Showing papers on "Spatial analysis published in 1989"

The design and analysis of spatial data structures

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Spatial pattern and ecological analysis

Abstract: The spatial heterogeneity of populations and communities plays a central role in many ecological theories, for instance the theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on. This paper will review how the spatial structure of biological populations and communities can be studied. We first demonstrate that many of the basic statistical methods used in ecological studies are impaired by autocorrelated data. Most if not all environmental data fall in this category. We will look briefly at ways of performing valid statistical tests in the presence of spatial autocorrelation. Methods now available for analysing the spatial structure of biological populations are described, and illustrated by vegetation data. These include various methods to test for the presence of spatial autocorrelation in the data: univariate methods (all-directional and two-dimensional spatial correlograms, and two-dimensional spectral analysis), and the multivariate Mantel test and Mantel correlogram; other descriptive methods of spatial structure: the univariate variogram, and the multivariate methods of clustering with spatial contiguity constraint; the partial Mantel test, presented here as a way of studying causal models that include space as an explanatory variable; and finally, various methods for mapping ecological variables and producing either univariate maps (interpolation, trend surface analysis, kriging) or maps of truly multivariate data (produced by constrained clustering). A table shows the methods classified in terms of the ecological questions they allow to resolve. Reference is made to available computer programs.

Spatial autocorrelation and sampling design in plant ecology

Abstract: Using spatial analysis methods such as spatial autocorrelation coefficients (Moran’s I and Geary’s c) and kriging, we compare the capacity of different sampling designs and sample sizes to detect the spatial structure of a sugar-maple (Acer saccharum L.) tree density data set gathered from a secondary growth forest of southwestern Quebec. Three different types of subsampling designs (random, systematic and systematic-cluster) with small sample sizes (50 and 64 points), obtained from this larger data set (200 points), are evaluated. The sensitivity of the spatial methods in the detection and the reconstruction of spatial patterns following the application of the various subsampling designs is discussed. We find that the type of sampling design plays an important role in the capacity of autocorrelation coefficients to detect significant spatial autocorrelation, and in the ability to accurately reconstruct spatial patterns by kriging. Sampling designs that contain varying sampling steps, like random and systematic-cluster designs, seem more capable of detecting spatial structures than a systematic design.

Geographical perspectives of space, time, and scale

Abstract: This article reviews space and time scales from a geographer's point of view. Because spatial phenomena come in incredibly different size classes, geographers have conducted analyses across many orders of spatial magnitude. Geographers seem adept at moving from one scale to another, but they are not prone to explicitly state these scales a priori. Moreover, in spite of many appeals for multiscaler research (e.g., Abler 1987; Miller 1970; Stone 1968; Kirkby 1985), this is seldom done, although higher-level information is often used to predict lower levels. Good multiscale work apparently meets data-handling thresholds rather quickly. Most geographic research is now conducted with a relativistic view of space rather than a view of space as a ‘container.’ Spatial scales for relative space are more difficult to define, however, than those for the absolute space of cartography and remote sensing. The relevant, important, and useful variables from a modeling standpoint change with spatial scale. By reviewing the literature on a topic in a systematic way, as was done here for physical climatology and orographie precipitation, this scale change in variables can be seen. We do not as yet have models of the changes in models caused by changes in scale. Spatial data violate nearly every requirement for parametric statistical analysis (Meentemeyer and Box 1987), which is partially responsible for fallacies and erroneous inference. Many of these problems are scale dependent. Based on the work of Harvey (1969), we see that there are three primary methodological problems in spatial analyses. There are first of all the differences in inference and relevant variables caused by different scales or hierarchical levels. This has been called the ‘scale problem’ in geographic literature. Secondly, the description and modeling of spatial patterns, as noted above, may defy easy solutions, and finally the relationships between spatial patterns and process remain a challenge. The geographic literature contains many examples of extrapolations to lower levels from higher levels. Often the higher levels have been more widely sampled geographically (e.g., weather and climate, topography) and may be data rich. Models which predict spatial patterns and process often use the data-rich higher levels as driving variables for lower levels. Young (1978) argues that central place theory in geography should be a component of hierarchy theory. Indeed it can be argued here that space is inherently hierarchical and needs to be more fully incorporated into hierarchy theory. As the various disciplines under the umbrella of the environmental sciences more fully incorporate the spatial dimension into their research agendas, problems associated with spatial scale will be encountered. Many of these problems have in varying degrees been recognized if not solved. Nevertheless it is worth noting Clark's (1985) warning, ‘No simple rules can automatically select the “proper” scale for attention.’ Good geographic models require good geographic coverage, but this may mean that lower-level details are simply not needed. As mentioned earlier, the question of whether one is working at a ‘fundamental’ level is never discussed in geography. The Long-Term Ecological Reserve (LTER) sites are a step in the right direction, but a geographer would prefer much more intensive spatial sampling, even if that means a sacrifice in accuracy or detail. Otherwise a spatial analysis may not be possible. It remains to be seen to what degree the reductionist sciences can contribute to IGBP. More work with explicitly stated scales is needed, as well as across-scales research. Scale has been treated philosophically in this essay. But I am reminded of Couclelis's caution, ‘Philosophizing in an empirical discipline is a sure sign of trouble’ (cited in Abler 1987).

Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems

Abstract: 1. Introduction: spatial effects and the role of configuration of data.- 1.1 Objectives and approaches.- 1.2 An overview of theoretical problems.- 1.3 A sketch of the methodology.- 1.4 An outline of the book.- 1.5 Omitted topics.- 2. Theoretical Problems Motivation.- 2.1 Introduction.- 2.2 The modifiable areal unit problem.- 2.3 The ecological fallacy problem.- 2.4 Problems in the estimation of the spatial correlogram.- 2.5 Summary and conclusion.- 3. The Configuration of Spatial Data in Regional Economics.- 3.1 Introduction.- 3.2 The nature of spatial data in regional economic analysis.- 3.3 Describing the configuration of irregular collecting areas.- 3.4 Conclusion.- Appendix 3.1 FORTRAN program to generate connectivity matrices with a considerably smaller matrix as an input.- Appendix 3.2 FORTRAN program to generate grouping matrices with a considerably smaller matrix as an input.- 4. Stochastic Spatial Processes.- 4.1 Stationary stochastic processes in two dimensions.- 4.2 Linear transformations of random processes.- 4.3 Inference on spatial stochastic processes.- 4.4 Summary and conclusion.- 5. Univariate Problems: The Modifiable Areal Unit Problem.- 5.1 Introduction.- 5.2 The scale problem : regular case.- 5.3 The scale problem : irregular case.- 5.4 The aggregation problem.- 5.5 Summary and conclusion.- Appendix 5.1 FORTRAN program for the recursive estimation of variance and covariance.- Appendix 5.2 FORTRAN program for the generation of pseudo-random regular zoning systems.- 6. Biyariate Problems: The Modifiable Areal Unit Problem and Correlation between Processes.- 6.1 Introduction.- 6.2 Scale and correlation between processes.- 6.3 Aggregation and correlation between processes.- 6.4 Summary and conclusion.- 7. Biyariate Problems: The Ecological Fallacy.- 7.1 Introduction.- 7.2 The ecological fallacy problem.- 7.3 Summary and conclusion.- Appendix 7.1: FORTRAN program for the generation of observations from a multivariate process with a very large variance-covariance matrix.- 8. The Dampening Effect of Spatial Correlograms.- 8.1 Introduction.- 8.2 The dampening effect.- 8.3 Simulation study.- 8.4 Summary and conclusion.- 9. Conclusion.- Appendices.- A.1 Population, employed and activity rates for local labour markets in Italy in 1981 Census.- A.2 Electricity consumption of Italian manufacturing industry in the first semester of 1985.- A.3 Quadrats counts of houses in Hukuno town, Tonami plain, Japan (Matui, 1932).- A.4 Weights of wheat plots of grain (Mercera Hall, 1911).- A.5 Simulation methods in two dimensions.- References.

