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The implementation of China's Beautiful Village Initiative was an extraordinary achievement and aroused extensive public attention. However, existing research mostly focuses on the construction and seldom on public attention towards the Beautiful Village Initiative. For this reason, this paper investigated the spatiotemporal characteristics of public attention based on the Baidu index using time-constrained clustering and the spatial autocorrelation test. Our results showed that the evolutionary process can be divided into three stages: very little national attention (2011-2012), injection of a strong impetus (2013-2015), and rooted in the people's minds (2016-2020). Spatially, provincial public attention demonstrated obvious spatial differentiation and stable spatial autocorrelation, with Low-Low clusters in Northwest China and High-High Clusters in East, Central, and North China. Spatial econometric models were further utilized to quantify the effects of socioeconomic factors on public attention. The results of the SEM model proved the existence of spatial spillover effects and indicated that the urbanization rate, population density, education level, and network popularity rate all positively affected public attention. The relationship between Beautiful Village construction and public attention was uncoordinated and, in most provinces, advances in public attention were ahead of the construction level. Our findings contribute to the understanding of public attention towards the Beautiful Village Initiative, and policy suggestions we proposed would be applied to increasing public awareness and participation. [ABSTRACT FROM AUTHOR]

Quantifying the dynamics of green infrastructure (GI) in agricultural peri-urban areas is of great significance to the regional ecological security, food security, and the sustainable development of urban integration. Based on remote sensing images, this study aims to provide a spatiotemporal dynamic assessment of the GI in Baisha District from 2007 to 2018 to improve the layout of GI and planning policies from the perspective of ecological security and food security. Research methods include landscape pattern indices, spatial autocorrelations, and grid analyses in this case study. The results suggest that ensuring the dominant position of farmland is critical to maintaining the composition and connectivity of the overall GI. The recreation, inheritance of farming culture, and ecosystem service functions of farmland should be improved to meet the growing needs of urban residents. GI includes the farmland, greenspace, and wetland on both sides of the Jialu River that should be retained and restored as much as possible to protect natural ecological processes. Simultaneously, construction of important urban facilities and residential areas in flooded areas should be banned. A part of the evenly distributed large greenspace patches should be moved to both sides of the Jialu River to increase the agglomeration effect of GI. Optimization measures in this case study also offer a perspective for other agricultural peri-urban areas that have experienced similar urbanization. [ABSTRACT FROM AUTHOR]

Moran's I statistic [Moran, (1950), 'Notes on Continuous Stochastic Phenomena', Biometrika, 37, 17-23] has been widely used to evaluate spatial autocorrelation. This paper is concerned with Moran's I-induced testing procedure in residual analysis. We begin with exploring the Moran's I statistic in both its original and extended forms analytically and numerically. We demonstrate that the magnitude of the statistic in general depends not only on the underlying correlation but also on certain heterogeneity in the individual observations. One should exercise caution when interpreting the outcome on correlation by the Moran's I-induced procedure. On the other hand, the effect on the Moran's I due to heterogeneity in the observations enables a regression model checking procedure with the residuals. This novel application of Moran's I is justified by simulation and illustrated by an analysis of wildfire records from Alberta, Canada. [ABSTRACT FROM AUTHOR]

The current study aimed at investigating cycles and the spatial autocorrelation pattern of anomalies of thunderstorms in Iran during different periods from 1961 to 2010. In this analysis, 50-year periods (1961–2010) of thunderstorm codes have been collected from 283 synoptic stations of Meteorological Organization of Iran. The study period has been divided into five different decades (1961–1970, 1971–1980, 1981–1990, 1991–2000, and 2001–2010). Spectral analysis and Moran's I were used to analyze cycles and the spatial autocorrelation pattern, respectively. Furthermore, in order to conduct the calculations, programming facilities of MATLAB have been explored. Finally, Surfer and GIS were employed to come up with the graphical depiction of the maps. The results showed that the maximum of positive anomalies mainly occurred in the northwestern and western parts of Iran due to their special topography, during all the five studied periods. On the other hand, the minimum of negative anomalies took place in central regions of the country because of lack of appropriate conditions (e.g., enough humidity). Moran's I spatial analysis further confirmed these findings as Moran's I depicts the positive and negative spatial autocorrelation patterns in line with negative and positive anomalies, respectively. However, in recent decades, this pattern has experienced a declining trend, especially in southern areas of Iran. The results of harmonic analysis indicated that mainly short-term and midterm cycles dominated Iran's thunderstorms. [ABSTRACT FROM AUTHOR]

