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Spatial Clustering (spatial + clustering)
Selected AbstractsAlgorithm for Spatial Clustering of Pavement SegmentsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 2 2009Chientai Yang This article formulates a new spatial search model for determining appropriate pavement preservation project termini. A spatial clustering algorithm using fuzzy c-mean clustering is developed to minimize the rating variation in each cluster (project) of pavement segments while considering minimal project scope (i.e., length) and cost, initial setup cost, and barriers, such as bridges. A case study using the actual roadway and pavement condition data in fiscal year 2005 on Georgia State Route 10 shows that the proposed algorithm can identify more appropriate segment clustering scheme, than the historical project termini. The benefits of using the developed algorithm are summarized, and recommendations for future research are discussed. [source] An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse DataGEOGRAPHICAL ANALYSIS, Issue 1 2001John Paul Ekwaru The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations. [source] Power of the Rank Adjacency Statistic to Detect Spatial Clustering in a Small Number of RegionsGEOGRAPHICAL ANALYSIS, Issue 1 2001John Paul Ekwaru The rank adjacency statistic D is a statistical method for assessing spatial autocorrelation or clustering of geographical data. It was originally proposed for summarizing the geographical patterns of cancer data in Scotland (IARC 1985). In this paper, we investigate the power of the rank adjacency statistic to detect spatial clustering when a small number of regions is involved. The investigations were carried out using Monte Carlo simulations, which involved generating patterned/clustered values and computing the power with which the D statistic would detect it. To investigate the effects of region shapes, structure of the regions, and definition of weights, simulations were carried out using two different region shapes, binary and nonhinary weights, and three different lattice structures. The results indicate that in the typical example of considering Canadian total mortality at the electoral district level, the D statistic had adequate power to detect general spatial autocorrelation in twenty-five or more regions. There was an inverse relationship between power and the level of connectedness of the regions, which depends on the weighting function, shape, and arrangement of the regions. The power of the D statistic was also found to compare favorably with that of Moran's I statistic. [source] A Class of Multiplicity Adjusted Tests for Spatial Clustering Based on Case,Control Point DataBIOMETRICS, Issue 1 2007Toshiro Tango Summary A class of tests with quadratic forms for detecting spatial clustering of health events based on case,control point data is proposed. It includes Cuzick and Edwards's test statistic (1990, Journal of theRoyal Statistical Society, Series B52, 73,104). Although they used the property of asymptotic normality of the test statistic, we show that such an approximation is generally poor for moderately large sample sizes. Instead, we suggest a central chi-square distribution as a better approximation to the asymptotic distribution of the test statistic. Furthermore, not only to estimate the optimal value of the unknown parameter on the scale of cluster but also to adjust for multiple testing due to repeating the procedure by changing the parameter value, we propose the minimum of the profile p-value of the test statistic for the parameter as an integrated test statistic. We also provide a statistic to estimate the areas or cases which make large contributions to significant clustering. The proposed methods are illustrated with a data set concerning the locations of cases of childhood leukemia and lymphoma and another on early medieval grave site locations consisting of affected and nonaffected grave sites. [source] Spatial clustering of childhood cancer in Great Britain during the period 1969,1993INTERNATIONAL JOURNAL OF CANCER, Issue 4 2009Richard J.Q. McNally Abstract The aetiology of childhood cancer is poorly understood. Both genetic and environmental factors are likely to be involved. The presence of spatial clustering is indicative of a very localized environmental component to aetiology. Spatial clustering is present when there are a small number of areas with greatly increased incidence or a large number of areas with moderately increased incidence. To determine whether localized environmental factors may play a part in childhood cancer aetiology, we analyzed for spatial clustering using a large set of national population-based data from Great Britain diagnosed 1969,1993. The Potthoff-Whittinghill method was used to test for extra-Poisson variation (EPV). Thirty-two thousand three hundred and twenty-three cases were allocated to 10,444 wards using diagnosis addresses. Analyses showed statistically significant evidence of clustering for acute lymphoblastic leukaemia (ALL) over the whole age range (estimate of