Showing papers on "Spatial analysis published in 1987"

Estimating uncertain spatial relationships in robotics

Abstract: In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map contains the estimates of relationships among objects in the map, and their uncertainties, given all the available information. The procedures provide a general solution to the problem of estimating uncertain relative spatial relationships. The estimates are probabilistic in nature, an advance over the previous, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering theory, which provides a solid basis for numerous extensions.

The Fractal Nature of Geographic Phenomena

Abstract: Fractal concepts have attracted substantial popular attention in the past few years. The key ideas originated in studies of map data, and many of the applications continue to be concerned with spatial phenomena. We review the relevance of fractals to geography under three headings; the response of measure to scale, self-similarity, and the recursive subdivision of space. A fractional dimension provides a means of characterizing the effects of cartographic generalization and of predicting the behavior of estimates derived from data that are subject to spatial sampling. The self-similarity property of fractal surfaces makes them useful as initial or null hypothesis landscapes in the study of geomorphic processes. A wide variety of spatial phenomena have been shown to be statistically self-similar over many scales, suggesting the importance of scale-independence as a geographic norm. In the third area, recursive subdivision is shown to lead to novel and efficient ways of representing spatial data in...

Scale Effects in Landscape Studies

Abstract: Landscape ecology cannot escape dealing with spatial analysis, spatial scale and scale-change effects. A landscape may appear to be heterogeneous at one scale but quite homogeneous at another scale, making spatial scale inherent in definitions of landscape heterogeneity and diversity. In analyzing disturbances and other aspects of landscape change, temporal scale (or temporal resolution of events) may also become an important factor, for similar reasons. The importance of integrating the disparate spatial and temporal scales in landscapes was emphasized by Risser in the preceding chapter.

Autocorrelation of gene frequencies under isolation by distance.

Abstract: Spatial autocorrelation statistics are used for description of geographic variation of gene frequencies, but the relationship of these indices with the parameters describing the genetic structure of populations is not established. A simple relation is derived here between kinship coefficient and a measure of spatial autocorrelation, Moran9s I . The autocorrelation coefficient of gene frequencies at a given distance is a direct function of the kinship at that distance, and an inverse function of the standardized gene frequency variance, F st . Under isolation by distance, the expected values of Moran9s I for any allele may be calculated by means of Malkcot-Morton function, which predicts an exponential decline of genetic similarity in space. This allows comparison of observed gene frequency patterns with the patterns that should be caused by interaction of short range migration and random genetic drift.

Geostatistics and its application to soil science

Abstract: . Geostatistics is principally the application of regionalized variable theory. The methods it embodies are applicable throughout the earth sciences for investigating the spatial variation of, and for estimating continuous random variables. The semi-variogram is the central tool of geostatistics. It can quantify the scale and intensity of spatial variation and it provides the essential spatial information for local estimation by kriging and for optimizing sample intensity. It can also be used in an exploratory manner to try to discover underlying causes of the variation. Geostatistical methods have been widely applied in the mining industry and there are many examples of their application in soil science. Their use is illustrated by a case study of soil spatial variation in the Wyre Forest of England.

Spatial Structure in Drosophila buzzatii Populations: Simple and Directional Spatial Autocorrelation

Abstract: The spatial structure of 12 allele frequencies was examined for 57 populations of the cactophilic fly Drosophila buzzatii from eastern Australia. Techniques include spatial-autocorrelation analysis and the newly developed directional spatial autocorrelation. Although 11 allele frequencies differ among localities, only 6 show spatial structure along one dimension. Directional correlograms support clines or major geographic trends in different directions for 5 allele frequencies. Spatial correlograms were also computed for genetic distances. The overall results-genetic heterogeneity among localities, weak correlation between allele-frequency surfaces, moderate spatial structure, and moderate parallelism of correlograms-permit elimination of genetic drift, selection against a single environmental gradient, or the effects of a single migrational event, and they suggest that selection operates on different spatial scales ranging from a continental one to a strictly local one. These results are interpreted in t...