The goal is to predict the final extent of the Ebola epidemic in West Africa, 2014–2015, well before its end. Our models are based on the nature of the reported data, and the social, medical, and technological conditions that existed in real time during the course of the epidemic. The spatial and temporal nature of Ebola transmission is considered. A modified, classical compartmental model is used to develop variations of Gompertz and logistic‐type predictive models. A map analysis strongly hints at the existence of an initial rural component of the transmission of the disease followed by an urban component. Cumulative and weekly data for the three countries illustrate how the disease intensified and spread over the 90 weeks of the epidemic. A spatial autocorrelation study shows the clustering locational changes of the disease over time. A cross‐correlation study gives credence to both a rural and an urban transmission pattern. Our early estimates of the toll of the disease, crude as they are, stand in marked contrast to some of the official estimates at the time, which greatly inflated the number of new cases. [ABSTRACT FROM AUTHOR]

Abstract: Species distribution models (SDMs) are widely used in ecology and related fields. They are frequently adopted to predict the expected occurrence (presence/absence) or abundance over large spatial scales, that is, to produce a species distribution map. Two issues that almost universally affect these models are measurement errors (especially imperfect detection) and residual spatial autocorrelation. We explored the effects of imperfect detection and autocorrelation in abundance models by simulating datasets which did or did not contain these two effects and analysing them with four different models that did or did not accommodate them. Specifically, we used a Poisson GLM as a baseline, an N‐mixture model accounting only for imperfect detection and two N‐mixture models that accounted for imperfect detection, but differed in their specification of spatial autocorrelation (CAR random effects vs. two‐dimensional splines). In a case study, we then applied these models to Common Redstart Phoenicurus phoenicurus data from the second Swiss Breeding Bird Atlas (1993–1996) and validated them using an independent monitoring dataset. We found that both imperfect detection and autocorrelation strongly affected the quality and the uncertainty of abundance maps, especially when they occurred together. Spatial N‐mixture models were well able to estimate the true abundance maps. Explicit modelling of measurement error and spatial autocorrelation can thus greatly improve the quality of SDMs and should not be ignored when producing large‐scale abundance or occurrence maps. [ABSTRACT FROM AUTHOR]

Choropleth map animation is widely used to show the development of spatial processes over time. Although animation congruently depicts change, the rapid succession of complex map scenes easily exceeds the human cognitive capacity, causing map users to miss important information. Hence, a reduction of the visual complexity of map animations is desirable. This article builds on research related to complexity reduction of static choropleth maps. It proposes value generalization of choropleth time-series data in space and time, by using a method that adapts to the degree of global spatiotemporal autocorrelation within the dataset. A combination with upstream algorithms for local outlier detection results in less complex map animations focusing on large-scale patterns while still preserving significant local deviations in space and time. An according software application allows for in-depth exploration of the spatial and temporal autocorrelation structures in time-series data and provides control over the whole process of generalization. [ABSTRACT FROM AUTHOR]

Rural in situ urbanization (RISU) indicates that rural settlements transfer into urban land within the original rural settlements area from the perspective of land use. A quantitative study from the perspective of land use can reveal trends of RISU and provide a basis for the promotion of the healthy and rational development of urbanization. Based on the investigation data of land use change of Beijing-Tianjin-Hebei in the period from 2003 through 2015, this paper analyses the spatio-temporal pattern of RISU through the elaboration of RISU and the construction of a rural in situ urbanization index (RISUI), and the driving mechanism of RISU is subsequently discussed. The results showed that RISU fluctuated during the study period in Beijing-Tianjin-Hebei, and its scale between different levels shifted from polarization to balance. RISU displayed a significant spatial agglomeration with the combined influence of siphon and spillover effects in Beijing and Tianjin, and its spatial scope expanded to the periphery with Beijing as the centre. The findings further revealed that RISU was developed under the comprehensive influence of population, economy, social public service and space. To realize the healthy and benign development of urbanization, the government should vigorously promote the RISU of small towns on the basis of resource environmental bearing capacity and the land construction foundation. Meanwhile, urban-rural construction land and the allocation of public service resources should also be arranged. [ABSTRACT FROM AUTHOR]

