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Analytical strategies for estimating suppressed and missing data in large regional and local employment, population, and transportation databases
Author(s) -
Guldmann JeanMichel
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1095
Subject(s) - census , cluster analysis , population , database , computer science , data mining , data science , construct (python library) , socioeconomic status , geography , machine learning , demography , sociology , programming language
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