EPV = 0.05, p = 0.002) and for ages 1,4 years (estimate of EPV = 0.03, p = 0.015). Soft-tissue sarcoma (estimate of EPV = 0.03, p = 0.04) and Wilms tumours (estimate of EPV = 0.04, p = 0.007) also showed significant clustering. Clustering tended to persist across different time periods for cases of ALL (estimate of between-time period EPV = 0.04, p =0.003). In conclusion, we observed low level spatial clustering that is attributable to a limited number of cases. This suggests that environmental factors, which in some locations display localized clustering, may be important aetiological agents in these diseases. For ALL and soft tissue sarcoma, but not Wilms tumour, common infectious agents may be likely candidates. © 2008 Wiley-Liss, Inc. [source] Cluster Detection Based on Spatial Associations and Iterated Residuals in Generalized Linear Mixed ModelsBIOMETRICS, Issue 2 2009Tonglin Zhang Summary Spatial clustering is commonly modeled by a Bayesian method under the framework of generalized linear mixed effect models (GLMMs). Spatial clusters are commonly detected by a frequentist method through hypothesis testing. In this article, we provide a frequentist method for assessing spatial properties of GLMMs. We propose a strategy that detects spatial clusters through parameter estimates of spatial associations, and assesses spatial aspects of model improvement through iterated residuals. Simulations and a case study show that the proposed method is able to consistently and efficiently detect the locations and magnitudes of spatial clusters. [source] Space-Time Hierarchical Radiosity with Clustering and Higher-Order WaveletsCOMPUTER GRAPHICS FORUM, Issue 2 2004Cyrille Damez Abstract We address in this paper the issue of computing diffuse global illumination solutions for animation sequences. The principal difficulties lie in the computational complexity of global illumination, emphasized by the movement of objects and the large number of frames to compute, as well as the potential for creating temporal discontinuities in the illumination, a particularly noticeable artifact. We demonstrate how space-time hierarchical radiosity, i.e. the application to the time dimension of a hierarchical decomposition algorithm, can be effectively used to obtain smooth animations: first by proposing the integration of spatial clustering in a space-time hierarchy; second, by using a higher-order wavelet basis adapted for the temporal dimension. The resulting algorithm is capable of creating time-dependent radiosity solutions efficiently. [source] Likelihood-based tests for localized spatial clustering of diseaseENVIRONMETRICS, Issue 8 2004Ronald E. Gangnon Abstract Numerous methods have been proposed for detecting spatial clustering of disease. Two methods for likelihood-based inference using parametric models for clustering are the spatial scan statistic and the weighted average likelihood ratio (WALR) test. The spatial scan statistic provides a measure of evidence for clustering at a specific, data-identified location; it can be biased towards finding clusters in areas with greater spatial resolution. The WALR test provides a more global assessment of the evidence for clustering and identifies cluster locations in a relatively unbiased fashion using a posterior distribution over potential clusters. We consider two new statistics which attempt to combine the specificity of the scan statistic with the lack of bias of the WALR test: a scan statistic based on a penalized likelihood ratio and a localized version of the WALR test. We evaluate the power of these tests and bias of the associated estimates through simulations and demonstrate their application using the well-known New York leukemia data. Copyright © 2004 John Wiley & Sons, Ltd. [source] Loglinear Residual Tests of Moran's I Autocorrelation and their Applications to Kentucky Breast Cancer DataGEOGRAPHICAL ANALYSIS, Issue 3 2007Ge Lin This article bridges the permutation test of Moran's I to the residuals of a loglinear model under the asymptotic normality assumption. It provides the versions of Moran's I based on Pearson residuals (IPR) and deviance residuals (IDR) so that they can be used to test for spatial clustering while at the same time account for potential covariates and heterogeneous population sizes. Our simulations showed that both IPR and IDR are effective to account for heterogeneous population sizes. The tests based on IPR and IDR are applied to a set of log-rate models for early-stage and late-stage breast cancer with socioeconomic and access-to-care data in Kentucky. The results showed that socioeconomic and access-to-care variables can sufficiently explain spatial clustering of early-stage breast carcinomas, but these factors cannot explain that for the late stage. For this reason, we used local spatial association terms and located four late-stage breast cancer clusters that could not be explained. The results also confirmed our expectation that a high screening level would be associated with a high incidence rate of early-stage disease, which in turn would reduce late-stage incidence