Showing papers on "Spatial analysis published in 1978"

Spatial autocorrelation in biology: 1. Methodology

Abstract: Spatial autocorrelation analysis tests whether the observed value of a nominal, ordinal, or interval variable at one locality is independent of values of the variable at neighbouring localities. The computation of autocorrelation coefficients for nominal, ordinal, and for interval data is illustrated, together with appropriate significance tests. The method is extended to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities being considered, and summarize the patterns of geographic variation exhibited by the response surface of any given variable. Autocorrelation analysis is applied to microgeographic variation of allozyme frequencies in the snail Helix aspersa. Differences in variational patterns in two city blocks are interpreted. The inferences that can be drawn from correlograms are discussed and illustrated with the aid of some artificially generated patterns. Computational formulae, expected values and standard errors are furnished in two appendices.

Spatial autocorrelation in biology: 2. Some biological implications and four applications of evolutionary and ecological interest

Abstract: Spatial autocorrelation analysis tests whether the observed value of a variable at one locality is significantly dependent on values of the variable at neighbouring localities. The method was extended by us in an earlier paper to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities, and summarize the patterns of geographic variation exhibited by the response surface of any given variable. Identical variation patterns lead to identical correlograms, but different patterns may or may not yield different correlograms. Similarity in the correlograms of different variation patterns suggests similarity in the generating mechanism of the pattern. The inferences that can be drawn from correlograms are discussed and illustrated. Examination and analysis of variation patterns of several characters or gene frequencies for one population, or of several populations in different places or at different times, permit some conclusions about the nature of the populational processes generating the observed patterns. Autocorrelation analysis is applied to four biological situations differing in the nature of the data (interval or nominal), in the type of grid connecting the localities (regular or irregular), and the field of application (evolution or ecology). The examples comprise genotypes of individual mice, blood group frequencies in humans, gene frequency variation in a perennial herb, and the distribution of species of trees. The implications of our findings are discussed.

An optimal zoning approach to the study of spatially aggregated data

Abstract: The quantitative revolution in geography, planning and the other spatial sciences involved the importation and application of statistical methods and mathematical model-building techniques which place little emphasis on the importance of space. Currently many studies are based on the application of a paradigm and a methodology which are intrinsically aspatial; they justify the adjective ‘spatial’ only in the limited sense that they use spatial data. This is particularly characteristic of those studies which are concerned with the analysis and modelling of data which have been spatially aggregated one or more times.

Comprehensive Flood Plain Studies Using Spatial Data Management Techniques.

Abstract: : A pilot study undertaken to develop and test analytical methodologies for application in comprehensive flood plain information studies is described. The methodology permits and encourages comprehensive, systematic, practical assessments of present and alternative future basin-wide development patterns as reflected by alternative land use patterns and physical works in terms of flood hazard, economic damage potential and selected environmental consequences. The analysis methodologies are centered about integrated use of computerized spatial, gridded geographic and resource data files. A family of special purpose utility computer programs access the data file and extract appropriate variables and interpret and format the data into specific analytical parameters that are subsequently formatted for input to traditional modeling computer programs. An example application to Trail Creek in Clarke County, Georgia, is described. (Author)

A spatial model for high plains agriculture

Abstract: A random field model, consisting of a regional trend component and a local interaction component, describes the spatial pattern of corn and wheat yields in the High Plains. The presence of spatial autocorrelation in the local component raises interesting statistical estimation problems. Temporal variations in the slope and interaction parameters are a function of climatic fluctuations. The local component reflects intercounty spatial autocorrelation in precipitation patterns.

On Searching for the Most Informative Spatial Pattern

Abstract: This paper is concerned with an inquiry into the way in which the organisation of a spatial data set affects the interpretation of the spatial phenomena which it records, in terms of its underlying pattern or density. It is argued that the number and configuration of zones affects the level of information which is imparted to the spatial analyst, and thus the quest becomes one in which the data set is to be reorganised spatially to impart maximum information. The paper sets out to define an appropriate measure of information and an algorithm designed to optimise its value. The information measure developed is in essence a modified Shannon entropy, modified to account for uneven zonal configurations and converging to Shannon entropy when the zoning system is equal-area and the distribution the best approximation to the density. The measure has some well-known aggregation properties which are presented and several interpretations of its form in spatial terms are made. Empirical measurements of this informat...

