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Jahrb Reg wiss (2011) 31:11–25 DOI 10.1007/s10037-011-0051-0 O R I G I N A L PA P E R

Measuring spatial co-agglomeration patterns by extending ESDA techniques Karsten Rusche · Uwe Kies · Andreas Schulte

Accepted: 15 March 2011 / Published online: 30 March 2011 © Springer-Verlag 2011

Abstract An emerging topic in spatial statistics is the analysis of agglomerations within a regional context. Often, these ‘spatial clusters’ are formed by effects of spatial co-agglomeration. This article introduces an extended bivariate Moran’s I statistic in a case study of German furniture industries. It allows to jointly account for the clustering of two different industries. The method is integrated into the context of Exploratory Spatial Data Analyses. Results show that the approach is a suitable tool for the detection and delineation of co-agglomerations in space by adding self-inclusion of cluster cores and by offering measures of statistical significance. Keywords Spatial clustering · Coagglomeration · Bivariate Moran’s I · Exploratory spatial data analysis JEL Classification C21 · L73 · R12 1 Introduction Empirical research on agglomeration economies can be subdivided into several fields of interest. Graham 2009 identifies three main strands of economic research: K. Rusche () Research Institute for Regional and Urban Development (ILS), Brüderweg 22–24, 44135 Dortmund, Germany e-mail: karsten.rusche@ils-forschung.de url: www.ils-forschung.de U. Kies · A. Schulte Wald-Zentrum (Centre of Forest Ecosystems), Westfälische Wilhelms-Universität, Robert-Koch-Str. 27, 48149 Münster, Germany U. Kies e-mail: uwe.kies@wald-zentrum.de url: www.wald-zentrum.de/kies


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(i) verify the existence of industrial agglomeration an measuring the strength of its localisation tendencies, (ii) identify actual sources of localisation and urbanisation externalities, (iii) determine productive effects of agglomeration economies. This article can be sorted to the first field of empirical literature because it introduces a tool for identifying the spatial structure of co-agglomeration patterns. But as it also focuses on spatial statistics and the local geography of agglomeration it belongs to a fourth field of agglomeration analyses: (iv) detection of agglomeration locations in space and delineating the spatial extend of regional clusters. According to this, the tool presented in this paper combines agglomeration research with the field of “Exploratory spatial data analysis” (ESDA). Here, economic activities and industrial location decisions are treated as phenomena in geographical space. Whereby a spatial topological structure of local observations is one key information to be included into the analysis (Le Gallo and Ertur 2003; Dall’erba 2005; Patacchini and Rice 2007). The existing literature on the spatial aspects of agglomeration economies is mainly focused on the determination of the spatial extend in which agglomeration economies show effect (Duranton and Overman 2005; Marcon and Puech 2003; Ellison and Glaeser 1997). The research presented here goes one step further. The more global measures of agglomeration answer the question “does a set of industries concentrate in space?”. Here, the research question is “if they do, where in space do industries co-locate?” In accordance to the ESDA approach this question is answered on the basis of tests for statistical significance. The methodological approach refers to the often used local Moran’s I statistic (Anselin 1995), but it augments the calculations by adding a self-inclusion of cluster cores. This advances the identification of cluster topologies in space and is to our knowledge new to the field of spatial statistics for the Moran’s I case. The improvements that can be achieved by this type of measure are evaluated within a case study of a set of German furniture industries. As in other countries, this sector can be seen as a good example for the occurrence of agglomeration economies (Rosenthal and Strange 2004) that enforce existing natural advantages (Glaeser 2008). The paper is structured as follows. First a short overview of theoretical aspects of agglomeration and coagglomeration tendencies is presented. Then the methodology is discussed in the framework of an ESDA approach. After that, the data used is shortly introduced and the results are shown and discussed. The article ends with a conclusion. 2 Spatial (co-)agglomeration Agglomeration can be characterized as a spatially constrained form of geographical concentration. Economic activity shows a tendency to concentrate at particular locations owing to three major sources of agglomeration economies: (i) scale, (ii) scope, (iii) complexity.


