Marc P. Armstrong

An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting

A factorial, computational experiment was conducted to compare the spatial interpolation accuracy of ordinary and universal kriging and two types of inverse squared-distance weighting. The experiment considered, in addition to these four interpolation methods, the effects of four data and sampling characteristics: surface type, sampling pattern, noise level, and strength of small-scale spatial correlation. Interpolation accuracy was measured by the natural logarithm of the mean squared interpolation error. Main effects of all five factors, all two-factor interactions, and several three-factor interactions were highly statistically significant. Among numerous findings, the most striking was that the two kriging methods were substantially superior to the inverse distance weighting methods over all levels of surface type, sampling pattern, noise, and correlation.

Exploring the Use of Buffer Analysis for the Identification of Impacted Areas in Environmental Equity Assessment

Though circular buffer zones are commonly used in environmental equity assessment, the results obtained may not be entirely accurate because physical processes rarely operate in a perfectly symmetrical manner. An integrated approach, known as geographic plume analysis, accounts for directional biases in the distribution of hazards by using a chemical dispersion model to identify areas that are likely to be exposed to toxic releases. In this paper we implement, evaluate, and compare circular and plume-based approaches to environmental equity analysis in the city of Des Moines, Iowa. For each approach, three strategies were selected to define the spatial extent and form of the buffer used to compute the population at risk. In all cases, the analyses are based on census data units and on locations of hazardous facilities listed in EPAs 1994 Toxic Release Inventory (TRI) database. At each toxic site we generated: (i) circles of radii 0.5 and 1 mile, and (ii) a composite plume footprint based on the chemical ...

Requirements for the development of GIS-based group decision-support systems

Though GIS technology has progressed rapidly during the past decade, the focus of most research and development activity has been placed on the development of single‐user systems. A mismatch exists, however, between the widespread single‐user model of GIS and the group‐based approach to decision making that is often adopted when semistructured public policy issues are addressed. GIS‐based spatial analysis and display methods must be expanded to encompass group decision making processes, and new tools must be developed that will enable group members to generate, evaluate, and illustrate the strong and weak points of alternative scenarios and come to a consensus about how to proceed toward a decision.

Using evolutionary algorithms to generate alternatives for multiobjective site-search problems

Multiobjective site-search problems are a class of decision problems that have geographical components and multiple, often conflicting, objectives; this kind of problem is often encountered and is technically difficult to solve. In this paper we describe an evolutionary algorithm (EA) based approach that can be used to address such problems. We first describe the general design of EAs that can be used to generate alternatives that are optimal or close to optimal with respect to multiple criteria. Then we define the problem addressed in this research and discuss how the EA was designed to solve it. In this procedure, called MOEA/Site, a solution (that is, a site) is encoded by using a graph representation that is operated on by a set of specifically designed evolutionary operations. This approach is applied to five different types of cost surfaces and the results are compared with 10 000 randomly generated solutions. The results demonstrate the robustness and effectiveness of this EA-based approach to geographical analysis and multiobjective decisionmaking. Critical issues regarding the representation of spatial solutions and associated evolutionary operations are also discussed.

Database organization strategies for spatial decision support systems

Abstract The selection of a data model is an important step in designing a spatial decision support system (SDSS) because the database is the foundation of the system, and other system components must draw upon the database to perform analysis and display functions. The diversity of representations that must be maintained to support these functions often results in a complex database. One way to simplify the implementation of a complex database is to exploit features of a data model embedded in database management software. A methodology for SDSS conceptual database design was developed using the Entity-Category-Relationship approach. To accommodate this conceptual structure in database management software, we reviewed available data models and concluded that the hybrid extended network model is appropriate for use in an SDSS context. To illustrate this methodology, we designed a logical database structure for a locational analysis SDSS.