rates. [source] An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse DataGEOGRAPHICAL ANALYSIS, Issue 1 2001John Paul Ekwaru The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations. [source] Power of the Rank Adjacency Statistic to Detect Spatial Clustering in a Small Number of RegionsGEOGRAPHICAL ANALYSIS, Issue 1 2001John Paul Ekwaru The rank adjacency statistic D is a statistical method for assessing spatial autocorrelation or clustering of geographical data. It was originally proposed for summarizing the geographical patterns of cancer data in Scotland (IARC 1985). In this paper, we investigate the power of the rank adjacency statistic to detect spatial clustering when a small number of regions is involved. The investigations were carried out using Monte Carlo simulations, which involved generating patterned/clustered values and computing the power with which the D statistic would detect it. To investigate the effects of region shapes, structure of the regions, and definition of weights, simulations were carried out using two different region shapes, binary and nonhinary weights, and three different lattice structures. The results indicate that in the typical example of considering Canadian total mortality at the electoral district level, the D statistic had adequate power to detect general spatial autocorrelation in twenty-five or more regions. There was an inverse relationship between power and the level of connectedness of the regions, which depends on the weighting function, shape, and arrangement of the regions. The power of the D statistic was also found to compare favorably with that of Moran's I statistic. [source] Spatial clustering of childhood cancer in Great Britain during the period 1969,1993INTERNATIONAL JOURNAL OF CANCER, Issue 4 2009Richard J.Q. McNally Abstract The aetiology of childhood cancer is poorly understood. Both genetic and environmental factors are likely to be involved. The presence of spatial clustering is indicative of a very localized environmental component to aetiology. Spatial clustering is present when there are a small number of areas with greatly increased incidence or a large number of areas with moderately increased incidence. To determine whether localized environmental factors may play a part in childhood cancer aetiology, we analyzed for spatial clustering using a large set of national population-based data from Great Britain diagnosed 1969,1993. The Potthoff-Whittinghill method was used to test for extra-Poisson variation (EPV). Thirty-two thousand three hundred and twenty-three cases were allocated to 10,444 wards using diagnosis addresses. Analyses showed statistically significant evidence of clustering for acute lymphoblastic leukaemia (ALL) over the whole age range (estimate of EPV = 0.05, p = 0.002) and for ages 1,4 years (estimate of EPV = 0.03, p = 0.015). Soft-tissue sarcoma (estimate of EPV = 0.03, p = 0.04) and Wilms tumours (estimate of EPV = 0.04, p = 0.007) also showed significant clustering. Clustering tended to persist across different time periods for cases of ALL (estimate of between-time period EPV = 0.04, p =0.003). In conclusion, we observed low level spatial clustering that is attributable to a limited number of cases. This suggests that environmental factors, which in some locations display localized clustering, may be important aetiological agents in these diseases. For ALL and soft tissue sarcoma, but not Wilms tumour, common infectious agents may be likely candidates. © 2008 Wiley-Liss, Inc. [source] War and Peace in Space and Time: The Role of DemocratizationINTERNATIONAL STUDIES QUARTERLY, Issue 1 2000Kristian S. Gleditsch Democratization reduces the risk of war, but uneven transitions toward democracy can increase the probability of war. Using country-level data on democratization and international war from the period 1875,1996, we develop a general additive statistical model reassessing this claim in light of temporal and spatial dependence. We also develop a new geopolitical database of contiguities and demonstrate new statistical techniques for probing the extent of spatial clustering and its impact on the relationship between democratization and war. Our findings reaffirm that democratization generally does reduce the risk of war, but that large swings back and forth between democracy and autocracy can increase war proneness. We show that the historical context of peace diminishes the risk of war, while a regional context plagued by conflict greatly magnifies it. [source] Biogeography of European land mammals shows environmentally distinct and spatially coherent clustersJOURNAL OF BIOGEOGRAPHY, Issue 6 2007H. Heikinheimo Abstract Aim, To produce a spatial clustering of Europe on the basis of species occurrence data for the land mammal fauna. Location, Europe defined by the following boundaries: 11°W, 32°E, 71°N, 35°N. Methods, Presence/absence records of mammal species collected by the Societas Europaea Mammalogica with a resolution of 50 × 50 km were used in the analysis. After pre-processing, the data provide information on 124 species in 2183 grid cells. The