The Role of Artificial Intelligence in the Integration of Remotely Sensed Data with Geographic Information Systems

Abstract: Although databases for geographic information systems (GIS) have been developed to manage digital map data, the integration of remotely sensed imagery and other collateral non-map information is rarely performed. For the most part, the use of sophisticated intelligent spatial databases, in which the user can query interactively about map, terrain, or associated imagery, is unknown in the GIS and cartographic community. In standard GIS systems, the ability to formulate complex queries requiring dynamic computation of factual and geometric properties is severely limited, often reflecting its origin as collections of thematic map overlays. Spatial database research requires the integration of ideas and techniques from many disciplines such as computer graphics, computational geometry, database methodology, image analysis, photogrammetry, and artificial intelligence. In this paper we discuss some ideas on how the scope of geographic information systems can be expanded by utilizing techniques from the Al community that may remedy deficiencies in user interfaces, spatial data representation, and its utilization. We draw on ongoing research at Carnegie Mellon University for examples of these techniques in the areas of image/map database and knowledge-based image interpretation.

KBGIS-II A knowledge-based geographical information system

Abstract: This paper describes the architecture and working of a recently implemented knowledge-based GIS (KBGIS-II) that was designed to satisfy several general criteria for GIS. The system has four major functions, query-answering, learning, editing and training. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial object language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multi-layered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new defin...

Quantitative analysis of mineral and energy resources

Abstract: 1. Integration of Data for Resource Evaluation.- Application of recent developments of regression analysis in regional mineral resource evaluation.- Poisson regression analysis and its application.- Measurement, sampling and interpretation.- Levels of information and probability mapping.- Review of geomathematical applications for mineral resources evaluation in China.- 2. Statistical Analysis of Geochemical Data.- New approaches to the recognition of anomalies in exploration geochemistry.- Univariate patterns in the design of multivariate analysis techniques for geochemical data evaluation.- Application of robust statistics in the analysis of geochemical data.- 3. Statistical Mehtods for Oil and Gas Resource Evaluation.- U.S. Geological Survey assessment methodology for estimation of undiscovered petroleum resources in play analysis of the Artic National Wildlife Refuge.- Statistical evaluation of petroleum deposits before discovery.- On assessing dry probabilities in offshore oil and gas exploration: an application of Bayes's theorem.- Statistical analysis of oil and gas discovery data.- 4. Image Processing in Earth Sciences.- Automated integration of mineral resource data by image processing and artificial intelligence.- Principles of mathematical morphology.- The use of geological image analysis in remote sensing and seismic attribute integration for resource assessment.- Statistical image processing.- 5. Statistical Analysis of Spatial Data.- Spatial analysis of patterns of land-based and ocean-floor ore deposits.- Point processes for the earth sciences.- 6. Geostatistics.- An unconventional approach to geostatistical estimation.- Case studies on modelling complex sulphide orebodies for ore reserve estimation using geostatistical methods.- A Bayesian approach to surface estimation.- Conditional simulation of indicator data. Case study of a multi-seam coal deposit.- Factorial kriging as a method to include spatial structure into classification. A case study on a sulphide orebody.- Geostatistical techniques for interpreting multivariate spatial information.- 7. Other Statisitcal Methodologies for Geoscience Data.- The shape of Lloydminster oil and gas deposit attribute data.- Confidence bands for the distribution and quantile functions for truncated and randomly censored data.- Geostatistical crustal abundance resource models.- 8. Artificial Intelligence in Earth Sciences.- The usage of Artificial intelligence in remote sensing: a review of applications and current research.- GEOVALUATOR, an expert system for resource appraisal: a demonstration prototype for kaolin in Georgia, U.S.A.- GEMS: a microcomputer-based expert system for digital image data.- On the understanding of geological cross-sections by expert systems.- 9. Other Contributed Papers.- Examples of spatial analysis and management in the geographical and conformal projection domains.- A classification procedure for exploitable reserves.- Drainage and divide networks derived from high-fidelity digital terrain models.- An algebraic model for data structure and manipulation as a design basis for integrated geoscience processing systems.- Case study on application of qualitative data analysis techniques to an uranium mineralization.- A shell for microcomputer exploration software used in developing countries.- Optimal exploration strategies: the synthesis of resource assessments and operations research.- Advantages and limitations of discovery process modeling: the case of the Northern West Siberian gas plays.- Some aspects of multivariate analysis.- Workshop Reports.- Current problems and future developments in multivariate analysis.- Current problems and future developments in oil and gas resource modeling and forecasting.- Current problems and future developments in spatial statistics and image processing.- Current problems and future developments in mineral and energy resource expert system development.- Current problems and future developments in the use of microcomputers.

