<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The power of today's computers allows us to perform computation on massive quantities of data on the one hand and produces enormous amounts of analysis output on the other, as noted by Griffith in his 2003 book. Besides, visualisation and spatial filtering (the core of considerations in Griffith's book) have a chance to be widely used in research practice, especially in geosciences and, more precisely, for georeferenced data. Following the idea proposed by Patuelli et al. (2006, 2009), we analysed the labour market in Poland, focusing on metropolitan areas and their surroundings. The analysis was performed on a data set for the unemployment rate in the 2,478 Polish communes. We took into account spatial autocorrelation and used spatial filtering techniques to construct components of an orthogonal map pattern. As shown in Tiefelsdorf &amp; Griffith (2007), the spatial filtering techniques could be employed in both, parametric and semi-parametric approaches. In this paper we adopted a parametric one.</p></abstract>

The power of today's computers allows us to perform computation on massive quantities of data on the one hand and produces enormous amounts of analysis output on the other, as noted by Griffith in his 2003 book. Besides, visualisation and spatial filtering (the core of considerations in Griffith's book) have a chance to be widely used in research practice, especially in geosciences and, more precisely, for georeferenced data. Following the idea proposed by Patuelli et al. (2006, 2009), we analysed the labour market in Poland, focusing on metropolitan areas and their surroundings. The analysis was performed on a data set for the unemployment rate in the 2,478 Polish communes. We took into account spatial autocorrelation and used spatial filtering techniques to construct components of an orthogonal map pattern. As shown in Tiefelsdorf & Griffith (2007), the spatial filtering techniques could be employed in both, parametric and semi-parametric approaches. In this paper we adopted a parametric one.

Analysis of the Labour Market in Metropolitan Areas: A Spatial Filtering Approach

Institute of Socio-Economic Geography and Spatial Management, Adam Mickiewicz University, Poznań, Poland

Quaestiones Geographicae

The power of today's computers allows us to perform computation on massive quantities of data on the one hand and produces enormous amounts of analysis output...

Analysis of the Labour Market in Metropolitan Areas: A Spatial Filtering Approach

Publicado en línea: 21 jun 2012

Páginas: 39 - 48

DOI: https://doi.org/10.2478/v10117-012-0017-5

Palabras clave
Moran's statistic, spatial autocorrelation, spatial dependence

This content is open access.

Analysis of the Labour Market in Metropolitan Areas: A Spatial Filtering Approach

Publicado en línea: 21 jun 2012

Páginas: 39 - 48

DOI: https://doi.org/10.2478/v10117-012-0017-5

Palabras claveMoran's statistic, spatial autocorrelation, spatial dependence

This content is open access.

Palabras clave
Moran's statistic, spatial autocorrelation, spatial dependence