Showing papers on "Spatial analysis published in 1991"

Statistics for spatial data

Abstract: Statistics for Spatial Data GEOSTATISTICAL DATA Geostatistics Spatial Prediction and Kriging Applications of Geostatistics Special Topics in Statistics for Spatial Data LATTICE DATA Spatial Models on Lattices Inference for Lattice Models SPATIAL PATTERNS Spatial Point Patterns Modeling Objects References Author Index Subject Index.

Bayesian image restoration, with two applications in spatial statistics

Abstract: There has been much recent interest in Bayesian image analysis, including such topics as removal of blur and noise, detection of object boundaries, classification of textures, and reconstruction of two- or three-dimensional scenes from noisy lower-dimensional views. Perhaps the most straightforward task is that of image restoration, though it is often suggested that this is an area of relatively minor practical importance. The present paper argues the contrary, since many problems in the analysis of spatial data can be interpreted as problems of image restoration. Furthermore, the amounts of data involved allow routine use of computer intensive methods, such as the Gibbs sampler, that are not yet practicable for conventional images. Two examples are given, one in archeology, the other in epidemiology. These are preceded by a partial review of pixel-based Bayesian image analysis.

Rejoinder (Bayesian image restoration,with two applications in spatial statistics)

Abstract: There has been much recent interest in Bayesian image analysis, including such topics as removal of blur and noise, detection of object boundaries, classification of textures, and reconstruction of two- or three-dimensional scenes from noisy lower-dimensional views. Perhaps the most straightforward task is that of image restoration, though it is often suggested that this is an area of relatively minor practical importance. The present paper argues the contrary, since many problems in the analysis of spatial data can be interpreted as problems of image restoration. Furthermore, the amounts of data involved allow routine use of computer intensive methods, such as the Gibbs sampler, that are not yet practicable for conventional images. Two examples are given, one in archeology, the other in epidemiology. These are preceded by a partial review of pixel-based Bayesian image analysis.

Integrating multi-criteria evaluation with geographical information systems

Abstract: Geographical information systems (GIS) provide the decision-maker with a powerful set of tools for the manipulation and analysis of spatial information. The functionality of GIS is, however, limited to certain deterministic analyses in key application areas such as spatial search. The integration of multi-criteria evaluation (MCE) techniques with GIS is forwarded as providing the user with the means to evaluate various alternatives on the basis of multiple and conflicting criteria and objectives. An example application based on the search for suitable sites for the disposal of radioactive waste in the UK using the Arc/Info GIS is included. The potential use of a combined GIS-MCE approach in the development of spatial decision support systems is considered.

Quantitative methods in landscape ecology : the analysis and interpretation of landscape heterogeneity

Abstract: Landscape ecology as a modern interdisciplinary science is increasingly making use of quantitative research techniques adopted from other fields. This text is intended for use by those wishing to acquaint themselves with new approaches to quantitative analysis of spatial heterogeneity at the landscape level. This book seeks to provide a cenceptual framework and illustrating potential applications for methods such as patterns analysis, spatial statistics, fractals, spatial modeling, broad-scale studies, and extrapolation across scale.

The accuracy of spatial databases

Abstract: Error modelling for the map overlay operation modelling error in overlaid categorical maps user considerations in landscape characterization knowledge-based approaches to determining and correcting areas of unreliability in geographical databases observations and comments on the generation and treatment of error in digital GIS data developing confidence limits on errors of suitability analyses in geographical information systems distance calculations and errors in geographical databases inclusion of accuracy data in a feature based, object- orientated data model accuracy and bias issues in surface representation modelling error in objects and fields frame independence spatial analysis modelling locational uncertainty via hierarchical tesselation minimum cross-entropy convex decompositions of pixel-indexed stochastic matrices - a geographical application of the Ising model the traditional and modern look at Tissot's indicatrix real data and real problems - dealing with large spatial databases the small number problems and the accuracy of spatial databases demand point approximations for location problems modelling realibility on statistical surfaces by polygon filtering scale-independent spatial analysis the effect of data aggregation on a poisson model of Canadian migration statistical methods for inference between incompatible zonal systems statistical effect of spatial data transformations - a proposed general framework learning to live with errors in spatial databases.