Dissolved oxygen is an essential controlling factor in the performance of facultative and maturation ponds since both take many advantages of algal photosynthetic oxygenation. The rate of this photosynthesis strongly depends on the time during the day and the location in a pond system, whose roles have been overlooked in previous guidelines of pond operation and maintenance (O&M). To elucidate these influences, a linear mixed effect model (LMM) was built on the data collected from three intensive sampling campaigns in a waste stabilization pond in Cuenca, Ecuador. Within two parallel lines of facultative and maturation ponds, nine locations were sampled at two depths in each pond. In general, the output of the mixed model indicated high spatial autocorrelations of data and wide spatiotemporal variations of the oxygen level among and within the ponds. Particularly, different ponds showed different patterns of oxygen dynamics, which were associated with many factors including flow behavior, sludge accumulation, algal distribution, influent fluctuation, and pond function. Moreover, a substantial temporal change in the oxygen level between day and night, from zero to above 20 mg O2·L-1, was observed. Algal photosynthetic activity appeared to be the main reason for these variations in the model, as it was facilitated by intensive solar radiation at high altitude. Since these diurnal and spatial patterns can supply a large amount of useful information on pond performance, insightful recommendations on dissolved oxygen (DO) monitoring and regulations were delivered. More importantly, as a mixed model showed high predictive performance, i.e., high goodness-of-fit (R² of 0.94), low values of mean absolute error, we recommended this advanced statistical technique as an effective tool for dealing with high autocorrelation of data in pond systems. [ABSTRACT FROM AUTHOR]

In this paper, we provide a spatiotemporal examination of German real-estate prices in 412 administrative districts. The price process is spatially autocorrelated and stationary over the considered period from 1995 to 2010. To quantify both spatial and temporal effects of the process, we apply different spatiotemporal models. These models are consistently estimated by the maximum likelihood approach, and they are compared with respect to the impact of shocks on fundamental quantities. Moreover, we interpret the economic importance of our results with respect to migrational issues. We show that the willingness of individuals to move or to commute decreased in Germany during the considered years. Furthermore, we include a detailed interpretation of the so-called ripple effect. [ABSTRACT FROM AUTHOR]

Air pollution episodes in China are frequent and a more comprehensive understanding of pollution sources and impacts is needed to design appropriate strategies and set emission reduction targets. This study analyzes PM 2.5 and PM 10 concentrations measured in 23 cities at 178 urban sites and at 23 corresponding “urban contrast” sites in China with the goals of understanding spatial and temporal trends and quantifying the regional component of PM pollution. The contrast sites, located an average of 29 km from cities in the upwind direction, are intended to represent “background” levels. Using daily measurements from April 2013 to March 2014, we assess compliance with air quality standards, PM 2.5 /PM 10 ratios and urban “increments,” defined as the increase in PM levels in the city compared to the contrast site. Spatial and temporal patterns at daily, monthly and annual levels are shown using distributions, correlations, spatial autocorrelation, and factor analyses. At the contrast sites, PM 2.5 and PM 10 concentrations averaged 56 ± 26 and 91 ± 44 μg m − 3 , respectively, and China's daily and annual average air quality standards were frequently exceeded. PM 2.5 and PM 10 concentrations in most cities exceeded levels at the corresponding contrast sites, but by an average of only 14 ± 14 and 26 ± 27 μg m − 3 , respectively. Seasonal changes in PM 2.5 and PM 10 concentrations and urban increments were striking, e.g., levels increased 2 to 3-fold in winter at several sites. The significance of exurban and regional sources of PM 2.5 is demonstrated by the small urban increments, the strong correlations across broad regions, and the correlation between daily levels at city and contrast sites. These sources will require control to achieve air quality goals, in particular, the PM 10 and PM 2.5 targets announced by the Chinese government in 2013. [ABSTRACT FROM AUTHOR]