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All three dimensions are to be detected as well on intra- and inter-industry level (Parr 2002a), and in particular the complexity factor influences the concentration towards spatial agglomeration. These three economies can mainly be referred as to be based on Marshall-Arrow-Romer (MAR) externalities that lead to agglomeration (Das and Finne 2008). This driving forces of economic activities do not lead do a spatial concentration itself, they rather induce mechanisms that lead to actions of firms and people ending in agglomeration: (i) sharing, (ii) matching, (iii) learning (Duranton and Puga 2004). Communication among business partners belonging to industries of a common value added chain occurs in various form, therefore Storper and Venables (2003) advocate the effect of face-to-face contact as an additional standard mechanism of agglomeration. These agglomeration forces inherently constrain business activities to occur within certain proximity. Urbanization economies, business contacts, overview of the competitive market structure and down- or upstream linkages can be named as the factors of agglomeration that assure a spillover of knowledge and economic activity across industries situated within geographical availability (Maurel and Sédillot 1999; Parr 2002b). These effects mainly concern not single isolated industries but foremost sets of industries of different sectoral composition. So, when concentration meets agglomeration (De Dominicis et al. 2007), economic theory infers an expected trend towards pronounced co-location of industries, i.e. spatial coagglomeration, because forces of complexity and business contacts particularly affect industries inter-related in value added chains (Rosenthal and Strange 2004). Spatially coagglomerated industries can be characterized as belonging to common regional industrial clusters (Porter 2000), which emerge through shared common markets, e.g. for goods and employees. From an evolutionary point of view, the existence of spatial coagglomerations can be seen as highly path dependent. So currently existing cluster regions developed through several historical stages and asserted their relative position in the economy (Ter Wal and Boschma 2009). Furthermore, the existing research aims at defining adequate procedures for the empirical study of clustering in geographical space. Different geostatistical measures have been proposed, which assess spatial patterns of events and indicate local concentrations in global space that are grouped together within one or more regions. In contrast to a local ‘concentration’, such a regional deviation of an industry from an average global trend has been defined as ‘agglomeration’ (Arbia 2001). The Moran’s I statistic is among the most popular measures, and its application in numerous studies investigating cases of regional agglomerations provided insight into the complexities behind local patterns of economic activity. From a sectoral economic perspective, such analyses are rather simplistic, as they can only offer a one-dimensional view of single industries, which however are generally embedded in a context of supply or value added chains relating them to other industries. An emerging, still rather unexplored topic in spatial statistics is therefore the regional association of paired industries, which has been described above as ‘coagglomeration’. The relevant question for cluster research is whether inter-industry


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relationships matter in spatial dimensions and how spatial measures could be extended to identify local regimes of sectoral coagglomeration (De Dominicis et al. 2007). A widely used measure is the popular Ellison-Glaeser agglomeration index (Ellison and Glaeser 1997), which indicates the degree of concentration of different sectors in a regional subset in relation to their distribution in a reference area. The Ellison-Glaeser coagglomeration index, an extension of the former index, allows for a relative ranking of concentration between different industries. Ellison et al. (2007) also have proposed a simplified coagglomeration index that does not require information on plant size. However, these indices fail to account for geographical location of observations and cannot describe local patterns. They are a-spatial in nature in view of the agglomeration concept adopted here, because they have not the capacity to assess the nature of the underlying local structures (Alecke et al. 2006) and hence can only be considered as global concentration measures. So, “spatial” in the context used here is defined as reference to a point in space and its relation to other neighbours, not the relative diffusion process within a global setting (as in Duranton and Overman 2005 or Marcon and Puech 2003, for example). In this research paper an approach based on an extended bivariate Moran’s I statistics (Anselin et al. 2002) is proposed as an ESDA analysis tool for coagglomeration analysis of interrelated industries or industry branch segments. The characteristics and requirements of adapting the bivariate Moran’s I to the context of global and local measurements of spatial coagglomeration are developed and demonstrated in a case study of two German wood-based industries.