Applications of Spatial Autocorrelation in Ecology

Abstract: The methods of spatial auto correlation analysis for both continuous and nominal variable are explained. Spatial correlograms depict autocorrelation as a function of geographic distance. They permit inferences from patterns to process. The Mantel test and its extensions are special ways of detecting autocorrelation in ecology. The methods are applied to the spatial distributions of ecological variables in two under story plants in the genus Aralia.

Cranial variation in European populations: a spatial autocorrelation study at three time periods.

Abstract: This study reports on spatial variation of 10 cranial variables in European populations at 3 time periods. Means for these variables, based on 137, 108, and 183 samples from the Early Medieval, Late Medieval, and Recent periods, were subjected to one-dimensional and directional spatial autocorrelation analyses. Significant spatial structure was found for most variables. It becomes more pronounced as time progresses. The spatial patterns are not strongly clinal. Correlograms based on distances computed from all variables are monotonic only to 900, 1,650, and 1,350 km for the three periods. Regional patterns are seen for most variables and become more structured and significant with time. There is little similarity among the correlograms of the variables at any one period and virtually none among periods. Inferences about spatial structure of these populations, based on spatial autocorrelation analysis, suggest a pattern dominated by migration, followed by expansion and admixture rather than selection or chance fluctuations. The patterns of morphometric change seem to reflect the patterns of linguistic change in these areas.

Design of a spatial data structure using the relational normal forms

Abstract: In previous work, a relational data structure aimed at the exchange of spatial data between systems was developed As this data structure was relational it was of first normal form, but compliance with the higher normal forms was not investigated Recently, a new procedural method for composing fully normalized data structures from the basic data fields has been developed by H C Smith, as an alternative to the process of non-loss decomposition which is difficult to understand Smith's method has been applied to data fields required to store points, lines and polygons in a chain-node spatial data model When geographic domain, coverage layer and map are also considered, the procedure naturally leads to a catalogue model, needed for the exchange of spatial data Although the method produces a fully normalized data structure, it is not as easy to identify which normal forms are responsible for the ultimate arrangement of the data fields into relations, but the benefits of these criteria for data b

The Spatial Analysis of Peace and War

Abstract: This article uses methodological insights to argue for a reconceptualization of the way in which we approach spatial data, and specifically for a rejection of the primacy of the nation state as the unit of analysis. We pay particular attention to the way in which the analysis of data with a geographic basis is undertaken, particularly in situations where some form of data aggregation has been undertaken, and in cases where there exists a wide variation in the size of data units. This analysis explores the implications of this variation, employing the technique of spatial autocorrelation. Our study of peace and war in Africa extends previous analyses by Starr and Most, and illustrates the way in which the measurement of spatial autocorrelation can be employed as a powerful analytical tool to identify relations of conflict and cooperation. This article builds on these findings with some speculations on the ways in which further research on conflict and cooperation might proceed, in both technical and concep...