This paper provides a geospatial analysis of regional inequality across provinces, prefectures and counties in China from 1997 to 2010 under a comparative spatiotemporal conceptual framework. Despite significant spatial agglomeration at all spatial scales, the extent of agglomeration shows an obviously downward trend from 2003 to 2006. Substantially stronger agglomeration of economic development is demonstrated at county scales. Local indicators of spatial autocorrelation (LISA) are employed to visualize the local spatial characteristics of economic growth. Four snapshots (in the years 1997, 2001, 2005, and 2010) of LISA indicate a dramatic north-shifting of hot spots of economic growth in response to the northward movement of foreign investors and spatial agglomeration besides institutional forces in China. Furthermore, local spatial agglomeration demonstrates a heterogeneous process: hot spots of economic development along the coast, cold spots in western China and no significant spatial clusters in central China. As the major carries of scale economies, metropolitan regions see decreasing internal agglomeration during this period with the exception of the Yangtze River Delta area, which shows a strong spatial spillover into its neighbourhood. Finally, LISA Markov and geovisualization methods are employed to predict the long-run properties of spatial distribution in multi-scalar China. The results show that downward co-movements of a county with its neighbours are more frequently encountered, perhaps resulting in the continuous concentration of poor areas in the long run. [ABSTRACT FROM AUTHOR]

Monitoring of fine particulate matter with diameter <2.5 μm (PM 2.5 ) started from 1999 in the US and even later in many other countries. The lack of historical PM 2.5 data limits epidemiological studies of long-term exposure of PM 2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM 2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM 10 ) and meteorological variables. Monitoring data of PM 10 , PM 2.5 , and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM 2.5 concentrations and the following predictor variables: spatiotemporal factors (PM 10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R 2 of 0.81, with PM 10 , meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM 2.5 concentrations, respectively. The model had a cross-validation R 2 of 0.83 for monthly PM 2.5 concentrations (N = 8170) and 0.79 for daily PM 2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R 2 of 0.61 for the daily model when PM 10 was replaced by total suspended particulate. Our model can robustly estimate historical PM 2.5 concentrations in California when PM 2.5 measurements were not available. [ABSTRACT FROM AUTHOR]

Extreme hydrometeorological conditions typically impact ecophysiological processes of terrestrial vegetation. Satellite based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in-situ observations. The question is if the density of existing ecological in-situ networks is sufficient for analyzing the impact of extreme events, or what are expected event detection rates of ecological in-situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small network, but also reveal a linear decay of detection probabilities towards smaller extreme events in log-log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the largest 8 (typically very large) extreme events; but only a ≥ 50 % chance of capturing the largest 39 events. These finding are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation issues and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in-situ networks can capture extreme events of a given size. Consistent with our theoretic considerations, we find that today's systematic network designs (i.e. NEON) reliably detects the largest extremes. But the extreme event detection rates are not higher than they would be achieved by randomly designed networks. Spatiotemporal expansions of ecological in-situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor their impacts in the terrestrial biosphere. [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]

• Constructed a GEE-based remote sensing ecological index. • Assessed the temporal and spatial changes of eco-environmental quality in Chang-Zhu-Tan metropolitan circle. • Proposed a framework for exploring the direct and indirect effects of natural and human factors on eco-environmental quality. • Urbanization threatens ecological environment. Accurately analyzing the spatial and temporal evolution of regional eco-environmental quality and exploring the response of eco-environmental quality to natural environment and human factors are essential to protect the regional ecological environment. This study analyzed the ecological environmental quality of Chang-Zhu-Tan metropolitan circle, central China from 2000 to 2020 based on Google Earth Engine (GEE) and Remote Sensing Ecological Index (RSEI). Subsequently, we introduced the Partial Least Squares Structural Equation Model (PLS-SEM) to explore the response of RSEI to the direct and indirect effects of natural and anthropogenic factors, and assessed the spatial autocorrelation of regional ecological environmental quality. The results showed that the mean value of RSEI in the study area was showing an increasing trend, with the lowest mean value of RSEI in 2010 (0.6508) and the highest mean value of RSEI in 2020 (0.6948). The eco-environmental quality level was mainly good and excellent, and the average area accounted for more than 70% of the total area, and showed an upward trend during 2000–2020. Climate, soil, terrain, and urbanization had different effects on the ecological environment, with climate, soil, and terrain had positive effects on the ecological environment, while urbanization had negative effects on it. The positive effect of climate on ecological environment was gradually weakened, while the negative effect of urbanization on ecological environment was gradually strengthened. Climate and urbanization weakened the positive effect of soil and terrain in different degrees. This study provides a method for rapid and accurate assessment of regional eco-environmental quality and exploration of complex interaction pathways of influencing factors, which is essential for balancing the relationship between regional development and nature. [ABSTRACT FROM AUTHOR]