Hierarchical Spatial Data Structures

Abstract: An overview is presented of the use of hierarchical spatial data structures such as the quadtree. They are based on the principle of recursive decomposition. The focus is on the representation of data used in image databases. The emphasis is on two-dimensional regions, points, rectangles, and lines.

What is Special About Spatial Data? Alternative Perspectives on Spatial Data Analysis (89-4)

Abstract: Author(s): Anselin, Luc | Abstract: Outlines general ideas on fundamental issues related to the distinctive characteristics of spatial data analysis as opposed to data analysis in general. Focuses on two issues that are often overlooked in technical treatments of the methods of spatial statistics and spatial econometrics. One is therelevance for spatial data analysis of the ongoing debate about methodology in the disciplines of statistics and econometrics. The other is much narrower and pertains to the role of spatial errors in modeling and analysis. Includes an extensive bibliography. Paper prepared for presentation at the Spring 1989 Symposium on Spatial Statistics, Past, Present and Future, Department of Geography, Syracuse University.

Orientation specificity in spatial memory: what makes a path different from a map of the path?

Abstract: Three studies investigated the factors that lead spatial information to be stored in an orientation-specific versus orientation-free manner. In Experiment 1, we replicated the findings of Presson and Hazelrigg (1984) that learning paths from a small map versus learning the paths directly from viewing a world leads to different functional characteristics of spatial memory. Whether the route display was presented as the path itself or as a large map of the path did not affect how the information was stored. In Experiment 2, we examined the effects of size of stimulus display, size of world, and scale transformations on how spatial information in maps is stored and available for use in later judgments. In Experiment 3, we examined the effect of size on the orientation specificity of the spatial coding of paths that are viewed directly. The major determinant of whether spatial information was stored and used in an orientation-specific or an orientation-free manner was the size of the display. Small displays were coded in an orientation-specific way, whereas very large displays were coded in a more orientation-free manner. These data support the view that there are distinct spatial representations, one more perceptual and episodic and one more integrated and model-like, that have developed to meet different demands faced by mobile organisms. Language: en

Spatial autocorrelation analysis of migration and selection.

Abstract: We test various assumptions necessary for the interpretation of spatial autocorrelation analysis of gene frequency surfaces, using simulations of Wright's isolation-by-distance model with migration or selection superimposed. Increasing neighborhood size enhances spatial autocorrelation, which is reduced again for the largest neighborhood sizes. Spatial correlograms are independent of the mean gene frequency of the surface. Migration affects surfaces and correlograms when immigrant gene frequency differentials are substantial. Multiple directions of migration are reflected in the correlograms. Selection gradients yield clinal correlograms; other selection patterns are less clearly reflected in their correlograms. Sequential migration from different directions and at different gene frequencies can be disaggregated into component migration vectors by means of principal components analysis. This encourages analysis by such methods of gene frequency surfaces in nature. The empirical results of these findings lend support to the inference structure developed earlier for spatial autocorrelation analysis.

Spatial decision support systems: a research agenda

Abstract: Definitions of geographic information systems often focus on the capture, storage, manipulation, analysis and display of spatial data - implying that geographic information systems implicitly are designed to support spatial decision-making. For many spatial problems, however, geographic information systems do not support decision-making effectively: analytical modelling capabilities are lacking and system designs are not flexible enough to accommodate variations in either the context or the process of spatial decision-making. One response to these needs is the development of spatial decision support systems. We draw a distinction between geographic information systems and spatial decision support systems in terms of system design, the types of problem to which each can be applied, and the decision-making processes supported. We classify the impediments to both the design and implementation of spatial decision support systems and outline a research agenda to address these problems. -Authors

Cluster Analysis as Applied to Regional Flood Frequency

Abstract: A statistical tecnhique for delineating groups of stations to be considered a region for regional flood frequency analysis is presented. The technique, which utilizes cluster analysis, allows the inclusion of a diversity of factors which might be considered to be of relevance when seeking stations in differing basins having similar extreme flow characteristics. The method incorporates both a basin similarity measure, imbedded in the clustering algorithm, and a regional homogeneity measure used to evaluate station partitionings obtained from the clustering algorithm. The result is that groups of stations are identified that can be considered sufficiently homogeneous to effect an efficient spatial data transfer. An application of the methodology to rivers in southern Manitoba, Canada, is presented to illustrate pertinent aspects of the procedure.

