Spatial and multidimensional analysis of the Dutch housing market using the Kohonen Map and GIS

Tom Kauko () and Roland Goetgeluk ()

ERSA conference papers from European Regional Science Association

Abstract: In this work the idea is to analyse general spatially identifiable housing market related data on Dutch districts (wijken) with the SOM (Kohonen Map) and a GIS. One of the authors has earlier carried out purely visual SOM analysis of that data, where patterns formed on a larger ‘map’ (the output matrix of the SOM) were used as a basis for classification of the Dutch housing market segments on a nationwide level. This way the SOM was used as a method for exploratory data analysis. Now we attempt a more rigorous method of determining the segmentation using a smaller ‘map’ size, in order to be able to export the SOM-output directly to a GIS-system to analyse it further. Two technical issues interest us: one, the robustness of the results – do the five basic housing market segments found in the earlier analysis prevail (we call these urban, urban periphery, pseudo-rural, traditional, and low-income segments); and two, which classes fit the real situation better and which worse, when using the RMSE for a measure of goodness? We also keep an eye on policy implications and aim at comparing our classifications with the ‘actual’ ones used in official discourse.

Date: 2005-08
New Economics Papers: this item is included in nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www-sre.wu.ac.at/ersa/ersaconfs/ersa05/papers/91.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wiw:wiwrsa:ersa05p91

Access Statistics for this paper

More papers in ERSA conference papers from European Regional Science Association Welthandelsplatz 1, 1020 Vienna, Austria.
Bibliographic data for series maintained by Gunther Maier ().