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WIREs Data Mining Knowl Discov
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Analytical strategies for estimating suppressed and missing data in large regional and local employment, population, and transportation databases

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Several analytical techniques are reviewed that aim to (1) estimate suppressed employment data in large regional or local economic databases, such as County Business Patterns, using goal‐programming optimization models, (2) estimate local population data, using regional Census data, remotely sensed and traditional data, and statistical modeling, and (3) transfer individual‐level transportation data gathered in national surveys of transportation behavior to construct reliable estimates for local area units (Census tracts), using clustering and regression techniques. These methodologies are illustrative of the rapidly expanding opportunities for improving socioeconomic databases, using new data sources and new and older techniques in innovative ways, thus contributing to knowledge discovery. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Statistics Application Areas > Government and Public Sector
A hypothetical example for suppressing data. (Reprinted with permission from Ref . Copyright 2009 Elsevier.)
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Algorithmic Development > Spatial and Temporal Data Mining
Algorithmic Development > Statistics
Application Areas > Government and Public Sector

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