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defined by a household that leaves a property and one that moves into the just-
vacated property. Parcel data are suited to this task when they encompass all home
ownership for a specific area; in the Twin Cities, for example, these data describe
over one million lots. This research utilizes the annual regional parcel dataset in
the TCMA compiled and managed by the regional government, the Metropolitan
Council, spanning the seven counties of Anoka, Carver, Dakota, Hennepin, Ramsey,
Scott, and Washington. Relevant information includes owner's name and date last
sold; other data vary by jurisdiction, such as square footage of houses and their
lots or dwelling type (for a review of these data and those from other locations, see
Manson et al. 2009 ). We identified about 4,800 origin-destination pairs for the years
2005 through 2007, which contain the most complete information for the region and
pertain to the period before the US housing market collapsed in 2008.
While developing migration chains from land parcel data is laborious, it can be
semiautomated. We developed migration chains for the Twin Cities by comparing
the owners of a parcel across years, detecting valid owner changes and matching
owners across years. We weeded out transactions, such as speculation and bank
sales, that represent ownership change without a household move. We also left out
condominiums and apartments given that many are not owner occupied (so renters
are not included). We developed software that embodied a multipart strategy to deal
with variations and errors in names. All names were uniformly formatted into the or-
der of first name, middle name, and last name. Then an intelligent name comparison
routine determined if two different names actually refer to the same person, family,
or organization. It employed a dictionary of abbreviations, which records various
forms of names for a single institution such as the city of Minneapolis and MPLS
and the Minnesota Department of Transportation and MNDOT. It also scanned all
parts and letters in two names, and if the percentage of matched parts or letters is
beyond a predefined criterion, the two names are defined as the same. For instance,
George Washington and G. Washington would be judged as the same person, and
George Washington and George and Martha Washington are the same household.
We then reviewed all matches manually to minimize dataset errors.
Semiautomated extraction of migration chains from parcel data is not a panacea
for migration research, but it offers significant advantages over other approaches.
While it can identify housing attributes, such as square feet of number of rooms,
it does not provide characteristic of movers, such as age or size of household. It is
far more extensive in coverage than most sales databases, much less expensive than
surveys of individuals, and provides a level of specificity not found in higher-level
data such as the census. As a result, this novel approach to migration data provides
critical spatial and temporal information at resolutions sufficient to test theories of
individual migration.
6.3.2
Agent-Based Modeling of Migration
We develop an agent-based model of the modified intervening opportunity theory
presented above that is calibrated and validated against migration chains derived
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