Assessing geospatial characteristics of snowplowing service in urban settings

Keywords: Snowplow, Geospatial analysis, GIS
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 08:00 AM (Eastern Time (US & Canada)) - 3/1/2022 09:20 AM (Eastern Time (US & Canada))
Room: Virtual 11

Abstract

Snowplowing is a common municipal service in many US northern cities. Snowplowing routing optimization as a typical urban operation research problem, has not been resolved thus far. In this study, we attempted to examine this problem from the geospatial perspective and its possible links to relevant socioeconomic factors. Based on GPS data recording snowplow drops for four storm events in City of Syracuse, New York, we created spatial distributions of snowplowing frequencies using GIS tools. Hot-spot analysis showed that their spatial patterns had similar local clusters, indicating that snowplowing service is apparently independent of snowstorm events. When linking these spatial patterns to the identified relevant factors, including nonwhite percentage, number of commuters, household income, and ground slope, using both multi-variate linear and geographically weighted regressions, we found that none of the factors could explain the spatial patterns. Realizing pitfalls of using GPS points to represent snowplowing frequencies, we switched to two new metrics, (1) number of snowplowing trucks passing through each census block, and (2) edge betweenness, which measures the number of shortest network paths through a road segment. These two metrics simplify the intrinsic difficulty in solving the snowplowing frequencies as a routing problem at different levels of details. We hypothesize that the spatial patterns of these metrics will be correlated to some of the identified relevant factors. Proving the hypothesis will allow us to reveal the potential social inequality in terms of the correlated factors, while disproving it will indicate that snowplowing service is irrelevant to social justice.