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Destructive aftershocks such as the Mw 7.2 Van earthquake on October 23, 2011, and the Hoy (Iran) earthquake with Mw 5.9 on February 23, 2020, occurred in the province of Van and its surroundings. In earthquake studies, the issue of examining the distribution and homogeneity of earthquake incidences with Geographic Information Systems (GIS) based via spatial autocorrelation techniques is frequently investigated. Van province and its surroundings are among the areas with high earthquake risk due to its location on the East Anatolian Compressive Tectonic Block. The aim of this study is to analyze the spatial patterns of earthquakes with magnitude Mw 4 and above that occurred in the province of Van and its surroundings during the instrumental period and to determine to cluster. Spatial cluster analyses play an important role in examining the distribution of seismicity. The data used in the study have been taken from the database system of the Earthquake Department of the Republic of Turkey Ministry of Interior Disaster and Emergency Management Presidency. Moran's I and Getis-Ord Gi methods from spatial autocorrelation techniques were preferred on the earthquake data set to be used in this research. It has aimed to determine the dangerous areas by testing the earthquake distributions in clustered regions via spatial autocorrelation techniques. [ABSTRACT FROM AUTHOR]

• Bike-sharing networks have a small-world property. • The connectivity of bike stations is highly related to their accessibility, while not to their intermediateness. • The spatial distributions of connectivity and accessibility of bike stations have a strong spatial autocorrelation. • Bike-sharing networks have a polycentric structure. To solve the last-mile problem and promote low carbon transportation, hundreds of cities around the world have launched bike-sharing services. A growing number of studies have been mining bike-sharing data from different cities to understand bike-sharing systems. However, there is still a lack of comprehensive understanding of a bike-sharing network structure. Thus, this paper attempts to use complex network theory and spatial autocorrelation analysis approaches to examine structural properties of bike-sharing systems, quantify the importance of bike stations in the network, and evaluate their spatial clustering patterns. This research analyzes bike-sharing trip data from five different sized bike-sharing systems in the United States, Canada, and China. The results of network analysis reveal that the bike-sharing networks have a small-world property with a small average path length and a high clustering coefficient. Compared with medium-scale networks, the connectivity and accessibility of bike stations are relatively low in the large-scale bike-sharing network. Also, the connectivity of bike stations is positively related to their accessibility while they are not highly correlated with their intermediateness. The spatial clustering analysis results indicate that the spatial distributions of connectivity and accessibility of bike stations have a strong global spatial autocorrelation. Also, the stations with high connectivity and accessibility are concentrated in the urban centers, and the stations with low connectivity and accessibility are clustered in the periphery of the urban areas. However, their intermediateness does not show a strong global spatial clustering pattern, which implies that bike-sharing networks consist of multiple sub-groups. The findings provide new insights for transport planners and managers to understand bike-sharing systems and to improve their service quality. [ABSTRACT FROM AUTHOR]

Highlights • A spatial autocorrelation validation is implemented to validate the significant patterns of the spatial clustering of check-in data. An exploratory spatial analysis and hotspot clustering method based on check-in data is proposed to detect urban hotspot and extract commercial area by geographic distribution metric with urban commercial hotspots. • An error ellipse is adopted to calculate the rough boundary of the commercial area, then a planar Delaunay iterative triangulation algorithm is proposed to determine the exact boundary of the commercial. Satellite images are used to adjust the fine boundary of the urban commercial area. • A time sequence extraction method of the commercial area is presented to analyze the trend of evolution in the city business space. Abstract Urban hot spot detection and commercial area analysis for city economic planning and dynamic urban planning is of vital importance. However, it is difficult to obtain a more accurate commercial area boundary in the past. Check-in data obtained by a social networking service (SNS) and/or a location-based service (LBS) is a type of crowd-sourced geographic data that can reveal mass daily life activities, which provides a new big data source for urban hot spot detection and commercial area analysis. In this paper, a dynamic urban commercial area extraction and monitoring approach is proposed using SINA Weibo (a social network) check-in data. First, a check-in data pre-process model is proposed to simplify the amount of check-in data and improve the efficiency of cluster analysis. The spatial autocorrelation validation is implemented to validate the significant patterns of the spatial clustering of check-in data. Then, an exploratory spatial analysis and hot spot clustering method based on check-in data is proposed to detect urban hot spots and extract commercial areas using a geographic distribution metric with urban commercial hot spots. Second, the hot spot cluster analysis results are taken to determine the center of the commercial area and calculate the distribution of an ellipse, which is adopted to obtain the rough boundary of the commercial area. A planar Delaunay iterative triangulation algorithm is presented to determine the exact boundary of the commercial area. Then, the time sequence extraction result of the commercial area is presented to analyze the evolutionary trend in the city business space. Finally, the Weibo check-in data from 2012 to 2014 of Wuhan city are taken as an example dataset for the commercial area extraction and detection with the proposed approach. The results show that the method can accurately determine the boundary of and changes within the commercial area in Wuhan city. This study provides a new method for the monitoring of hot spots and the geographical situations of city commercial areas. [ABSTRACT FROM AUTHOR]

