Noudéhouénou Lionel

Jaderne Houssou

Jean-Loup Guillaume
email: jean-loup.guillaume@univ-lr.fr

Armelle Prigent
email: armelle.prigent@univ-lr.fr

Review and comparison of similarity measures and community detection algorithms for clustering of network constrained trajectories

Keywords: complex networks, network-constrained trajectories, trajectory similarity, trajectory clustering MOTS-CLÉS : graphes de terrain, trajectoires contraintes par un réseau, similarité de trajectoires, clustering de trajectoires

Trajectory analysis is a study field that is experiencing a renewed interest mainly due to the increase and availability of datasets, generated by users either in the real world by the means of sensors such as GPS, or in virtual environments thanks to the digital footprint they leave when visiting a commercial website for example. In this paper we present the process of converting raw trajectories into network-constrained ones and we review similarity measures for trajectories as well as how they can be used to represent trajectories as graphs. We also describe some approaches to cluster graph of trajectories. Then we assess the trajectories similarity measures and the algorithms for graph clustering. In particular we show that some similarity measures are inadequate for clustering graph of trajectories since they are not discriminative enough.

Introduction

Trajectory analysis is a study field that is experiencing a renewed interest mainly due to the increase and availability of mobility datasets generated by sensors such as GPS. The access to that large amount of data allows researchers and data scientists to extract relevant knowledge like user behavior, road traffic and occupation or frequent mobility patterns that can be monetized by companies through value-added applications as intelligent transportation systems. Moreover, this knowledge is necessary to meet populations needs concerning smart and sustainable cities concept and to deal with the fast demographic evolution and the rapid economic development in some regions.

However, trajectories are not limited to human mobility measured with GPS. Other kinds of trajectories describing for example animal displacements in nature or pages to pages moves of website users are also studied. Independently from the way they are conducted, researches dealing with knowledge extraction through trajectory analysis are usually related to definition and comparison of distance or similarity measures, trajectories representation and clustering of trajectories. They also generally concern trajectories that occur in free space while network-constrained trajectories which are quite common has only been considered by a small handful of studies. Accordingly, we choose to focus on trajectories that are constrained by an underlining network (road network, hyperlinks, etc.) in our literature review.

For instance, in [START_REF] Wang | An effectiveness study on trajectory similarity measures[END_REF] authors compare several distance measures. They transform taxi trajectories from the city of Beijing, by adding noise, re-sampling (addition, deletion of points) or deforming (points shifting) them. Then they assess the performances of the different distance measures by computing distance between transformed trajectories and their originals. They thus succeed in determining by experimentation the advantages and disadvantages of the studied distance measures.

In [START_REF] Besse | Review and perspective for distancebased clustering of vehicle trajectories[END_REF], authors rather define a new distance measure between vehicle trajectories called Symmetrized Segment-Path Distance (SSPD). It has the advantage of being symmetrical and takes account of the geometrical characteristics of the trajectories. The authors compare their new distance to those commonly used in the literature using hierarchical clustering and clustering by affinity propagation and get good results. [START_REF] Hwang | Spatio-temporal similarity analysis between trajectories on road networks[END_REF] propose a method to retrieve similar trajectories from road network space. It consists in a first step to use Points Of Interest (POIs) like roads intersections or places to identify spatially similar trajectories. In the second step they use a temporal distance also based on POIs to refine the trajectory similarity evaluation. Then they apply a clustering using a shifted Hilbert curve to assess their method and get coherent results.

Unlike the previous works, [START_REF] Guo | A graph-based approach to vehicle trajectory analysis[END_REF] define a graph-based representation of trajectories where nodes are trajectories points and where edges link only points that share the same trajectory. They apply a spatially constrained graph partition method on the obtained graph to cluster trajectories by regions.

In the same way, [START_REF] Khalil El Mahrsi | Co-clustering networkconstrained trajectory data[END_REF] propose a new representation of trajectories in graph form. The nodes of this graph correspond to the trajectories and are connected by edges whose weights indicate the similarity between trajectories. The authors also define a new similarity measure between trajectories similar to the TF-IDF (Term Frequency -Inverse Document Frequency) originally used for documents. They then apply a community detection method based on modularity to cluster the trajectories. These new approaches in trajectory representation and clustering has aroused our interest and we propose to enrich it by comparing different similarity measures and various methods of community detection for the clustering of trajectories represented as graph.