data were clustered using the k -means and probabilistic expectation maximization (EM) clustering algorithms. The resulting geographical pattern of clusters was compared against climate variables and against an environmental stratification of Europe based on climate, geomorphology and soil characteristics (EnS). Results, The mammalian presence/absence data divide naturally into clusters, which are highly connected spatially and most strongly determined by the small mammals with the highest grid cell incidence. The clusters reflect major physiographic and environmental features and differ significantly in the values of basic climate variables. The geographical pattern is a fair match for the EnS stratification and is robust between non-overlapping subsets of the data, such as trophic groups. Main conclusions, The pattern of clusters is regarded as reflecting the spatial expression of biologically distinct, metacommunity-like entities influenced by deterministic forces ultimately related to the physical environment. Small mammals give the most spatially coherent clusters of any subgroup, while large mammals show stronger relationships to climate variables. The spatial pattern is mainly due to small mammals with high grid cell incidence and is robust to noise from other subsets. The results support the use of spatially resolved environmental reconstructions based on fossil mammal data, especially when based on species with the highest incidence. [source] Industry Characteristics Linked to Establishment Concentrations in Nonmetropolitan AreasJOURNAL OF REGIONAL SCIENCE, Issue 2 2000Yunsoo Kim In this paper we investigate industry characteristics associated with the clustering of establishments in three-digit SIC manufacturing industries in nonmetropolitan areas. The dispersion parameter k of the negative binomial distribution is selected as the measure of industry spatial concentration. Associations between industry characteristics and spatial concentration are investigated using OLS regression analysis. Our findings indicate that the spatial clustering of establishments is positively related to industry average establishment size, reliance on natural resource inputs, labor intensity, cost shares of professional and technical employees, and cost shares of low-skilled workers. Agglomeration is negatively related to multiplant structure, employment in precision production, and reliance on local product and input markets. [source] The tick Ixodes ricinus: distribution and climate preferences in the western PalaearcticMEDICAL AND VETERINARY ENTOMOLOGY, Issue 2 2006A. ESTRADA-PEÑA Abstract In this study, multivariate spatial clustering on monthly normalized difference vegetation index (NDVI) maps is used to classify ecological regions over the western Palaearctic. This classification is then used to delineate the distribution and climate preferences of populations (clades) of the tick Ixodes ricinus L. (Acari: Ixodidae) from a geographically extensive dataset of tick records and a gridded 2.5-km resolution climate dataset. Using monthly layers of the NDVI, regions of similar ecological attributes were defined and nine populations with significant differences in critical climate parameters (P < 0.005) were detected. Grouping of tick records according to other categories, such as political divisions, a 4°× 4° grid overlying the study area, or the CORINE) and USGS) vegetation classification schemes did not provided significantly separated populations (P= 0.094,0.304). Factor analysis and hierarchical tree clustering provided an ecological overview of these tick clades: two Mediterranean and one Scandinavian (western) clades are clearly separated from a node that includes clades of different parts of central Europe and the British Isles, with contrasting affinities between the different clades. The capture records of these ecologically separated clades produce a clear bias when bioclimate envelope modelling is applied to the mapping of habitat suitability for the tick in the western Palaearctic. The best-performing methods (Cohen's kappa = 0.834,0.912) use partial models developed with data from each ecoregion, which are then overlapped over the region of study. It is concluded that the use of ecologically derived ecoregions is an objective step in assessing the presence of ecologically different clades, and provides a guide in the development of data partitioning for habitat suitability modelling. [source] Fine-scale natal homing and localized movement as shaped by sex and spawning habitat in Chinook salmon: insights from spatial autocorrelation analysis of individual genotypesMOLECULAR ECOLOGY, Issue 14 2006H. M. NEVILLE Abstract Natal homing is a hallmark of the life history of salmonid fishes, but the spatial scale of homing within local, naturally reproducing salmon populations is still poorly understood. Accurate homing (paired with restricted movement) should lead to the existence of fine-scale genetic structuring due to the spatial clustering of related individuals on spawning grounds. Thus, we explored the spatial resolution of natal homing