The Bayesian bridge between simple and universal kriging

Abstract: Kriging techniques are suited well for evaluation of continuous, spatial phenomena. Bayesian statistics are characterized by using prior qualified guesses on the model parameters. By merging kriging techniques and Bayesian theory, prior guesses may be used in a spatial setting. Partial knowledge of model parameters defines a continuum of models between what is named simple and universal kriging in geostatistical terminology. The Bayesian approach to kriging is developed and discussed, and a case study concerning depth conversion of seismic reflection times is presented.

Method and means for lineage tracing of a spatial information processing and database system

Abstract: A lineage information processor enables a user to obtain information concerning the various data layers in a spatial data base which contributed to any particular data layer of interest. The component software parses input commands and determines if those commands to the spatial data processing and information systems are valid. The lineage information processor also creates a knowledge representation of the spatial database comprising a meta-database consisting of a semantic network that describes the various data layers in the spatial database and the relationships among these layers. The semantic network consists of parent and child links symbolizing the relationship among data layers, nodes describing the data layers in the spatial database, frames comprising attributes that describe the input data layers, the commands and command modifiers acting on those data layers, and characteristics of the final products. By means of rule-based processing, the lineage information processor does not permit combinations of data layers that are incompatible, and creates commands that can alter incompatible data layers so that the layers can be combined in the desired fashion. A query capability is also provided that enables a user to query in a flexible fashion, the lineage information processor concerning the lineage of data layers in the spatial database.

Spatial data-analysis of regional counts

Abstract: Counts data from spatially continguous regions offer a challenge to the statistician both from the data analytic and the statistical modeling point of view. Important applications include epidemiological studies (e. g., cancer mortality over the counties of the USA) and Census surveys (e. g., undercount over the Census blocks of an urban area). It has long been recognized by time-series analysts that data close together in time usually exhibit higher dependence than those far apart. Time-series data analysis relies on methods of data transformation, detrending, and autocorrelation plotting. It is our intention in this article to generalize this approach to a spatial setting. To do this we consider a small spatial data set of 100 observations. Through the use of a square-root transformation, a weighted median polish and a variogram analysis of the median-polish residuals, we represent the transformed data as a trend plus stationary error. Thus we show how standard data-analytic techniques can be modified both to mitigate and to exploit the spatial relationships.

Spatial Displays and Spatial Instruments

Abstract: The conference proceedings topics are divided into two main areas: (1) issues of spatial and picture perception raised by graphical electronic displays of spatial information; and (2) design questions raised by the practical experience of designers actually defining new spatial instruments for use in new aircraft and spacecraft. Each topic is considered from both a theoretical and an applied direction. Emphasis is placed on discussion of phenomena and determination of design principles.

Statistical analysis of spatial data in the presence of missing observations: a methodological guide and an application to urban census data.

Abstract: In this paper a simple introduction and guide to a widely applicable method for estimating missing data in fields of enquiry such as census maps or LANDSAT images are presented. The method given is a maximum likelihood procedure. This is argued to have the particularly favourable characteristics (1) that its distribution properties are known, (2) it is applicable both to regularly and to irregularly spaced observations, (3) it can handle different spatial configurations of missing cells, (4) it makes full use of the information contained in the known spatial data (particularly its spatial autocorrelation), (5) it has no systematic tendency to error, and (6) it provides ‘probability limits’. The algorithm is presented in the form of a simple tutorial guide. An example, of median income levels in Houston, is worked through in detail for missing cells in census data. The example is characterised by a variable mean and a general variance — covariance matrix.