This paper present a review of some similarity measures and assess performances of two community detection algorithms used in the context of trajectories clustering.

In Section 2 we present some preprocessing steps wich must be performed when confronted with GPS based trajectories that have to be fitted on a network. Then we present several measures of similarity between trajectories in Section 3 which are not dedicated to network-constrained trajectory but can be somehow adapted. In Section 4 we discuss the clustering of trajectories represented as graph. Finally, we present some experimental results using trajectories in the city of Porto in 5.

Trajectories preprocessing

When studying trajectories in real environments it is often necessary to associate raw mobility data from GPS to positions on a map. For instance, when studying car movements in a city, we expect that the recorded positions belong to road segments as displacements are mainly carried out on road networks and we therefore want to ensure that the trajectories do not go through buildings. The matching of GPS data to positions on a map is called map matching and several approaches have been proposed to deal with this problem. They are categorized in four groups as stated in [START_REF] Quddus | Current map-matching algorithms for transport applications: State-of-the art and future research directions[END_REF]:

-Geometric approaches: map matching is done point-to-point, point-to-curve or curve-to-curve. In point-to-point approaches, each recorded point is matched to the closest point on a road segment [START_REF] Bernstein | An introduction to map matching for personal navigation assistants[END_REF] while for point-tocurve approaches each GPS point is associated to the closest road segment [START_REF] White | Some map matching algorithms for personal navigation assistants[END_REF] and for curve-to-curve approaches entire pieces of the recorded trajectory are matched to road segments on the map [START_REF] Phuyal | Method and use of aggregated dead reckoning sensor and gps data for map matching[END_REF].

-Topological approaches: the similarity between the trajectories and the road network is assessed using the geometry of road segments as well as their topological properties (connectivity, adjacency, containment or contiguity) [START_REF] Yu | A road-matching method for precise vehicle localization using belief theory and kalman filtering[END_REF][START_REF] Greenfeld | Matching gps observations to locations on a digital map[END_REF][START_REF] Yu | Improved positioning of land vehicle in its using digital map and other accessory information[END_REF][START_REF] Quddus | A general map matching algorithm for transport telematics applications[END_REF].

-Probabilistic approaches: an error zone is derived from the error variance associated with the GPS, around each recorded point. If only one road segment intersect the error zone then the point is matched to it, otherwise additional criteria as heading, connectivity and closeness are used to select the best road segment [START_REF] Ochieng | Map-matching in complex urban road networks[END_REF][START_REF] Zhao | Vehicle location and navigation systems[END_REF].

-Advanced approaches: other concepts such as Kalman filters [START_REF] Kim | Efficient use of digital road map in various positioning for its[END_REF], particle filters [START_REF] Gustafsson | Particle filters for positioning, navigation, and tracking[END_REF], Dempster-Shafer belief theory (El Najjar, Bonnifait, 2005), fuzzy logic [START_REF] Fu | A hybrid map matching algorithm based on fuzzy comprehensive judgment[END_REF][START_REF] Quddus | A high accuracy fuzzy logic based map matching algorithm for road transport[END_REF] and hidden Markov model [START_REF] Newson | Hidden markov map matching through noise and sparseness[END_REF] have also been proposed.

According to [START_REF] Newson | Hidden markov map matching through noise and sparseness[END_REF], the hidden Markov model approach correctly identifies up to 99.89% of road segments when the sampling time is less than 30 seconds. Since these performances are better than the ones of other approaches presented in [START_REF] Quddus | Current map-matching algorithms for transport applications: State-of-the art and future research directions[END_REF], we selected the hidden Markov model for the map-matching of our trajectories. We will now describe it with more details.

Hidden Markov Model

A Hidden Markov Model (HMM) is a probabilistic model that generates sequences from transitions between the states of a Markov chain [START_REF] Aggarwal | Data mining: the textbook[END_REF]. The term "hidden" comes from the fact that the states of the chain are not visible to the user but correspond to observations that are visible.