3 Method The literature on agglomeration and its spatial configuration suggests the use of two different sets of measures to assess the occurrence and strengths of economic concentration (Arbia 2001). The first measure is the popular Ellison-Glaeser coagglomeration index (EG), which computes the crossproduct of pairwise industries’ employment shares in local units and allows for a relative ranking of concentration between different industries (Ellison et al. 2007) (1). As already noted, the EG index only provides a global indication of the strength of industrial concentration, neglecting the geographical topology of the observations. EG coagglomeration index N (si1 − xi )(si2 − xi ) γc = i=1 (1)  2 1− N i=1 xi s share of total employment of industry 1 or 2 in region i; x regions share in aggregate employment. The second measure is the Moran’s I, a monovariate statistic widely used to measure global and local autocorrelation as a proxy for regional clustering in spatial events. The topological neighbourhood relationships are defined by means of a spatial weights matrix, which encodes the spatial relationships between the local observation units. The resulting spatial pattern is evaluated for statistical significance based


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on randomised permutation tests that yield pseudo significance levels (Anselin 1988, 1995). The global Moran’s I statistic indicates the strength of spatial autocorrelation in the data, i.e. the significance of clustering of events in space. This global analysis can also be transferred into a common taxonomy of spatial disproportionality measures of concentration (Bickenbach and Bode 2008). But because this global measure provides only slightly more insight into agglomeration structures compared to the EG index, local indicators of spatial association (LISA) (Anselin 1995) need to be considered, although this local approach still lacks generalization into a common taxonomy. The local Moran’s I statistic divides the global Moran’s I measure into four different clustering schemes of neighbourhood relationships: high-high, high-low, low-low and low-high. Aiming at measuring the concentration of observations in space the HH and HL clusters can be understood as agglomerations (Guillain and Le Gallo 2006). Here this second, local spatial analysis is developed further to take into account the small scale distribution of industrial coagglomerations. Accordingly, the bivariate Moran’s I has been proposed by Anselin et al. (2002) as a measure for space-time autocorrelation. Contrary to the monovariate measure, the bivariate Moran’s I relates a first variable x in the core to a second variable y in its spatial neighbourhood as the bivariate. This rather multivariate analysis of spatial distributions is based on a seminal work by Wartenberg (1985). In the spatial agglomeration literature the potential use of bivariate measures has already been put forward (Guillain and Le Gallo 2006). Only very few experiences are available dealing with the bivariate local Moran statistic (Reich et al. 1994; Zhu et al. 2007), which however did not yet investigate spatial coagglomeration. In this research the bivariate Moran’s I measure was adopted for the purpose of a bidimensional spatial analysis of the regional association among pairs of different industries x and y. Regarding the mathematical formulation it is necessary to define a spatial weights matrix that identifies the connectivity in space between the regions under study. The common way of encoding the spatial weights matrices for monovariate analysis sets all diagonal elements to zero by convention, because the centre region cannot predict itself and is therefore not included into the calculation of spatial lags (Anselin 1988; Pace and Gilley 1997). This also means that the original bivariate Moran’s I formula excludes the target local unit from the neighbourhood measurement (i = j ). In the context of spatial industrial coagglomeration this neighbourhood concept is not realistic, because an industry y located within the same local unit of industry x belongs as well to the neighbourhood of x and might theoretically have an important impact on the identification of the coagglomeration. Therefore the target local unit needs to be ‘self-included’ into the list of neighbours when computing the Moran’s I statistics for the second industry (i = j ). Here a ‘self-included’ version, the extended bivariate Moran’s I∗ was adopted (2) and (3), whereby ‘∗’ indicates the inclusion of the core region in analogy to the G and G∗ indices terminology (Ord and Getis 1995). It is important to note that the cross product x  x of standardized values sums up to n, hence it is irrelevant whether to use x or y as a denominator. Bivariate Global Moran’s I x  Wy ∗ (2) I(d) =  xx