This paper presents the outcomes of a study on thewater quality of Dubai Creek which aimed to assess its eutrophication status. Field water quality data from stations along the creek collected in 2012 and 2013 were used. Ordinary least squares (OLS) and spatial autocorrelation analyses were used as part of geographic information system (GIS)-based exploratory regression analysis to study the relationship between chlorophyll-a and nutrients, specifically total nitrogen and phosphate. Multiple logistic regression analysis was used to study the vulnerability of the creek to eutrophication. Results showed unique trends of spatiotemporal variability of chlorophyll-a and nutrients. OLS modeling showed high correlation between field and modeled chlorophyll-a values between Al Garhoud Bridge and Sanctuary stations, located about 2 km upstream and downstream of the Sewage Treatment Plant (STP) Outfall station. Furthermore, results showed the lower half of the creek was more vulnerable to eutrophication than the upper, which was believed to be due to the location of the STP station, poor flushing, shallow water depth, and irregular circulation patterns in the creek. Accordingly, this study recommends development of a mitigation plan in order to control the levels of nutrients in the creek. [ABSTRACT FROM AUTHOR]

Building on previous work, we derive an optimization model for a two-state stochastic environment and evaluate the fitnesses of five reproductive strategies across generations. To do this, we characterize spatiotemporal variation and define grain (=patch) size as the scale of fitness autocorrelation. Fitness functions of environmental condition are Gaussian. The strategies include two specialists on each of the environmental conditions; two generalists that each fare equally well under both conditions, but one (a conservative bet hedger) optimizes the shape of the fitness function; and a diversified bet hedger producing an optimal mix of the two specialists within individual broods. When the environment is primarily in one of the two states, the specialist on that state achieves the highest fitness. In the more interesting situation where the two environments are equally prevalent in the long term, with low-moderate environmental variation, a generalist strategy (that copes with both states well) does best. Higher variation favors diversified bet hedgers, or surprisingly, specialists, depending mainly on whether spatial or temporal variation predominates. These strategies reduce variance in fitness and optimize the distribution of offspring among patches differently: specialists by spreading offspring among many independently varying patches, while diversified bet hedgers put all offspring into a few patches or a single patch. We distinguish features consistent with strategies like diversified bet hedgers that spread risk in time from features linked to strategies like specialists that spread risk in space. Finally, we present testable hypotheses arising from this study and suggest directions for future work. [ABSTRACT FROM AUTHOR]

Background: The incidence of hand, foot, and mouth disease (HFMD) is extremely high, and has constituted a huge disease burden throughout Beijing in recent years. This study aimed to determine the spatiotemporal distribution and epidemic characteristics of HFMD.Methods: Descriptive statistics was used to analyze the data and estimate the epidemic peaks in 2009-2014. Space-time scanning detected spatiotemporal clusters and identified high-risk locations. Global and local Moran's I statistics were used to measure the spatial autocorrelation. Geocoding was performed in ArcGIS, based on the present address codes of the patients and the centroids of the towns. Maps were created in ArcGIS to show the geographic spread of HFMD.Results: In total, 220,451probable cases of HFMD were reported in Beijing between January 2009 and December 2014: 12,749 (5.78 %) were laboratory confirmed, and 35 (0.02 %) were fatal. The median age of reported cases was 3.12 years (interquartile range 1.96-4.39). Coxsackievirus A16 (CV-A16), enterovirus 71 (EV-A71), and other enteroviruses accounted for 39.31, 35.36, and 25.33 % of the 12,749 confirmed cases, respectively. Many more severe cases were caused by EV-A71 (χ (2) = 186.41, df = 1, P < 0.001) and other enteroviruses (χ (2) = 156.44, df = 1, P < 0.001) than by CV-A16. A large single distinct peak occurred between May and July each year. Spatiotemporal clusters of HFMD were identified in Beijing during 2009-2014. The most likely clusters were detected and tended to move from the southwest (Fengtai and Daxing) southeastwards to Daxing and Tongzhou in 2009-2014. The incidence of HFMD was not randomly distributed, but showed global and local spatial autocorrelations.Conclusions: There were obvious spatiotemporal clusters of HFMD in Beijing in 2009-2014. High-incidence areas mainly occurred at the junctions of urban and rural zones. More attention should be paid to the epidemiological and spatiotemporal characteristics of HFMD to establish new strategies for its control. Health issues should be especially promoted in kindergartens and at urban-rural junctions. [ABSTRACT FROM AUTHOR]