Highlights • Cargo truck crash clusters are identified using spatial statistical methods. • Global clustered pattern and random distribution of crash attributes are observed. • Recurrent locations on highway Ruta 5 are identified as crash attribute hot spots. • Spatial co-occurrence identified strong relationships among attributes and outcomes. Abstract The growing number of cargo trucks on highway crashes in recent years due to the increase in freight movement in Chile motivates this study to identify the formation of persistent crash clusters on highway Ruta 5 (R5). Two spatial statistical methods (Moran’s I and Getis-Ord Gi*) were used to determine whether crashes on this highway showed spatial clustering over time from a global and local perspective. Globally, recurrent crash clusters are spatially correlated on vertical curves and straight highway sections on northern R5 with different truck types and with the tractor-trailer units during rainy days on southern R5. The local spatial autocorrelation results suggest that the contributing causes related to the loss of control of the vehicle, the fatigue and imprudence of the driver, and crashes involving tractor units with trailer tend to cause persistent rollover crash clusters throughout R5. Overall, clustering of crash attributes with high values (i.e., hot spots) occurring on highway locations with vertical curves and on cloudy days predominated in the northern R5, and the largest number of recurrent hot spots occurred on sunny days along southern R5. A hot spot spatial co-occurrence analysis was further performed to identify the strong relationships between the studied crash attributes, and the crash and injury types as outcomes. The indication of high risk for the clustering of cargo trucks on highways crashes provides a basis for improving highway safety and reduce the associated social and economic costs. [ABSTRACT FROM AUTHOR]

Moran's Index for spatial autocorrelation and localized index for spatial association have been widely applied in many research fields as the first step to explore and assess the spatial dependency in a set of geographic events. This article presents extensions to the equations for calculating global and localized spatial autocorrelation so to include the temporal attribute values of the geographic events being analyzed. The extended equations were successfully implemented and tested with a real world data set. In addition, simulated data sets were used to reveal how the extended equations performed. Beyond the usefulness of the extended equations, we suggest that care be taken with regard to assessing spatiotemporal patterns under the normality and randomization assumptions as different outcomes from different assumptions would require different approaches for interpretation. [ABSTRACT FROM AUTHOR]

A fundamental problem in linguistics is how literary texts can be quantified mathematically. It is well known that the frequency of a (rare) word in a text is roughly inverse proportional to its rank (Zipf’s law). Here we address the complementary question, if also the rhythm of the text, characterized by the arrangement of the rare words in the text, can be quantified mathematically in a similar basic way. To this end, we consider representative classic single-authored texts from England/Ireland, France, Germany, China, and Japan. In each text, we classify each word by its rank. We focus on the rare words with ranks above some threshold Q and study the lengths of the (return) intervals between them. We find that for all texts considered, the probability SQ(r) that the length of an interval exceeds r, follows a perfect Weibull-function, SQ(r) = exp(−b(β)rβ), with β around 0.7. The return intervals themselves are arranged in a long-range correlated self-similar fashion, where the autocorrelation function CQ(s) of the intervals follows a power law, CQ(s) ∼ s− γ, with an exponent γ between 0.14 and 0.48. We show that these features lead to a pronounced clustering of the rare words in the text. [ABSTRACT FROM AUTHOR]

Volatility is a key variable in the modeling of financial markets. The most striking feature of volatility is that it is a long-range correlated stochastic variable, i.e. its autocorrelation function decays like a power-law τ − β for large time lags. In the present work we investigate the determinants of such feature, starting from the empirical observation that the exponent β of a certain stock’s volatility is a linear function of the average correlation of such stock’s volatility with all other volatilities. We propose a simple approach consisting in diagonalizing the cross-correlation matrix of volatilities and investigating whether or not the diagonalized volatilities still keep some of the original volatility stylized facts. As a result, the diagonalized volatilities result to share with the original volatilities either the power-law decay of the probability density function and the power-law decay of the autocorrelation function. This would indicate that volatility clustering is already present in the diagonalized un-correlated volatilities. We therefore present a parsimonious univariate model based on a non-linear Langevin equation that well reproduces these two stylized facts of volatility. The model helps us in understanding that the main source of volatility clustering, once volatilities have been diagonalized, is that the economic forces driving volatility can be modeled in terms of a Smoluchowski potential with logarithmic tails. [ABSTRACT FROM AUTHOR]

The spatial distribution of commercial activities is vital to support healthy lifestyles and to achieve livable public spaces and environmental, social and economic sustainability in our cities. However, commercial activities require a constant flow of expenditure for their own viability. As a result, understanding the spatial and temporal distribution of expenditure is fundamental, although the lack of detailed, complete data sources has impeded this task until now. Bank card data paves the way for a new urban geography of expenditure, thanks to its fine spatial and temporal granularity along with the uniform coverage of all commercial sectors. In this paper, we analyze temporal, spatial, and spatiotemporal distributions of expenditure at the intraurban scale of the city of Madrid (Spain), combining spatial statistical tools (Getis-Ord General for global autocorrelation and Getis-Ord Gi* hot spot analysis for local autocorrelation) with k-means cluster analysis and spatiotemporal tools (Time Series Clustering analysis and Temporal Hot Spot Analysis). Our analysis confirms the strong center-periphery gradient described in previous literature, but with a CBD integrated by distinct specialized areas. The paper demonstrates that bank card data has a great potential to support a new geography of expenditure that could strengthen decision-making in planning and retailing. [Display omitted] • Bank card data supports novel, improved, geographical analysis of expenditure. • Spatial and temporal granularity gain detail, also disaggregated by sector. • Center-periphery gradient patterns, but a greater complexity within the CBD. • Spatiotemporal analyses uncover areas with similar expenditure across time. [ABSTRACT FROM AUTHOR]

