Spatial autocorrelation

Watershed land cover is widely used as a predictor of stream-ecosystem condition. However, numerous spatial factors can confound the interpretation of correlative analyses between land cover and stream indicators, particularly at broad spatial scales. We used a stream-monitoring data set collected from the Coastal Plain of Maryland, USA to address analytical challenges presented by (1) collinearity of land-cover class percentages, (2) spatial autocorrelation of land cover and stream data, (3) intercorrelations among and spatial autocorrelation within abiotic intermediaries that link land cover to stream biota, and (4) spatial arrangement of land cover within watersheds. We focused on two commonly measured stream indicators, nitrate-nitrogen (NO 3 -N) and macroinvertebrate assemblages, to evaluate how different spatial considerations may influence results. Partial correlation analysis of land-cover percentages revealed that simple correlations described relationships that could not be separated from the effects of other land-cover classes or relationships that changed substantially when the influences of other land-cover classes were taken into account. Partial Mantel tests showed that all land-cover percentages were spatially autocorrelated, and this spatial phenomenon accounted for much of the variation in macroinvertebrate assemblages that could naively be attributed to certain classes (e.g., percentage cropland). We extended our use of partial Mantel tests into a path-analytical framework and identified several independent pathways between percentage developed land and instream measurements after factoring out spatial autocorrelation and other confounding variables; however, under these conditions, percentage cropland was only linked to nitrate-N. Further analyses revealed that spatial arrangement of land cover, as measured by areal buffers and distance weighting, influenced the amount of developed land, resulting in a threshold change in macroinvertebrate-assemblage composition. Moreover, distanceweighted percentage cropland improved predictions of stream nitrate-N concentrations in small watersheds, but not in medium or large ones. Collectively, this series of analyses clarified the magnitude and critical scales of effects of different land-cover classes on Coastal Plain stream ecosystems and may serve as an analytical framework for other studies. Our results suggest that greater emphasis should be placed on these important spatial considerations; otherwise, we risk obscuring the relationships between watershed land cover and the condition of stream ecosystems.

This paper employs individual firm data in order to check the existence of industryspatial effects alongside other microeconomic determinants of R&D investment. Spatial proximity is defined by a measure of firms' industry distance based on trade intensity between sectors. The spatial model specified here refers to the combined spatial autoregressive model with autoregressive disturbances (SARAR). In modelling the outcome for each location as dependent on a weighted average of the outcomes of other locations, outcomes are determined simultaneously. The results of the spatial two stage least square estimation suggest that in their R&D decision firms benefit from spillovers originating from neighbouring industries.

Summary Biogeographical models of species’ distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). Most models still largely ignore or inappropriately take into account important features of species’ distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species’ predictions in a bottom‐up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) inco...

The spatiotemporal correlation in disease incidence rates resulting from the spatial arrangement of neighboring geographical units is often conceptualized through constructing contiguity-based spatial weights. However, these weights specifications are not meant for capturing the spatial relationships across multiple spatial scales and disjoint spatial units. Modifications to existing spatial weights specifications are highly required. Hence, this study used supra-adjacency matrix in network science to analyze the spatial autocorrelation of COVID-19 incidence rates at Sarawak's district and Malaysia's state levels. Flight routes between these regions were embedded as spatial interaction submatrix to represent their interlayer adjacency. Segmentation of data based on respective Sarawak's and Malaysia's daily cases was conducted to investigate the consistency in the spatial autocorrelation and the type of local clustering. When global spatial autocorrelations at state level were high, both the Sarawak districts' and Malaysia states' incidence rates became more spatially related with the inclusion of spatial interaction. Several districts, including Sibu, in Sarawak were now classified as high-high cluster with supra-adjacency weights. These high-high clusters can only be discovered with second-order contiguity weights in previous literature. The numbers of significant spatial clusters and outliers in district level were substantially greater than its state-level counterpart. This research provides evidence on how spatial dependencies of disease incidence rates between two spatial aggregation levels and geographically disjoint regions can be quantified using supra-adjacency matrix for disease surveillance. Capturing inter-layer spatial dependencies allows for more targeted interventions such as optimizing vaccine distribution and planning mobility restrictions during pandemics.

Veterinary education in India plays a critical role in strengthening livestock development, rural service delivery, and One Health imperatives. Despite the steady expansion of veterinary colleges, their distribution remains uneven, often misaligned with livestock density and human resource needs. This paper employs a Composite Need Gap Index (NGI) to assess state-wise disparities in veterinary education capacity. The NGI integrates five indicators-Veterinarian Availability Ratio (VAR), Institution Coverage Ratio (ICR), Seat Intensity (SI), College Intensity (CI), and Seats per Node (SPN)-to generate a composite ranking of states. Results reveal that eastern and central states such as West Bengal, Jharkhand, and Bihar face acute shortages, while others require selective consolidation and specialization. Findings underscore the importance of evidence-based planning in line with the National Education Policy (NEP 2020). Embedding NGI into policy, accreditation, and funding frameworks can ensure equitable veterinary colleges distribution, enhance educational outcomes, and contribute to sustainable rural development.

