distributions of predicted risk and incident ASCVD were determined using global and local spatial autocorrelation statistics. Different models were tested to identify explanatory variables (alcohol consumption, body mass index, socio-economic status, anxiety disorder, major depressive disorder, Mediterranean diet, sport activity, polygenic risk score, mean noise, concentration of PM2.5, normalized difference vegetation index, land surface temperature). Results 6203 individuals (56% women, mean age 52.5 ± SD 10.7) with a median follow-up of 10 years (IQR, 6-10) were included in the analyses. Over the period, there was persistent geographical clustering of predicted risk and ASCVD (Figures 1 and 2). Predicted risk and incident ASCVD marginally overlapped spatially. Body-mass index (BMI) and alcohol consumption explained most of the spatial distribution of predicted risk, reducing high-risk clusters from 286 to 5 and low-risk clusters from 391 to 44. For ASCVD, high clusters either persisted or were reinforced depending upon locations, after adjustment for potential risk factors, with an increase from 100 to 118. Low clusters reduced from 293 to 76 after the adjustment. Incidence rate of ASCVD was 2.5% (IC95%, 3.7-2.4%]) in high-risk clusters compared to the rest of the population. Conclusions Using a population-based cohort, we showed that in an urban area there exists clusters of high and low incidence of ASCVD. Geographical distribution of clusters of high clinical risk score was not congruent with that of incident ASCVD, limiting the use of clinical risk scores in identifying areas where ASCVD may develop.

Study on monitoring water area in irrigation area by local space self-correlation index

Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z > 1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.

Epidemiological Characteristics and Spatial-Temporal Analysis of Tuberculosis at the County-Level in Shandong Province, China, 2016-2020.

(1) Background: Tuberculosis (TB) is an infectious disease that seriously endangers health and restricts economic and social development. Shandong Province has the second largest population in China with a high TB burden. This study aimed to detect the epidemic characteristics and spatio-temporal pattern of reported TB incidence in Shandong Province and provide a scientific basis to develop more effective strategies for TB prevention and control. (2) Methods: The age, gender, and occupational distribution characteristics of the cases were described. The Seasonal-Trend LOESS decomposition method, global spatial autocorrelation statistic, local spatial autocorrelation statistics, and spatial-temporal scanning were used to decompose time series, analyze the spatial aggregation, detect cold and hot spots, and analyze the spatio-temporal aggregation of reported incidence. (3) Results: A total of 135,185 TB cases were reported in Shandong Province during the five years 2016-2020. Men and farmers are the main populations of TB patients. The time-series of reported tuberculosis incidence had a long-term decreasing trend with clear seasonality. There was aggregation in the spatial distribution, and the areas with a high reported incidence of TB were mainly clustered in the northwest and southeast of Shandong. The temporal scan also yielded similar results. (4) Conclusions: Health policy authorities should develop targeted prevention and control measures based on epidemiological characteristics to prevent and control TB more effectively.

Detecting, quantifying, and mapping urban trees' structural defects using infrared thermography: Implications for tree risk assessment and management

Urban trees are a fundamental and key component of urban green areas, however, they are subject to several stresses which can compromise their mechanical integrity through the development of defects such as wood decay. In this study, we evaluated the structural health state of trees in four urban parks in the city of Mytilene, Greece, using structural traits of the trees, their trunk surface temperature distribution as recorded using infrared thermography, and spatial statistics both at single tree and at park level. We developed thermal indices by analyzing data from 334 trees belonging to three main species (Robinia pseudoacacia, Morus alba and Melia azedarach). We estimated temperature spatial dependence across each tree trunk using Moran's I index, while statistically significant spatial clusters were assessed using local spatial autocorrelation statistics. Relationships between tree traits, thermal, and spatial indices were established using linear and logistic regression models. Finally, we used the Getis-Ord Gi* statistic for the identification of risky tree hotspots and we applied the kriging geostatistical procedure for mapping of such hotspots. Our results have shown that the thermal and spatial indices can sufficiently predict different types of structural defect, and to identify hotspots of risky trees and their spatial extent. This approach can successfully contribute to tree risk assessment for a more effective urban park management.

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia.

IntroductionThe unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission.MethodologyWe obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™.ResultsAt the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.Discussion and ConclusionBoth analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.