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Bivariate Local Moran’s I ∗ Ii(d) = xi



wij yj

(3)

j

i, j indices of local units (i = j ); n total number of local units i; x observed standardized z-value for local activity of industry a; y observed standardized z-value for local activity of industry b; wij spatial distance weights matrix; d neighbourhood threshold distance. In a bivariate coagglomeration analysis, two potential pairings of two industries are possible, which can be understood as two different directions of the analysis: x → y and vice versa y → x. Both directions are valid coagglomeration concepts and are required to be analysed in parallel, yet it might depend on the particular research question to decide which pairing of industries is more suitable. In order to identify a coagglomeration in its complete, largest extent, a straightforward approach to merge both results was selected by applying a GIS union operation, which integrates both potential spatial coagglomeration linkages in one result.

4 Data Wood panel and furniture industries represent two key branches of the so-called forest sector, which comprises numerous interrelated industries with a close linkage of their associated activities to the common resource wood and therewith to the forest (e.g. forestry, manufacturing of various wood and paper products, timber trade) (European Commission 1999). Economic research on this sector is contributing to a growing understanding of a large forest cluster in Europe’s national economies (Hazley 2000; UNECE and FAO 2005; Kies et al. 2008). On the regional level, cases of concentration have been investigated in the wood industries (Aguilar 2008; Herruzo et al. 2008). Recent research concerning the German forest sector provided first hand evidence for strong regionally diverging trends (Klein et al. 2009) and significant agglomeration and coagglomeration tendencies, in particular in the wood panel and furniture industries (Kies et al. 2009). The most interesting aspect here is that the location of the German forest industries is clustered in the region of EasternWestphalia in the middle of Germany. As there are many other regions in Germany that have at least the same access to relevant raw materials (wood-rich Länder like Bavaria or Baden-Württemberg) it can be assumed that there are agglomeration forces present, that lead to spatial clustering in this region. Accordingly, “something beyond locating near raw materials sources is taking place” (Rosenthal and Strange 2004). The case study focused on a particularly interesting pair of industries in the forest sector. The special case of the German wood panel and furniture industries represents a linkage between a primary wood processing industry and a secondary downstream manufacturing industry, which are connected by supply-chain relationships based on wood products: in this case, wood panels are crucial semi-finished supplies for furniture production. This pair of industries is very suitable for a first testing of the bivariate coagglomeration index for the following reasons:


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1. The definition of the wood panels (20.2) and furniture industries (36.1) according to the NACE Rev.1.1 (EUROSTAT 2002) allows for a further differentiation of the industries into branch segments. Thus the measure’s behaviour on different sectoral sub-class levels of the classification can be investigated. 2. The pronounced interdependence of wood panel and furniture industries presents a good basis for the test of a geostatistical index. 3. The existing literature (Bonkamp 2005; Litzenberger 2007; Kies et al. 2009) provides descriptive evidence for an established regional furniture cluster in the Eastern Westphalia region of North Rhine Westphalia, which represents a prime example for a coagglomeration but so far lacks a geostatistical proof. The dataset’s source is the official statistics of employees with social insurance registration, which is maintained by the German Federal Employment Agency (Bundesagentur für Arbeit 2007) and assesses around 80% of the workforce. The dataset records the distribution of employees of eight NACE classes in 439 counties and urban districts of Germany in 2006. The data is analysed in tabular and cartographic form using GIS techniques. The computation of the bivariate Moran’s I and the extended bivariate Moran’s I* was programmed in R (Bivand et al. 2008) based on the following specific settings: The spatial weights matrix is constructed using a simple distance band around the counties’ geographical centres. Within this distance band a standard contiguity coding scheme was adopted. A 50 km distance band proofed to be most suitable for the spatial classification of German counties (Bode 2008; Rusche 2008) and empirical research indicates, that at this distance agglomeration forces are the strongest at a disaggregated sectoral view (Duranton and Overman 2005). Statistical inference is based on conditional permutation tests (Moran’s I computed through 9.999 permutations), where the observed patterns are compared against random patterns of neighbourhood relationships (Baumont et al. 2003). Spatial coagglomeration is defined as follows: where absolute employment of industry x in a local unit (county) shows a statistically significant autocorrelation (at least 1% confidence level) with industry y in the neighbourhood, the local units are classified as coagglomerations. This conservative threshold was selected, because the calculation of such significance levels for small scale local indicators of spatial association is confronted with severe methodological issues like multiple comparisons (Anselin 1995). Further research will be necessary to assess the statistical sensitivity of the bivariate Moran’s I∗ approach. Another issue is the occurrence of Null fields in a considerable number of local observations, typical for regional statistics of small-sized industries or lower sectoral classes, which may influence the analysis (Lafourcade and Mion 2007).