The degree of social vulnerability may vary according to the conditions and backgrounds of different locations, yet spatial clustering phenomena may exist when nearby spatial units exhibit similar characteristics. This study applied spatial autocorrelation statistics to analyze the spatial association of vulnerability among townships in Taiwan. The vulnerability was first assessed on the basis of a social vulnerability index that was constructed using Fuzzy Delphi and analytic hierarchy process methods. Subsequently, the corresponding indicator variables were applied to calculate standardized vulnerability assessment scores by using government data. According to the results of the vulnerability assessment in which T scores were normalized, the distribution of social vulnerabilities varied among the townships. The scores were further analyzed using spatial autocorrelation statistics for spatial clustering of vulnerability distribution. The LocalGstatistic identified 42 significant spatial association pockets, whereas the GlobalGstatistic indicated no spatial phenomenon of clustering. This phenomenon was verified and explained by applying Moran'sIstatistics to examine the homogeneity and heterogeneity of spatial associations. Although both statistics were originally designed to identify the existence of spatial clustering, they serve diverse purposes, and the results can be compared to obtain additional insights into the distribution patterns of social vulnerability. [ABSTRACT FROM AUTHOR]

Purpose: We sought to determine whether significant variation in the incidence of clinically relevant psychoses existed at an ecological level in an urban French setting, and to examine possible factors associated with this variation. We aimed to advance the literature by testing this hypothesis in a novel population setting and by comparing a variety of spatial models.Methods: We sought to identify all first episode cases of non-affective and affective psychotic disorders presenting in a defined urban catchment area over a 4 years period, over more than half a million person-years at-risk. Because data from geographic close neighbourhoods usually show spatial autocorrelation, we used for our analyses Bayesian modelling. We included small area neighbourhood measures of deprivation, migrants' density and social fragmentation as putative explanatory variables in the models.Results: Incidence of broad psychotic disorders shows spatial patterning with the best fit for models that included both strong autocorrelation between neighbouring areas and weak autocorrelation between areas further apart. Affective psychotic disorders showed similar spatial patterning and were associated with the proportion of migrants/foreigners in the area (inverse correlation). In contrast, non-affective psychoses did not show spatial patterning.Conclusions: At ecological level, the variation in the number of cases and the factors that influence this variation are different for non-affective and affective psychotic disorders. Important differences in results-compared with previous studies in different settings-point to the importance of the context and the necessity of further studies to understand these differences. [ABSTRACT FROM AUTHOR]

This study examines the relationships between land-use, transportation infrastructure, household, and individual characteristics and the resulting average and daily trip distances using data from the 1999 Mid-Ohio Area Household Travel Survey. Spatial error models are developed to analyze the determinants of average and daily trip distances at the person level. First, new neighborhood categories are created usingK-means cluster analysis and several land-use and built-environment variables. Then, these new neighborhood categories (such as central city residential areas, medium-density suburbs, low-density suburbs, etc.) are used as independent variables to explain the resulting average and total trip distances, while controlling for sociodemographics, access to transit and bicycle facilities, and spatial autocorrelation. The results indicate that the residential location characteristics are a significant factor in explaining trip distances; people who live in areas that are away from urban centers and with a low mix of employment and population travel longer distances. For the Central Ohio Region, residing in lowest-density suburbs may add up to 44% to average trip distances and 33% to daily trip distances. [ABSTRACT FROM PUBLISHER]

In recent years, the topic of classification which is advantageous in terms of time and cost is of great interest in various fields. In particularly, when a large number of series should be analyzed, it is much more practical to classify the series into similar groups, and to make an estimation for each corresponding group rather than to make prediction for every given series individually. For this reason, some studies have been carried out in order to develop classification and clustering methods using characteristics of the time series. In this study, model based approaches: Maharaj's p-value based distance, Piccolo's AR distance, Cepstral based distance and free model based methods: Autocorrelation based distance, Chouakria-Douzal dissimilarity measure, Minkowski distance are compared in terms of clustering performances of time series. The performances of the clustering methods are also investigated for different ordered sets of the processes, and correlation structures among the series. In the result of the study, it is obtained that Maharaj's p-value based distance is the best method regarding to clustering performance, and Piccolo's AR distance based clustering is the least affected method by the different order of the processes. [ABSTRACT FROM AUTHOR]

Nearly all societies have now committed themselves to sustainable development by integrating some form of environmental quality, social equity, and economic welfare into their day-to-day activities. As such, there remains a strong political desire for the comprehensive assessment of conditions that evaluate the current status, measure progress, and help set future development goals. Indicators have been nominated as universal tools for progressing sustainable development across scales; however, there remains no consensus regarding the best approach to their design or use. While several studies have investigated the associations between indicators of sustainable development, few have directly addressed the question of how multiple measures can be used simultaneously to assess sustainability regionally. Building upon previous studies, this paper presents a quantitative and spatial assessment of 25 multi-metric indices across 36 European nations. The goals of this research were (1) to increase understanding of indicator complexity and (2) provide an applied example of their simultaneous use for regional assessment. Global Moran’sI-test and Pearson’s correlation coefficient (r) analysis were used to test spatial autocorrelation and multicollinearity, respectively. From the 25 composite indices, an overall rank was also provided for each country. Lastly, Ward’s cluster analysis was used to create country bundles of similarity. Our findings revealed that environmental performance index, global information networking institute coefficient, and happy planet index were numerically and spatially random. Cluster analysis revealed a four-bundle solution, while Norway, Switzerland, and Sweden ranked highest. This approach shows promise for systematically describing, visualizing, and monitoring sustainable development at the continental scale. [ABSTRACT FROM AUTHOR]

This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. Simulation results show that this metric aggregates time series according to their serial dependence structure better than a metric based on the sample autocorrelations. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates stock markets reasonably well according to size and the level of development. Furthermore, despite the substantial evolution of individual variance ratio statistics, the clustering pattern remains fairly stable across different time periods. [ABSTRACT FROM AUTHOR]

