Stan Openshaw

A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets

Abstract This paper presents the first of a new generation of spatial analytical technology based on a fusion of statistical, GIS and computational thinking. It describes how to build what is termed a Geographical Analysis Machine (GAM), with high descriptive power. A GAM offers an imaginative new approach to the analysis of point pattern data based on a fully automated process whereby a point data set is explored for evidence of pattern without being unduly affected by predefined areal units or data error. No prior information or specification of particular location-specific hypotheses is required. If geographical data contain strong evidence of pattern in geographical space, then the GAM will find it. This technology is demonstrated by an analysis of data on cancer for northern England.

Ecological Fallacies and the Analysis of Areal Census Data

In many countries census data are only reported for areal units and not at the individual level. This custom raises the spectre of ecological fallacy problems. In this paper, a 10% sample census (from the United Kingdom) and individual census data (from Italy) are used to provide an empirical demonstration of the nature and magnitude of these problems. It is concluded that ecological fallacy effects are endemic to areal census data, although their magnitude is perhaps not as large as might have been expected. The principal difficulty is that there is at present no way of predicting in advance the degree of severity likely to be associated with particular variables and particular techniques. Finally, a suggestion is made concerning how the potentially serious practical consequences can be reduced.

A hybrid multi-model approach to river level forecasting

Abstract This paper presents four different approaches for integrating conventional and AI-based forecasting models to provide a hybridized solution to the continuous river level and flood prediction problem. Individual forecasting models were developed on a stand alone basis using historical time series data from the River Ouse in northern England. These include a hybrid neural network, a simple rule-based fuzzy logic model, an ARMA model and naive predictions (which use the current value as the forecast). The individual models were then integrated via four different approaches: calculation of an average, a Bayesian approach, and two fuzzy logic models, the first based purely on current and past river flow conditions and the second, a fuzzification of the crisp Bayesian method. Model performance was assessed using global statistics and a more specific flood related evaluation measure. The addition of fuzzy logic to the crisp Bayesian model yielded overall results that were superior to the other individual and integrated approaches.

Applying soft computing approaches to river level forecasting

Abstract This paper assesses one of many potential enhancements to conventional flood forecasting that can be achieved through the use of soft computing technologies. A methodology is outlined in which the forecasting data set is split into subsets before training with a series of neural networks. These networks are then recombined via a rule-based fuzzy logic model that has been optimized using a genetic algorithm. The methodology is demonstrated using historical time series data from the Ouse River catchment in northern England. The model forecasts are assessed on global performance statistics and on a more specific flood-related evaluation measure, and they are compared to benchmarks from a statistical model and naive predictions. The overall results indicate that this methodology may provide a well performing, low-cost solution, which may be readily integrated into existing operational flood forecasting and warning systems.

Algorithms for automated line generalization1 based on a natural principle of objective generalization

Abstract This article describes a new set of algorithms for locally–adaptive line generalization based on the so-called natural principle of objective generalization. The drawbacks of existing methods of line generalization are briefly discussed and the algorithms described. The performance of these new methods is compared with benchmarks based on both manual cartographic procedures and a standard method found in many geographical information systems.

An Empirical Study of Some Spatial Interaction Models

This paper attempts to focus attention away from a mathematical and theoretical perspective to one where empirical and statistical aspects can assume greater significance. The need for this change of emphasis is demonstrated by the results of an empirical study of a selection of nine spatial-interaction models. Not only do the theory-based models fail to achieve significantly better levels of performance than some empirically derived models, but both types of model fail to achieve satisfactory levels of descriptive performance. These models are shown to be sensitive to the choice of calibration procedure and various kinds of errors in the constant and independent variables. It is suggested that the use of these models should always be accompanied by estimates of confidence limits, and that there is an urgent need for the development of new model designs which can at least provide accurate descriptions of observed reality.

An Empirical Study of Some Zone-Design Criteria

The results obtained from studies of spatially aggregated data are not independent of the choice of zoning system. The paper investigates the effects of different zone-design criteria on a linear-regression model. It is concluded that there is unlikely to be either a simple or general-purpose solution to the problem.

Modelling spatial interaction using a neural net

Neurocomputing has the potential to revolutionise many areas of urban and regional modelling by providing a general purpose systems modelling tool in applications where data exist. This chapter examines the empirical performance of a feedforward neural net as the basis for representing the spatial interaction contained within journey to work data. The performance of the neural net representation is compared with various types of conventional model. It is concluded that there is considerable potential for many more neural net applications in this and related areas.

Neural Network, Genetic, and Fuzzy Logic Models of Spatial Interaction

The author investigates the extent to which smart computational methods can be used to create new and better performing types of spatial interaction model. He briefly describes the application of three different computationally intensive modelling technologies and compares the performance of the resulting models on a benchmark data set. It would appear that performance improvements of up to a factor of two can be obtained at the cost of a few orders of magnitude increase in compute times.

Building a prototype Geographical Correlates Exploration Machine

The paper describes a exploratory procedure for data analysis for use within GIS. The objective is to search digital map databases for the presence of geographical relationships that may be useful for descriptive purposes, as a pointer towards areas for further investigation, and as a means of generating hypotheses for subsequent testing. A prototype Geographical Correlates Exploration Machine (GCEM) is demonstrated by searching for possible linkages between children with leukaemia and a selection of environmental coverages. Arc Info is used for the GIS parts and a Cray X-MP/48 supercomputer for the analysis. Ultimately, it is envisaged that GCEM will be able to run entirely within a GIS workstation environment.