Measuring Pedestrian-Friendly Attributes of Streets in Seattle: A Computational Approach

Abstract

This research project aims to enhance pedestrian-friendliness in Seattle by utilizing computational methods to analyze street elements' various measurements. Python, a programming language commonly used in geospatial data analysis, generates geographic visualizations (geo-visualizations) that provide insights into the current state of street infrastructure in Seattle. In order to establish the significance of this research objective, the paper begins by examining the existing plans and initiatives published by the City of Seattle and reviewing literature by renowned urban design theorists to emphasize the importance of pedestrian-friendly streets and street elements. The methodology of this paper is quantitative, involving the collection of numeric data from official websites, which serves as a reliable source for coding purposes. The ultimate goal of this study is to assist the City of Seattle in managing street feature data and make computational methods more accessible to urban design students and researchers. This paper identifies potential avenues for future research by leveraging computational methods, showcasing the transformative capabilities of coding in manipulating urban design data. This approach empowers individuals to generate visualizations by crafting lines of code, opening up new possibilities for analyzing and understanding urban environments.