Geoprocessing Journey-to-Work Data: Delineating Commuting Regions in Dalarna, Sweden

Abstract

Delineation of commuting regions has always been based on statistical units, often municipalities or wards. However, using these units has certain disadvantages as their land areas differ considerably. Much information is lost in the larger spatial base units and distortions in self-containment values, the main criterion in rule-based delineation procedures, occur. Alternatively, one can start from relatively small standard size units such as hexagons. In this way, much greater detail in spatial patterns is obtained. In this paper, regions are built by means of intrazonal maximization (Intramax) on the basis of hexagons. The use of geoprocessing tools, specifically developed for the processing of commuting data, speeds up processing time considerably. The results of the Intramax analysis are evaluated with travel-to-work area constraints, and comparisons are made with commuting fields, accessibility to employment, commuting flow density and network commuting flow size. From selected steps in the regionalization process, a hierarchy of nested commuting regions emerges, revealing the complexity of commuting patterns.

An evolutionary approach to the delimitation of labour market areas: an empirical application for Chile

“…These rules force the amalgamation of the starting BTUs to form LMAs based on local maxima and mergers are determined without reconsideration of previous decisions. An example is the hierarchical aggregation procedure known as Intramax (Masser & Brown, 1975; see Landré, 2012, for an extensive list of applications). In this family of methods, mergers at each step are locally optimalthe pair of groups of BTUs that maximize a form of interaction index is merged.…”

A functional interaction approach to the definition of meso regions: The case of the Czech Republic

“…All approaches have their pros and cons (see for example, Coombes, 2010;Mitchell and Watts, 2010;Landré, 2012;Landré and H�kansson, 2013). Clustering methods (numerical taxonomy methods) are based on a selected criterion, which is gradually decreased until all the resulting regions meet it (Watts, 2009).…”

“…The Intramax algorithm identifies functional regions through the hierarchical aggregation of basic spatial units, with the objective of forming homogeneous clusters (Landré, 2012). The interaction measure is used as an objective function in the hierarchical clustering algorithm and it can be expressed by the formula:…”

“…Each pair of regions is examined separately in each step and is amalgamated on the basis of the maximum value for the objective function. The objective function can be calculated only if all ∑ k T ik > 0 and all ∑ k T kj > 0 (Landré, 2012 limits of self-containment and size are set by the researcher. The values for these four parameters β4) have resulted from extensive testing and are presented in Table 2.…”

“…Each pair of regions is examined separately in each step amalgamated on the basis of the maximum value for the objective function. The objective function c calculated only if all ∑kTik > 0 and all ∑kTkj > 0 (Landré, 2012).…”