5 Results The EG coagglomeration index was computed for all possible pairings of industry branches under study and compiled in a crosstable (Table 1; the internal pairings of classes belonging to the same industries are not shown here). It must be noted that, in contrast to the conventional EG index, the coagglomeration index cannot be


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Table 1 Ellison-Glaeser coagglomeration index of the German wood panel and furniture industries, 2006 (based on Ellison et al. 2007; Bundesagentur für Arbeit 2007) Industry a

NACE

Industry b Wood-based panels total

Veneer, plywood, fibreboard

Particle boards

20.2

20.20.1

20.20.2 0.0099

Furniture total

36.1

0.0095

0.0093

Chair and seat furniture

36.11

0.0065

0.0064

0.0067

Office and shop furniture

36.12

0.0056

0.0047

0.0075

Kitchen furniture

36.13

0.0195

0.0206

0.0169

Other furniture

36.14

0.0100

0.0098

0.0106

Note: EG index measured through total employment per county Table 2 Bivariate global Moran’s I and I∗ coagglomeration index of the German wood particle board and kitchen furniture industries, 2006 (based on Anselin et al. 2002; Bundesagentur für Arbeit 2007) NACE pair (x; y)

36. 13 vs. 20.20.2

20.20.2 vs. 36.13

Neighbours

Absolute values

Location quotient

I

I

p-value

p-value

i = j

0.1348

0.0012

0.0302

0.0489

i=j

0.1315

0.0004

0.0297

0.0461

i = j

0.1247

0.0018

0.0177

0.2776

i=j

0.1235

0.0011

0.0289

0.0508

compared to reference levels for significance, because the range of computed values depends on the input data distribution (Ellison et al. 2007). The results allow for a ranking of concentration trends in different branches. The internal pairings of classes (industry x paired with itself) indicate that the strongest concentration trends occur in particle board production (NACE 20.20.2: 0.1166) and the kitchen furniture industry (NACE 36.13: 0.1386). The strongest coagglomerations, i.e. precisely speaking ‘co-concentrations’ (following the presented terminology), are detected in the pairwise combination of the kitchen furniture industry with its input industries in the wood panel industries (NACE 36.13 vs. 20.20.1 and 20.20.2). This pair of wood-based industries, which has the strongest direct ties relating to the value added chain (wood particle boards represent the major supply product for kitchen furniture production), will also be the main focus of the further analysis. Table 2 presents the results of the global bivariate autocorrelation analysis applying the Moran’s I statistics. The global spatial relationship between the two industries of interest occurs to be rather weak at considerably low levels of statistical significance. Although the adapted bivariate Moran’s I∗ tends to produce lower values than the standard I (owing to the adapted calculation procedure), no striking differences between the two measures can be detected. The question here is: does local spatial investigation offer more insights into spatial structures? Figure 1 depicts the spatial distribution pattern of employment in the