A Simulation Study of Some Contextual Classification Methods For Remotely Sensed Data

Abstract: Various methods for contextual classification of multispectral scanner data have been developed during the last 15 years, aiming at increased accuracy in classified images. The methods have for a large part been of four main types: 1) neighborhood-based classification based on stochastic models for the classes over the scene and for the vectors given the classes; 2) simultaneous classification of all pixels, using, e.g., Markov random-field models; 3) relaxation methods that iteratively modify posterior probabilities using information from an increasing neighborhood; and 4) methods using ordinary noncontextual rules based on transformed data. In the present paper a selection of these methods is presented and compared using computer-gented data on different scenes. Spatial autocorrelation is present in the data. Error rates are compared, and an attempt is made to characterize what kind of errors each particular method makes.

Spatial and functional representation language for structural design

Abstract: Knowledge-based systems for structural design developed to date have used simple geometric representations which have not provided adequate spatial reasoning. Shape grammars are suggested as a representation for a knowledge-based system capable of performing spatial and functional reasoning. The representation needs to serve all disciplines involved in the design process, where different semantics of each discipline are associated with the same spatial information about design objects. The representation is demonstrated in the building design environment, where possible structural systems can be generated dependent upon the building's spatial layout.

Spatial kd-Tree: A Data Structure for Geographic Database

Abstract: Geographic objects in two dimensional space are usually represented as points, lines, and regions. To retrieve these data objects from the database efficiently according to their spatial locations and spatial relationships, an efficient indexing mechanism is necessary. The kd-trees proposed in the literature are either unsuitable for indexing non-zero size objects such as line and region or require duplication of indexes. In this paper an alternative index structure called spatial kd-tree is proposed to facilitate the processing of queries concerning geographic information. The spatial kd-tree partitions a set of records on two dimensional space into small groups based on their spatial proximity. The structure not only provides efficient retrieval of objects but also maintains high storage efficiency.

A computer-based test battery for the assessment of static and dynamic spatial reasoning abilities

Abstract: A battery of 10 computerized tests of spatial ability is described. It includes 5 tests that require reasoning about static spatial displays and 5 tests that require reasoning about dynamically displayed spatial information. An integrated software package for task presentation and data analysis is described, along with a summary of results from a validation study comparing performance on the computer-based tests with performance on standardized paper-and-pencil tests of spatial abilities. Finally, research applications of the current battery are discussed.

Spatial Data for Modeling Wildlife Habitat

Abstract: Three models are presented that use the presence and spatial characteristics of land‐cover types to calculate quality of wildlife habitat. The modeling approaches include the use of a Geographic Information System (GIS) and a multiple variable data base for modeling habitat characteristics, and an Integrated Point Area model (IPA) and a Point Specific Estimator model (PSE) for estimation of quality from single variable data bases. Applications of these models illustrate the capability and sensitivity of spatial models and parameters derived from map and remote sensor data. Results included a verification of the IPA model, a sensitivity analysis of parameters in the PSE model, and a test of the GIS model. Model evaluations revealed the value of these approaches for supplying habitat quality ratings. The models together constitute an evaluation of each element of a comprehensive experiment, even though elements are missing from individual experiments.

A Mathematical Structure for Analyzing Maps

Abstract: The growing use of computers in environmental management is profoundly changing data collection procedures, analytic processes, and even the decision-making environment itself. The emerging technology of geographic information systems (GIS) is expanding this revolution to integrate spatial information fully into research, planning, and management of land. In one sense, this technology is similar to conventional map processing involving traditional maps and drafting aids, such as pens, rub-on shading, rulers, planimeters, dot grids, and acetate sheets for light-table overlays. In another sense, these systems provide advanced analytic capabilities, enabling managers to address complex issues in entirely new ways. This report discusses a fundamental approach to computer-assisted map analysis that treats entire maps as variables. The set of analytic procedures for processing mapped data forms a mathematical structure analogous to traditional statistics and algebra. All of the procedures discussed are available for personal computer environments.