Using the 2006 New Zealand Census data, we examine the spatial clustering of the four largest ethnic minority groups—Chinese, Indian, Māori and Samoan—in Auckland. To guide our analysis, we employ three theoretical models—the immigrant enclave, ethnic community and place stratification models—that have helped explain the residential patterns of ethnic minority groups in immigrant destination countries. The results of spatial autocorrelation analysis show that all four ethnic minority groups form ethnic neighbourhoods. Indian neighbourhoods are more reflective of the immigrant enclave model as these neighbourhoods are socio-economically impoverished and have a higher percentage of foreign born. On the other hand, Chinese neighbourhoods are better explained by the ethnic community model, which describes an ethnic neighbourhood based more on preference than economic necessity. While the overall findings conform to patterns found in other countries, the severely socially deprived characteristics of Samoan and Māori neighbourhoods are reflective of the place stratification model. [ABSTRACT FROM PUBLISHER]

Cluster analysis continues to be an important exploratory technique in scientific inquiry. It is used widely in geography, public health, criminology, ecology, and many other fields. Spatial cluster detection is driven by geographic information corresponding to the location of activities, requiring appropriate and meaningful treatment of space and spatial relationships combined with observed attributes of location and events. To date, this has meant utilizing dedicated measures and techniques to structure and account for distance, neighbors, contiguity, irregular geographic morphology, and so on. Unfortunately, all spatial clustering approaches, regardless of their theoretical underpinning, statistical foundation, or mathematical specification, have limitations in accuracy, sensitivity, and the computational effort required for identifying clusters. As a result, a major challenge in practice is determining which technique(s) will provide the most meaningful insights for a particular substantive issue or planning context. The purpose of this article is to provide an overview and evaluation of spatial clustering techniques, identifying the strengths and weaknesses of the most widely applied approaches. Results suggest that performance varies significantly in terms of accuracy, sensitivity, and computational expense. This is noteworthy because the misidentification of clusters, whether false positives or false negatives, has the potential to bias not only hypothesis formulation but also pragmatic facets of policy, process, and planning efforts within a region. [ABSTRACT FROM AUTHOR]

Nowadays ubiquitous sensor stations are deployed worldwide, in order to measure several geophysical variables (e.g. temperature, humidity, light) for a growing number of ecological and industrial processes. Although these variables are, in general, measured over large zones and long (potentially unbounded) periods of time, stations cannot cover any space location. On the other hand, due to their huge volume, data produced cannot be entirely recorded for future analysis. In this scenario, summarization, i.e. the computation of aggregates of data, can be used to reduce the amount of produced data stored on the disk, while interpolation, i.e. the estimation of unknown data in each location of interest, can be used to supplement station records. We illustrate a novel data mining solution, named interpolative clustering, that has the merit of addressing both these tasks in time-evolving, multivariate geophysical applications. It yields a time-evolving clustering model, in order to summarize geophysical data and computes a weighted linear combination of cluster prototypes, in order to predict data. Clustering is done by accounting for the local presence of the spatial autocorrelation property in the geophysical data. Weights of the linear combination are defined, in order to reflect the inverse distance of the unseen data to each cluster geometry. The cluster geometry is represented through shape-dependent sampling of geographic coordinates of clustered stations. Experiments performed with several data collections investigate the trade-off between the summarization capability and predictive accuracy of the presented interpolative clustering algorithm. [ABSTRACT FROM AUTHOR]

This study provides explicit formulas for the moments and the autocorrelation function of the number of jumps over a given interval for a self-excited Hawkes process. These computations are possible thanks to the affine property of this process. Using these quantities an implementation of the method of moments for parameter estimation that leads to an fast optimization algorithm is developed. The estimation strategy is applied to trade arrival times for major stocks that show a clustering behavior, a feature the Hawkes process can effectively handle. As the calibration is fast, the estimation is rolled to determine the stability of the estimated parameters. Lastly, the analytical results enable the computation of the diffusive limit in a simple model for the price evolution based on the Hawkes process. It determines the connection between the parameters driving the high-frequency activity to the daily volatility. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 34:548-579, 2014 [ABSTRACT FROM AUTHOR]

Air quality is known to be a key factor in affecting the wellbeing and quality of life of the general populous and there is a large body of knowledge indicating that certain underrepresented groups may be overexposed to air pollution. Therefore, a more precise understanding of air pollution exposure as a driving cause of health disparities between and among ethnic and racial groups is necessary. Utilizing 52,613 urban census tracts across the United States, this study investigates age, racial, educational attainment and income differences in exposure to benzene pollution in 1999 as a case. The study examines spatial clustering patterns of these inequities using logistic regression modeling and spatial autocorrelation methods such as the Global Moran's I index and the Anselin Local Moran's I index. Results show that the age groups of 0 to 14 and those over 60 years old, individuals with less than 12 years of education, racial minorities including Blacks, American Indians, Asians, some other races, and those with low income were exposed to higher levels of benzene pollution in some census tracts. Clustering analyses stratified by age, education, and race revealed a clear case of disparities in spatial distribution of exposure to benzene pollution across the entire United States. For example, people aged less than 4 years from the western south and the Pacific coastal areas exhibit statistically significant clusters. The findings confirmed that there are geographical-location based disproportionate pattern of exposures to benzene air pollution by various socio-demographic factors across the United States and this type of disproportionate exposure pattern can be effectively detected by a spatial autocorrelation based cluster analysis method. It is suggested that there is a clear and present need for programs and services that will reduce inequities and ultimately improve environmental conditions for all underrepresented groups in the United States. [ABSTRACT FROM AUTHOR]

Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocalmaxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images. [ABSTRACT FROM AUTHOR]

Highlights: [·] We explored the pollution characters of metals in street dust of Beijing. [·] Area-source pollution and point-source pollution exist simultaneously. [·] We identified the spatial autocorrelation intensities and ranges of metals. [·] Metal pollution anomalies were identified by cluster and outlier analyses. [·] Urban activities strongly influence the distributions of metals. [ABSTRACT FROM AUTHOR]