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Fig. 1 Spatial distribution of employment in the wood particle boards and kitchen furniture industries

wood particle boards (rectangular symbols) and kitchen furniture industries (circular symbols) across German counties. Clearly the observer’s eye can identify an outstanding ‘area of interest’ of these two industries, which comprises several counties situated in the Western federal state of North Rhine-Westphalia in the so-called Eastern Westphalia region (shown in the zoom frame). The focal point is the small county of Herford (HF) (population 254 500 in 2005), accounting for around 5 800 employees in the kitchen furniture industry alone, which is surrounded by other counties revealing high concentrations of both industries. However, it must be noted that this local distribution pattern is still quite complex: counties with high concentrations of particle boards do not directly correspond to counties with the highest concentrations of kitchen furniture, but they are neighbours. Figure 2 depicts the results of the autocorrelation analysis with the standard and the adapted bivariate local Moran’s I/I∗ putting a focus on the Eastern Westphalia region. Although other concentrations occur in both industries across Germany, none of them are directly co-located. Only one other county in Baden-Württemberg is classified as a coagglomeration, where high concentrations of particle board and kitchen furniture industries are situated in two adjacent neighbouring countries (Ravensburg and Sigmaringen). Figure 2 maps only HH clusters, because significant HL clusters were not identified in the analysis. Applying the standard bivariate Moran’s I to kitchen furniture industries in the neighbourhood of wood panel industries (Fig. 2a), four neighbouring counties are classified as highly significant (p < 0.01, HH∗∗∗ ) and delineate an outstanding re-


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Fig. 2 Results of spatial autocorrelation analysis

gional coagglomeration (OS, MI, GT, LIP). A fifth county (HF) attains only medium significance (p < 0.05, HH∗∗ ). When applying the adapted bivariate local Moran’s I∗ (Fig. 2b), all five counties are classified as highly significant.


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Furthermore the inverse direction of industry pairing is analysed, in which the variables are exchanged vice versa: while Figs. 2(a) and 2(b) show kitchen furniture (y) in the neighbourhood of particle boards (x), Figs. 2(c) and 2(d) depict particle boards (y) in the neighbourhood of kitchen furniture (x), which is a slightly different spatial query: in Fig. 2(c), showing the standard I, only three counties (MI, HF, GT) are classified as highly significant, whereas the two other adjacent counties (OS, LIP) are only significant at p < 0.10 (HH∗ ). In Fig. 2(d), showing the extended I∗ , two differences occur in the classification compared to Fig. 2(c). First the county of Lippe (LIP), which shows the highest concentration of employment in particle boards across Germany (1 038 employees in 2006), was classified as a highly significant coagglomeration unit. Second the Hochsauerlandkreis (HSK), which is located a little more far to the south and disconnected from the former counties, but which is characterized by high employment levels in both industries, is included into the cluster. In total, six counties are classified belonging to this coagglomeration. The differences of results in Fig. 2(a)–(b) showing higher overall significance levels vs. Fig. 2(c)–(d) are dependent on the analysis direction. The results are coherent with their expectation, because wood particle board concentrations occur only in three adjacent counties (GT, HF , LIP) of the region, contrary to kitchen furniture, which occur in all five countries and thus have a higher neighbourhood impact. The results portray the need that both analysis directions require parallel consideration in a coagglomeration analysis. Furthermore the differences between Moran’s I and I∗ in the classification are notable: the counties LIP and HSK, which both show high concentrations of panel industries, receive no or only low significance scores using the standard Moran’s I (Fig. 2(c)), while the Moran’s I∗ classifies them as medium to highly significant. This difference occurs because the self-included Moran’s I∗ accounts for local coconcentrations also within the counties. The example reflects the capacity of the bivariate local Moran’s I∗ approach for a more realistic classification of local coagglomerating units, different in shape and significance level distribution from the standard Moran’s I. In the depicted case study, merging Fig. 2(b) and 2(d) is therefore considered as the best cartographic delineation of the regional coagglomeration cluster of particle board and kitchen furniture industries in Germany.

