A network-based method to harmonize data classifications

Dario Diodato

No 1843, Papers in Evolutionary Economic Geography (PEEG) from Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography

Abstract: A frequent problem in research is the harmonization of data to a common classification, whether that is in terms of ? to name a few examples ? industries, commodities, occupations, or geograph- ical areas. Statistical offices often provide concordance tables, to match data through time or with different classifications, but these concordance tables alone are often not sufficient to define a clear methodology on how the matching should be performed. In fact, the concordance tables have, in numerous occasions, a many-to-many mapping of classifications. The issue is exacerbated when two or more concordance tables are concatenated. In this Jupyter notebook, I discuss a network- based abstraction of this problem and propose, as a general solution, a method that identifies the network components (or the network communities) to make data converge to a new classification. The method simplifies the issue and reduces greatly conversion errors.

Keywords: classification; concordance; harmonization; network; Python; Jupyter (search for similar items in EconPapers)
JEL-codes: C65 C82 C88 (search for similar items in EconPapers)
Date: 2018-12, Revised 2018-12
New Economics Papers: this item is included in nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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