using genetic associations among individual Chinook salmon (Oncorhynchus tshawytscha) in an interconnected stream network. We also investigated the relationship between genetic patterns and two factors hypothesized to influence natal homing and localized movements at finer scales in this species, localized patterns in the distribution of spawning gravels and sex. Spatial autocorrelation analyses showed that spawning locations in both sub-basins of our study site were spatially clumped, but the upper sub-basin generally had a larger spatial extent and continuity of redd locations than the lower sub-basin, where the distribution of redds and associated habitat conditions were more patchy. Male genotypes were not autocorrelated at any spatial scale in either sub-basin. Female genotypes showed significant spatial autocorrelation and genetic patterns for females varied in the direction predicted between the two sub-basins, with much stronger autocorrelation in the sub-basin with less continuity in spawning gravels. The patterns observed here support predictions about differential constraints and breeding tactics between the two sexes and the potential for fine-scale habitat structure to influence the precision of natal homing and localized movements of individual Chinook salmon on their breeding grounds. [source] Fine-scale spatial genetic correlation analyses reveal strong female philopatry within a brush-tailed rock-wallaby colony in southeast QueenslandMOLECULAR ECOLOGY, Issue 12 2004S. L. HAZLITT Abstract We combine spatial data on home ranges of individuals and microsatellite markers to examine patterns of fine-scale spatial genetic structure and dispersal within a brush-tailed rock-wallaby (Petrogale penicillata) colony at Hurdle Creek Valley, Queensland. Brush-tailed rock-wallabies were once abundant and widespread throughout the rocky terrain of southeastern Australia; however, populations are nearly extinct in the south of their range and in decline elsewhere. We use pairwise relatedness measures and a recent multilocus spatial autocorrelation analysis to test the hypotheses that in this species, within-colony dispersal is male-biased and that female philopatry results in spatial clusters of related females within the colony. We provide clear evidence for strong female philopatry and male-biased dispersal within this rock-wallaby colony. There was a strong, significant negative correlation between pairwise relatedness and geographical distance of individual females along only 800 m of cliff line. Spatial genetic autocorrelation analyses showed significant positive correlation for females in close proximity to each other and revealed a genetic neighbourhood size of only 600 m for females. Our study is the first to report on the fine-scale spatial genetic structure within a rock-wallaby colony and we provide the first robust evidence for strong female philopatry and spatial clustering of related females within this taxon. We discuss the ecological and conservation implications of our findings for rock-wallabies, as well as the importance of fine-scale spatial genetic patterns in studies of dispersal behaviour. [source] Post-ice age recolonization and differentiation of Fucus serratus L. (Phaeophyceae; Fucaceae) populations in Northern EuropeMOLECULAR ECOLOGY, Issue 7 2003J. A. Coyer Abstract The seaweed Fucus serratus is hypothesized to have evolved in the North Atlantic and present populations are thought to reflect recolonization from a southern refugium since the last glacial maximum 18 000,20 000 years bp. We examined genetic structure across several spatial scales by analysing seven microsatellite loci in populations collected from 21 localities throughout the species' range. Spatial auto-correlation analysis of seven microsatellite loci revealed no evidence for spatial clustering of alleles on a scale of 100 m despite limited gamete dispersal in F. serratus of , 2 m from parental individuals. Pairwise , analysis suggested that the minimal panmictic unit for F. serratus was between 0.5 and 2 km. Isolation by distance was significant along some contiguous coastlines. Population differentiation was strong within the Skagerrak,Kattegat,Baltic Seas (SKB) (global ,= 0.17) despite a short history of , 7500 years. A neighbour-joining tree based on Reynold's distances computed from the microsatellite data revealed a central assemblage of populations on the Brittany Peninsula surrounded by four well-supported clusters consisting of the SKB, the North Sea (Ireland, Helgoland), and two populations from the northern Spanish coast. Samples from Iceland and Nova Scotia were most closely aligned with northwest Sweden and Brittany, respectively. When sample sizes were standardized (N = 41), allelic diversity was twofold higher for Brittany populations than for populations to the north and threefold higher than southern populations. The Brittany region may be a refugium or a recolonized area, whereas the Spanish populations most likely reflect present-day edge populations that have undergone repeated bottlenecks as a consequence of thermally induced