Trend-surface models with regional and local scales of variation with an application to aerial survey data

Abstract: This article specifies a trend-surface model with spatially correlated errors as a model for spatial variation with three scale components, site, local, and regional scales. An iterative scheme is proposed to estimate parameters, and three different ways of modeling the spatially correlated error component are considered, including maximum likelihood. The article evaluates the three approaches and brings together some recent developments in spatial statistics for parameter estimation and inference. There is discussion of an example to describe spatial variation in marine pollution levels monitored from an aerial survey.

CBD Land Values and Multiple Externalities

Abstract: Generalized spatial models of urban activity emphasize the importance of a land value gradient that declines with distance from the central point within an urban area. Empirical tests of this phenomenon consistently support the theory (Diamond 1980; Chicoine 1981; Asabere and Harvey 1985). However, there has been much less research on the nature of land value gradients within the narrower geographic confines of the Central Business District (CBD) itself. Utilizing a unique data set of actual vacant land transactions, this study empirically explores the pattern of land values within a major metropolitan CBD. A behavioral model of land values is specified and several alternative functional forms for the general model are estimated. These estimates are then compared to estimates of a non-behavioral model that is based on purely spatial relationships, trend surface analysis. A close look at the spatial implications of both models reveals that there are many pitfalls in the analysis of land values within a tightly bound geographic area. In particular, there are inherent statistical problems in utilizing micro data in the confines of a CBD. Also, the confounding influence of street externalities may distort exact locational centrality of the dominant point in the CBD. Finally, care must be taken to prevent the tendency to interpret spatial data beyond the actual spatial confines of the data itself.

Mapping and map analysis: new tools for land evaluation

Abstract: . Rural planning studies require knowledge about the attributes and spatial distributions of the natural resources of areas to be developed. When these data are available only in the form of paper maps simple technology limits the planner in the number of questions that can be answered quickly and effectively. Geographical information systems now enable mapped data to be stored and linked to other relevant spatial information so that many kinds of questions about the natural resources can be answered, and various scenarios can be compared before they are carried out. The methods used for data analysis in geographical information systems are explained briefly and are illustrated by using an example of locating a simple earth dam in a small catchment in Kisii District, Kenya. Although these new tools are often technically excellent, the results they give can be no better than the quality of the data and the models used for analysis allow. There is still much work to he done on the propagation of errors in geographical analysis, whether done with the aid of the computer or not.

GIS and remote sensing data integration

Abstract: Geographic Information Systems are concerned with the digital capture of spatial data and spatially related attributes and their linkage relative to one another. Most importantly, geographic information processing deals with the query, analyses, reporting and output of these data. Remote sensing has always provided a primary source of geographic data to these systems, although not in the digital sense. Environmentally based surveys such as those developed for forest inventory, have relied heavily on aerial photography since its inception as a large area operational tool four decades ago; however, digital remote sensing has now been brought to a point in its development where real applications have been demonstrated. The functional requirements to develop an operational interface for forestry related applications are discussed, including both graphics display and data processing problems.

Teaching spatial autocorrelation by simulation

Abstract: A computer simulation game to help teach spatial autocorrelation is presented. Its use assumes that students have a minimal background in introductory statistics and its focus is on efficiently and effectively searching through a sampling distribution, seeking map patterns with particular levels of spatial autocorrelation. The sampling distribution is constructed using randomisation, and the search process is guided by calculations of Geary's contiguity ratio. Experiences with this simulation exercise at SUNY/Buffalo are briefly summarised.

