Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 1.939

Analytical strategies for estimating suppressed and missing data in large regional and local employment, population, and transportation databases

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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.)
[ Normal View | Magnified View ]

Browse by Topic

Algorithmic Development > Spatial and Temporal Data Mining
Algorithmic Development > Statistics
Application Areas > Government and Public Sector

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts