An Empirical Taxonomy of Common Curb Zoning Configurations in Seattle

Abstract

This work applies an unsupervised clustering algorithm to blockface zoning data to identify typical curb configurations in a city. Data is obtained via the city of Seattle’s (Washington, USA) open data portal. To compare the distribution of blockfaces of varying length, all blockfaces are normalized where each zone type is presented as a percentage of the total blockface length in an order-preserving format. Common zone sequences are identified via k-modes clustering, where an optimal choice of k is cross-validated, quantifying the number of curb configurations to represent the majority of Seattle’s blockfaces. All documented code and data are open source and available at https://github.com/pnnl/curbclustering.