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Spatial Methods (spatial + methods)
Selected AbstractsCoefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regressionECOGRAPHY, Issue 2 2009L. Mauricio Bini A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; "OLS models" hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation. [source] Detecting spatial hot spots in landscape ecologyECOGRAPHY, Issue 5 2008Trisalyn A. Nelson Hot spots are typically locations of abundant phenomena. In ecology, hot spots are often detected with a spatially global threshold, where a value for a given observation is compared with all values in a data set. When spatial relationships are important, spatially local definitions , those that compare the value for a given observation with locations in the vicinity, or the neighbourhood of the observation , provide a more explicit consideration of space. Here we outline spatial methods for hot spot detection: kernel estimation and local measures of spatial autocorrelation. To demonstrate these approaches, hot spots are detected in landscape level data on the magnitude of mountain pine beetle infestations. Using kernel estimators, we explore how selection of the neighbourhood size (,) and hot spot threshold impact hot spot detection. We found that as , increases, hot spots are larger and fewer; as the hot spot threshold increases, hot spots become larger and more plentiful and hot spots will reflect coarser scale spatial processes. The impact of spatial neighbourhood definitions on the delineation of hot spots identified with local measures of spatial autocorrelation was also investigated. In general, the larger the spatial neighbourhood used for analysis, the larger the area, or greater the number of areas, identified as hot spots. [source] EVAPOTRANSPIRATION DYNAMICS AT AN ECOHYDROLOGICAL RESTORATION SITE: AN ENERGY BALANCE AND REMOTE SENSING APPROACH,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2006Jason W. Oberg ABSTRACT: Little work is reported where spatial methods are employed to monitor evapotranspiration (ET) changes as a result of vegetation and wetland restoration. A remote sensing approach with the Surface Energy Balance Algorithm for Land (SEBAL) for estimating ET at The Nature Conservancy's Glacial Ridge prairie-wetland restoration site in northwestern Minnesota is presented. The calibrated 24-hour ET from SEBAL was estimated with an average error of prediction of ,4.3 percent. Monthly, interseasonal, and seasonal ET for the period of June to September (2000 to 2003) from three adjacent land-uses: a hydrologic control preserved wetland; a treated or restored site; and a nontreated or impacted wetland, were used in the study. Results from comparing ET behavior to the preserve suggest restoration efforts have affected monthly and seasonal ET within the treated site. Spatial average standard deviations of the seasonal ET within the preserve, treated, and nontreated sites give 47.3, 75.7, and 109.9 mm, respectively, suggesting hydrologic stabilization within the treated site. Monthly and interseasonal comparisons show similar behavior to that of the seasonal data, where monthly correlations suggest increasing agreement within the treated site, approaching those within the preserve. [source] A review and discussion of prospective statistical surveillance in public healthJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2003Christian Sonesson Summary. A review of methods suggested in the literature for sequential detection of changes in public health surveillance data is presented. Many researchers have noted the need for prospective methods. In recent years there has been an increased interest in both the statistical and the epidemiological literature concerning this type of problem. However, most of the vast literature in public health monitoring deals with retrospective methods, especially spatial methods. Evaluations with respect to the statistical properties of interest for prospective surveillance are rare. The special aspects of prospective statistical surveillance and different ways of evaluating such methods are described. Attention is given to methods that include only the time domain as well as methods for detection where observations have a spatial structure. In the case of surveillance of a change in a Poisson process the likelihood ratio method and the Shiryaev,Roberts method are derived. [source] Path-breaking books in regional sciencePAPERS IN REGIONAL SCIENCE, Issue 1 2004Brigitte S. Waldorf Regional science; urban economics; spatial methods; New Economic Geography; history Abstract. This article presents a collection of regional science books that long-standing members of the Regional Science Association International (RSAI) identified as path-breaking books. The most frequently nominated books include the "classics" by Isard, the seminal books in urban economics by Alonso, Muth and Mills, methods books by Miernyk, Wilson, Anselin, and Cliff and Ord, textbooks by Beckmann and Richardson, as well as the recent contribution by Fujita, Krugman and Venables. Reviews of these books, written by leading scholars from different continents, make up the major contribution of this article and are a testimony to the far-reaching influence of regional science in the academic literature. [source] Improving the precision of cotton performance trials conducted on highly variable soils of the southeastern USA coastal plainPLANT BREEDING, Issue 6 2007B. T. Campbell Abstract Reliable agronomic and fibre quality data generated in Upland cotton (Gossypium hirsutum L.) cultivar performance trials are highly valuable. The most common strategy used to generate reliable performance trial data uses experimental design to minimize experimental error resulting from spatial variability. However, an alternative strategy uses a posteriori statistical procedures to account for spatial variability. In this study, the efficiency of the randomized complete block (RCB) design and nearest neighbour adjustment (NNA) were compared in a series of cotton performance trials conducted in the southeastern USA to identify the efficiency of each in minimizing experimental error for yield, yield components and fibre quality. In comparison to the RCB, relative efficiency of the NNA procedure varied amongst traits and trials. Results show that experimental analyses, depending on the trait and selection intensity employed, can affect cultivar or experimental line selections. Based on this study, we recommend researchers conducting cotton performance trials on variable soils consider using NNA or other spatial methods to improve trial precision. [source] | ||||||||||