Private forest owners’ sense of landownership: Motives, influential factors and landscape context

Legal ownership of land defines the formal relationship between a landowner and land. Besides legal ownership, there is also psychological ownership which landowners can develop toward land. Despite the abundant literature on the relationships between landowners and land, it remains unclear under which circumstances a sense of ownership over land occurs and what impacts individual feelings may have on landscape dynamics if aggregated or scaled up. Building upon the psychological ownership theory, we interviewed 442 private forest owners in Croatia about the benefits of landownership. By using a Multiple Indicators Multiple Causes (MIMIC) structural equation model and local spatial autocorrelation statistics, we 1) analyzed whether landownership benefits correlate to three motives for psychological ownership, i.e. having a place, efficacy and effectance, and self-identity; 2) analyzed the factors stimulating the motives; and 3) located the areas of spatial concentration of psychological landownership (i.e. hotspots/coldspots). Landownership benefits showed a strong association with the corresponding motives indicating that private forest owners experience landownership as a connection between the self and forest. Physical work in the forest and tending young forests increase psychological landownership. The land tenure system and duration of ownership plays no role. The biggest hotspot appeared in the most developed area, possibly preventing land transfer. Coldspots, in contrast, emerged across the country, pointing to land which could potentially change ownership after the land market regulation expires in 2023. The findings expand the understanding of land possession beyond the dis(investment) paradigm and help predict hotspots of land transfers at local to regional scales.

Measuring Local Spatial Autocorrelation with Data Reliability Information

Local spatial autocorrelation (SA) measures have been used in exploratory spatial data analysis, particularly in detecting spatial clusters. Existing local SA measures, however, are likely unreliable and biased because they compare only estimates among neighboring spatial units, ignoring errors associated with these estimates. The spatial Bhattacharyya coefficient (SBC) compares probability distributions by considering both estimates and their standard errors. Therefore, it was proposed to be a global SA measure. This article argues that the local version of SBC can serve as a local SA statistic, addressing the deficiency of existing local SA statistics that fail to consider estimate error in their formulations. Significance tests for local SBC are conducted under conditional and total randomization assumptions by using a generalized randomization approach. Simulation experiments and empirical analyses of American Community Survey data show that local SBC complements traditional local SA measures by incorporating data reliability information of estimates in SA assessment. This study shows that the direct comparison of estimate distributions in neighboring units in local SBC is more similar to the comparison of neighboring estimates in local Geary than to the comparison of mean deviations in local Moran. Thus, local SBC can effectively detect zonal boundaries.

A longitudinal study of e-commerce diversity in Europe

Owing to increased access to the Internet and the development of electronic commerce, e-commerce has become a common method of shopping in all countries. The purpose of this study is more precisely to research e-commerce diversity in Europe at the regional level and develop the conception of “E-commerce Supply Chain Management”. Statistical data derived from the European Statistical Office were applied to analyse the spatial diversity of e-retailing. Assessments of the regional diversity of e-retailing applied geographic information systems and exploratory spatial data analysis methods such us global and local spatial autocorrelation statistics. Clusters of regions with similar household preferences related to online shopping were identified. A spatial visualisation of the e-retailing diversity phenomenon may be utilised for the reconfiguration of supply chains and to adapt them to actual household preferences related to shopping methods.

Diversity of the selected elements of agricultural potential in the European Union countries

Agricultural importance in determining the directions of respective regions results from its production potential. The agricultural potential of a given country is determined by natural resources, ways of using them, natural conditions, workforce resources, technical resources and basic economic conditions. In this paper, only income and rural population are taken under consideration to describe the agricultural potential. Currently, European Union countries are functioning under the assumptions of the Common Agricultural Policy, assuming, among other things, increasing agricultural productivity, ensuring an adequate standard of living for the rural population and stabilising markets. The European Union (EU) is one of the world’s leading exporters and importers of agricultural products. The obtained results allowed the identification in 2010 and 2018 of countries with high and low values of income and population potential. It is characteristic that within both potentials, population and income, the countries with the lowest potentials are the most numerous group. Poland and Romania stand out against the background of all countries, where due to the high share of people working in agriculture, the population’s potential has the highest values. Denmark is also an outstanding country for which income potential has the highest value. This study aims to examine the diversity of selected elements of agricultural potential in the European Union countries. The research was conducted using, among other potential models and global and local spatial autocorrelation statistics. The analysis covered the years 2010 and 2018 by applying statistical data (Eurostat, Statistical Yearbook of Agriculture).

Spatial Patterns and Spatial Autocorrelations of Wetland Changes in China During 2003-2013

