Spatial Access Ratio, Albuquerque, NM

GIS-based spatial access measures have been used extensively to monitor social equity and to help develop policy and planning for provision of public services. However, uncertainties in the road datasets used to calculate measures of spatial access remain largely underreported. These uncertainties might result in biases within decision-making that strives for social equity based on seemingly egalitarian accessibility metrics. To better understand and address these uncertainties, we evaluated variations in travel impedance resulting from street layer uncertainty (e.g. proprietary, free, and volunteer-information-based streets) and its propagation in a multi-modal enhanced 2-step floating catchment area (MM-E2SFCA) model of spatial accessibility for car and bus transportation, using datasets in the metropolitan area of Albuquerque, NM, USA. We proposed and demonstrated a novel approach as a solution – the spatial access ratio (SPAR). Results indicate that travel impedance disagreement among different street sources propagate through the modeling process to effect Spatial Access Index (SPAI) estimates. Less urbanized regions were found to experience higher street-source variations when compared with the core-metropolitan area. SPAR reduced uncertainties introduced by the choice of model parameter or street datasets, providing a suitable alternative to SPAI for analyses that do not require an absolute measure of supply to demand ratio. Careful selection of street source data and consideration of the potential for bias, particularly for less urbanized areas and areas reliant on public transportation, is warranted when leveraging SPAI to inform policy.