We present a clustering analysis of luminous red galaxies (LRGs) in Stripe 82 from the Sloan Digital Sky Survey (SDSS). We study the angular two-point autocorrelation function, w(θ), of a selected sample of over 130 000 LRG candidates via colour-cut selections in izK with the K-band coverage coming from UKIRT (United Kingdom Infrared Telescope) Infrared Deep Sky Survey (UKIDSS) Large Area Survey (LAS). We have used the cross-correlation technique of Newman to establish the redshift distribution of the LRGs. Cross-correlating them with SDSS quasi-stellar objects (QSOs), MegaZ-LRGs and DEEP Extragalactic Evolutionary Probe 2 (DEEP2) galaxies, implies an average redshift of the LRGs to be z ≈ 1 with space density, ng ≈ 3.20 ± 0.16 × 10−4 h3 Mpc−3. For θ ≤ 10 arcmin (corresponding to ≈10 h−1 Mpc), the LRG w(θ) significantly deviates from a conventional single power law as noted by previous clustering studies of highly biased and luminous galaxies. A double power law with a break at rb ≈ 2.4 h−1 Mpc fits the data better, with best-fitting scale length, r0, 1 = 7.63 ± 0.27 h−1 Mpc and slope γ1 = 2.01 ± 0.02 at small scales and r0, 2 = 9.92 ± 0.40 h−1 Mpc and γ2 = 1.64 ± 0.04 at large scales. Due to the flat slope at large scales, we find that a standard Λ cold dark matter ($\Lambda \rm$CDM) linear model is accepted only at 2–3σ, with the best-fitting bias factor, b = 2.74 ± 0.07. We also fitted the halo occupation distribution (HOD) models to compare our measurements with the predictions of the dark matter clustering. The effective halo mass of Stripe 82 LRGs is estimated as Meff = 3.3 ± 0.6 × 1013 h−1 M⊙. But at large scales, the current HOD models did not help explain the power excess in the clustering signal.We then compare the w(θ) results to the results of Sawangwit et al. from three samples of photometrically selected LRGs at lower redshifts to measure clustering evolution. We find that a long-lived model may be a poorer fit than at lower redshifts, although this assumes that the Stripe 82 LRGs are luminosity-matched to the AAΩ LRGs. We find stronger evidence for evolution in the form of the z ≈ 1 LRG correlation function with the above flat two-halo slope maintaining to s ≳ 50 h− 1 Mpc. Applying the cross-correlation test of Ross et al., we find little evidence that the result is due to systematics. Otherwise, it may represent evidence for primordial non-Gaussianity in the density perturbations at early times, with flocalNL = 90 ± 30. [ABSTRACT FROM PUBLISHER]

Abstract: The objective was to analyze the spatial food environment of the Washington, DC metropolitan area. Poorer food sources (convenience stores, fast food, and pharmacies) were the most spatially clustered. Co-Location results suggest strong positive autocorrelation among food environment establishment categories, and competitive effects between pharmacies and convenience stores. Grocery stores were present throughout the region. Changes in the retail food environment, a measure potentially important for diet-related health, occurred through the changing ratio of restaurants to fast food as well as the increased presence of convenience stores. [Copyright &y& Elsevier]

Abstract: Automatic Noise Recognition was performed in two stages: (1) feature extraction based on the pitch range, found by analyzing the autocorrelation function and (2) classification using a classifier trained on the extracted features. Since most environmental noise types change their acoustical characteristics over time, we focused on the “pitch range” of the sounds in order to extract features. Two different classifiers, Support Vector Machines (SVM) and k-means clustering, were performed and compared using the proposed features. The SVM and k-means clustering classifiers achieve recognition rates up to 95.4% and 92.8%, respectively. Although both classifiers provided high accuracy, the SVM-based classifier outperformed the k-means clustering classifier by approximately 7.4%. [Copyright &y& Elsevier]

This article examines the determinants of Net Interest Margins (NIM) in Tunisia and tests some of the bank's characteristics that are derived mainly from the dealership model (Ho and Saunders, 1981). The research considers the heterogeneity of individual banks through the use of random-effect as well as fixed-effect models. It tests the robustness of the results by running the Wooldridge test for autocorrelation in panel data and robust cluster estimation. Operating Costs (OC) and Bank Capital (BC) are found to be consistent to the theoretical model implying positive association to NIM. In addition, NIM is positively related to Opportunity Costs of Bank Reserves (OCBR), Implicit Interest Payments (IIP) and negatively related to Quality of Management (QM). [ABSTRACT FROM AUTHOR]

This paper introduces a method for the analysis of regional linguistic variation. The method identifies individual and common patterns of spatial clustering in a set of linguistic variables measured over a set of locations based on a combination of three statistical techniques: spatial autocorrelation, factor analysis, and cluster analysis. To demonstrate how to apply this method, it is used to analyze regional variation in the values of 40 continuously measured, high-frequency lexical alternation variables in a 26-million-word corpus of letters to the editor representing 206 cities from across the United States. [ABSTRACT FROM AUTHOR]

