David O’Sullivan

The Geographic Scale of Metropolitan Racial Segregation

This article addresses an aspect of racial residential segregation that has been largely ignored in prior work: the issue of geographic scale. In some metropolitan areas, racial groups are segregated over large regions, with predominately white regions, predominately black regions, and so on, whereas in other areas, the separation of racial groups occurs over much shorter distances. Here we develop an approach—featuring the segregation profile and the corresponding macro/micro segregation ratio—that offers a scale-sensitive alternative to standard methodological practice for describing segregation. Using this approach, we measure and describe the geographic scale of racial segregation in the 40 largest U.S. metropolitan areas in 2000. We find considerable heterogeneity in the geographic scale of segregation patterns across both metropolitan areas and racial groups, a heterogeneity that is not evident using conventional “aspatial” segregation measures. Moreover, because the geographic scale of segregation is only modestly correlated with the level of segregation in our sample, we argue that geographic scale represents a distinct dimension of residential segregation. We conclude with a brief discussion of the implications of our findings for investigating the patterns, causes, and consequences of residential segregation at different geographic scales.

Geographical information science: critical GIS

© 2006 SAGE Publications 10.1177/0309132506071528

Agent-Based Models – Because They’re Worth It?

We address the question of when the relative complicatedness of spatial agent-based models (ABMs) compared to alternative modelling approaches can be justified. The spectrum of ABM types from simple, abstract models to complicated models aspiring to realism makes a single answer impossible. Therefore we focus on identifying circumstances where the advantages of ABMs outweigh the additional effort involved. We first recall the reasons for building any model: to simplify the phenomena at hand to improve understanding. Thus, the representational detail of ABMs may not always be desirable. We suggest that critical aspects of the phenomena of interest that help us to assess the likely usefulness of ABMs are the nature of the decisions which actors make, and how their decisions relate to the spatio-temporal grain and extent of the system. More specifically, the heterogeneity of the decision-making context of actors, the importance of interaction effects, and the overall size and organization of the system must be considered. We conclude by suggesting that there are good grounds based on our discussion for ABMs to become a widely used approach in understanding many spatial systems.

Strategic directions for agent-based modeling: avoiding the YAAWN syndrome

In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances – in terms of model complexity, model evaluation, and model structure – can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from ‘yet another model’ to doing better science with models.

Do Physicists Have Geography Envy? And What Can Geographers Learn from It?

Recent years have seen an increasing amount of work by physicists on topics outside their traditional research domain, including geography. We explore the scope of this development, place it in a historical context dating back at least to statistical physics in the nineteenth century and trace the origins of more recent developments to the roots of computational science after World War II. Our primary purpose is not historical, however. Instead, we are concerned with understanding what geographers can learn from the many recent contributions by physicists to understanding spatiotemporal systems. Drawing on examples of work in this tradition by physicists, we argue that two apparently different modes of investigation are common: model-driven and data-driven approaches. The former is associated with complexity science, whereas the latter is more commonly associated with the fourth paradigm, more recently known as “big data.” Both modes share technical strengths and, more important, a capacity for generalization, which is absent from much work in geography. We argue that although some of this research lacks an appreciation of previous geographical contributions, when assessed critically, it nevertheless brings useful new perspectives, new methods, and new ideas to bear on topics central to geography, yet neglected in the discipline. We conclude with some suggestions for how geographers can build on these new approaches, both inside and outside the discipline.

Graph-assisted landscape monitoring

The structure of computational spatial analysis has mostly built on data lattices inherited from cartography, where visualization of information takes priority over analysis. In these framings, spatial relationships cannot easily be encoded into traditional data lattices. This hinders spatial analysis that emphasizes how interactions among spatial entities reflect mutual inter-relationships. This paper explores how graph theoretic principles can support spatiotemporal analysis by enabling assessment of spatial and temporal relationships in landscape monitoring.

Exploring spatial scale in geography

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing exploring spatial scale in geography as the reading material.

The Use of Hybrid Agent Based Systems to Model Petrol Markets

The petrol price market is a highly sensitive and competitive market with many processes combining at different temporal and spatial scales to affect each petrol station’s prices. Previous models developed to represent the relationship between petrol and a variable are empirical and mathematical. These suffer from a number of problems, chiefly: the parameters are all on the same scale (behaviours executed at the ‘micro’ level are not tied to ‘global’ level variables like oil prices); the parameters are often difficult to estimate and lack realism; very little, if any, account of any geographical effects is taken, and, finally, mathematical models by their nature only consider quantitative parameters and therefore miss out on qualitative, behavioural information.

More bark than bytes? Reflections on 21+ years of geocomputation

This year marks the 21st anniversary of the International GeoComputation Conference Series. To celebrate the occasion, Environment and Planning B invited some members of the geocomputational community to reflect on its achievements, some of the unrealised potential, and to identify some of the on-going challenges.

Don’t panic! The need for change and for curricular pluralism

The proposal for a more quantitative geography curriculum from Johnston et al. (2014) is a welcome contribution to ongoing debates. However, their arguments rely in part on an overly pessimistic assessment of the current status of quantitative methods in the discipline – perhaps reflecting their UK focus. They also underplay the importance of geometry and the models of theoretical geography to any comprehensive treatment of quantitative methods in contemporary geography. These are themes that should be considered in any modern geography curriculum. The future of quantitative methods in geography seems secure and is likely to lead to different curricula in different geographical contexts.