An introduction to urban geographic information systems

Abstract: Information in the organization Geographic information systems defined Applications of geographic information systems Topological data structures Geographic base files Land records information The model urban GIS project Applications digest Glossary Index

Linear Models for Multivariate, Time Series, and Spatial Data

Abstract: Multivariate linear models discrimination and allocation frequency analysis of time series time domain analysis linear models for spatial data.

Dynamic Graphics for Exploring Spatial Data with Application to Locating Global and Local Anomalies

Abstract: We explore the application of dynamic graphics to the exploratory analysis of spatial data. We introduce a number of new tools and illustrate their use with prototype software, developed at Trinity College, Dublin. These tools are used to examine local variability—anomalies—through plots of the data that display its marginal and multivariate distributions, through interactive smoothers, and through plots motivated by the spatial auto-covariance ideas implicit in the variogram. We regard these as alternative and linked views of the data. We conclude that the most important single view of the data is the Map View: All other views must be cross-referred to this, and the software must encourage this. The view can be enriched by overlaying on other pertinent spatial information. We draw attention to the possibilities of one-many linking, and to the use of line-objects to link pairs of data points. We draw attention to the parallels with work on Geographical Information Systems.

Spatial Interaction and Spatial Autocorrelation: A Cross-Product Approach

Abstract: A cross-product statistic is used to demonstrate that spatial interaction models are a special case of a general model of spatial autocorrelation. A series of traditional measures of spatial autocorrelation is shown to have a cross-product form. Several interaction models are shown to have a similar form. A general spatial statistic is developed which indicates that the relationship between the two types of models is particularly strong when the focus is on measurements from a single point.

Design of a Lineage-Based Meta-Data Base for GIS

Abstract: A conceptual design is presented for a lineage meta-data base system that documents data sources and geographic information system (GIS) transformations applied to derive cartographic products. Artificial intelligence techniques of semantic networks are used to organize input-output relationships between map layers, and frames are used for storing lineage attributes characterizing source, intermediate, and product layers. An example indicates that a lineage meta-data base enables GIS users to determine the fitness for use of spatial data sets.

Problems with handling spatial data — the voronoi approach

Abstract: Experience with the handling of spatial data on a computer led to the identification of a variety of “awkward” problems, including interpolation, error estimation and dynamic polygon building and e...

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

Abstract: This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

The temporal and spatial analysis of tree age distributions

Abstract: The temporal and spatial patterns of tree establishment and stand disturbance history are often based on the interpretation of age-class frequency distributions. In particular, the presence of even-aged groups of trees is often used as compelling evidence of past disturbance. However, even-aged groups of trees may be indistinguishable in an age distribution if several different-aged patches occur, especially if their ages overlap. For two different types of forest we used spatial autocorrelation analysis to statistically test for the presence of even-aged patches in tree age data. Ordination and cluster analysis were subsequently applied to a matrix of association measures that reflected both spatial proximity and age similarity to identify even-aged groups of trees. Although the method worked well for our forests, which contained light-demanding tree species, it is likely to be less applicable to forests dominated by shade-tolerant species, because trees may be of many different ages if they were present...

Testing inferences about micro-evolutionary processes by means of spatial autocorrelation analysis.

Abstract: We generated numerous simulated gene-frequency surfaces subjected to 200 generations of isolation by distance with, in some cases, added migration or selection. From these surfaces we assembled six data sets comprising from 12 to 15 independent allele-frequency surfaces, to simulate biologically plausible population samples. The purpose of the study was to investigate whether spatial autocorrelation analysis will correctly infer the microevolutionary processes involved in each data set. The correspondence between the simulated processes and the inferences made concerning them is close for five of the six data sets. Errors in inference occurred when the effect of migration was weak, due to low gene frequency differential or low migration strength; when selection was weak and against a background with a complex pattern; and when a random process- isolation by distance-was the only one acting. Spatial correlograms proved more sensitive to detecting trends than inspection of gene-frequency surfaces by the human eye. Joint interpretation of the correlograms and their clusters proved most reliable in leading to the correct inference. The inspection and clustering of surfaces were useful for determining directional components. Because this method relies on common patterns across loci, as many gene frequencies as feasible should be used. We recommend spatial autocorrelation analysis for the detection of microevolutionary processes in natural populations.

