Measuring Urban Sprawl Based on Massive Street Nodes and the Novel Concept of Natural Cities

TL;DR

This paper introduces a new method for measuring urban sprawl using street nodes and natural city boundaries derived from OpenStreetMap data, enabling cross-country comparisons of urban sprawl levels.

Contribution

It proposes a novel approach utilizing street nodes as proxies for population and urban extent, validated with US data and applied to European countries for standardized sprawl classification.

Findings

Street nodes are significantly correlated with city population.

Street nodes have a linear relationship with city area.

Sprawling cities are identified as those below the regression line.

Abstract

In this paper, we develop a novel approach to measuring urban sprawl based on street nodes and naturally defined urban boundaries, both extracted from massive volunteered geographic information OpenStreetMap databases through some data-intensive computing processes. The street nodes are defined as street intersections and ends, while the naturally defined urban boundaries constitute what we call natural cities. We find that the street nodes are significantly correlated with population of cities. Based on this finding, we set street nodes as a proxy of population to measure urban sprawl. We further find that street nodes bear a significant linear relationship with city areal extents. In the plot with the x axis representing city areal extents, and the y axis street nodes, sprawling cities are located below the regression line. We verified the approach using urban areas and population from the US census, and then applied the approach to three European countries: France, Germany, and the United Kingdom for the categorization of natural cities into three classes: sprawling, compact, and normal. This categorization sets a uniform standard for cross comparing sprawling levels across an entire country. Keywords: Street networks, openstreetmap, volunteered geographic information, GIS