The present paper presents a technique to estimate spectra from experimental data sampled at discrete points in time. The interest of the current paper is to investigate spatiotemporal dependencies in supersonic turbulent flows like boundary layers and shock wave/turbulent boundary-layer interactions. The experimental data are measured by means of a dual-particle-image-velocimetry system that gives access to a certain amount of temporal information that is also in high-speed flows. The cross-correlation information in space made available by the dual-particle-image-velocimetry system can be used to reconstruct the autocorrelation for delay times ranging between the discrete points measured with the dual-particle-image-velocimetry system. This can be achieved for convection-dominated regions of the flow by moving into the convected frame of reference: similar to Taylor's hypothesis. The principle and suitability of the suggested technique is demonstrated in a combined numerical-experimental approach. The large-eddy simulations give access to fully time-resolved data and can thus be used to verify the accuracy of the autocorrelation and spectra estimated from experimental data. Also, the choice of time delays for the measurements can be optimized based on the numerical results. [ABSTRACT FROM AUTHOR]

A two-dimensional stochastic-diffusive energy balance model (EBM) formulated on a sphere by G. R. North et al. is explored and generalized. Instantaneous and frequency-dependent spatial autocorrelation functions and local temporal power spectral densities are computed for local sites and for spatially averaged surface temperature signals up to the global scale. On time scales up to the relaxation time scale given by the effective heat capacities of the ocean mixed layer and land surface, respectively, scaling features are obtained that are reminiscent of what can be derived from the observed temperature field. On longer time scales, however, the EBM predicts a transition to a white-noise scaling, which is not reflected in the observed records. A fractional generalization, which can be considered as a spatial generalization of the zero-dimensional, long-memory EBM of M. Rypdal and K. Rypdal, is proposed and explored. It is demonstrated that this generalized model describes qualitatively the main correlation characteristics of the temperature field reported in the literature and those derived herein from 500-yr-long control simulations of the NorESM Earth system model. A further generalization of the model, to include long-term persistence in the stochastic forcing, is also discussed. [ABSTRACT FROM AUTHOR]

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This paper develops a method to identify three-dimensional anisotropic spatiotemporal autocorrelation with an application to real estate markets. A large literature modeling spatiotemporal autocorrelation in house prices assumes that the spatiotemporal dependence structure is isotropic: a function of only distances between observations, and therefore the direction effect is dismissed. If the importance of direction is dismissed or understated, an estimation result would be biased and therefore less precise unless the distribution of observations is in rare case of being directional homogeneous. This paper thus proposes a local anisotropic spatiotemporal approach to improve estimation performance. The methodology is illustrated by using data on single-family house transactions in the San Francisco Bay Area. The empirical results suggest that the proposed three-dimensional anisotropic modeling technique can reduce both in-sample estimation and out-of-sample forecast errors. [ABSTRACT FROM AUTHOR]

In this research, we present a spatio-temporal analytical framework including spatio-temporal visualization (STV), space-time kernel density estimation (STKDE), and spatio-temporal-autocorrelation-analysis (STAA), to explore human mobility patterns and intra-urban communication dynamics. Experiments were conducted using large-scale detailed records of mobile phone calls in a city. The space-time path, time series graphs, vertical Bézier curves, STKDE, STAA, and related techniques in 3D GIS as well as statistical tests have been suggested for different spatio-temporal analysis tasks. We also investigated several statistical measures that extend the classic spatial association indices for spatio-temporal autocorrelation analysis. The spatial order of weighted matrix was found to have more significant effects than the temporal neighbors on influencing the autocorrelation strength of hourly phone calls. [ABSTRACT FROM PUBLISHER]

