The neighborhood is the central analytical entry point into a wide range of research topics, but it is an open question as to what defines a neighborhood. Most quantitative neighborhood classification methods are based on the assumption that neighborhoods are composed of places with similar spatial and socioeconomic characteristics. While this assumption is both convenient and valuable in neighborhood classification, it tends to overlook critical features of lived experience, particularly human activities such as migration. This paper examines neighborhood classification through the lens of migration patterns in the Twin Cities Metropolitan Area of Minnesota. This study uses a parcel dataset to derive a near complete depiction of intraurban migration, which is then coupled to a new combination of methods informed by migration concepts to construct and analyze neighborhood structure. The results of this approach illustrate the value of combining data, method, and theory of human migration in neighborhood classification.
Bibliographical noteFunding Information:
1This work is supported in part by the National Aeronautics and Space Administration New Investigator Program in Earth-Sun System Science (NNX06AE85G), Center for Urban and Regional Affairs’ Faculty Interactive Research Program, and the University of Minnesota. The authors gratefully acknowledge the efforts of the editor, anonymous reviewers, and Prof. John S. Adams, all of whom greatly improved the quality of the article. Responsibility for the opinions or errors expressed herein is solely that of the authors. 2Correspondence concerning this article should be addressed to Shipeng Sun, School of Natural Resources & Environment, University of Michigan, Ann Arbor, Michigan 48109; phone: 734-764-2550; email: shipengs@ umich.edu and Steven M. Manson, Department of Geography, University of Minnesota, 267 19th Avenue South, Minneapolis, Minnesota 55455; phone: 612-625-4577; fax: 612-624-1044; email: firstname.lastname@example.org
- intraurban migration
- parcel data
- urban segmentation