Over the past two decades, geographical accessibility of urban resources for population living in residential areas has received an increased focus in urban health studies. Operationalising and computing geographical accessibility measures depend on a set of four parameters, namely definition of residential areas, a method of aggregation, a measure of accessibility, and a type of distance. Yet, the choice of these parameters may potentially generate different results leading to significant measurement errors.
The aim of this paper is to compare discrepancies in results for geographical accessibility of selected health care services for residential areas (i.e. census tracts) computed using different distance types and aggregation methods.
First, the comparison of distance types demonstrates that Cartesian distances (Euclidean and Manhattan distances) are strongly correlated with more accurate network distances (shortest network and shortest network time distances) across the metropolitan area (Pearson correlation greater than 0.95). However, important local variations in correlation between Cartesian and network distances were observed notably in suburban areas where Cartesian distances were less precise.
Second, the choice of the aggregation method is also important: in comparison to the most accurate aggregation method (population-weighted mean of the accessibility measure for census blocks within census tracts), accessibility measures computed from census tract centroids, though not inaccurate, yield important measurement errors for 5% to 10% of census tracts.
Although errors associated to the choice of distance types and aggregation method are only important for about 10% of census tracts located mainly in suburban areas, we should not avoid using the best estimation method possible for evaluating geographical accessibility. This is especially so if these measures are to be included as a dimension of the built environment in studies investigating residential area effects on health. If these measures are not sufficiently precise, this could lead to errors or lack of precision in the estimation of residential area effects on health.