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.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1995.tb00338.x

Local Indicators of Spatial Association—LISA

‘The Production of Space’, in: Frans Jacobi, Imagine, Space Poetry, Copenhagen, 1996, unpaginated.

The Production of Space

Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at different points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigating the null hypothesis that the data may be described by a global model rather than a non-stationary one and also for testing whether individual regression coefficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U.K. census relating car ownership rates to social class and male unemployment. The paper concludes by discussing ways in which the technique can be extended.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1996.tb00936.x

Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

Transactions of the Institute of British Geographers

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.

Analysing Ecological Data

The concept of scale in human geography has been profoundly transformed over the past 20 years. And yet, despite the insights that both empirical and theoretical research on scale have generated, there is today no consensus on what is meant by the term or how it should be operationalized. In this paper we critique the dominant – hierarchical – conception of scale, arguing it presents a number of problems that cannot be overcome simply by adding on to or integrating with network theorizing. We thereby propose to eliminate scale as a concept in human geography. In its place we offer a different ontology, one that so flattens scale as to render the concept unnecessary. We conclude by addressing some of the political implications of a human geography without scale.

/pdf/human-geography-without-scale-ckthh2kr5u.pdf

Human geography without scale

NGOs and the globalization of managerialism: A research framework

Recent work by geographers concerned with the enduring presence of racism has called for an interrogation of the privileges and contingencies of whiteness. Central to this project of denaturalizing White Identity has been the disclosure of its co-constitution with a host of social practices. Building on the work of critical theorists in the humanities and social sciences concerned with masculinist and post-colonial epistemologies, this paper outlines a socio-spatial epistemology of whiteness. Whiteness’s central tenets are an essentialist and non-relational construction of space and identity that underwrite its claims to be realized independent of an Other. Spatially, this refusal manifests itself in the deployment of discursive categories associated with scales, boundaries and extensivity in ways that reify space into discrete, unrelated parcels. We discuss some of the implications of this non-relational construction of space and identity in the context of residential segregation and spatial mobility. The paper concludes by noting that historically and geographically speciec forms of whiteness have drawn upon a common socio-spatial framing and that further study in this eeld will beneet anti-racist activism by disclosing the workings of racialization in numerous human geographic contexts.

White socio-spatial epistemology

Distance decay is one of geography's core concepts, yet its own spatial properties have been largely neglected. We illustrate the use of the expansion method in assessing the spatial variation of distance-decay parameters within the general context of gravity models. The approach enables the portrayal of “space warping,” said to occur when equivalent distances have spatially uneven effects on interaction. The concepts are examined empirically through the estimation of a competing destination model for out-migration from five State Economic Areas over the 1965–70 period. Four of the five origins are found to have spatially unstable frictions of distance. We conclude by suggesting that distance decay is a contextual, rather than a universal, effect.

Warped space: a geography of distance decay*

1. An Introduction to the Expansion Method and to its Applications Emilio Casetti and John Paul Jones III 2. The Dual Expansion Method: An Application for Evaluating the Effects of Population Growth on Development Emilio Casetti 3. Paradigmatic Dimensions of the Expansion Method John Paul Jones III 4. A Contextual Expansion of the Welfare Model Janet E. Kodras 5. A Comparison of Drift Analyses and the Expansion Method: The Evaluation of Federal Policies on the Supply of Physicians Stuart A. Foster, Wilpen L. Gorr and Francis C. Wimberly 6. Personal Characteristics in Models of Migration Decisions: An Analysis of Destination Choice in Ecuador Mark Ellis and John Odland 7. Alternative Approaches to the Study of Metropolitan Decentralization Shaul Krakover 8. Long Wave Spatial and Economic Relationships in Urban Development Shaul Krakover and Richard L. Morrill 9. An Investigation of the Dynamics of Development Inequalities via Expanded Rank-Size Functions C. Cindy Fan 10. Identifying Hierarchical Development Trends in the Hungarian Urban System using the Expansion Method Darrick R. Danta 11. An Exploration of the Relationship between Sectoral Labor Shares and Economic Development Kavita Pandit 12. Production Function Estimation and the Spatial Structural of Agriculture Sent Visser 13. Incorporating the Expansion Method into Remote Sensing-Based Water Quality Analyses Marty Miles, Douglas A. Stow and John Paul Jones III 14. Innovation Diffusion Theory and the Expansion Method Michael Sonis 15. Spatial Dependence and Spatial Heterogegity: Model Specification Issues in the Spatial Expansion Paradigm Luc Anselin 16. Generating Varying Parameter Models using Cubic Spline Functions Robert Q. Hanham

John Paul Jones

Papers

Human geography without scale

NGOs and the globalization of managerialism: A research framework

White socio-spatial epistemology

Warped space: a geography of distance decay*

Applications of the Expansion Method