†max planck institute for psycholingusitics, nijmegen ‡leverhulme centre for human evolutionary studies, cambridge This paper builds on a previous work in which we attempted to retrieve a phylogenetic signal using abstract structural features alone, as opposed to cognate sets, drawn from a sample of Island Melanesian languages, both Oceanic (Austronesian) and (non-Austronesian) Papuan (Science 2005[309]: 2072-75 ). Here we clarify a number of misunderstandings of this approach, referring particularly to the critique by Mark Donohue and Simon Musgrave (in this same issue of Oceanic Linguistics), in which they fail to appreciate the statistical principles underlying computational phylogenetic methods. We also present new analyses that provide stronger evidence supporting the hypotheses put forward in our original paper: a reanalysis using Bayesian phylogenetic inference demonstrates the robustness of the data and methods, and provides a substantial improvement over the parsimony method used in our earlier paper. We further demonstrate, using the technique of spatial autocorrelation, that neither proximity nor Oceanic contact can be a major determinant of the pattern of structural variation of the Papuan languages, and thus that the phylogenetic relatedness of the Papuan languages remains a serious hypothesis. 1. This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek, by means of a Programma grant (Dunn and Reesink) and a Vidi grant (Terrill); support from the Max Planck Institute for Psycholinguistics is also gratefully acknowledged. We would like to thank Åshild Naess for updating her typological questionnaire for this paper on the basis of her Äiwoo fieldwork. We thank Russell Gray, Simon Greenhill, Fiona Jordan, Asifa Majid, and Doug Marmion for comments on earlier versions of this paper. We also appreciate the fact that Mark Donohue and Simon Musgrave made their paper available to us for comment prior to publication.

ReSUMO Following the enlargement process of the European Union that occurred from the 1980s onward, regional policies have been reinforced decreasing the disparities of GDP per capita among the European Union-15 Member States. However, this convergence trend is not observed at the intra-national level in which regional economic disparities are increasing. In the present paper we proceed with an empirical exercise of convergence of continental Portuguese municipalities between 1998 and 2009. Differently from the common literature, we use a spatial econometric approach as a means to embody the role of space and geography, considering both spatial dependence and spatial heterogeneity. The first conclusion points to the existence of a strong beta-convergence process during this period. The results also sustain the existence of two convergence clubs, a former composed by the inland municipalities with higher convergence velocity and a latter formed by the coastal municipalities with lowe...

El propósito de este artículo es analizar la difusión y dinámica temporal de la dependencia espacial. Con este objetivo, en primer lugar se diferenciará entre dependencia espacial contemporánea y no contemporánea, discutiendo distintas especificaciones de modelos espaciales de regresión que recojan ambos tipos de dependencia espacial. En segundo lugar, extenderemos estas especificaciones a modelos SUR espaciales que incluyan retardos tanto espaciales como espacio temporales y que recojan la dinámica temporal de la dependencia espacial mediante una única estructura. Los modelos desarrollados se aplicarán a la estimación de la Renta Familiar disponible en las provincias españolas.

The long-tailed pygmy rice rat Oligoryzomys longicaudatus (Rodentia, Cricetidae) experiences marked population density fluctuations. This species is the major reservoir of the Andes orthohantavirus associated with the hantavirus pulmonary syndrome in southern Argentina, and information on dispersal patterns at different population densities, or differences in dispersal between the sexes during those periods, could contribute to understanding risk of viral transmission among individuals. We examined the spatial genetic structure of O. longicaudatus at a local scale to elucidate how variation in population density influences dispersal patterns. Effective dispersal levels were estimated through spatial genetic autocorrelation (SGA) analyses in periods of different density, and for females and males separately, within an area of 510 × 180 m, using seven microsatellite loci. In autumn (high-intermediate and intermediate densities), SGA was primarily determined by females, which show phil...

Our aim was to describe vegetation heterogeneity at a regional scale in northeastern Patagonia and to identify the environmental variables associated to it. The study area encompasses 13 144 km 2 and is characterized by a mixture of species typical of Patagonian steppes and Monte Desert. We performed 48 vegetation relevés, which were randomly assigned to a training set and to a validation set (32 and 16 relevés, respectively). Training set was subjected to cluster analysis, which allowed the identification of two plant communities one related to Patagonian steppes and another to the Monte desert. We derived 3 attributes of the seasonal curve of the NDVI as indicators of ecosystem function: the seasonal amplitude (SA), the date of the maximum (DOM), and the large seasonal integral (LSI). We explored the relationship between NDVI attributes and communities by classification tree analysis. LSI was the strongest predictor among NDVI attributes, separating both communities without misclassification errors. Patagonian steppes occupy areas with higher LSI. The partial RDA analysis explained 38.1% of total data variation, of which 16.5% was ascribed to environment, 7.9% to space, and 13.7% to spatial component of environment. Patagonian steppes are closer to the coast, in areas exhibiting higher annual precipitation and lower annual temperature range than Monte deserts. Our results indicate the occurrence of two plant communities in the study area and highlight the significance of climatic variables to explain their spatial distribution. As most scenarios of future climate predict greater annual thermal amplitude in the study area, the limit between both communities could be displaced eastward.

Context Wildfires play a crucial role in maintaining ecological and societal functions of North American boreal forests. Because of their contagious way of spreading, using statistical methods dealing with spatial autocorrelation has become a major challenge in fire studies analyzing how environmental factors affect their spatial variability. Objectives We aimed to demonstrate the performance of a spatially explicit method accounting for spatial autocorrelation in burn rates modelling, and to use this method to determine the relative contribution of climate, physical environment and vegetation to the spatial variability of burn rates between 1972 and 2015. Methods Using a 482,000 km 2 territory located in the coniferous boreal forest of eastern Canada, we built and compared burn rates models with and without accounting for spatial autocorrelation. The relative contribution of climate, physical environment and vegetation to the burn rates variability was identified with variance partitioning. Results Accounting for spatial autocorrelation improved the models' performance by a factor of 1.5. Our method allowed the unadulterated extraction of the contribution of climate, physical environment and vegetation to the spatial variability of burn rates. This contribution was similar for the three groups of factors. The spatial autocorrelation extent was linked to the fire size distribution. Conclusions Accounting for spatial autocorrelation can highly improve models and avoids biased results and misinterpretation. Considering climate, physical environment and vegetation altogether is essential, especially when attempting to predict future area burned. In addition to the direct effect of climate, changes in vegetation could have important impacts on future burn rates.