Soil geographic data bases

Abstract: AS an element of the National Cooperative Soil Survey, the Soil Conservation Service has established soil geographic data bases to improve the storage, manipulation, and retrieval of soil map information. People involved in agriculture, agribusiness, forestry, environmental protection, and land use planning and management often have difficulty locating and interpreting soil data. Frequently, these data are not in a form that permits easy analysis, particularly if the data must be analyzed together with other resource information. Soil and other natural resource data are unique in that spatial distribution and variability on the landscape are important components. Soil data often must be integrated with other spatial data. SCS has established three soil geographic data bases representing different scales of soil mapping. Each data base links digitized soil map unit delineations with computerized data for each map unit, giving the proportionate extent of the component soils and their properties. With these computerized data bases, users will be able to store, retrieve, analyze, and display soil data efficiently. They will also be able to integrate the data with other spatially referenced resource and demographic data in geographic information systems. The three data bases The three soil geographic data bases include the Soil Survey …

Maximum likelihood estimation with missing spatial data and with an application to remotely sensed data

Abstract: The paper examines the small and large lattice properties of the exact maximum likelihood estimator for a spatial model where parameter estimation and missing data estimation are tackled simultaneously, A first order conditional autoregressive model is examined in detail. The paper concludes with an empirical analysis of remotely sensed data.

A reactive data structure for geographic information systems

Abstract: We introduce a Reactive Data Structure, that is a spatial data structure with detail levels. The two properties, spatial organization and detail levels, are the basis for a Geographic Information System with a multi-scale database. A reactive data structure is a novel type of data structure catering to S. It is presented here as a modification of the binary space partioning tree that includes detail levels. This tree is one of the few spatial data structures that do not organize the space in a rectangular manner. An application of the reactive data structure in thematic mapping is given. -Author

7+b2 criteria for assessing and comparing spatial data structures

Abstract: Spatial data structures have evolved under the influence of several forces: 1) Database technology, with its emphasis on modeling and logical organization; 2) the long history of data structures developed in response to requirements from other applications; and 3) the recent rapid progress in computational geometry, which has identified typical queries and access patterns to spatial data. Rather than attempting a comprehensive survey of many spatial data structures recently developed, we aim to identify the key issues that have created them, their common characteristics, the requirements they have to meet, and the criteria for assessing how well these requirements are met. As a guideline for tackling these general goals, we begin with a brief history and recall how past requirements from other applications have shaped the development of data structures. Starting from the very early days, five major types of applications generated most of the known data structures. But the requirements of these applications do not include one that is basic to spatial data: That objects are embedded in Euclidian space, and access is mostly determined by location in space.

A tesselated probabilistic representation for spatial robot perception and navigation

Abstract: The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.

Geographic information systems: an important technology for transportation planning and operations

Abstract: A Geographic Information System (GIS) is a computerized data base management system for the capture, storage, retrieval, analysis, and display of spatial (i.e., locationally defined) data. The purpose of this paper is to explain why GIS technology is important to transportation professionals, describe how a number of transportation agencies are using GIS, and provide insight on how to participate in this technology. Transportation agencies are still in their infancy with respect to exploiting the power and possibilities offered by GIS technology. The usefulness of spatially integrated data to transportation is examined and the distinction is made between GIS and other data base systems that use spatial data. The benefits of GIS are summarized, and examples of GIS activities at the FHWA and state highway agencies are described. Sources for digital geocoded data, including U.S. Geological Survey digital line graphs and Bureau of the Census topologically integrated geographic encoding and referencing files, are discussed.

Unsupervised training area selection in forests using a nonparametric distance measure and spatial information

Abstract: A new unsupervised technique that automatically delineates areas with a similar tone is described. The proposed algorithm grows a region of homogeneous tone around a seed pixel; membership criteria for the region is based upon a nonparametric distance measure. The thematic image output can be used to define training areas for a supervised classifier. Two commonly used unsupervised strategies for delineating training areas (viz., clustering and uniform texture mapping) are compared with the proposed technique using SPOT digital data collected over a multi-aged forest plantation in south-east Australia.

