2014
DOI: 10.1007/s00168-014-0652-y
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Spatial fragmentation of industries by functions

Abstract: We show that key functions are spatially clustered with, or dispersed from, each other even within manufacturing industries in West Germany, and that these clustering or dispersion patterns have changed significantly during recent decades. Estimating levels and changes (1992–2007) of localizations and colocalizations of selected functions (production, headquarter services, R&D) within 27 West German industries by means of K densities, we identify two broad groups of industries. In “fragmenting” industries, whi… Show more

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Cited by 5 publications
(4 citation statements)
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“…Several recent studies have found similar trends in other developed countries (see, e.g., Brunelle, 2013, for Canada andBade, Bode, andCutrini, 2015, for Germany). In effect, this implies an increasing specialization by functions and occupations instead of traditional sectoral divisions.…”
Section: This Classifies Cities (Metropolitan Areas Plus Those Counties Not Included In Any Metropolitansupporting
confidence: 59%
“…Several recent studies have found similar trends in other developed countries (see, e.g., Brunelle, 2013, for Canada andBade, Bode, andCutrini, 2015, for Germany). In effect, this implies an increasing specialization by functions and occupations instead of traditional sectoral divisions.…”
Section: This Classifies Cities (Metropolitan Areas Plus Those Counties Not Included In Any Metropolitansupporting
confidence: 59%
“…At present, the methods used to identify morphological characteristics of industry clusters mainly include (1) the set threshold value, based on expert opinions or experiences, (2) clustering algorithms, e.g., Local Getis-Ord Gi statistic [37], DBSCAN (density-based spatial clustering of applications with noise) [20], (3) the distance-based method, for example, Ripley's K [38], L, and M functions [39,40], and (4) methods based on density contour. Chen and Yu [41] compared the spatial distribution structure of social and economic elements in a city to the undulating terrain surface and introduced the contour tree method [22,37] into urban center recognition.…”
Section: Geographic Distribution and Identification Methodsmentioning
confidence: 99%
“…In order to reach such objectives, at first the occupations were separated in two groups, white-collar occupations and blue-collar occupations, according to Duranton and Puga (2005) and Bade et al (2004;2015). Later, the locational quotient (LQ) was calculated for each occupational group in the industrial sector as a whole and each city size group.…”
Section: Final Commentsmentioning
confidence: 99%