In a Hidden Markov Model used for map-matching, the Markov chain states (nodes) are grouped into layers individually associated with a point on the trajectory to be processed and represent the road segments closest to that point. Each node of a layer has an emission probability quantifying the likelihood that the point associated with the layer belongs to the road segment represented by this node. This probability depends on the measurement error of the GPS used and follows a zero mean Gaussian law [START_REF] Newson | Hidden markov map matching through noise and sparseness[END_REF]) defined as :

p(z t |r i ) = 1 σ z √ 2π e -0.5( ||z t -x t,i || greatcircle σz ) 2 (1)
where z t is the recorded GPS point; r i is a road segment close enough to z t ; x t,i is the point associated to z t on r i ; ||z t -x t,i || greatcircle is the great circle distance between z t and x t,i .

A transition probability is also defined between each pair of nodes coming from two consecutive layers. It indicates the probability of moving from one node to another and therefore from one road segment to another, taking into account the distance between them and the topology of the road network. The transition probability follows an exponential distribution described by [START_REF] Newson | Hidden markov map matching through noise and sparseness[END_REF]

p(d t ) = 1 β e -d t β (2)
where d t is the difference between the great circle distance and the road network distance of two consecutive points of a trajectory, β is an adjustment parameter depending on d t Once the model is constructed, it is represented as a network of nodes affected by the emission and transition probabilities on which the Viterbi algorithm is applied to find the best path on the road network corresponding to the inaccurate trajectory recorded on the GPS. Figure 1 illustrates a Hidden Markov Model in which the best path is selected [START_REF] Goh | Online map-matching based on hidden markov model for real-time traffic sensing applications[END_REF].

Trajectories similarity measures

Since we seek to partition a graph of trajectories into clusters, it is necessary to evaluate the similarity between trajectories. However, the literature most often proposes distance measures between trajectories, which is not adapted to our study. To compensate this lack, we simply convert the distance measures into measurements of similarity using :

similarity = 1 1 + distance (3)
we add 1 to the distance at the denominator, to obtain a normalized similarity between 0 and 1.

The distance measures between trajectories that we have exploited are the Symmetrized Segment-Path Distance (SSPD), Dynamic Time Warping (DTW), Edit Distance with Real Penalty (ERP) and Edit Distance on Real Sequence (EDR). They are among the most widely used in the literature and each has its own unique characteristics. However, none of them is a panacea to the problem of proximity (geographic and / or structural) measurement between trajectories. These distances can be subdivided into two groups: warping distances (DTW, ERP and EDR) that make it possible to compare trajectories of different lengths and take into account the time shifting between trajectories, and shape-based distances (SSPD) that compare the trajectories based on their geometrical characteristics [START_REF] Besse | Review and perspective for distancebased clustering of vehicle trajectories[END_REF].

Dynamic Time Warping (DTW) distance

DTW helps to find the similar patterns between two trajectories of different lengths taking into account the time dimension if necessary. For two trajectories A and B, DTW is computed according to the equation [START_REF] Yoon | Social itinerary recommendation from usergenerated digital trails[END_REF]:

DT W (A, B) =                0, if n = 0 and m = 0 ∞, if n = 0 or m = 0 d(Head(A), Head(B)) + min      DT W (A, Rest(B)) DT W (Rest(A), B) DT W (Rest(A), Rest(B))
(4) where d(A,B) can be any of the distance functions defined on points and Head(X) (resp. Rest(X)) is the first element of X (resp. all but the first element of X). The notations Head(X) and Rest(X) will keep the same meaning in rest of the paper as well as n and m which are the respective lengths of trajectories A and B. This equation results in a dynamic programming algorithm that evaluates all possible combinations of points between the two trajectories and retains the one where the distance is minimal. It is parameter-free but greedy in calculation time and resources and is also sensitive to noise [START_REF] Wang | An effectiveness study on trajectory similarity measures[END_REF].