We propose a new quantitative approach for the joint interpretation of velocity and attenuation tomography images, performed through the lateral separation of scattering and intrinsic attenuation. The horizontal P-wave scattering attenuation structure below Campi Flegrei Caldera (CFC) is imaged using the autocorrelation functions (ACF) of P-wave vertical velocity fluctuations. Cluster analysis (CA) is then applied to interpret the images derived from ACF and the available P-wave total attenuation images at 2000 m quantitatively. The analysis allows the separation of intrinsic and scattering attenuation on a 2-D plane, adding new geophysical constraints to the present knowledge about this volcanic area. The final result is a new, quantitative image of the past and present tectonic and volcanological state of CFC. P-wave intrinsic dissipation dominates in an area approximately located under the volcanic centre of Solfatara, as expected in a region with a large presence of fluids and gas. A north-south scattering attenuation region is mainly located below the zone of maximum uplift in the 1982-1984 bradiseismic crisis, in the sea side of the Pozzuoli bay, but also extending below Mt Nuovo. This evidence favours the interpretation in terms of a hard but fractured body, contoured by strong S-wave scatterers, corresponding to the Caldera rim: the region is possibly a section of the residual magma body, associated with the 1538 eruption of Mt Nuovo. [ABSTRACT FROM AUTHOR]

Accurate characterization of line-edge roughness (LER) and line-width roughness (LWR) is essential to cope with the growing challenge of device variability in large-scale integrations. The accuracy is affected markedly by statistical noise, which is caused by the finiteness of a number of samples. The statistical noise produces random oscillatory fluctuations of autocorrelation function (ACF) of LER/LWR. These fluctuations are obstacles to estimating LWR statistics by comparing experimental and theoretical ACFs. Using the Monte Carlo (MC) method to prepare pseudoexperimental ACFs (MC-ACFs), the authors found that an error η of the estimates is minimized in the case when a ratio of a fitting-window size to a correlation length is 0.3 or smaller, being less affected by the statistical noise. η under a fixed sampling interval is determined by the total number Nall of width data used to obtain the MC-ACF. This comes from the fact that the MC-ACF is obtained after averaging approximately for Nall times. The authors also investigated the case when LWR consisted of two components that had different correlation lengths. They confirmed that η of both components increase with a decrease in their occupancies in the entire LWR. This, together with a large correlation length, makes it difficult to accurately characterize the longer-correlation component, which is mostly minor (small occupancy) in actual cases. This difficulty is also an obstacle to estimating the shorter-correlation component, because the statistics of the former are mostly the prerequisites for analyzing the latter. These facts make a stark contrast to a power-spectral-density (PSD) fitting method, where at least the shorter-correlation component is estimated with almost the same accuracy as in the case of a single component. Based on these results, the authors propose to investigate PSDs, rather than ACFs, in the case of multicomponent LWR. [ABSTRACT FROM AUTHOR]

Abstract: A cross-sectional serological survey for Neospora caninum was carried out in Swedish beef cattle in order to estimate the seroprevalence and investigate any geographical patterns of the infection. Blood samples were collected from 2754 animals in 2130 herds and analysed for presence of antibodies to N. caninum. The study included 18.5% of all Swedish beef herds. Spatial autocorrelation was assessed by calculating Moran''s I and cluster detection was performed using LISA- and spatial scan statistics. The seroprevalence of N. caninum was 2.8% (95% CI: 2.18–3.44). Positive samples were found only in the southern half of Sweden. Moran''s I was positive (I =0.045, p <0.01) and indicated global spatial autocorrelation. Using the spatial scan test based on a Bernoulli model, a cluster comprising 16 postal code areas and including 19 seropositive animals was identified. The results indicate that there is a cluster of higher risk of N. caninum (relative risk=3.95) in the southern part of Sweden, in the counties of Kronoberg and Skåne. This is the first report on the seroprevalence of N. caninum in beef cattle in Sweden. [Copyright &y& Elsevier]

Abstract: Increments in financial markets have anomalous statistical properties including fat-tailed distributions and volatility clustering (i.e., the autocorrelation functions of return increments decay quickly but those of the squared increments decay slowly). One of the central questions in financial market analysis is whether the nature of the underlying stochastic process can be deduced from these statistical properties. We have shown previously that a class of variable diffusion processes has fat-tailed distributions. Here we show analytically that such models also exhibit volatility clustering. To our knowledge, this is the first case where clustering of volatility is proven analytically in a model. Our results are compatible with the viewpoint that variable diffusion processes are possible models for financial markets. [Copyright &y& Elsevier]

Identification of priority areas is a fundamental goal in conservation biology. Because of a lack of detailed information about species distributions, conservation targets in the Zhoushan Archipelago (China) were established on the basis of a species-area-habitat relationship (choros model) combined with an environmental cluster analysis (ECA). An environmental-distinctness index was introduced to rank areas in the dendrogram obtained with the ECA. To reduce the effects of spatial autocorrelation, the ECA was performed considering spatial constraints. To test the validity of the proposed index, a principal component analysis-based environmental diversity approach was also performed. The priority set of islands obtained from the spatially constrained cluster analysis coupled with the environmental-distinctness index had high congruence with that from the traditional environmental-diversity approach. Nevertheless, the environmental-distinctness index offered the advantage of giving hotspot rankings that could be readily integrated with those obtained from the choros model. Although the Wilcoxon matched-pairs test showed no significant difference among the rankings from constrained and unconstrained clustering process, as indicated by cophenetic correlation, spatially constrained cluster analysis performed better than the unconstrained cluster analysis, which suggests the importance of incorporating spatial autocorrelation into ECA. Overall, the integration of the choros model and the ECA showed that the islands Liuheng, Mayi, Zhoushan, Fodu, and Huaniao may be good candidates on which to focus future efforts to conserve regional biodiversity. The 4 types of priority areas, generated from the combination of the 2 approaches, were explained in descending order on the basis of their conservation importance: hotspots with distinct environmental conditions, hotspots with general environmental conditions, areas that are not hotspots with distinct environmental conditions, and areas that are not hotspots with general environmental conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a new distance metric that enables the clustering of general climatic time series. Clustering methods have been frequently used to partition a domain of interest into distinct climatic zones. However, previous techniques have neglected the time series (autocorrelation) component and have also handled seasonal features in a suboptimal way. The distance proposed here incorporates the seasonal mean and autocorrelation structures of the series in a natural way; moreover, trends and covariate effects can be considered. As an important by-product, the methods can be used to statistically assess whether two stations can serve as reference stations for one another. The methods are illustrated by partitioning 292 weather stations within the state of Colorado into six different zones. [ABSTRACT FROM AUTHOR]