Knowledge-based classification of landsat thematic mapper digital imagery

Abstract: An innovative approach to identifying land use and land cover categories through computer-assisted analyses of digital satellite remote sensing image data is presented. The technique is based on the subfield of artificial intelligence known as expert systems, commonly referred to as knowledge-based systems. The methodology developed embodies expert image analyst rules and heuristics in a knowledge base which is used to classify regions of a Landsat Thematic Mapper image. Expert knowledge about and image attributes from spectral, spatial, and temporal domains are addressed. The procedures for image and ancillary data preprocessing, knowledge acquisition, knowledge-based image analysis, and traditional image classification are discussed. Both the deterministic knowledge-based image analysis approach and the traditional statistical maximum likelihood approach were applied to multidate Landsat Thematic Mapper digital imagery to derive land use and land cover information. It was found that a knowledge-based approch to the classification of specially-processed, digital remote sensing imagery, coupled with spatial information, produced results superior, in terms of accuracy and visual comprehensibility, to those achieved through conventional per-pixel, supervised classification of multispectral data alone. It was illustrated that the knowledge-based method developed permits the inclusion of heuristics and decision criteria not possible in the strictly numerical, algorithmic approach. Because of the success of this prototype knowledge-based image analysis system, and because of its parallelism with human visual image analysis processes, additional research into this area is recommended and briefly discussed.

Remote Sensing Technologies and Spatial Data Applications.

Abstract: : In the last decade, significant new tools have become available for planners, managers and scientists working in hydrologic engineering. Two new and significant tools are the widespread availability of spaceborne multi-spectral remote sensing systems, and the development of more sophisticated and less expensive micro computer work stations for both image processing and spatial data(GIS)analyses. This paper describes an evaluation of emerging remote sensing and spatial data capabilities and applications performed for the Corps of Engineers Hydrologic Engineering Center at Davis, California. It first surveys recent and planned spaceborne remote sensing systems providing data relevant to the hydrologic community. Next, integraded digital image processing and Geographic Information Systems(GIS)available today on microcomputers for applied hydrologic analyses are reviewed. Finally, the interaction of these capabilities is examined in the context of specific hydrologic engineering and planning tasks, ranging from real-time flood forecasting, to urban watershed modeling, to snow cover, evaporation, and soil moisture estimation. Keywords: Remote sensing; Hydrology; Imagery; Satellite flood; Geographic information system; Precipitation. (rh)

Research for Reducing the Labor Intensive Nature of High-Resolution Terrain Analysis Feature Extraction

Abstract: : Based on terrain analyst productivity estimates of 1000 man-hours per 15 by 15 arc-minute area, the time required to complete a single terrain analysis of the world's land surface exceeds several hundred thousand man-years. Another dilemma arises from the way we currently store and use spatial data. Current geographic information system techniques emphasize a 'brute-force' search approach to spatial storage, query and analysis. If global high- resolution terrain data were available, the response time for certain 'brute- force' data base queries might approach the above time estimates for compilation. The following research strategies are discussed which address the high-resolution dilemmas. First, terrain feature extraction should be approached from a 'minimum compilation', 'maximum analysis' strategy. In other words, map only the key terrain components, and gather additional information by thorough analysis and inferencing from this compiled spatial data. This basic approach parallels techniques used extensively in manual photo-based terrain analysis. Secondly, knowledge needs to be incorporated into all phases of terrain data compilation, storage and analysis. Low-level geometric knowledge of spatial features can be used to organize and group data together that are important at a higher symbolic level of terrain understanding. Similarly, high-level knowledge and models of regional factors such as climate and geomorphology can be used to constrain 'brute-force' search, detect errors and handle incomplete information. Exploitation of terrain knowledge in digital spatial information technology can reduce the 'data-rich' requirement and 'knowledge poor' state of current systems.

Advanced techniques for the storage and use of very large, heterogeneous spatial databases. The representation of geographic knowledge: Toward a universal framework

Abstract: A new approach to building geographic data models that is based on the fundamental characteristics of the data is presented. An overall theoretical framework for representing geographic data is proposed. An example of utilizing this framework in a Geographic Information System (GIS) context by combining artificial intelligence techniques with recent developments in spatial data processing techniques is given. Elements of data representation discussed include hierarchical structure, separation of locational and conceptual views, and the ability to store knowledge at variable levels of completeness and precision.