Edit Distance with Real Penalty (ERP) / on Real Sequence (EDR)

ERP and EDR are edit distances that can compare trajectories of different sizes with time shifting. An edit distance, also known as the Levenshtein distance between two chains, defines the minimal cost necessary to transform one of the sequences into the other by using a series of transformation operations [START_REF] Aggarwal | Data mining: the textbook[END_REF]. According to [START_REF] Zheng | Computing with spatial trajectories[END_REF] EDR and ERP are defined as :

EDR(A, B) =          n if m = 0 m if n = 0 min{EDR(Rest(R), Rest(S) + subcost, otherwise EDR(Rest(R), S) + 1, EDR(R, Rest(S)) + 1}
(5) where

subcost = 0, if d(Head(A),Head(B)) ≤ 1, otherwise (6)
ERP (A, B) =                n l |s i -g|, if m = 0 m l |r i -g|, if n = 0 min      ERP (Rest(A), Rest(B)) + d(Head(A), Head(B)), ERP (Rest(A), B) + d(Head(A), g), otherwise ERP (A, Rest(B)) + d(Head(B), g) (7)
ERP is based on the L1 norm or Manhattan distance and is a metric while EDR is not. ERP assigns a real penalty to the matched points whereas EDR assigns them a discrete penalty (0 or 1). EDR depends on a matching point parameter which makes it robust to noise while ERP uses a constant called gap to process unpaired points [START_REF] Zheng | Computing with spatial trajectories[END_REF]. ERP and EDR apply to standardized trajectories [START_REF] Wang | An effectiveness study on trajectory similarity measures[END_REF].

Symmetrized Segment-Path Distance (SSPD)

SSPD is a shape-based distance that use point-to-segment distance. It measures the average of the minimum distances between each point of a trajectory and all segments of a second one and vice versa. It is parameter-free, can compare trajectories of different size and is time insensitive. The definition of SSPD distance is [START_REF] Besse | Review and perspective for distancebased clustering of vehicle trajectories[END_REF]:

SSP D(A, B) = SP D(A, B) + SP D(A, B) 2 (8)
where

SP D(A, B) = 1 n A n A i=1 D pt (p A i , B) (9)
and

D pt (p A i , B) = min j∈[0,...,n2-1]Dps(p A i ,s B j ) (10) D ps (p A i , s B j )
is the distance from the point p A i to the segment s B j .

Longest Common SubSequence similarity

Besides these distances, we also used the LCSS similarity which is equal to the ratio between the length of the longest common subsequence of two trajectories and the length of the shortest of them. This common subsequence consists of points considered to be matched when the distance between them is less than a predetermined proximity threshold. LCSS is a measure which is robust to noise but does not consider the entire length of the trajectories and is dependent on a parameter. It is calculated as [START_REF] Zheng | Computing with spatial trajectories[END_REF]:

LCSS(A, B) =          0, if n = 0 or m = 0 1 + LCSS(Rest(A), Rest(B)), i f d(Head(A), Head(B)) ≤ , and |n -m| < δ max(LCSS(Rest(A), B), LCSS(A, Rest(B))), otherwise (11)
Where is a matching threshold between two points and δ is a parameter used to control how far in time we can go in order to match a given point from one trajectory to a point from another trajectory.

Sim LCSS (A, B) = LCSS(A, B) min(n, m) (12)

Trajectories clustering

Using the similarities between the trajectories data, we construct a graph whose nodes are the trajectories themselves and edges bear weights indicating the similarity between two trajectories. This representation of trajectories is inspired by [START_REF] Khalil El Mahrsi | Co-clustering networkconstrained trajectory data[END_REF]. Once the trajectory graph is obtained, the next step consists in detecting the groups of similar trajectories. It is a well-known problem in graph theory: the detection of communities. The communities in the graphs are considered as groups in which the nodes are densely connected to each other and weakly connected to the nodes of other groups. However, detecting communities and large-scale graphs is a very difficult task classified as Np-hard [START_REF] Fortunato | Community detection in graphs[END_REF]. The methods available in the literature for detecting communities are [START_REF] Fortunato | Community detection in graphs[END_REF]:

-Traditional methods such as graph partitioning, hierarchical clustering, partition clustering -Methods based on statistical inference -Divisive methods -Spectral methods -Modularity-based methods -Method using dynamic algorithms (spin models, random walk) These methods serve as basis for the design of community detection algorithms, which best performers [START_REF] Fortunato | Community detection in graphs[END_REF] are the Louvain algorithm [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF] that makes a direct optimization of the modularity and the InfoMap algorithm [START_REF] Rosvall | The map equation[END_REF] based on map equation and using information theory and random walks. We thus select these two algorithms for the detection of community in our graph of trajectories.