Landscape Fragmentation and Dispersal in a Model of Riparian Forest Dynamics

Abstract Although several computer simulation models of forest tree growth have been developed at the stand level, they do not address the impact of mainland landscape structure and forest fragmentation on the forest dynamics of spatially extensive areas. To model forest dynamics at the landscape level, a model must explicity consider the dispersibility of tree species within the landscape. A computer simulation model, seedflo , is constructed which models tree species dispersal based on empirical evidence. The analyses test the hypothesis that a model with seed dispersal differs in its projections from a model that assumes ubiquitous dispersal. For species in a riparian corridor, seedflo projects lower diversity in more fragmented landscapes and increases the abundance of those species with higher dispersal probabilities, especially bird-dispersed species, and toward species with average dispersal abilities which normally compete with species with low dispersal probabilities. The effects of considering landscape fragmentation and dispersal are amplified when environmental stress, in this case flooding, is increased. The results conform to theoretical expectations and indicate that seed dispersal should be incorporated in landscape simulation models.

A theoretical approach to the use of cyberinfrastructure in geographical analysis

This paper presents a theoretical approach that has been developed to capture the computational intensity and computing resource requirements of geographical data and analysis methods. These requirements are then transformed into a common framework, a grid‐based representation of a spatial computational domain, which supports the efficient use of emerging cyberinfrastructure environments. Two key types of transformational functions (data‐centric and operation‐centric) are identified and their relationships are explained. The application of the approach is illustrated using two geographical analysis methods: inverse distance weighted interpolation and the spatial statistic. We describe the underpinnings of these two methods, present their conventional sequential algorithms, and then address their latent parallelism based on a spatial computational domain representation. Through the application of this theoretical approach, the development of domain decomposition methods is decoupled from specific high‐performance computer architectures and task scheduling implementations, which makes the design of generic parallel processing solutions feasible for geographical analyses.

Dispersal probability and forest diversity in a fragmented landscape

The diversity of forest stands may be affected by landscape fragmentation during periods of climatic change. A modified version of the jabowa-foret model of the dynamic processes of establishment, growth, and death of forest trees is used in a spatially explicit framework to elucidate differences in the effects of both spatial structure and spatial processes. In cases with and without climatic change, the effects of including random or structured fragmentation and successively lower dispersal probabilities (increased chance of long-distance dispersal) are examined in simulation experiments. The exclusion of very low dispersal probability (p < 0.001) has an important effect on species richness. Barriers and random fragmentation also lower diversity. Climatic change has little effect on diversity alone or in addition to fragmentation; changes in composition result. These results indicate that rare events, especially of the type seldom recorded in observations of seed dispersal, are extremely important. The results of our simulation experiments indicate that model scale must be addressed in more detail.

Using Genetic Algorithms to Create Multicriteria Class Intervals for Choropleth Maps

Abstract During the past three decades a large body of research has investigated the problem of specifying class intervals for choropleth maps. This work, however, has focused almost exclusively on placing observations in quasi-continuous data distributions into ordinal bins along the number line. All enumeration units that fall into each bin are then assigned an areal symbol that is used to create the choropleth map. The geographical characteristics of the data are only indirectly considered by such approaches to classification. In this article, we design, implement, and evaluate a new approach to classification that places class-interval selection into a multicriteria framework. In this framework, we consider not only number–line relationships, but also the area covered by each class, the fragmentation of the resulting classifications, and the degree to which they are spatially autocorrelated. This task is accomplished through the use of a genetic algorithm that creates optimal classifications with respect to multiple criteria. These results can be evaluated and a selection of one or more classifications can be made based on the goals of the cartographer. An interactive software tool to support classification decisions is also designed and described.

Geographic Information Technologies and Personal Privacy

Abstract Concepts of privacy are fluid. They change according to historical contingencies and are mediated by technology. Geospatial technologies are now altering the way privacy is being considered. Remote sensing technologies can be used to observe, or infer, the locations of individuals from space, from remotely piloted aircraft, and from fixed terrestrial observation points. Other geospatial technologies can be used to track movements and to recover individual-level information from maps. These changes are welcomed by some, since they provide a certain level of public safety (e.g., E-911). In other cases, however, a lack of awareness about the sinister aspects of surveillance may lead to complacency. Where personal privacy is eroded, individuals should be aware of the limitations of technology and the degree to which it may be applied to monitor their activities.