The Shape of Mobility: Measuring the Distance Decay Function of Household Mobility

Abstract

A well-known challenge to studies examining the distance of residential mobility patterns is that the estimates are often constrained to patterns only within a particular metro area or between metro areas. Thus, studies are unable to estimate the entire distance decay functional form. Using a unique data set on the distance of the most recent move for a large sample of households in twenty-three metropolitan areas in the United States over three waves, we flexibly estimate the distance decay function for the entire sample, as well as for a series of subpopulations based on key demographic information.

对检视居住能动性模式的距离之研究而言, 一个众所皆知的挑战便是, 评估经常限于仅在一个特定的大都会地区或大都会之间的模式。因此, 这些研究无法评估全面的距离衰退之函数形式。我们运用美国二十三个大都会地区中, 三波大型採样的家户晚近迁徙距离之独特数据集, 弹性地评估全部採样以及根据主要人口信息的分组人口系列的距离衰退函数。

Un reto bien conocido de los estudios que examinan la distancia de los patrones de movilidad residencial es que los cálculos a menudo están restringidos a patrones solamente presentes dentro de un área metropolitana particular, o entre áreas metropolitanas. Por eso, los estudios no están en capacidad de calcular toda la forma funcional derivada de la declinación del efecto distancia. Usando un conjunto de datos único sobre la distancia del desplazamiento más reciente en una muestra amplia de hogares en veintitrés áreas metropolitanas de los Estados Unidos, en tres oleadas, calculamos con flexibilidad la función de declinación del efecto distancia para toda la muestra, lo mismo que para una serie de subpoblaciones, basados en información demográfica clave.

Key Words:

• distance decay
• gravity model
• residential mobility

关键词:

• 距离衰减
• 中立模型
• 居住能动性。

Palabras clave:

• declinación del efecto distancia
• modelo gravitacional
• movilidad residencial

Acknowledgments

The research in this article was conducted while John R. Hipp was a Special Sworn Status researcher of the U.S. Census Bureau at the Triangle Census Research Data Center. Research results and conclusions expressed are those of the author and do not necessarily reflect the views of the Census Bureau. This article has been screened to ensure that no confidential data are revealed.

Funding

This research is supported in part by National Science Foundation grant BCS-0827027.

Notes

¹ Studies have also extended the gravity flow model by including various sociocultural characteristics of the destination as predictors of the direction of mobility for interstate flows (Herting, Grusky, and Van Rompaey 1997). Whereas the early studies focused almost exclusively on the gravity flow model and the importance of physical distance, later work increasingly incorporated other important characteristics of metropolitan areas that might influence migration destinations (Ferguson and Kanaroglou 1995).

² The metropolitan areas are Anaheim, Baltimore, Buffalo, Chicago, Cleveland, Dallas, Denver, Fort Worth, Houston, Indianapolis, Los Angeles, Milwaukee, Minneapolis, Oakland, Philadelphia, Phoenix, Portland (Oregon), Riverside-San Bernardino, Sacramento, St. Louis, San Diego, San Francisco, Seattle, Tampa, and Washington, DC.

³ These were obtained from the MABLE/Geocore Web site located at the Missouri Census Data Center Web site (see http://mcdc2.missouri.edu/websas/geocorr90.shtml).

Additional information

Notes on contributors

John R. Hipp

JOHN R. HIPP is a Professor in the Departments of Criminology, Law and Society, and Sociology at the University of California, Irvine, Irvine, CA 92697. E-mail: [email protected]. His research interests focus on how neighborhoods change over time, how that change both affects and is affected by neighborhood crime, and the role that networks and institutions play in that change. He approaches these questions using quantitative methods as well as social network analysis.

Adam Boessen

ADAM BOESSEN is an Assistant Professor in the Department of Criminology and Criminal Justice at the University of Missouri–St. Louis, St. Louis, MO 63121. E-mail: [email protected]. His primary research interests include neighborhoods and crime, geography and space, and social networks.