Cities provide unique opportunities for integrating humans into ecology. Using data from a socio-ecological inventory of metropolitan Phoenix, Arizona, we explore the contribution of human-related variables to explaining observed variation in soil nitrate-N (NO 3 --N) and total carbon (C) concentrations across the city, agricultural fields, surrounding desert, and mixed regions. Conventional modeling approaches in such a setting would lead to examination of spatial relationships over the entire study area or on subsets of the data independently. However, the spatial relationships for NO 3 --N and C may be different in each of these regions. Here we estimate the correlation coefficients for influential variables toward soil NO 3 --N and C across the entire region, while at the same time accounting for potentially differing spatial patterns in each of these regions. Soil NO 3 --N shows markedly greater spatial autocorrelation in the desert regions, while the soil C shows varying amounts of spatial relationships in the different regions.

Objective: To present a geographic information systems (GIS) method for exploring the spatial pattern of injuries and to demonstrate the utility of using this method in conjunction with classic ecological models of injury patterns. Design: Profiles of patients' socioeconomic status (SES) were constructed by linking their postal code of residence to the census dissemination area that encompassed its location. Data were then integrated into a GIS, enabling the analysis of neighborhood contiguity and SES on incidence of injury. Setting: Data for this analysis (2001-2006) were obtained from the British Columbia Trauma Registry. Neighborhood SES was calculated using the Vancouver Area Neighborhood Deprivation Index. Spatial analysis was conducted using a join-count spatial autocorrelation algorithm. Patients: Male and female patients over the age of 18 and hospitalized from severe injury (Injury Severity Score .12) resulting from an assault or intentional self-harm and included in the British Columbia Trauma Registry were analyzed. Results: Male patients injured by assault and who resided in adjoining census areas were observed 1.3 to 5 times more often than would be expected under a random spatial pattern. Adjoining neighborhood clustering was less visible for residential patterns of patients hospitalized with injuries sustained from self-harm. A social gradient in assault injury rates existed separately for men and neighborhood SES, but less than would be expected when stratified by age, gender, and neighborhood. No social gradient between intentional injury from self-harm and neighborhood SES was observed. Conclusions: This study demonstrates the added utility of integrating GIS technology into injury prevention research. Crucial information on the associated social and environmental influences of intentional injury patterns may be under-recognized if a spatial analysis is not also conducted. The join-count spatial autocorrelation is an ideal approach for investigating the interconnectedness of injury patterns that are rare and occur in only a small percentage of the population.

Traffic accident prediction is crucial to preventive measures against accidents and effective traffic management. Identifying hotspots can facilitate the selection of the most critical survey points to note the contributing features. In this research, an effort has been made to identify hotspots and predict traffic accident occurrences in an urban area. Accident data was obtained from the Rescue 1122 Emergency Services of Faisalabad, and hotspots were identified using Moran's I in ArcGIS. Results showed that most hotspots were located around the General Transport Stand (GTS) area due to the maximum number of road users. The temporal investigations showed that the accident occurrence was significant from 1 to 2 p.m. The identified hotspots were further investigated by conducting a field survey. Essential features such as road geometric features, road furniture, and traffic data were used for developing Machine Learning Algorithms for accident prediction. Using Computer Vision, traffic data was extracted from recorded videos. Random forest, linear regression, and Decision tree algorithms were developed using Python in the Jupyter Notebook environment. The decision tree algorithm showed a maximum accuracy of 84.4%. The analysis of contributing factors revealed that road measurements had the maximum effect on accident occurrence.

hilechanised forest harvest operations are a significant source of soil corrlpacrlon for which intensive rillage is prescribed to a!leviate soil compaction and ensure successful regerreration of planted pine trzes. Soil strength is a poiential indicaror of comi;zction status of a harvest tract due tc its sensitivity and the ease of data collection with a cone penetrometer, but estimates may vary widely throughout a harvest tract. A IoSlolIy pine (Pinus raeda L.) plantation that had been harvested in winter 1998 was studied to assess soil strength and its spatial qualities through the measurement of soil strengrh on tw.o sampling scales, and to identify areas ofthe harvest tract where tillage operations would be beneficial. Cone index measurements indicated a high degree of variability in soil strength regardless of the scale of measurement, and high soil strength !evels throughout the soil profile. Spatial dependence was high in thesurface and immediate sabsurface soil layers ofeach point grid system and was attributed to the impact oftraffic or topographic position on soil strength. Spatial dependence was not detectable for the lowest subsoil layers of the large-scale sampling scheme. The -short ranges of spatial correlation associated with cone index estimations and the presence o f compacted subsoil layers throughout the study area suggested the need to perform tillage throughout the harvest tract to ensure alleviation of subsoil compaction for adequate regeneration.

Instead of defining a priori target populations, sociodemographic variables and sector of residence are combined in order to identify geographically meaningful clusters of households on the French Riviera. A Bayesian classifier produces uncertainty-based clusters whose uncertain knowledge is represented through an interactive geo-dataviz solution. Cluster characteristics, sociodemographic content of geographic sectors, socio-spatial contrasts between neighboring sectors and proximity in variable space are explored taking into consideration the uncertain character of the available knowledge.