Estimating the long-term health impact of air pollution using an ecological spatio-temporal study design is a challenging task, due to the presence of residual spatio-temporal autocorrelation in the health counts after adjusting for the covariate effects. This autocorrelation is commonly modelled by a set of random effects represented by a Gaussian Markov random field (GMRF) prior distribution, as part of a hierarchical Bayesian model. However, GMRF models typically assume the random effects are globally smooth in space and time, and thus are likely to be collinear to any spatially and temporally smooth covariates such as air pollution. Such collinearity leads to poor estimation performance of the estimated fixed effects, and motivated by this epidemiological problem, this paper proposes new GMRF methodology to allow for localised spatio-temporal smoothing. This means random effects that are either geographically or temporally adjacent are allowed to be autocorrelated or conditionally independent, which allows more flexible autocorrelation structures to be represented. This increased flexibility results in improved fixed effects estimation compared with global smoothing models, which is evidenced by our simulation study. The methodology is then applied to the motivating study investigating the long-term effects of air pollution on respiratory ill health in Greater Glasgow, Scotland between 2007 and 2011. [ABSTRACT FROM AUTHOR]

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]

Understanding animal movement is a key challenge in ecology and conservation biology. Relocation data often represent a complex mixture of different movement behaviors, and reliably decomposing this mix into its component parts is an unresolved problem in movement ecology. Traditional approaches, such as composite random walk models, require that the timescales characterizing the movement are all similar to the usually arbitrary data-sampling rate. Movement behaviors such as long-distance searching and fine-scale foraging, however, are often intermixed but operate on vastly different spatial and temporal scales. An approach that integrates the full sweep of movement behaviors across scales is currently lacking. Here we show how the semivariance function (SVF) of a stochastic movement process can both identify multiple movement modes and solve the sampling rate problem. We express a broad range of continuous- space, continuous-time stochastic movement models in terms of their SVFs, connect them to relocation data via variogram regression, and compare them using standard model selection techniques. We illustrate our approach using Mongolian gazelle relocation data and show that gazelle movement is characterized by ballistic foraging movements on a 6-h timescale, fast diffusive searching with a 10-week timescale, and asymptotic diffusion over longer timescales. [ABSTRACT FROM AUTHOR]

Convergences of house prices have been studied for over three decades, but yet have been confirmed because of spatial heterogeneity and autocorrelations in house prices. A spatio-temporal approach was recently proposed to address the spatial and temporal issues related to house prices. However, most previous studies placed the focus on the spatial heterogeneity and autocorrelations from geographical locations, which neglected other spatial factors. In order to overcome this shortfall, this research argued a demographical distance, constructed by demographical structure and housing market scales, to investigate the house price convergences in Australian capital cities. The results confirmed the house price levels in Canberra, Brisbane and Perth converged to the house price level in Sydney. [ABSTRACT FROM PUBLISHER]

Abstract: Station and gridded precipitation data from all over the Indus basin were analyzed for the periods 1951 to 2010 and 1986 to 2010. The non-parametric Mann–Kendall trend test was applied to determine whether statistically significant changes in precipitation amounts occurred over time, in due consideration of autocorrelation in the data. In addition, linear regression trend lines were fitted to the precipitation series by the method of least squares. We also investigated whether the precipitation in 2010, the year of a devastating flood, was the highest, second highest or third highest recorded annual and monsoonal total in the periods under observation. The use of gridded precipitation data unveiled a spatial pattern of precipitation trends in the Indus River basin. For the shorter time period, we found stronger evidence of positive trends in monsoonal precipitation time series compared to annual precipitation. Significant positive precipitation trends were primarily detected in the mountains: in the northwest (Hindu Kush and Sulaiman Mountains) and in the east (Himalayas) of the Upper Reaches of the Indus River. Negative precipitation trends, most of which are not significant, were detected in the northeast of the Upper Reaches (Karakorum and Transhimalaya) and in the lowlands. Compared to previous years, annual and especially monsoonal precipitation totals in 2010 were extremely high in the Himalayas, the Hindu Kush, the Sulaiman Mountains, and in the central lowlands. Strong monsoon precipitation in the arid high mountainous regions, not used to these amounts of precipitation, played a major role in the 2010 floods in Pakistan. [Copyright &y& Elsevier]