Modelling soil map-unit inclusions by Monte Carlo simulation

Abstract: Error and uncertainty in spatial databases have gained considerable attention in recent years. The concern is that, as in other computer applications and, indeed, all analyses, poor quality input data will yield even worse output. Various methods for analysis of uncertainty have been developed, but none has been shown to be directly applicable to an actual geographical information system application in the area of natural resources. In spatial data on natural resources in general, and in soils data in particular, a major cause of error is the inclusion of unmapped units within areas delineated on the map as uniform. In this paper, two alternative algorithms for simulating inclusions in categorical natural resource maps are detailed. Their usefulness is shown by a simplified Monte Carlo testing to evaluate the accuracy of agricultural land valuation using land use and the soil information. Using two test areas it is possible to show that errors of as much as 6 per cent may result in the process of...

Integration of remote sensing and GIS: Data and data access

Abstract: The integration of remote sensing tools and technology with the spatial analysis orientation of geographic information systems is a complex task. In this paper, we focus on the issues of making data available and useful to the user. In part, this involves a set of problems which reflect on the physical and logical structures used to encode the data. At the same time, however, the mechanisms and protocols which provide information about the data, and which maintain the data through time, have become increasingly important. We discuss these latter issues from the viewpoint of the functions which must be provided by archives of spatial data.

The Mathematical Modeling of Spatial and Non-Spatial Information in Geographic Information Systems

Abstract: Geographic Information Systems (GIS) use a variety of approaches to model spatial information and the data processing associated to spatial analysis. Each of these primitive data models has its own set of inherent strengths and weaknesses which determine how its users view the spatial world and reason about it. Further, each GIS application has developed its own jargon to describe complex spatial interactions not usually addressed in natural languages. To study how humans reason about space should require us to examine these logical approaches to spatial reasoning, especially those which extend natural language concepts with their own more application specific jargon. To extend this study to encompass methods for automated spatial reasoning in a manner applicable to existing GIS environments and applications requires a logically consistent framework for the models themselves, for the manners in which they interact, and for the spatial concepts that they can represent.

Simulation of non‐Gaussian space random functions for modeling transport in groundwater

Abstract: The recent developments and successful applications of covariance-related stochastic methods to groundwater fields experiments suggest that further efforts in developing spatial characterization methods are warranted. One area that deserves further attention is that of characterizing multimodal distributions, such as fractured rock masses, dolomite rocks with dissolution channels, and sand-shale formations. Traditionally used covariance functions may not be enough to fully characterize spatial continuity applications where the pattern of spatial continuity depends on the specific magnitude level of the attribute, for example, hydraulic conductivity. Alternative models based on multiple indicator covariances or mixture of populations offer greater flexibility. Applying such models to the case of perfect stratification, a stochastic simulations is proposed which allows generating different realizations, all sharing the same vertical conductivity covariance yet differing by other spatial statistics and leading to widely different transport characteristics. A mere covariance-based approach would have failed to distinguish between these alternatives. Investigation of tracer transport in bimodal stratified formations suggests a dependence of the mean advection velocity on the dispersion coefficients as a result of a mechanism that enhance longitudinal dispersion directly proportional to lateral pore scale dispersion and entails a diversion of a substantial portion of the plume into the low-conductivity layers.more » This mechanism may not be evident when the class-specific spatial structures of the conductivities are ignored.« less

Remote sensing and geographical information systems for resource management in developing countries.

