Stan Openshaw

Bio: Stan Openshaw is an academic researcher from University of Leeds. The author has contributed to research in topics: Census & Population. The author has an hindex of 38, co-authored 102 publications receiving 8573 citations. Previous affiliations of Stan Openshaw include University of Manchester & Newcastle University.

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.

Abstract: The author examines problems related to the fact that in many countries census data are only reported for areal units and not at the individual level. Attention is paid to the question of ecological fallacy problems that arise from this situation. Data from a 10 percent sample of the United Kingdom population and individual census data from Italy are used to illustrate the problem. "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." (EXCERPT)

A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling

Abstract: The paper describes a geographical solution to the scale and aggregation problems frequently encountered in studies of spatially aggregated data. Instead of trying to model the effects of scale and aggregation, the problem is inverted. An attempt is made to identify a set of zones which optimizes an objective function related in some way to the performance of a model subject to whatever constraints may be relevant. In this way scale and aggregation problems become one of optimal-zone design. A heuristic procedure is described which may solve this problem and it is demonstrated by reference to an empirical study. Finally, there is a brief discussion of some of the possible implications for the study of spatially aggregated data and of the role of the optimal-zone design approach in spatial model building. THE conventional approach to spatial analysis involves the application of a model to a study area which has been partitioned into zones. The definition of these zonal boundaries involves the selection of the scale of the study and the aggregation of data to match the choice of scale. In nearly all cases, there are an incredibly large number of alternative scales and aggregations which could be used. It follows, therefore, that spatial data and the patterns and processes they describe are the product of a particular set of zonal boundaries, and that qualitative or quantita- tive studies of spatial data are not invariant with the choice of these boundaries. Scale is an abstract concept which cannot be easily measured except in relative terms. The best surrogates are probably the size and number of zones used to partition a study area. The scale problem arises because of uncertainty about the number of zones needed for a particular study. The aggregation problem arises because of uncertainty about how the data is to be aggre- gated to form a given number of zones. These problems always occur in the design of zones for the study of spatial data; and the two together represent one of the greatest unsolved problems facing spatial study today. Current zone design procedures are typically haphazard. Zones are mainly based on considerations of convenience and the existence of readily available data, but on occasions may be selected at a certain scale in order to isolate a particular spatial pattern. However, in many cases current knowledge of spatial phenomena is insufficient to define with any precision the scale and the aggregation needed. In other studies zone design is not regarded as being very important, and the relationship between the choice of zones and the results is seldom investigated, even when the data is sufficient for such a study. It is suggested, therefore, that the distribution of zone-dependent results may be widespread throughout the geographical literature, wreaking havoc with at least some of it. The purpose of this paper is to provide a geographical solution to certain aspects of the

Local Indicators of Spatial Association—LISA

Abstract: The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.

Local Spatial Autocorrelation Statistics: Distributional Issues and an Application

Abstract: The statistics Gi(d) and Gi*(d), introduced in Getis and Ord (1992) for the study of local pattern in spatial data, are extended and their properties further explored. In particular, nonbinary weights are allowed and the statistics are related to Moran's autocorrelation statistic, I. The correlations between nearby values of the statistics are derived and verified by simulation. A Bonferroni criterion is used to approximate significance levels when testing extreme values from the set of statistics. An example of the use of the statistics is given using spatial-temporal data on the AIDS epidemic centering on San Francisco. Results indicate that in recent years the disease is intensifying in the counties surrounding the city.

Analysing Ecological Data

Abstract: Introduction.- Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- Introduction to mixed modelling.- Univariate tree models.- Measures of association.- Ordination--first encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- Introduction to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis--Introduction.- Common trends and sudden changes.- Analysis and modelling lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugual.- Crop pollination by honeybees in an Argentinean pampas system using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: using generalised least squares, nonmetric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species--spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analysis of morphometric turtle data--size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisheries landings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: a time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.

Ethnic Diversity and Economic Performance

Abstract: We survey and assess the literature on the positive and negative effects of ethnic diversity on economic policies and outcomes. Our focus is on both focus both cities in developed countries (the US) and villages in developing countries. We also consider the endogenous formation of political jurisdictions and we highlight several open issues in need of further research.