Stewart Fotheringham

Understanding the Geography of Post-Traumatic Stress: An Academic Justification for Using a Spatial Video Acquisition System in the Response to Hurricane Katrina

In the aftermath of a disaster like Hurricane Katrina, remote-sensing methods are often employed in an effort to assess damage. However, their utility may be limited by the aerial perspective and image resolution. The Spatial Video Acquisition System (SVAS), in conjunction with a Geographic Information System (GIS), has the potential to be a complementary methodology for obtaining damage assessment information as well as capturing recovery related geographies associated with post-traumatic stress. An example is provided from the Lower 9th Ward of New Orleans with data that could be used to predict neighborhood post-traumatic stress. Results reveal six dimensions in which a SVAS can improve existing disaster-related data collection approaches: organization, archiving, transferability, evaluation, objectivity, and feasibility.

A Migration Modelling System to Support Government Decision-making

The planning support system described in this chapter is a prototype internal migration modelling system developed for the UK’s Department of Environment, Transport and the Regions during 2000. It forms part of the Government’s drive towards evidence-based policy-making. It is designed to be run by civil servants on stand-alone desk-top computers primarily in order to gauge the likely impact on between-area migration flows of alternative economic and policy scenarios. The system is based on a two-stage representation of the migration process, the first stage predicting out-migration from each of 100 areas of the UK, and the second predicting the distribution of these migrants between destinations. A user-friendly front-end allows the alteration of the levels of the determinant variables for running scenarios and the easy visualisation of the migration impacts through tables and maps. The system is now being refined in a second phase of work for the client, which includes the preparation of a scoping report on future enhancements. The chapter outlines the policy context of this work and the client’s requirements, describes the modelling approach and the basic structure of the initial model, and presents an example of running a trial scenario.

Combining Geovisual Analytics with Spatial Statistics: the Example of Geographically Weighted Regression

Abstract An attempt is made to facilitate interpretation of the results of a spatial statistical method – Geographically Weighted Regression (GWR) – using a geovisual exploratory approach. The GWR parameter space is treated as a multivariate dataset and explored in a geovisual exploratory environment with the goal to identify spatial and multivariate patterns that describe the spatial variability of the parameters and underlying spatial processes.

Analysing Access to Hospital Facilities with GIS

The location of a facility can be examined in the context of several classical frameworks in economic geography and operation research where the facility is located such that some objective function is minimised (ie. Love et al., 1988; Ghosh and McLafferty, 1987; Wrigley, 1988; Fotheringham and O’Kelly, 1989). A frequently encountered objective, for example, is to find the optimal location of a new facility in terms of minimising the average time it takes individuals to travel to the nearest facility. That is, a new facility is added to an existing spatial distribution of facilities in order to achieve the maximum reduction in average travel times (the p-median problem). Other objective functions can of course be used to locate the new facility: it could for example, be located so that the maximum distance any individual has to travel is minimised (the minimax problem). Another slant on the problem is to simultaneously locate a set of facilities and to determine the allocation of demand to each of these facilities (the location-allocation problem). Still another is to model the choice of a facility by an individual as a probabilistic function of the attributes of each facility rather than as a deterministic one (a spatial interaction problem).