Guide Manual for the Creation of Grid Cell Data Banks

Abstract: : Spatial Data Management techniques are rapidly becoming practical tools for use by Corps of Engineers field offices in a variety of their responsibilities. Various aspects of these techniques have been applied in traditional Survey and Phase I General Design Memorandum Studies and on a large scale in the Expanded Flood Plain Information (XFPI) studies of the Corp's Flood Plain Management Services program. The use of grid data, e.g., spatial data stored in computer files in a specific grid cell format, has been determined to be the only spatial data management technique that offers significant analytical opportunities when compared to polygon oriented approaches. The grid structure successfully used in applications to date have included square, rectangular, and triangular cells, the latter being an emerging method with particular potential in the Management of terrain (topographic) data. This manual is intended to be initial documentation of the basic procedures that are necessary for the successful creation of a grid cell data bank. The manual was prepared primarily to aid the XFPI pilot studies in which gridded data banks are being created as a major focal point of the studies. However, anyone interested in spatial data management should find that the manual contains valuable information.

Flood Damage Assessments Using Spatial Data Management Techniques.

Abstract: : The Corps of Engineers Hydrologic Engineering Center has developed techniques that perform the spatial data analysis approach and individual structure approach and work is near completion on an integrated analysis package. The capability therefore exists to perform damage appraisals in a manner that encourages a general geographic and land use approach (thus greatly facilitating the study of nonstructural measures) while preserving the ability to analyze individual, unique structures should the need arise. This paper discusses the basic concepts of a spatial data management approach to damage appraisals and highlight(s) its integrated use with more traditional individual structure approaches. Selected example results are presented. (Author)

Forests and Pyramids: Using Image Hierarchies to Understand Landsat Images

Abstract: Computer-based Landsat image interpretation has neglected the spatial organization of the image in favour of the spectral and temporal organization. A brief survey of techniques that exploit spatial information, including multistage sampling, is given. Semantically-guided region-merging methods have been used successfully but they require sophisticated and expensive list processing facilities. Similar semantic and spatial sensitivity can be introduced by exploiting a pyramidal, hierarchical representation of the image advocated by Kelly, Tanimoto and Levine. The image pyramid is constructed bottom-up with the original image as the base. Each level is a reduced resolution version of the level below, constructed by averaging the signatures of adjacent pixels at the lower level. By classifying pixels at the higher levels one is efficiently classifying semantically uniform regions in the original image. If, however, a region''s signature lies in the spectral overlap of two or more classes its subregions will have to be considered for classification. Several refinements of this technique, including the use of semantically-based region splitting and merging techniques at each level of the pyramid, are described. .br These techniques are used to classify forest cover types on Vancouver Island in a Landsat image. The results of several initial experiments indicate that, compared to a baseline of a traditional supervised maximum-likelihood classifier, the cost of maintaining the pyramid is balanced by the vast reduction in the number of pixel classifications. The spatial homogeneity or readability of the segmented image, as measured by the number of regions, is improved by a factor of three while the accuracy of the classification is unaffected or slightly improved. When the region splitting and merging techniques are applied at each level of the imaqe pyramid the accuracy and the readability of the final segmentation both increase markedly. It is thereby demonstrated that these pyramidal techniques offer many of the advantages of the semantically-driven region-merging approach in a more flexible and efficient fashion. Indeed the two approaches have been combined to achieve substantial benefits for Landsat image interpretation.

Preliminary Design for a Spatial Data Management System

Abstract: : Spatial Data Management is a technique for organizing and retrieving information by positioning it in a Graphical Data Space. In contrast to conventional database management systems (DBMSs) which require that information be stored and retrieved by specifying attributes as numbers or strings of text, Spatial Data Management allows a user to employ the spatial location and visual appearance of information in order to find it. The underlying concept is that spatial location is, for many purposes, easier to remember and work with than the keywords of an ordinary DBMS. By allowing a datum to be stored in proximity to related pieces of information, a Spatial Data Management System (SDMS) frees the user of the need to know the exact name or location of the information that he seeks. Instead, he can locate it by 'browsing' until he finds something that he can identify visually. Spatial management of data can be combined with conventional DBMS techniques to yield a system which provides both means of access to information.

Digital Analysis of Multi-Channel Radar Data at CCRS

Abstract: Because of the coarse spatial resolution of LANDSAT scanner data, the four or five spectral radiance measurements for each pixel contribute more to successful target identification than does the spatial information. On the other hand, the higher spatial resolutions achieved by synthetic aperture radar (SAR) lead to an increased importance of textural features in pattern classification algorithms. Thus, it is not surprising that automated schemes developed for use on LANDSAT data often work very poorly on radar data. Moreover, the coherent speckle, which is characteristic of SAR imagery, precludes pixel by pixel classification unless the data are smoothed considerably.