How does the spatial scale impact accessibility analysis and equity? The case of accessibility to supermarkets in Montreal

Abstract

In place-based accessibility analyses, larger spatial scales, such as census tracts or traffic analysis zones, are often chosen for their practicality and ease of data management. However, larger spatial scales can introduce errors or biases, a phenomenon commonly known as the Modifiable Areal Unit Problem (MAUP). Although MAUP has been addressed in accessibility studies, most compare biases between larger spatial scales and do not use building lots as the reference unit. In this paper, we consider building lots to be the smallest unit of analysis for transport and urban planning. Those that do often fail to estimate the direction and magnitude of overestimation or underestimation or quantify and identify which population groups are most affected. This paper addresses this gap by quantifying the misestimation of accessibility to supermarkets in Montreal, using the cumulative opportunities measure and time to the nearest amenity, by comparing residential building lots with dissemination blocks, dissemination areas, and census tracts. We also measured the cumulative opportunities using three spatial approaches: centroid-to-centroid within census geographies, centroid-to-amenity, and average accessibility. We found that the larger the spatial scale, the higher the misestimation. Census tracts (CTs), the larger spatial scale, show a mean overestimation of 132 % to 154 %, dissemination areas (DAs) range from 111 % to 136 %, and dissemination blocks (DBs) range from 99 % to 131 %. Overestimation affects 14–54 % of dwellings in census tracts, 9–47 % in dissemination areas, and 5–46 % in dissemination blocks, depending on the mode of transport. The centroid-to-amenity generally performs the best in terms of misestimation, and we found mixed results across income groups. These findings can inform decision-makers about the importance of using small spatial scales and optimal approaches to minimize bias and improve city resource allocation.