Wetlands play an important role in maintaining ecosystem functions. Wetlands in China have suffered intensive human disturbance, especially before 2000, resulting in great losses and degradation. Therefore, two national wetland resource surveys were carried out by the Chinese government during 1998-2003 and 2008-2013 to determine the status of wetlands, understand their dynamics, and provide substantial data that can aid scientific wetland conservation. Based on the survey data, the spatial pattern and spatial autocorrelation were explored using the standard deviation ellipse as well as global and local spatial autocorrelation statistics. GIS mapping was employed to display the results via the visualization of the spatial patterns and relationships. Results indicate that:① Changes in the wetlands of China are significant and exhibit obvious regional differences. The center of the ellipse of the total wetlands has moved to the west, and the degree of spatial differentiation between natural wetlands and artificial wetlands is increasing. There is no significant spatial autocorrelation for changes in artificial wetlands. ② The changes in natural wetlands are significantly spatially autocorrelated and clustered, which are identified by global Moran's I and local Moran's I. The hotspots of natural wetland change are concentrated primarily in Qinghai, Tibet, and Sichuan. The individual hotspot is in Inner Mongolia and the cold spot is in Henan. The difference in spatial autocorrelation between natural and artificial wetland changes indicates that natural wetland changes have shown spatial continuity, while artificial wetland changes have shown strong spatial randomness. ③ Some useful spatial associations are used to delineate wetland conservation effects. Then, three major or five minor effective protection management regions are identified. Wetland conservation efforts should be continuously strengthened and improved, especially in the middle-lower Yangtze River region of central China. The use of Moran statistics helps to reveal spatial autocorrelation and identify the conservation effects in wetland changes, which can provide a basis for decision-making in regional wetland conservation and management systems.

Compositional and contextual factors associated with drug overdose deaths in the United States

Background: In 2017, the US Department of Health and Human Services declared the Opioid epidemic a public health emergency. In the US, emergency rooms treat more than 1,000 people each day for drug overdose, and 115 of them die. This study examines compositional and contextual factors associated with drug overdose deaths rates in the US. Methods: Local spatial autocorrelation statistics were used to estimate hot spot areas to identify census tracts with high risk of drug overdose death. Logistic regressions investigated the relationship between drug overdose death rates and various compositional and contextual variables across census tracks. Results: The adjusted logistic model shows that compositional variables: depression (OR = 2.47 [2.37–2.58]), poor mental health (OR = 1.71 [1.63–1.79]), median age 1.41 (1.36–1.47) and the percentage of people with a high school diploma (OR = 1.30 [1.24–1.35]) were positively associated with the rate of drug overdose deaths. On the other hand, contextual variables: the percentage having health insurance (OR = 0.66 [0.64–0.69]), the Theil’s H index (OR = 0.69 [0.66–0.71]), population density (OR = 0.80 [0.77–0.84]), poverty (OR = 0.90 [0.86–0.95]), and median household income (OR = 0.91[0.86–0.96]) were negatively associated with drug overdose deaths. Discussion: The analysis reveals a consistently strong association between compositional mental health factors and census tract-level death rates from drug overdose.

Spatiotemporal Distribution of Tuberculosis during Urbanization in the New Urban Area of Nanchang City, China, 2010-2018.

Background: Urbanization will play a key role in ending the tuberculosis (TB) epidemic by 2030, but understanding the relationship between urbanization and the health threats posed by TB is incomplete. Therefore, this study aimed to explore the spatiotemporal distribution of TB at the township level during urbanization in the new urban area of Nanchang. Methods: Seasonal-trend decomposition of time series analysis was used to explore the seasonal distribution and trend analysis. Global and local spatial autocorrelation statistics, and space–time scan statistics were performed to detect the spatiotemporal clusters of TB cases in the new urban area of Nanchang from 2010 to 2018. Results: A total of 3245 TB cases were reported in the study area from 2010 to 2018. Of all the TB cases, 68% occurred in individuals older than 40 years old, 73.2% were male cases, and 56.6% were farmers. The primary seasonal peak was in late spring (April), and a smaller peak was in early autumn (September). The results of local indicators of spatial association showed that Jiaoqiao town and Changleng town might be “High–High” clusters. The most likely spatiotemporal cluster was located in the southwest of the study area in 2010, which included five towns, and then shifted to the northeast gradually. Across 2010 to 2018, nine spatiotemporal clusters were identified. The most likely cluster was located at the northeast of the study area. The center of this area was in Nanji town with a circle radius of 43.74 kilometers. Conclusions: The spatial clusters of TB incidence shifted to the rural region and the fringe of the new urban area of Nanchang. Targeted management strategies for urban migrants in the process of urbanization should be strengthened.

Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China.

BackgroundGuangxi is one of the provinces having the highest notification rate of tuberculosis in China. However, spatial and temporal patterns and the association between environmental diversity and tuberculosis notification are still unclear.ObjectiveTo detect the spatiotemporal pattern of tuberculosis notification rates from 2010 to 2016 and its potential association with ecological environmental factors in Guangxi Zhuang autonomous region, China.MethodsWe performed a spatiotemporal analysis with prediction using time series analysis, Moran’s I global and local spatial autocorrelation statistics, and space-time scan statistics to detect temporal and spatial clusters of tuberculosis notifications in Guangxi between 2010 and 2016. Spatial panel models were employed to identify potential associating factors.ResultsThe number of reported cases peaked in spring and summer and decreased in autumn and winter. The predicted number of reported cases was 49,946 in 2017. Moran's I global statistics were greater than 0 (0.363–0.536) during the study period. The most significant hot spots were mainly located in the central area. The eastern area exhibited a low-low relation. By the space-time scanning, the clusters identified were similar to those of the local autocorrelation statistics, and were clustered toward the early part of 2016. Duration of sunshine, per capita gross domestic product, the treatment success rate of tuberculosis and participation rate of the new cooperative medical care insurance scheme in rural areas had a significant negative association with tuberculosis notification rates.ConclusionThe notification rate of tuberculosis in Guangxi remains high, with the highest notification cluster located in the central region. The notification rate is associated with economic level, treatment success rate and participation in the new cooperative medical care insurance scheme.