La llegada de la modernidad, los diferentes sistemas económicos, los estándares de vida y hasta los patrones de consumo difundidos, han generado diferencias en las distintas sociedades, situación que se manifiesta de manera preponderante en las áreas urbanas, en otras palabras, las ciudades actuales presentan diversas desigualdades en el orden social, económico y territorial, entre quienes la habitan, por lo que podemos encontrar en una misma urbe áreas que demuestran y expresan la riqueza y la pobreza en diferentes grados, y patrones de distribución. La noción de pobreza se aplica no solo a personas u hogares, sino también a lugares o zonas geográficas. Si bien los estudios sobre pobreza abordan principalmente a la población y a los hogares, esta tiene una dimensión no siempre considerada y es su dimensión espacial. En este escenario, en la que se encuentran cientos y miles de personas en cada contexto, la dimensión espacial, resulta difícil de identificar y de delimitar, por lo que las técnicas multivariadas pueden resultar complejas para la determinación de las áreas vulnerables. Es por esto que, es habitual la utilización de índices o indicadores estadísticos, en los estudios generalizados sobre esta temática, aunque los mismos dejan de lado la cuestión espacial . Este trabajo tiene por objetivo explorar la identificación de áreas urbanas con rasgos de vulnerabilidad social y económica aplicando un análisis multicriterio en base a información correspondiente al censo de población, hogares y viviendas del año 2010, en la ciudad de Formosa, promoviendo métodos estadísticos-espaciales para la exploración. La pobreza, a la que aludiremos como situación socioeconómicamente vulnerable, es un término polisémico que se atribuye a diversas situaciones como la carencia, la insuficiencia de recursos, la privación o la imposibilidad de cubrir determinadas necesidades consideradas básicas, por lo que se alude a un concepto complejo. En cuanto a esto último, la Dirección Provincial de Estadística de la provincia de Buenos Aires (2010), menciona que: a pesar del carácter multidimensional y complejo que puede tener, se dice que la pobreza es una condición en la cual una o más personas tienen un nivel de bienestar inferior al mínimo necesario para la sobrevivencia. (p.31) Replicando la idea de Sen (1992) sobre la pobreza, esta puede verse como un problema de subsistencia o de desigualdad, aunque no exista una correspondencia unívoca, como una cuestión de privación relativa o absoluta. Entonces, de manera general se puede definir a la pobreza en ambos términos, en cuanto al primero, las personas tenderían a percibir su propia situación en función del bienestar de los demás, por lo tanto, dependerá del grupo social de referencia, ya que difieren de una sociedad a otra, como a través del tiempo, entonces estas están determinadas culturalmente; mientras que el enfoque absoluto, enfatiza que, si bien la sociedad determina ciertas necesidades, no puede negarse la existencia de un núcleo básico que

The African coffee white stem borer (CWSB) Monochamus leuconotus is a destructive pest of Arabica coffee in Africa. Documentation on outbreaks, spatiotemporal development and the relationship with different environmental conditions and coffee production system is limited. To underpin effective control measures, we studied aspects of local and regional pest drivers in Eastern Uganda. At the local scale, we (i) characterized the temporal development of CWSB and explored associations with environmental and shade‐related indicators. During two growing seasons and on 84 coffee plots, we recorded CWSB incidence/infestation and microclimate on an altitudinal gradient and different shading systems. The bimodal rainfall, altitude and shade affected CWSB development through their effect on minimum temperature. At the landscape level, we (ii) analyzed the spatial pattern of CWSB. Data on CWSB were collected on 180 plots. Pest incidence showed a spatial arrangement varying by districts. A possi...

The genetic structure of the clonally reproducing Sudden Oak Death (SOD) pathogen in California was investigated using seven variable microsatellites. A total of 35 multilocus genotypes were identified among 292 samples representative of populations from 14 forest sites and of the nursery trade. AMOVA indicated significant genetic variability both within (44.34%) and among populations (55.66%). Spatial autocorrelation analyses indicated that Moran's index of similarity reached a minimum of 0.1 at 350 m, increased to 0.4 at 1500 m and then decreased to zero at 10 km. These results suggest a bimodal pattern of spread, with medium range dispersal (1500-10 000 m) putatively attributed to the presence of strong winds. Lack of genetic structure was identified for three groups of populations. One group notably included the nurseries' population and two forest populations, both linked to early reports of the pathogen. A neighbour-joining analysis based on pairwise Φ ST values indicated that the clade inclusive of the nurseries' populations is basal to all California populations. A network analysis identified three common genotypes as the likely founders of the California infestation and proposes a stepwise model for local evolution of novel genotypes. This was supported by the identification in the same locations of novel genotypes and of their 1-or 2-step parents. We hypothesize that the few undifferentiated population groups indicate historical human spread of the pathogen, while the general presence of genetically structured populations indicates that new infestations are currently generated by rare medium or long-range natural movement of the pathogen, followed by local generation of new genotypes.

We analyzed the community of ectomycorrhizal fungi of an autochthonous white poplar (Populus alba L.) stand in the Kovilj-Petrovaradin marshes (Serbia), and examined its seasonal dynamics. Ectomycorrhizal types were identified by combining morphological and anatomical descriptions with molecular methods (sequencing of ITS region of ribosomal DNA). In two seasons, 20 ectomycorrhizal types were recorded, from which 11 types were identified to the species level, six were determined to the genus level, two types were determined to the family level and one type remained unidentified. Number of ectomycorrhizal types, number of fine roots, percentage of vital mycorrhizal roots, diversity indexes and abundance of exploration types did not differ significantly between autumn and spring. During both seasons, the most abundant types were: Entoloma sp., Tuber maculatum, Cenococcum geophilum, Tuber rufum and Peziza sp. Due to the high variation of the ectomycorrhizal types-based Shannon-Weaver diversity index in poplar stands, and the fact that poplars form dual mycorrhizal association, this index is not recommended as a reliable index for bioindication in poplar.

Summary Partially, clonal species are common in the Tree of Life. And yet, population genetic models still mostly focus on the extremes: strictly sexual versus purely asexual reproduction. Here, we present an R package built upon genclone software including new functions and several improvements. The RClone package includes functions to handle clonal data sets, allowing (i) checking for data set reliability to discriminate multilocus genotypes (MLGs), (ii) ascertainment of MLG and semi‐automatic determination of clonal lineages (MLL), (iii) genotypic richness and evenness indices calculation based on MLGs or MLLs and (iv) describing several spatial components of clonality. RClone allows the one‐shot analysis of multipopulation data sets without size limitation, suitable for data sets now increasingly produced through next‐generation sequencing. A major improvement compared to existing software is the ability to determine the threshold to cluster similar MLGs into MLLs, based on impl...