Experimentations

Dataset

The data set used is Taxi Service Trajectory [START_REF] Moreira-Matias | Predicting taxipassenger demand using streaming data[END_REF]. It contains more than 1000000 trajectories performed by 442 taxis and recorded from 01/07/2013 to 30/06/2014 in the city of Porto (Portugal). Each trajectory is composed of a set of coordinates recorded every 15 seconds from a GPS. For the purposes of our study we will only process 1000 trajectories extracted from this dataset and whose durations are between 5 and 30 minutes. We apply this filter in order to retain only trajectories of intermediate size which are better suited for preprocessing and to prune outliers.

Trajectories preprocessing

The raw GPS coordinates formatted in JSON (javascript Object Notation) are provided as input to the map matching method using HMM, as described in Section 2, which outputs a trajectory matched to the road network and formatted in GeoJSON format. The trajectories obtained after this first treatment sometimes have loops, that do not exist in the original data, which we eliminate automatically with a script developed for this purpose. We then prune the trajectories points that are not points of interest to reduce trajectories size while retaining their geometric and topological characteristics. All the trajectories resulting from the preprocessing step are recorded in an indexed database.

Hardware and software used

Our experiments are conducted on an HP Zbook computer, containing an Intel i7-6700HQ octocore processor clocked at 8 * 2.60Ghz with 16Gb of Ram and running under Ubuntu 16.04 LTS. The map used in our study corresponds to the map of the administrative region of the city of Porto in Portugal and was downloaded at https://www.openstreetmap.org. We transform it into a routable graph of points of interest whose nodes are most often intersections thanks to the open-source tool Osm2Po. The barefoot open source library (https://github.com/bmwcarit/barefoot) written in Java and implementing the hidden Markov model has been used to associate the points of our trajectories with the road segments of the city of Porto. It proposes an "online" map matching, that is, as the data is recorded and an "offline" map maptching only possible when all the positions have been recorded. In our case we use the "offline" option. We also use the open-source library "trajectory_distance" written in python (https://github.com/bguillouet/traj-dist) to evaluate the distances and similarity described above. The pre-processed trajectories are saved and indexed using the PostgreSQL database management system with its POSTGIS extension.

Evaluation of the clustering

Obtaining ground truth communities for empirical trajectories is challenging. Since this is a very first comparison of different similarity measures and graph clustering algorithms, we rely on very simple techniques to evaluate the different techniques. Our main indicator of the goodness of a similarity is how well it is to cluster the trajectories. More precisely, community detection algorithm are designed to find clusters of highly connected nodes (or nodes connected with links of high weight) which are connected to each other by few links (or with links of low weight).

Therefore, once the graph of similarity between trajectories is built, we partition it and measure the average intra and inter-cluster weight and also compare it to the average weight on the whole network. If the network can be partitioned well, we should observe that the intra-cluster average weight should be significantly higher than the average weight and the inter-cluster weight.

As indicated in Table 1 and 2 we can observe that EDR and in a lesser extent LCSS are not discriminative enough: intra-cluster, inter-cluster and global average weights are very similar. This is confirmed by the observation of the distribution of the weights (not displayed here by lack of space) which shows for EDR that weights are homogeneously distributed. On the contrary, SSPD and even more DTW and ERP generate similarities much more discriminative: most of them are close to 0 while only few are significant. This is reflected in the ratio of intra/inter cluster average weight that is very high.

The difference between both algorithms is not very significant. In terms of quality, no algorithm gives better results on all datasets.

Conclusion

In this paper we presented a first comparative study of different similarity measure and graph-based clustering algorithms for network-constrained trajectories. While some trajectories are already mapped to a network, most of them require some preprocessing. It is for instance the case for GPS based trajectories that must be first mapped to a road network from which a set of interest points are extracted to form the nodes of the network. We aslo presented several state-of-the-art distance or similarity measures that can be used to compare trajectories as well as community detection algorithms. Finally, we presented some comparative results of clustering of trajectories based on these similarities and we have shown that some of them are inadequate for clustering, at least from a network point of view.