Abstract: 1. Principles of Remote Sensing: Electromagnetic Radiation, Reflectance and Emissivity.- 2. Principles of Remote Sensing: Interaction of Electromagnetic Radiation with the Atmosphere and the Earth.- 3. Spectral Characteristics of Vegetation, Soil and Water in the Visible, Near-infrared and Middle-infrared wavelengths.- 4. Remote Sensing Systems: Sensors and Platforms.- 5. The Processing and Interpretation of Remotely-sensed Satellite Imagery: A Current View.- 6. A Quantitative approach to remote sensing: Sensor calibration and comparison.- 7. The current status of ERS-1 and the role of radar remote sensing for the management of natural resources in developing countries.- 8. Vegetation Canopy Reflectance: Factors of Variation and Application for Agriculture.- 9. Remote Sensing for Vegetation Monitoring on Regional and Global Scales.- 10. Remote Sensing and Agricultural Production Monitoring in Sahelian Countries.- 11. Rainfall Estimation in Africa using Remote Sensing Techniques.- 12 Watershed Degradation - Use of Thermal Data and Vegetation Indices as Indicators of Environmental Changes - Hydrological Implications of Changes in Land Surface Cover.- 13. Remote Sensing for Tropical Forest Monitoring: An Overview.- 14. Basic Principles of Geographic Information Systems.- 15. Computer Systems for Geographic Information Systems.- 16. Data Input and Output.- 17. Spatial Databases.- 18. Data Analysis and Modelling.- 19. Errors in Geographic Information Systems.- 20. Spatial Data Analysis in Raster Based GIS: An Introduction to Geometric Characterization.- 21. An Introduction to Expert Systems in Spatial Data Analysis.- 22. Land Use Model Using a Geographical Information System.- 23. Soil Geographic Database: Structure and application example.- 24. Integration of GIS and Remote Sensing in Land Use and Erosion Studies.- 25. GIS Education and Training.

Using spatial information to analyze correlations between test structure data (semiconductor IC manufacture)

Abstract: A modeling strategy is presented that captures the dependence of performance on the spatial position of the chips on the water. The information from this model can be used to determine whether the variance and correlation of parameters are due to either random variation or deterministic function of wafer position. The modeling strategy covers deterministic variations of the mean as a function of wafer position. The authors provide a method to determine the amount of correlation which is due to the common spatial dependence (referred to as spatial correlation). When coupled with knowledge about the manufacturing process, the diagnosis system can determine the physical reasons for the yield loss. The problem is formalized and a solution is developed. Extensions to this model are discussed. >

Highway accidents: a spatial and temporal analysis

Abstract: A temporal, spatial, and spatial-temporal autocorrelation analysis of highway accidents on the Indiana Toll Road from 1983 to 1987 is presented. Applications of von Neumann's ratio, Moran's I, nearest-neighbor analysis, and a spatial-temporal autocorrelation coefficient to a transportation network situation are illustrated. Applications of these methods to transport network attributes, such as accidents, have not appeared previously. The main objectives are to determine whether these techniques are sensitive enough to distinguish different patterns in the accident distributions and whether these patterns are explainable. The analysis involved 10 sets of accident data, categorized by date of occurrence and location on an east-west roadway. Only 2 of the 10 revealed positive temporal autocorrelation (clustering in time), 5 revealed positive spatial autocorrelation (clustering in space), and between 6 and 9, depending on the method used, revealed positive spatial-temporal autocorrelation (clustering in time and space). These results suggest that observed autocorrelations in accidents are a function of weather conditions or traffic volumes, or a combination of the two.

Multiple species effects and spatial autocorrelation in detecting species associations

Abstract: . The traditional approach to the analysis of species association within a community, based upon co-occurrence in sampling units such as quadrats, has been to test all pairs of species, using a 2 × 2 contingency table for each pair. It has long been recognised that all these tests are not independent of each other, but there is an additional problem in that the association between any particular pair may depend on the combination of the other species that are present or on the environmental factors that determine that combination. We use a 2k contingency table to examine this problem and find that pairwise associations are not independent of the other species. The second problem that we consider is the effect of spatial autocorrelation in the data which makes the statistical tests too liberal. In the absence of a derived solution for a deflation factor to correct the test statistic calculated from a 2k table, we describe a Monte Carlo approach that provides an approximate solution to this problem. In our data the amount of deflation that is necessary for a 2k table is small compared to the amount required for the 2 × 2 tables used to test pairwise association.