Published In

Plos One

Document Type

Article

Publication Date

3-30-2025

Subjects

Urban areas -- census, Urban Geography -- United States

Abstract

Comparative urban research in the USA has an unacknowledged data and methodological problem at the metropolitan scale, rooted in geographic and definitional boundary changes of urban areas across time. In this article, we introduce a new spatial dataset, decision criteria, and methodological protocol for longitudinal and comparative research with US metropolitan statistical areas (MSAs)—known as ‘metros’—in a way that centers a ‘city-centric’ approach to comparison while significantly reducing spatial error and bias. First, we review gaps and limitations of existing approaches and identify three major but previously unacknowledged sources of error, including a new source of bias we call ‘spanning error.’ Next, we explain our methodological protocol and decision criteria, which are guided by the twin aims of reducing spatial bias and ensuring metropolitan consistency over time. We then introduce our improved dataset, which covers the 50 largest MSAs from 1980-2020. We argue that by centering the urban area as the fundamental unit of analysis—a city-centric approach—our methodology and dataset provides robust and dynamic metropolitan definitions that advance comparative urban studies while improving precision and accuracy in urban data analysis across different time scales. We discuss broader applications of our methodology and identify advantages and limitations over existing techniques, including potential applications of this work in policy, planning, and future research.

Rights

Copyright (c) 2025 The Authors Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

10.1371/journal.pone.0316750

Persistent Identifier

https://archives.pdx.edu/ds/psu/43260

Publisher

Public Library of Science (PLoS)

Download

Share

COinS