Measuring neighbourhood spatial accessibility to urban amenities : does aggregation error matter ?

Neighbourhood spatial accessibility (NSA) refers to the ease with which residents of a given neighbourhood can reach amenities. NSA indicators have been used to inform urban policy issues, such as amenity provision and spatial equity. NSA measures are, however, susceptible to numerous methodological...

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Veröffentlicht in:Environment and planning. A 2002-07, Vol.34 (7), p.1185-1206
Hauptverfasser: HEWKO, J, SMOYER-TOMIC, K.E, HODGSON, M.J
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Sprache:eng
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Zusammenfassung:Neighbourhood spatial accessibility (NSA) refers to the ease with which residents of a given neighbourhood can reach amenities. NSA indicators have been used to inform urban policy issues, such as amenity provision and spatial equity. NSA measures are, however, susceptible to numerous methodological problems. We investigate one methodological issue, aggregation error, as it relates to the measurement of NSA. Aggregation error arises when, for the purpose of distance calculations, a single point is used to represent a neighbourhood, which in turn represents an aggregation of spatially distributed individuals. NSA to three types of recreational amenities (playgrounds, community halls, and leisure centres) in Edmonton, Alberta, Canada is used to assess whether aggregation error affects NSA measures. The authors use exploratory spatial data analysis techniques, including local indicators of spatial association, to examine aggregation-error effects on NSA. By integrating finer resolution data into NSA measures, we demonstrate that aggregation error does affect NSA indicators, but that the effect depends on the type of amenity under investigation. Aggregation error is particularly problematic when measuring NSA to amenities that are abundant and have highly localized service areas, such as playgrounds. We recommend that, when analyzing NSA to these types of amenities, researchers integrate finer resolution data to indicate the spatial distribution of individuals within neighbourhoods better, and hence reduce aggregation error.
ISSN:0308-518X
1472-3409
DOI:10.1068/a34171