In statistical data analysis it is often important to compare, classify, and cluster different time series. For these purposes various methods have been proposed in the literature, but they usually assume time series with the same sample size. In this article, we propose a spectral domain method for handling time series of unequal length. The method make the spectral estimates comparable by producing statistics at the same frequency. The procedure is compared with other methods proposed in the literature by a Monte Carlo simulation study. As an illustrative example, the proposed spectral method is applied to cluster industrial production series of some developed countries. [ABSTRACT FROM AUTHOR]

The article deals with one of the big analytical issues in spatial data analysis: spatial autocorrelation. This phenomenon is described as a key concept and potential problem of spatial analysis, but especially as a method of spatial analysis. The method is introduced first by reviewing the basic methodological framework including some related issues (the choice of spatial weighting system etc.) and second by presenting empirical examples of its application. Spatial autocorrelation statistics detect the presence of interdependence between the values of data at neighbouring locations. In addition to global spatial autocorrelation, measuring the overall degree of clustering, and calculated, for example, by Moran's I, emphasis is placed on the local analysis of spatial autocorrelation. This local form of spatial autocorrelation is based on the premise that the presence of spatial autocorrelation can vary across the study area, and it is fully in line with contemporary developments in spatial analysis. The results of LISA (local indicators of spatial association, local Moran) can be mapped for the purpose of identifying clusters. The empirical examples based on aggregate statistical data at the municipal level highlight the relevance and usefulness of analysis of spatial autocorrelation and show how these analyses can be used in social research and can improve our understanding of spatial processes. [ABSTRACT FROM AUTHOR]

We assess patterns of genetic diversity of a neotropical leaflitter frog, Eleutherodactylus ockendeni, in the upper Amazon of Ecuador without a priori delineation of biological populations and with sufficiently intensive sampling to assess inter-individual patterns. We mapped the location of each collected frog across a 5.4 × 1 km landscape at the Jatun Sacha Biological Station, genotyped 185 individuals using five species–specific DNA microsatellite loci, and sequenced a fragment of mitochondrial cytochrome b for a subset of 51 individuals. The microsatellites were characterized by high allelic diversity and homozygote excess across all loci, suggesting that when pooled the sample is not a panmictic population. We conclude that the lack of panmixia is not attributable to the influence of null alleles or biased sampling of consanguineous family groups. Multiple methods of population cluster analysis, using both Bayesian and maximum likelihood approaches, failed to identify discrete genetic clusters across the sampled area. Using multivariate spatial autocorrelation, kinship coefficients and relatedness coefficients, we identify a continuous isolation by distance population structure, with a first patch size of ca. 260 m and apparently large population sizes. Analysis of mtDNA corroborates the observation of high genetic diversity at fine scales: there are multiple haplotypes, they are non-randomly distributed and a binary haplotype correlogram shows significant spatial genetic autocorrelation. We demonstrate the utility of inter-individual genetic methods and caution against making a priori assumptions about population genetic structure based simply on arbitrary or convenient patterns of sampling.Heredity (2007) 99, 506–515; doi:10.1038/sj.hdy.6801025; published online 15 August 2007 [ABSTRACT FROM AUTHOR]

The creation of a spatial weights matrix by a procedure called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, is dependent on the use of a local spatial autocorrelation statistic. The result is (1) a vector that identifies those spatial units that are related and unrelated to contiguous spatial units and (2) a matrix of weights whose values are a function of the relationship of the ith spatial unit with all other nearby spatial units for which there is a spatial association. In addition, the AMOEBA procedure aids in the demarcation of clusters, called ecotopes, of related spatial units. Experimentation reveals that AMOEBA is an effective tool for the identification of clusters. A comparison with a scan statistic procedure (SaTScan) gives evidence of the value of AMOEBA. Total fertility rates in enumeration districts in Amman, Jordan, are used to show a real-world example of the use of AMOEBA for the construction of a spatial weights matrix and for the identification of clusters. Again, comparisons reveal the effectiveness of the AMOEBA procedure. [ABSTRACT FROM AUTHOR]

The article discusses the numerical classification or cluster analysis and the methods of ordination in biogeography. It offers the changing patterns and problems in the application of numerical classification and ordination and gradient analysis methods. The spatial autocorrelation, spatial dependence and the application of ordination techniques in biogeography and ecology are discussed. The two components of the spatial structure and application in plant and animal communities shown by P. Legendre are presented. It also offers new developments in numerical classification and ordination methods .

