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Spatial accessibility measures are an important policy tool for managing healthcare provision and reducing health inequality. The two-step floating catchment area technique, in common with many alternative methodologies, requires that demand-side population be estimated using spatial interpolation techniques. This article studies the implications of adopting differing spatial representations of population on healthcare accessibility modeling outcomes. Results indicate that a dasymetric model yields lower accessibility scores than a standard pro rata model. More important, the difference is spatially disproportionate, suggesting that the degree of disadvantage experienced in rural areas may be greater than has previously been recognized.

Areal interpolation involves the transfer of data (often socioeconomic statistics and especially population data) from one zonation of a region to another, where the two zonations are geographically incompatible. This process is inevitably imprecise and is subject to a number of possible errors depending on the assumptions inherent in the methods used. Previous analysts have had only limited information with which to compare the results of interpolation and so assess the errors. In this paper a Monte Carlo simulation method based on modifiable areal units is employed. This allows multiple interpolations of population to be conducted from a single set of source zones to numerous sets of target zones. The properties of the full error distribution associated with a particular interpolation model can then be examined. The method based on dasymetric mapping consistently gave the highest accuracy of those tested, whereas the areal weighting method gave the lowest. More important than the results presented is the potential for future testing of other methods in increasingly complex situations.

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A large body of research has examined relationships between accessibility to green space and a variety of health outcomes with many researchers finding benefits in terms of levels of physical activity and relationships with levels of obesity, mental health, and other health conditions. Such studies often use spatial analytical techniques to examine relationships between distance to such spaces and health data collated at an individual survey respondent's home address or, more commonly, derived from area-based census measures summarised at a centroid. Generally, such measures are becoming more sophisticated and have moved on from the use of straightforward Euclidean-based measures to those based on network distance. However, few studies tend to use a combination of approaches or seek to establish the implications of incorporating alternative measures of accessibility on potential relationships. Using a database of green spaces (and associated attributes) and a detailed network dataset for the city of Cardiff, Wales, we examine the sensitivity of findings to the ways in which different metrics are calculated. This is illustrated by examining the variations in association between such metrics and a census-based deprivation index widely used in health studies to measure socioeconomic conditions. Our findings demonstrate that not only will the distances to green spaces vary according to the methodologies adopted but that any study that aims to investigate relationships with attributes of the nearest green space should acknowledge that matches may vary widely according to the techniques used. We conclude by warning against the use of inappropriate methodologies in examining access to green space which may directly influence directions (and levels) of association and hence may limit their relevance in wider geographical contexts.

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