cycles of recolonization and extinction. [source] The luminosity dependence of clustering and higher order correlations in the PSCz surveyMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 4 2000István Szapudi We investigate the spatial clustering of galaxies in the PSCz galaxy redshift survey, as revealed by the two-point correlation function, the luminosity mark correlations and the moments of counts-in-cells. We construct volume-limited subsamples at different depths and search for a luminosity dependence of the clustering pattern. We find no statistically significant effect in either the two-point correlation function or the mark correlations and so we take each subsample (of different characteristic luminosity) as representing the same statistical process. We then carry out a counts-in-cells analysis of the volume-limited subsamples, including a rigorous error calculation based on the recent theory of Szapudi, Colombi & Bernardeau. In this way, we derive the best estimates to date of the skewness and kurtosis of IRAS galaxies in redshift space. Our results agree well with previous measurements in both the parent angular catalogue and the derived redshift surveys. This is in contrast with smaller, optically selected surveys, where there is a discrepancy between the redshift space and projected measurements. Predictions from cold dark matter theory, obtained using the recent semi-analytical model of galaxy formation of Benson et al., provide an excellent description of our clustering data. [source] THE ,THICK MARKET' EFFECT AND AGGLOMERATION IN HIGH-GROWTH INDUSTRIESPACIFIC ECONOMIC REVIEW, Issue 2 2005Mikhail M. Klimenko In the model, agglomerative effects result from positive feedback between competitive forces in the upstream and downstream segments of a high-technology industry, rather than as a result of traditional scale economies in the manufacturing of standardized products. The model assumes that firms in the upstream service supply industry have ex ante uncertain costs and compete in Bertrand fashion for the independent demands of downstream firms. This framework explains the mechanism of spatial clustering in industries with a high rate of innovation. [source] Chemotopic odorant coding in a mammalian olfactory system,THE JOURNAL OF COMPARATIVE NEUROLOGY, Issue 1 2007Brett A. Johnson Abstract Systematic mapping studies involving 365 odorant chemicals have shown that glomerular responses in the rat olfactory bulb are organized spatially in patterns that are related to the chemistry of the odorant stimuli. This organization involves the spatial clustering of principal responses to numerous odorants that share key aspects of chemistry such as functional groups, hydrocarbon structural elements, and/or overall molecular properties related to water solubility. In several of the clusters, responses shift progressively in position according to odorant carbon chain length. These response domains appear to be constructed from orderly projections of sensory neurons in the olfactory epithelium and may also involve chromatography across the nasal mucosa. The spatial clustering of glomerular responses may serve to "tune" the principal responses of bulbar projection neurons by way of inhibitory interneuronal networks, allowing the projection neurons to respond to a narrower range of stimuli than their associated sensory neurons. When glomerular activity patterns are viewed relative to the overall level of glomerular activation, the patterns accurately predict the perception of odor quality, thereby supporting the notion that spatial patterns of activity are the key factors underlying that aspect of the olfactory code. A critical analysis suggests that alternative coding mechanisms for odor quality, such as those based on temporal patterns of responses, enjoy little experimental support. J. Comp. Neurol. 503:1,34, 2007. © 2007 Wiley-Liss, Inc. [source] Genetic structure of the endangered perennial plant Eryngium alpinum (Apiaceae) in an alpine valleyBIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 4 2008MYRIAM GAUDEUL We investigated the genetic structure of Eryngium alpinum (Apiaceae) in an Alpine valley where the plant occurs in patches of various sizes. In a conservation perspective, our goal was to determine whether the valley consists of one or several genetic units. Habitat fragmentation and previous observations of restricted pollen/seed dispersal suggested pronounced genetic structure, but gene dispersal often follows a leptokurtic distribution, which may lead to weak genetic structure. We used nine microsatellite loci and two nested sampling designs (50 × 50 m grid throughout the valley and 2 × 2 m grid in two 50 × 10 m quadrats). Within the overall valley, F -statistics and Bayesian approaches indicated high genetic homogeneity. This result might be explained by: (1) underestimation of long-distance pollen/seed dispersal by in situ experiments and (2) too recent fragmentation events to build up genetic structure. Spatial autocorrelation revealed isolation by distance on the overall valley but this pattern was much more pronounced in the 50 × 10 m quadrats sampled with a 2-m mesh. This was probably associated with limited primary seed dispersal, leading to the spatial clustering of half-sibs around maternal plants. We emphasize the interest of nested sampling designs and of combining several statistical tools. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society, 2008, 93, 667,677. [source] Spatial Cluster Detection for Weighted Outcomes Using Cumulative Geographic ResidualsBIOMETRICS, Issue 3 2010Andrea J. Cook Summary Spatial cluster detection is an important methodology for identifying regions with excessive numbers of adverse health events without making strong model assumptions on the underlying spatial dependence structure. Previous work has focused on point or individual-level outcome data and few advances have been made when the outcome data are reported at an aggregated level, for example, at the county- or census-tract level. This article proposes a new class of spatial cluster detection methods for point or aggregate data, comprising of continuous, binary, and count data. Compared with the existing spatial cluster detection methods it has the following advantages. First, it readily incorporates region-specific weights, for example, based on a region's population or a region's outcome variance, which is the key for aggregate data. Second, the established general framework allows for area-level and individual-level covariate adjustment. A simulation study is conducted to evaluate the performance of the method. The proposed method is then applied to assess spatial clustering of high Body Mass Index in a health maintenance organization population in the Seattle, Washington, USA area. [source] A Class of Multiplicity Adjusted Tests for Spatial Clustering Based on Case,Control Point DataBIOMETRICS, Issue 1 2007Toshiro Tango Summary A class of tests with quadratic forms for detecting spatial clustering of health events based on case,control point data is proposed. It includes Cuzick and Edwards's test statistic (1990, Journal of theRoyal Statistical Society, Series B52, 73,104). Although they used the property of asymptotic normality of the test statistic, we show that such an approximation is generally poor for moderately large sample sizes. Instead, we suggest a central chi-square distribution as a better approximation to the asymptotic distribution of the test statistic. Furthermore, not only to estimate the optimal value of the unknown parameter on the scale of cluster but also to adjust for multiple testing due to repeating the procedure by changing the parameter value, we propose the minimum of the profile p-value of the test statistic for the parameter as an integrated test statistic. We also provide a statistic to estimate the areas or cases which make large contributions to significant clustering. The proposed methods are illustrated with a data set concerning the locations of cases of childhood leukemia and lymphoma and another on early medieval grave site locations consisting of affected and nonaffected grave sites. [source] Bayesian Detection and Modeling of Spatial Disease ClusteringBIOMETRICS, Issue 3 2000Ronald E. Gangnon Summary. Many current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks. In this paper, we develop a Bayesian procedure for drawing inferences about specific models for spatial clustering. The proposed methodology incorporates ideas from image analysis, from Bayesian model averaging, and from model selection. With our approach, we obtain estimates for disease rates and allow for greater flexibility in both the type of clusters and the number of clusters that may be considered. We illustrate the proposed procedure through simulation studies and an analysis of the well-known New York leukemia data. [source] HeLa Cell Entry by Guanidinium-Rich ,-Peptides: Importance of Specific Cation,Cell Surface InteractionsCHEMBIOCHEM, Issue 8 2007Terra B. Potocky Abstract Short cationic oligomers, including arginine-rich peptides and analogous ,-amino acid oligomers (", -peptides"), can enter the cytoplasm and nucleus of a living cell from the extracellular medium. It seems increasingly clear that multiple entry pathways are possible, depending upon the structure of the guanidinium-rich molecule, the type of cell, and other factors. We have previously shown that conformational stability and spatial clustering of guanidinium groups increase the HeLa cell entry efficiency of short helical , -peptides bearing six guanidinium groups, results that suggest that these , -peptides could be useful tools for studying the entry process. Here we describe studies intended to identify the point in the entry process at which helix stability and spatial arrangement of guanidinium groups exert their effect. Our results suggest that key distinctions involve the mode of interaction between different guanidinium-rich ,-peptides and the HeLa cell surface. A specific guanidinium display appears to be required for proper engagement of cell-surface heparan sulfate proteoglycans and concomitant induction of endocytic uptake. [source] | |||||||||||||||||||