A new method for analysing spatial patterns was designed based on the variance of moving window averages (VMWA), which can be directly calculated in geographical information systems or a spreadsheet program (e.g. MS Excel). Different types of artificial data were generated to test the method. Regardless of data types, the VMWA method correctly determined the mean cluster sizes. This method was also employed to assess spatial patterns in historical plant disease survey data encompassing both airborne and soilborne diseases. The results obtained using the VMWA method were generally different from those obtained with Lloyd's index of patchiness and beta-binomial distribution methods, were in partial agreement with the results from spatial analysis by distance indices, and were highly consistent with the results from semivariogram and spatial autocorrelation analysis methods. Results demonstrated that the VMWA method can be applied to many types of data, including binomial diseased or healthy plant counts, incidence, severity, and number of diseased plants or pathogen propagules although directional and edge effects may limit its application. [ABSTRACT FROM AUTHOR]

Time series analysis under stationary assumption has been well established. However, stationary time series models are not plausible to describe the real world. Indeed, relatively long stretches of time series data should contain either slow or rapid changes in the spectra. To develop a general non-stationary theory, we have to pay careful attention to constituting a suitable model, otherwise the observations obtained in the future give no information about the present structure. Dahlhaus [1–4] has introduced an important class of non-stationary processes, called locally stationary processes which have the time varying spectral densities. In this paper, for a clustering problem of stock returns in Tokyo Stock Exchanges, we propose nonparametric approach based on generalized integral functional measures of the time varying spectral densities. The generalized measures include Gaussian Kullback–Leibler information and Chernoff information measures. The clustering results well extract the features of the relationship among the companies. [ABSTRACT FROM AUTHOR]

We investigate the clustering properties of galaxies in the recently completed ELAIS-S1 redshift survey through their spatial two-point autocorrelation function. We used a subsample of the ELAIS-S1 catalogue covering approximately 4 deg2 and consisting of 148 objects selected at 15μm with a flux>0.5 mJy and a redshift. We detected a positive signal in the correlation function that in the range of separationsis well approximated by a power law with a slopeand a correlation length, at the 90 per cent significance level. This result is in good agreement with the redshift-space correlation function measured in more local samples of mid-infrared-selected galaxies such as theIRASPoint Source Catalog (PSCz) redshift survey. This suggests a lack of significant clustering evolution of infrared-selected objects out tothat is further confirmed by the consistency found between the correlation functions measured in a localand a distantsubsample of ELAIS-S1 galaxies. We also confirm that optically selected galaxies in the local redshift surveys, especially those of the SDSS sample, are significantly more clustered than infrared objects. [ABSTRACT FROM AUTHOR]

Because of the increasing number of collected atoms (up to millions) in the three-dimensional atom probe, derivation of chemical or structural information from the direct observation of three-dimensional images is becoming more and more difficult. New data analysis tools are thus required. Application of a discrete Fourier transform algorithm to three-dimensional atom probe datasets provides information that is not easily accessible in real space. Derivation of mean particle size from Fourier intensities or from three-dimensional autocorrelation is an example. These powerful methods can he used to detect and image nano-segregations. Using three-dimensional 'bright- field' imaging, single nano-segregations were isolated from the surrounding matrix of an iron-copper alloy. Measurement of the inner concentration within clusters is, therefore, straightforward. Theoretical aspects related to filtering in reciprocal space are developed. [ABSTRACT FROM AUTHOR]

This paper analyses the asymptotic behaviour of the autocorrelation structure exhibited by squares of time series with a Wold expansion where the input error is a sequence of random variables with mean zero and finite kurtosis. Two important cases are discussed: (i) when the errors are independent and, (ii) when the errors are uncorrelated but their squares are correlated. Both situations are addressed when the process exhibits short or long memory. Consequences of these results on certain models widely used in many disciplines are also discussed. [ABSTRACT FROM AUTHOR]

The simple model NMEAR (1) is described for a stationary dependent sequence of random variables which have a mixed exponential marginal distribution; the model is a first-order stochastic difference equation with random coefficients and is first-order Markovian. It should be broadly applicable for stochastic modelling in operations analysis. In particular, it provides a model for simulating interarrival times in queuing systems when these random variables are overdispersed relative to an exponential random variable, and moreover are positively correlated. The model also has capability to model a variable which may be zero. but which otherwise is exponentially distributed. Such variables are found as waiting times in queuing models. Because of the (random) linearity of the process, it is easily extended to the modelling of cross-coupled sequences of interarrival and service times. The model can also be extended quite simply to a mixed exponential process with mixed pth order autoregresslve and qth order moving average correlation structure, NMEAR (p, q), so that non-Markovian dependence can be handled. [ABSTRACT FROM AUTHOR]

Foot-and-mouth disease (FMD) is highly endemic in Bangladesh. Using passive surveillance data (case records from all 64 districts of Bangladesh, 2014–2017) and district domestic ruminant population estimates, we calculated FMD cumulative incidence per 10,000 animals at risk per district, conducted cluster (Moran's spatial autocorrelation and scan statistics) and hotspot analysis (local indicator of spatial association statistic), created predictive maps and identified risk factors using a geographically weighted regression model. A total of 548,817 FMD cases in cattle and buffalo were reported during the four-year study period. The highest proportion (31.5%) of cases were reported during the post-monsoon season, and from Chattogram (29.2%) division. Five space-time clusters, 9 local clusters, and 14 hotspots were identified. Overall, higher cumulative incidences of FMD were consistently predicted in eastern parts of Bangladesh. The precipitation in the pre-monsoon season (p = 0.0008) was positively associated with FMD in Bangladesh. Results suggest climate plays an important role in the epidemiology of FMD in Bangladesh, and high risk zones exist. In a resource limited-setting, hotspots and clusters should be prioritized for vaccination coverage, and surveillance for FMD should be targeted in eastern areas of Bangladesh and during the post-monsoon season. [ABSTRACT FROM AUTHOR]

Background: Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data.Methods: An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran's I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5 s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0.Results: The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron's I = 0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p = 0.023 and p = 0.011). Compared to the diarrhea cases in the period 2007-2008, no much reduction was observed in the period 2015-2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1-33.8% and 1.3-96.1% in the period 2007-2008 and 0.6-29.1% and 10.4-92.0% in the period 2015-2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively.Conclusion: Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5 s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions. [ABSTRACT FROM AUTHOR]