1. Partially, clonal species are common in the Tree of Life. And yet, population genetic models still mostly focus on the extremes: strictly sexual versus purely asexual reproduction. Here, we present an R package built upon GENCLONE software including new functions and several improvements. 2. The RClone package includes functions to handle clonal data sets, allowing (i) checking for data set reliability to discriminate multilocus genotypes (MLGs), (ii) ascertainment of MLG and semiautomatic determination of clonal lineages (MLL), (iii) genotypic richness and evenness indices calculation based on MLGs or MLLs and (iv) describing several spatial components of clonality. RClone allows the one-shot analysis of multipopulation data sets without size limitation, suitable for data sets now increasingly produced through next-generation sequencing. 3. A major improvement compared to existing software is the ability to determine the threshold to cluster similar MLGs into MLLs, based on implemented simulations of sexual events. Several functions allow data importation, conversion and exportation with adegenet, Genetix or Arlequin. 4. RClone is provided with two vignettes to handle analysis on one (RClone_quickmanual) or several populations (RClone_qmsevpops).

The current focus is on the overall pattern of seed storage present in the fragments of the soil of piedmont and alluvial landscapes of the environment. The present study predicted the seed banks of both soils of alluvial and piedmont zones in different ecological conditions and evaluate the potential of seeds in the restoration of both environments. The composition of the seed bank of soil is mainly affected by the alluvial environment and the structure of cleared area shows that more species of germinating annual grasses and growable seeds with the higher total number. Extant vegetation structures have an important role in the diversity of soil seed reservoirs, whose composition corresponded with the openness of the site. When in situ soil seed bank is recruited, it helps to restore only some components of the plant community in an alluvial environment. In our current research, it was confirmed that seed richness was higher in number at lower elevation (alluvial) than that at high elevation (piedmont). Seed richness showed a significant negative correlation with anions, cations, while significantly positive with altitude that suggests the richness pattern of the overall seed bank of the area is influenced by various environmental factors.

Econometricians have begun to devote more attention to spatial interactions when carrying out applied econometric studies. In part, this is motivated by an explicit focus on spatial interactions in policy formulation or market behavior, but it may also reflect concern about the role of omitted variables that are or may be spatially correlated. The classic models of spatial autocorrelation or

This article presents the results of mapping the flow routes of surface runoff in an area characterized by profound changes in relief caused by anthropic interventions. We chose the Tucum stream upper basin (Sao Pedro, São Paulo -Brazil) as a case study because it is marked by the advance of urbanization on land with strong susceptibility to erosion, with the presence of linear erosive features (ravines and gullies) and a complex history of interventions seeking to control them. We obtained primary data from a topographic survey in the field and carried out operations to develop a Hydrologically Consistent Digital Elevation Model (HCDEM) in the ArcGIS 10.1 program. Then, the Concentrated Flow Map of the area was elaborated from the modeling using the Multiple Flow algorithm in the ArcView 3.2 program. The results showed that topographic features of detail, especially technogenic terraces, have exercised marked control over the flow routes of surface runoff, contributing to generating erosive advance vectors of the ravines and gullies.

Utilizando datos agregados por vecindario para 2010 en el Área Metropolitana de Monterrey (AMM), México, este trabajo investiga el efecto de las características del entorno en la delincuencia. Se corrobora la existencia de autocorrelación espacial en los datos, es decir, se encuentra que vecindarios con altos índices de delincuencia inciden positivamente en los índices de delincuencia de los vecindarios cercanos. Después de controlar por el sesgo causado por la autocorrelación espacial y la censura de los datos, se encuentra evidencia de que la probabilidad de ser víctima de un delito y la probabilidad de que alguien se convierta en delincuente está positivamente relacionada con el desempleo, el porcentaje de jóvenes varones y la existencia de centros comerciales en el vecindario.

Purpose. In large cities creative industries tend to geographically concentrate. The purpose of this paper is to empirically test the hypothesis that this geographic concentration derives from the benefits on the innovative urban milieu.
Design/methodology/approach. A spatial regression model is estimated using as dependent variable the number of creative firms per census block in the Metropolitan city of Rome. Empirical results show that the estimated coefficient of the spatially lagged dependent variable is significantly positive, indicating that the number of creative firms in a census block is influenced by the number of creative firms in neighboring blocks. This enables to explore the conditions accounting for the concentration of creative industries.
Practical and Social implications. This paper suggests that knowledge externalities influencing the creative firm’s spatial distribution can be interpreted, in an urban scale, in terms of local spatial spillovers, which take the form of spatial dependence. The empirical analysis revealed the existence of a spontaneous creative geography within the metropolitan city of Rome, which is important to further analyse and interpret, if we want to look at the creative clustering from a policy perspective. Creative clusters represent a good opportunity for local governments to catch up with innovation and entrepreneurship policies therefore they need evidence about the existence and the potential role of creative clusters, if they are to encouraging creative industrial growth in particular places.
Originality of the study. The paper aims to fill the gap between the regional and the urban scale of analysis in empirical studies on creative industries.

Habitat network connectivity influences colonization dynamics, species invasions, and biodiversity patterns. Recent theoretical work suggests dendritic networks, such as those found in rivers, alter expectations regarding colonization and dispersal dynamics compared with other network types. As many native and non-native species are spreading along river networks, this may have important ecological implications. However, experimental studies testing the effects of network structure on colonization and diversity patterns are scarce. Up to now, experimental studies have only considered networks where sites are connected with small corridors, or dispersal was experimentally controlled, which eliminates possible effects of species interactions on colonization dynamics. Here, we tested the effect of network connectivity and species interactions on colonization dynamics using continuous linear and dendritic (i.e., river-like) networks, which allow for active dispersal. We used a set of six protist species and one rotifer species in linear and dendritic microcosm networks. At the start of the experiment, we introduced species, either singularly or as a community within the networks. Species subsequently actively colonized the networks. We periodically measured densities of species throughout the networks over 2 weeks to track community dynamics, colonization, and diversity patterns. We found that colonization of dendritic networks was faster compared with colonization of linear networks, which resulted in higher local mean species richness in dendritic networks. Initially, community similarity was also greater in dendritic networks compared with linear networks, but this effect vanished over time. The presence of species interactions increased community evenness over time, compared with extrapolations from single-species setups. Our experimental findings confirm previous theoretical work and show that network connectivity, species-specific dispersal ability, and species interactions greatly influence the dispersal and colonization of dendritic networks. We argue that these factors need to be considered in empirical studies, where effects of network connectivity on colonization patterns have been largely underestimated.

