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.