Analysis of Lipid Order States and Domains in Lipid Bilayer Simulations.

We propose a general procedure to analyze lipid order states and domains in lipid bilayer simulations using surface areas and hydrophobic thicknesses of lipids. In our approach, the observable order states of individual lipids are inferred by a hidden Markov model analysis of their time series and by considering the deformation of a lipid in different packing environments. The assigned lipid order states are mapped onto the Voronoi tessellation of lipids, from which the ordered and disordered lipids are robustly clustered by the Getis-Ord local spatial autocorrelation statistics. The usefulness of this method is illustrated by its application to the quinary mixed bilayers consisting of cholesterol (Chol), 1,2-dimyristoyl- sn-glycero-3-phosphocholine (DMPC), 1,2-dimyristoyl- sn-glycero-3-phosphoethanolamine (DMPE), 1-palmitoyl-2-oleoyl- sn-glycero-3-phosphocholine (POPC), and 1-palmitoyl-2-oleoyl- sn-glycero-3-phosphoethanolamine (POPE), where any phospholipid type does not show strong preference over the other types to be enriched in lipid domains. The independent order state analysis for each lipid type allows straightforward applications of our method to arbitrarily complex bilayer simulations.

Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data

We propose a new estimator of spatial autocorrelation of areal incidence or prevalence rates in small areas, such as crime and health indicators, for correcting spatially heterogeneous sampling errors in denominator data. The approach is dubbed the heteroscedasticity‐consistent empirical Bayes (HC‐EB) method. As American Community Survey (ACS) data have been released to the public for small census geographies, small‐area estimates now form the demographic landscape of neighborhoods. Meanwhile, there is growing awareness of the diminished statistical validity of global and local Moran’s I when such small‐area estimates are used in denominator data. Using teen birth rates by census tracts in Mecklenburg County, North Carolina, we present comparisons of conventional and new HC‐EB estimates of Global and Local Moran’s I statistics created on ACS data, along with estimates on ground truth values from the 2010 decennial census. Results show that the new adjustment method dramatically enhances the statistical validity of global and local spatial autocorrelation statistics.

BUILT-UP AREA DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGES USING MULTI-SCALE WAVELET TRANSFORM AND LOCAL SPATIAL STATISTICS

Abstract. Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

Assessing the Association between Trust and Concentration Area of Migrant Ethnic Minority in Sydney

AbstractThere is ongoing concern that concentrations of migrant ethnic minority groups could increase distrust within Australian communities. This article assesses whether the existence of an area of high migrant ethnic minority concentration associates with low propensity to trust. After identification using Local Spatial Autocorrelation Statistics and controlling for other variables, a regression technique indicated significantly low trust associated with this area in 2006. Further investigation shows that the association of low level of trust and the concentration area was not as severe in 2010 and had even smaller and insignificant association in 2005.

Inter-decadal variation of spatial aggregation of Trichiurus japonicus in East China Sea based on spatial autocorrelation analysis

Marine fish shows high heterogeneity in spatial aggregation. We analyzed the inter-deca-dal variations of stock density for Trichiurus japonicus in East China Sea (ECS) using geo-statistical approaches such as spatial autocorrelation and hotspot analysis, based on the data of T. japonicus from both bottom trawl fishery and research surveys in the open waters of ECS during 1971 to 2011, combined with the sea surface temperature (SST) and surface salinity data in the PN section in August. The global spatial autocorrelation statistics showed that Moran's I firstly decreased and then went up, indicating that the spatial aggregation patterns of T. japonicus was weakened in the beginning and then increased during 1971 to 2011. The surface salinity in the PN section displayed the opposite trend during the same period. The local spatial autocorrelation statistics showed that the population firstly moved to the southern ECS and then to the northern ECS except in 1971 in which the population concentrated in the middle of ECS because of the restriction of offshore fishing ground. The movement of hotspot areas of T. japonicus adaptively varied with the first EOF mode of SST in summer (sumEOF1), which indicated that the hotspot areas first moved southeastward with decreasing sumEOF1, and moved northeastward with increasing sumEOF1, but all of the hotspot areas were close to the northward branch of the Kuroshio Current.