We construct a hybrid, stage-structured mathematical model to study whether trapping of the invasive predatory crayfish Procambarus clarkii can prevent local extinctions of the California newt (Taricha torosa), a species of special concern native to Santa Monica Mountain streams. Specifically, we numerically and analytically determine under what conditions trapping can drive the crayfish population size to zero. We observe the persistence or the time to extinction for newt populations under corresponding trapping scenarios. No simulations allow for long-term coexistence of newts and crayfish, although multiple scenarios delay newt extinction by several years in the presence of crayfish. We predict that crayfish extinction and newt persistence become more likely as the quantity of trapping resources, frequency of trapping implementation, and susceptibility of the crayfish population to trapping increases. We quantify the effectiveness of different crayfish trapping regimes at delaying the time until the newt population goes extinct. Predictions made with our model inform restorative efforts and crayfish management.

This paper aims to lay bare healthcare inequality and spatial interaction in health services in Turkey for the period 1997–2006. While inequality was explored using Theil index, Moran I and LISA ststistics were used in order to reveal spatial interaction. The obtained results indicate that both intra-regional and inter-regional inequality decreased during the mentioned time period. Furthermore, it was determined that the inequality concerning the specialist physician group is higher than the practitioner and total practitioner groups. Also, there is evidence of significant spatial clustering for all three physician groups.

La descripción e interpretación de los patrones espaciales de los árboles permite extraer conclusiones sobre los factores que condicionan tanto el establecimiento de las cohortes como su variación temporal. En el presente trabajo se analizan las distribuciones espaciales de tamaño y mortalidad del roble (Quercus robur L.) en dos parcelas de bosque. Obtenemos evidencias significativas de agregación para las clases de tamaño pequeñas y de distribución aleatoria en la clase de tamaño mayor. La disponibilidad de espacios abiertos parece ser el principal factor que condiciona la regeneración, estructura y mortalidad en las poblaciones de roble. Una de las poblaciones estudiadas se habría originado sin limitación de espacios abiertos, presentando poca organización espacial y un patrón aleatorio de la mortalidad, lo cual es indicativo de una escasa incidencia de la competencia intraespecífica. En otra población, que corresponde a una parcela de bosque maduro con escasos espacios abiertos, la regeneración del roble está asociada a «fase de claro», formándose grupos compuestos por árboles de tamaños similares y en distintas fases de madurez. En este caso, la mortalidad de roble tiene lugar predominantemente dentro de los grupos formados por árboles de poca talla y está, al menos en parte, causada por competencia intraespecífica. Palabras clave: patrones espaciales, dinámicas de poblaciones, K de Ripley, autocrrelación espacial, mortalidad, competencia intraespecífica, Quercus robur L..

The Association Navigator is an interactive visualization tool for viewing large tables of correlations. The basic operation is zooming and panning of a table that is presented in graphical form, here called a “blockplot”. The tool is really a tool box that includes, among other things: (1) display of p-values and missing value patterns in addition to correlations, (2) mark-up facilities to highlight variables and sub-tables as landmarks when navigating the larger table, (3) histograms/barcharts, scatterplots and scatterplot matrices as “lenses” into the distributions of variables and variable pairs, (4) thresholding of correlations and p-values to show only strong and highly significant p-values, (5) trimming of extreme values of the variables for robustness, (6) “reference variables” that stay in sight at all times, and (7) wholesale adjustment of groups of variables for other variables. The tool has been applied to data with nearly 2,000 variables and associated tables approachin...

The aim of this work is to study the spatio-temporal effects of a grassland fire in Lithuania. Immediately after the fire, a experimental plot was designed in a east-faced slope. Vegetation cover and height were measured 10, 17, 31 and 46 days after the fire (vegetation cover was only measured until 31 days after the fire because in the last measurement campaign the plot was completely covered). The results showed that vegetation recovered very fast. Ten days after the fire vegetation cover and height distribution were heterogeneous, decreasing with the time due to vegetation spread. Vegetation recovered was specially observed between 17 and 31 days after the fire due vegetation recuperation. This increase might reduce the soil vulnerability to erosion However, the spatial structure of this recuperation was different in both variables, and spatial autocorrelation was higher in vegetation cover than vegetation in height in all measurements. Despite these differences, vegetation cover and height values were higher in the bottom part of the plot that was attributed to lower fire severity and ash and nutrient transport.

Marketing science models typically assume that responses of one entity (firm or consumer) are unrelated to responses of other entities. In contrast, models constructed using tools from spatial statistics allow for crosssectional and longitudinal correlations among responses to be explicitly modeled by locating entities on some type of map. By generalizing the notion of a map to include demographic and psychometric representations, spatial models can capture a variety of effects (spatial lags, spatial autocorrelation, and spatial drift) that impact firm or consumer decision behavior. Marketing science applications of spatial models and important research opportunities are discussed. * This paper is based upon the discussions of the Spatial Models in Marketing seminar at the Sixth Invitational Choice Symposium, June 2004. Eric T. Bradlow, Bart Bronnenberg and Gary J. Russell served as co-chairs of the session.