An efficient use of quadtrees in a geographical information system

Abstract: With the increase in volume of spatial data now available, more effective ways must be found of storing and processing these data. This paper presents a compacted version of the linear quadtree and a spatially-referenced index method that can significantly reduce the storage requirements of a set of images and the time taken to process spatial queries. The index acts as a high-level summary of a regular-sized portion of the underlying image and so can be used to avoid examining areas of the image where none of the required features is present. Some example results are given. A method for the optimization of spatial searches is presented which takes into account the area and distribution of features within an image. Finally, a method for directly associating the edges of features with the individual nodes of a quadtree is reported. This is important since the edges of objects are no longer explicitly present in linear quadtrees and so must be recalculated when they are required for part of a query...

Probabilistic Potentiometric Surface Mapping

Abstract: The paper provides a methodology to estimate the spatial variation of potentiometric surfaces, allowing for possible uncertainties such as instrument errors, human errors, and statistical errors. The spatial variation is expressed in terms of the mean estimation, the coefficient of variation of the estimation, and the variance of the data scatter. The methodology is illustrated by a case study. The paper provides the criteria to decide the best estimation model when a number of theoretically consistent models are available to estimate spatial variation. A procedure to detect anomalous data is discussed in the paper through an example. The paper also provides a procedure to estimate the confidence of the mean spatial variation estimation, and describes steps one should take to improve this confidence, if it is needed. A technique is also given to estimate the random‐error component of the measurements.

Adding intelligence to scientific data management

Abstract: NASA plans to solve some of the problems of handling large-scale scientific data bases by turning to artificial intelligence (AI) are discussed. The growth of the information glut and the ways that AI can help alleviate the resulting problems are reviewed. The employment of the Intelligent User Interface prototype, where the user will generate his own natural language query with the assistance of the system, is examined. Spatial data management, scientific data visualization, and data fusion are discussed.

The acquisition of spatial data from archival photographs and their application to geomorphology

Abstract: This thesis discusses the development and application of an analytical photogrammetric technique which enables accurate spatial data, of known quality, to be derived from archival photographs. Such a facility represents an important advancement, particularly for geomorphologists, because the effects of geomorphological process can be assessed quantitatively and directly by comparing spatial data derived from photographs at different epochs. Sources of archival photographs of England are identified and the type, quantity, range and age of each major collection is discussed. Existing methods of deriving spatial data from photographs are reviewed and illustrated by previous research, with particular emphasis upon the limitations associated with each method. The technique that was developed is based upon a self calibrating bundle adjustment and both the functional and stochastic models suitable for successful restitution of archival photographs were established. Five computer programs were developed and the algorithms associated with each are given. These programs are run sequentially and assist in rapid restitution of archival photography and to derive measures of data quality. The technique is applied successfully to a forty year old sequence of archival photographs, obtained from a variety of sources, of the Black Ven landslide, Dorset, England. Spatial data was derived from five photographic epochs, at approximately 10 year intervals, using an analytical plotter. A secondary aim of the research was to extend existing techniques and devise new methods of processing these spatial data, for geomorphological purposes. Several techniques were found to be especially valuable including: the production of morpho-genetic maps; DTI's of difference; evolutionary models; animated sequences and distributions of slope angle. The latter has shown that the evolutionary model of 'dynamic equilibrium is valid for the Black Ven landslides. All aspects of data quality are examined, particularly the functional model used in the self-calibrating bundle adjustment. This least squares estimating procedure is found to be perfectly adequate for the successful restitution of archival photographs.

Object-oriented locational analysis

Abstract: An object oriented approach to the analysis of spatial data is presented. We begin by describing object oriented programming, and then define a structure of spatial and analytical objects within the problem domain of locating facilities. From these objects a set of object classes and inheritance structures is created. The spatial and analytical objects are represented using frames. A Smalltalk implementation of a heuristic location-allocation algorithm is also described. -Authors