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Symbolic approach in regional analyses

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  • Justyna Wilk

Abstract

Regional studies cover a spectrum of diversified phenomena and problems including social, economic and environmental ones, which refer to territorial units. Owing to their specific characteristics they are most frequently of both multivariate and complex nature. Conducting regional research is associated with the need to consider such difficulties as large data sets, insufficient precision of phenomena description, disregarding territorial diversification of a given phenomenon, as well as incomplete description of problems. The objective of the paper is to suggest solutions to these problems by means of symbolic approach application which basically consists in presenting phenomena in the form of symbolic data. The first part of the paper discusses specific nature of symbolic data, methods for collecting symbolic data and methods for these data analysis. The second part presents an empirical example referring to the assessment of labour market situation in Polish regions (NTS-2) using symbolic data and cluster analysis.

Suggested Citation

  • Justyna Wilk, 2012. "Symbolic approach in regional analyses," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(3), pages 581-600, December.
    • Handle: RePEc:csb:stintr:v:13:y:2012:i:3:p:581-600
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    Citations

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    Cited by:

    1. Justyna Wilk, 2015. "Using symbolic data in gravity model of population migration to reduce modifiable areal unit problem (MAUP)," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(2), pages 243-264, June.
    2. Wilk Justyna, 2015. "Using Symbolic Data in Gravity Model of Population Migration to Reduce Modifiable Areal Unit Problem (MAUP)," Statistics in Transition New Series, Polish Statistical Association, vol. 16(2), pages 243-264, June.
    3. Justyna Wilk, 2015. "Using Symbolic Data In Gravity Model Of Population Migration To Reduce Modifiable Areal Unit Problem (Maup)," Statistics in Transition New Series, Polish Statistical Association, vol. 16(2), pages 243-264, June.

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