Variation in forest canopy openness in¯uences the growth and survival of seedlings below, and is therefore of practical importance when wishing to restock a forest with valuable timber species after logging. In this study, growth and light environment of dipterocarp seedlings in an enrichment planting project in secondary lowland forest in Borneo were analysed using geostatistics, in order to determine the spatial relationships between canopy openness and seedling performance. Seedling growth was correlated with canopy openness measures up to 50 m away along planting lines but was uncorrelated across planting lines, because the dense understorey between planting lines blocked lateral light. It is recommended that planting be conducted in patches rather than along lines, so that understorey clearance can allow light from canopy gaps in all directions to reach seedlings.

Social exclusion represents one of the main societal challenges outlined by the Europe 2020 strategy. An influential strand of the theoretical literature argues that cultural consumption may have positive social effects, favouring tolerance and fighting prejudice, thus reducing social exclusion. However, this claim has not been tested on the field. This work investigates empirically the relation between social exclusion and consumption of cultural goods in a spatial framework. Using Eurostat data, we compute the Local Moran Transition Probability Matrix for Italian Regions (NUTS-2) over the 2004-2014 timespan. The results show that social exclusion decreases significantly with cultural consumption. In particular, the spatial clusters that emerge when mapping social exclusion break up when controlling for cultural consumption. These findings point out to the importance of governmental support to cultural goods as an instrument to fight social exclusion. In this sense, cultural goods are to be viewed not only as merit goods and sources of entertainment, but also as a societal heritage and a policy instrument.

The curvature of the terrestrial surface enters into the category of primary parameters, directly derived, on the strenght of the altimetric values stored up in the grid type elevation structures. Geomorphological, it indicates the changing rate of the slope or of the orientation per unit lenght, in the XY plan. There are many types of curvatures, but in the more important morphometric analyses they are the following: profile curvature, plan curvature, tangential curvature, longitudinal curvature, transverse curvature, maximum curvature, minimum curvature, medial curvature, general curvature. This article aims at highlighting the differentiations that appear in the qualitative interpretation, in a geomorphologic sense, the results of the processing curvatures’ varieties in a series of GIS type programmes. At the same time, we had as our goal, the identification of the causes for these interpretative disparities, causes that are found in the mathematical apparatus used at the elabora...

The spatial dependence present in a natural stand of Eucalyptus pilularis (Smith) dominated mixed species forest was characterised and modelled. Two wildfires imposed a significant spatial dependence on the post disturbance stand. It was hypothesised that spatial variation in the intensity of the wildfires generated the observed structures. The influence of patch formation, micro-site variability and competitive influences were also noted in the residuals of a distance-dependent individual-tree growth model. A methodology capable of modelling these complicated patterns of observed dependence was sought, and candidates included the spatial interaction, direct specification and Papadakis methods. The spatial interaction method with a moving average autoregression was identified as the most appropriate method for explicitly modelling spatial dependence. Both the direct specification and Papadakis methods failed to capture the influence of competition. This study highlights the possibil...

Incorporating spatial econometric tools in Hedonic Pricing (HP) models for environmental valuation has become the standard approach in the literature. The effect of house prices on other house prices is taken into account and usually measured by distance or contiguity in spatial weight matrices. Disaggregate house sale datasets are composed from observations each at a specific location and time. Nevertheless, the symmetric spatial weight matrices commonly employed in HP studies ignore the temporal dimension in disaggregate house sale data. Thus not only are previous house sales taken to affect subsequent house prices, but so do future house sales. However, information does not travel backwards in time; hence there is a clear theoretical impossibility of actual future prices affecting current/past prices. Estimates derived from HP models where spatial dependence is incorrectly specified or ignored will exhibit inaccuracies. This paper proposes an alternative specification of spatial weights in HP that includes spatial effects on each sale price only from preceding house sales. The temporal aspect of spatial effects is then developed further by specifying a time decay rate to capture the diminishing effect over time of preceding sale prices to succeeding house prices. This novel specification of spatial weight matrices is shown to have a significant effect on estimates of house price depreciation from aircraft noise. Monetary values of aircraft noise externality are successfully derived from the HP models for Athens Airport.

Previous studies have evaluated the overall structure of populations of Fusarium pseudograminearum (teleomorph, Gibberella coronicola ), causal agent of cereal crown rot, but there is no information available on spatial relationships of genetic variation in field populations. Three 1-m-row sections in crown-rot-affected wheat fields in the Australian grain belt were intensively sampled to estimate population genetic parameters and the spatial aggregation, or clustering, of disease aggregates and genotypes. Estimates of population genetic parameters based on amplified fragment length polymorphisms (AFLPs) indicated that the genetic diversity in isolates from the 1-m-row populations described a significant portion of the diversity recorded for corresponding field and regional populations. In point pattern analysis, there was physical clustering and aggregation of F. pseudograminearum isolates from two of the three sites. Analysis of the spatial distribution of clonal haplotypes (DICE similarity ≥ 97%) indicated significant aggregation of clones in all three 1-m-row populations. Based on matrix comparison tests, both mating types and genetic distances had significant spatial aggregation for at least two of the three 1-m-row populations. This is consistent with the presence of non-random spatial genetic structure due to clonal aggregation. High levels of genetic diversity and spatial structuring of disease and genotypes in at least two of the three 1-m-row populations is consistent with the hypothesis that stubble is a primary inoculum source in no-tillage farming systems, resulting in aggregated patterns of disease and allowing for haplotypes to be maintained in the field over a number of annual cropping cycles.

Ectomycorrhizal fungal (EMF) communities are highly diverse at the stand level. To begin to understand what might lead to such diversity, and to improve sampling designs, we investigated the spatial structure of these communities. We used EMF community data from a number of studies carried out in seven mature and one recently fire-initiated forest stand. We applied various measures of spatial pattern to characterize distributions at EMF community and species levels: Mantel tests, Mantel correlograms, variance/mean and standardized variograms. Mantel tests indicated that in four of eight sites community similarity decreased with distance, whereas Mantel correlograms also found spatial autocorrelation in those four plus two additional sites. In all but one of these sites elevated similarity was evident only at relatively small spatial scales (<2.6 m), whereas one exhibited a larger scale pattern ($25 m). Evenness of biomass distribution among cores varied widely among taxa. Standardized variograms indicated that most of the dominant taxa showed patchiness at a scale of less than 3 m, with a range from 0 to P17 m. These results have implications for both sampling scale and intensity to achieve maximum efficiency of community sampling. In the systems we examined, cores should be at least 3 m apart to achieve the greatest sampling efficiency for stand-level community analysis. In some cases even this spacing may result in reduced sampling efficiency arising from patterns of spatial autocorrelation. Interpretation of the causes and significance of these patterns requires information on the genetic identity of individuals in the communities.

BAASS (Bayesian Approaches for Adaptive Spatial Sampling) is a set of computational routines developed to support the design and deployment of spatial sampling programs for delineating contamination footprints, such as those that might result from the accidental or intentional environmental release of radionuclides. BAASS presumes the existence of real-time measurement technologies that provide information quickly enough to affect the progress of data collection. This technical memorandum describes the application of BAASS to a simple example, compares the performance of a BAASS-based program with that of a traditional gridded program, and explores the significance of several of the underlying assumptions required by BAASS. These assumptions include the range of spatial autocorrelation present, the value of prior information, the confidence level required for decision making, and "inside-out" versus "outsidein" sampling strategies. In the context of the example, adaptive sampling combined with prior information significantly reduced the number of samples required to delineate the contamination footprint.

Background: We investigated changes in the spatial distribution of schistosomiasis in Mali following a decade of donorfunded control and a further 12 years without control. Methodology/Principal Findings: National pre-intervention cross-sectional schistosomiasis surveys were conducted in Mali in 1984-1989 (in communities) and again in 2004-2006 (in schools). Bayesian geostatistical models were built separately for each time period and on the datasets combined across time periods. In the former, data from one period were used to predict prevalence of schistosome infections for the other period, and in the latter, the models were used to determine whether spatial autocorrelation and covariate effects were consistent across periods. Schistosoma haematobium prevalence was 25.7% in 1984-1989 and 38.3% in 2004-2006; S. mansoni prevalence was 7.4% in 1984-1989 and 6.7% in 2004-2006 (note the models showed no significant difference in mean prevalence of either infection between time periods). Prevalence of both infections showed a focal spatial pattern and negative associations with distance from perennial waterbodies, which was consistent across time periods. Spatial models developed using 1984-1989 data were able to predict the distributions of both schistosome species in 2004-2006 (area under the receiver operating characteristic curve was typically .0.7) and vice versa. Conclusions/Significance: A decade after the apparently successful conclusion of a donor-funded schistosomiasis control programme from 1982-1992, national prevalence of schistosomiasis had rebounded to pre-intervention levels. Clusters of schistosome infections occurred in generally the same areas accross time periods, although the precise locations varied. To achieve long-term control, it is essential to plan for sustainability of ongoing interventions, including stengthening endemic country health systems.

This paper studies the convergence of per-capita GDP across European regions over a fairly long period. Most of the works are based on either crosssectional or fixed-effects estimates. we propose the estimation of convergence in per-capita GDP across European regions by making use of panel-data models extended to include spatial error autocorrelation and spatially lagged dependent variable (Anselin, 1988; Elhorst, 2002). This will allow us to extend the traditional β-convergence model to include a rigorous treatment of the spatial correlation among the intercept terms. A spatial analysis of such intercept terms will also be performed in order to shed light on the concept spatially conditional convergence.

Spatial statistical analysis was undertaken to study the  variability of potato yield data due to soil fertility pattern. Seed potato yield were measured from field uniformity trial conducted at Hollota and Kulumsa agricultural research centers in year 2001 on  an area  of 0.15  hectare at each site.  The  harvested area was divided in to  basic units of 1.2x1.5 m and a total of 1658 and 931 plots were considered from  Hollota  and Kulumsa  respectively.  The  basic units were combined in different plot sizes to acquire the required plot dimension.
In this study, the Monte Carlo test  for completely  spatial  randomness  is applied and the result shows no complete spatial randomness detected in the series of potato yield data for both sites.
Thus, to set a model adjusted for spatial pattern, Moran’s index and Geary’s coefficient were applied to test for global and local spatial autocorrelation respectively. The result shows  positive spatial  autocorrelation  detected among potato yield data using  Rook’s weighted  neighboring  plot  relations. The result also shows, increasing plot  size will  not  generally  make the observed spatial autocorrelation insignificant.
An autoregressive model, that is adjusted for presence of  spatial autocorrelation in simulated plot size of 12m2  is fitted for row and column effect for each  site.  The  result  shows  significant  positive  association between neighboring plots row effect and the adjusted  potato  yield.  In addition, the result from comparison of model adjusted for presence of spatial autocorrelation and conventional OLS based analysis of variance shows the autocorrelation parameter accounts significant percent of variation among potato yield in both sites.

The aim of this study was to describe spatial patterns of the distribution of leprosy and to investigate spatial clustering of incidence rates in the state of Ceará, Northeast Brazil. The average incidence rate of leprosy for the period of 1991 to 1999 was calculated for each municipality of Ceará. Maps were used to describe the spatial distribution of the disease, and spatial statistics were applied to explore large-and small-scale variations of incidence rates. Three regions were identified in which the incidence of leprosy was particularly high. A spatial gradient in the incidence rates was identified, with a tendency of high rates to be concentrated on the North-South axis in the middle region of the state. Moran's I statistic indicated that a significant spatial autocorrelation also existed. The spatial distribution of leprosy in Ceará is heterogeneous. The reasons for spatial clustering of disease rates are not known, but might be related to an heterogeneous distribution of other factors such as crowding, social inequality, and environmental characteristics which by themselves determine the transmission of Mycobacterium leprae.