Objectives To identify the meteorological drivers of dengue vector density and determine high- and low-risk transmission zones for dengue prevention and control in Cairns, Australia. Methods Weekly adult female Ae. aegypti data were obtained from 79 double sticky ovitraps ( SOs) located in Cairns for the period September 2007-May 2012. Maximum temperature, total rainfall and average relative humidity data were obtained from the Australian Bureau of Meteorology for the study period. Time series-distributed lag nonlinear models were used to assess the relationship between meteorological variables and vector density. Spatial autocorrelation was assessed via semivariography, and ordinary kriging was undertaken to predict vector density in Cairns. Results Ae. aegypti density was associated with temperature and rainfall. However, these relationships differed between short (0-6 weeks) and long (0-30 weeks) lag periods. Semivariograms showed that vector distributions were spatially autocorrelated in September 2007-May 2008 and January 2009-May 2009, and vector density maps identified high transmission zones in the most populated parts of Cairns city, as well as Machans Beach. Conclusion Spatiotemporal patterns of Ae. aegypti in Cairns are complex, showing spatial autocorrelation and associations with temperature and rainfall. Sticky ovitraps should be placed no more than 1.2 km apart to ensure entomological coverage and efficient use of resources. Vector density maps provide evidence for the targeting of prevention and control activities. Further research is needed to explore the possibility of developing an early warning system of dengue based on meteorological and environmental factors. [ABSTRACT FROM AUTHOR]

The computation of Moran's I index and his statistics test relies mainly on an exogenous specification of a spatial weights matrix. However, the exogenous weights matrix is usually developed in a strictly spatial context, even when data are collected over time. This paper develops a spatio-temporal weights matrix and uses the new definition to evaluate spatial dependence using Moran's I index applied to real estate data for Québec City from 1986 to 1996. The results are compared with the original Moran's I index using a strictly spatial weights matrix specification based on Euclidian distance or contiguity. The findings suggest that ignoring the temporal dimension could lead to misinterpretation of the 'real' measure of spatial dependence over time. However, the time dimension cannot explain the total spatial autocorrelation since the Moran's I index is still significant even when adjusting for time consideration. The differences between the estimated indices and statistics depend on the structure of the spatial and the temporal weights matrices that are used to construct the complete spatio-temporal weights matrix. [ABSTRACT FROM AUTHOR]

Abstract: As more and more real time spatio-temporal datasets become available at increasing spatial and temporal resolutions, the provision of high quality, predictive information about spatio-temporal processes becomes an increasingly feasible goal. However, many sensor networks that collect spatio-temporal information are prone to failure, resulting in missing data. To complicate matters, the missing data is often not missing at random, and is characterised by long periods where no data is observed. The performance of traditional univariate forecasting methods such as ARIMA models decreases with the length of the missing data period because they do not have access to local temporal information. However, if spatio-temporal autocorrelation is present in a space–time series then spatio-temporal approaches have the potential to offer better forecasts. In this paper, a non-parametric spatio-temporal kernel regression model is developed to forecast the future unit journey time values of road links in central London, UK, under the assumption of sensor malfunction. Only the current traffic patterns of the upstream and downstream neighbouring links are used to inform the forecasts. The model performance is compared with another form of non-parametric regression, K-nearest neighbours, which is also effective in forecasting under missing data. The methods show promising forecasting performance, particularly in periods of high congestion. [Copyright &y& Elsevier]

The article responds to comments on the article "Extending Moran’s Index for Measuring Spatiotemporal Clustering of Geographic Events. It mentions an issue to consider for any further research in the realm of spatiotemporal autocorrelation is the very different ways events are related to each other spatially and temporally. It states that the study would stimulate others to further devise spatiotemporal analytics to benefit Geographic Information System (GIS) community.

The article comments on the article "Extending Moran’s Index for Measuring Spatiotemporal Clustering of Geographic Events" by J. Lee and S. Li. It formalizes the extended Moran coefficient (MC) space-time weights matrix for the two cases of contemporaneous and spatially lagged autocorrelation. It concludes that the Lee and Li paper should have had a much more comprehensive contextualization.