6 Conclusion The advantage of spatial statistics in economic cluster research is their potential for a deeper local analysis of regional structures. In combination with Geographical Information Systems (GIS), which have rapidly expanded to numerous domains (Longley et al. 2005), they represent powerful tools to visually explore large datasets and identify complex spatial structures or patterns changes over time. A particularly innovative aspect for regional research is that these measures explicitly assess the statistical significance of spatial structures. This enhances as well the quality of cartographic visualisation, because subjective visual assessments of spatial concentrations in simple mapping procedures can thus be confirmed or rejected.


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In summary, it can be noted that the bivariate global Moran’s I, in its standard form as well as in the adapted `self-included´ form, can extend the conventional EG indices: (i) by considering the spatial distribution of observations (by means of a spatial topology in the form of a weights matrix), (ii) by adding statistical confidence levels (pseudo-significance) to industrial coagglomeration analysis. Bivariate global Moran’s I measures can be applied to identify global coagglomeration trends in datasets of interrelated industries. The bivariate local Moran’s I, in particular the adapted ‘self-included’ form I∗ , offers a tool that allows localizing and delineating regional coagglomerations and sectoral clusters according to geostatistical significance criteria. However, this initial case study should be regarded as a first step for spatial coagglomeration statistics. Further refinement of the coagglomeration methodology of the bivariate Moran’s I∗ is required as well as further investigation and testing of the index among other pairs of related industries. Further research should encircle the following questions emerging from this case study’s outcomes: (i) The results demonstrate that ‘self-including’ the core unit of an assembled neighbourhood (i = j ) truly matters in cluster delineation. However it is not yet clear what implications this has on the statistical sensitivity, because it inevitably increases the inherent autocorrelation effects of the analysis. This question relates to the multiple comparisons problem and requires further investigation. (ii) Further thought should also be given to generalize the pairing of industries question. The presented approach to integrate both neighbourhood directions through a GIS union query is pragmatic, but could be improved towards a mathematical decision procedure, e.g. based in input-output relations or intellectual spillovers (Glaeser 2008). (iii) The first promising tests of the bivariate Moran’s I presented here show the capabilities of local coagglomeration analysis for spatial analysis. Measures for the spatial delineation of regional clusters could as well be of interest for a bivariate extension, for example a bivariate AMOEBA (Aldstadt and Getis 2006). (iv) Theoretically the mono- and bivariate Moran’s I indices could also offer a suitable procedure for the identification of an opposite spatial structure to a coagglomeration, i.e. the regional segmentation of industries or branches, which describes the occurrence of regionally separated centres through centrifugal (dispersive) forces, such as undesired competition for resources. As a targeting tool for applied regional sciences, the extended Moran’s I approach can provide crucial baseline information for cluster development and management. Localizing industrial clusters and potentially competitive centres, this geostatistical targeting of clusters in the form of visual maps presents mostly new, previously inaccessible information for decision makers in industry, government and research. The identification of nationally and regionally outstanding employment hotspots of interrelated industries encircles areas of interest for targeted business support activities and cluster research (e.g. the formation of enterprise networks, joint cluster development initiatives, research in regional industry structure and its competitive factors).


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Economic research acknowledges the fact that most industrial clusters emerged historically (Ter Wal and Boschma 2009) without a steering policy and were triggered through complex sets of specific regional competitive factors (Porter 2000; Bröcker et al. 2003; Brenner 2004), which remain yet to be investigated in the case of this resource-based sector and its structurally diverse industries. The authors remain yet careful about recommending the initiation of targeted cluster planning or management activities: cluster management has become an overly popular, inflationary concept in regional development and policy that has often been applied deliberately on a weak scientific basis and resulted in precipitated attempts to form and support ‘wishful thinking’-cluster initiatives (Enright 2003; Kiese and Schätzl 2008). In conclusion, this research presents initial methodologies and first-hand case study findings for the specific topic ‘regional coagglomeration analysis’ in spatial statistics. The bivariate Moran’s I statistic has revealed major potential for the investigation and mapping of sectoral clusters, which lead to a number of subsequent research questions. This research contributes further insight into the geospatial dimension of industrial density, dynamics and linkages of economic activity.

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