Many extensions still possible for this work. First, a deeper analysis of the results has to be performed so as to give precise conclusions on the usefulness of the similarity measures in this context. Next, a more proper validation of the results of the clustering should be done. For instance we might be able to label the different communities that might correspond to commuting trips, travels from the city to the airport, etc. Another solution might be to use an application case, for instance trajectory prediction, to check whether the clusters are meaningful or not. Finally, the last short term perspective is to take time constraints into account in particular to evaluate the similarity of trajectories.

Figure 1 .

 1 Figure 1. A Hidden Markov Model illustration.

Table 1 .

 1 Quality of the partitioning for Louvain and the 5 similarity index: number of clusters obtained, number of links of the weighted graph, global average weight, average weight of intra-cluster links, average weight of inter-cluster links.

	Sim.	Nb of clusters Links	Average similarity Intra-cluster similarity inter-cluster similarity
	LCSS 8	96586	0.184	0.217	0.123
	DTW 19	499500 5.72e-05	0.812	2.68e-05
	EDR	5	499500 0.675	0.687	0.672
	ERP	17	499500 4.24e-05	0.438	1.39e-07
	SSPD 16	499500 4.79e-04	0.401	3.33e-04

Table 2 .

 2 Quality of the partitioning for Infomap and the 5 similarity index: number of clusters obtained, number of links of the weighted graph, global average weight, average weight of intra-cluster links, average weight of inter-cluster links.

	Sim.	Nb of clusters Links	Average similarity Intra-cluster similarity inter-cluster similarity
	LCSS 14	96586	0.184	0.319	0.121
	DTW 26	499500 5.72e-05	0.629	2.26e-05
	EDR	1	499500 0.675	0.675	Nan
	ERP	12	499500 4.24e-05	6.9e-03	1.82e-08
	SSPD 23	499500 4.79e-04	0.64	4.53e-04

RIA. Volume -n o 1/2017


Anne Albert-Cromarias

Alexandre Asselineau
email: alexandre.asselineau@escdijon.eu

PROXIMITY COOPERATION DRIVING INNOVATION. THE NATUROPÔLE CASE-FILE

Keywords: territory, proximity, collaborative innovation, SME JEL : D22 -Firm Behavior: Empirical Analysis / L25 -Firm Performance: Size, Diversification, and Scope / R11 -Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

After a long period in the back seat, the proximity-competitiveness link is now emerging to the forefront as a focus for study. Proximity is thought to bring a number of advantages, including fostering the emergence of innovative business practices: for smaller organizations, cooperationfacilitated by proximity tiesmay often prove decisive in resolving certain organizational or managerial issues. To gain deeper insight into these dimensions, we led an exploratory study focusing on Naturopôle -a 'micro-cluster' harnessing together four French SMEs. Despite being based in a territory that, on paper, offers few economic asset-strengths, these four firms have managed to achieve outstanding growth by implementing innovative collaborative business projects. The first results from this research warrant confirmation show that starting out with a tough territorial groundbase can spark local firms to develop collaborative business practices as a platform for strategic, functional and social innovations geared to the overarching goal of competitive differentiation.

Introduction

Fifty years of social and economic transformation have reshaped our spatio-temporal frame, culminating in what today could be qualified as a 'long-space short-time' frame where firms are forced into longer-distance, faster-timespan interactions [START_REF] Torrès-Blay | Organisation, stratégie et territoire à l'aube de la nouvelle économie[END_REF].

Globalization may have become a contingent feature of today's market arena, but many SMEs still evolve in a "proximity mix" (Torrès, 2004), commonly imposed by their geographic location.

This paper examines how SMEs can tackle worldwide competition by becoming sufficiently innovative to turn their local setting into a plus, specifically through cooperative interplay with neighbouring firms.

After recapping on the theoretical foundations grounding our research (section I), we outline the case-brief of the four SMEs organized into a 'micro-cluster' in a rural French territory and show how they innovate (section II), before moving on to keynote our reread of how these firms forged their success (section III).

SMEs, globalization, and proximity

SMEs and 'glocalization'

After a long period in the back seat, the proximity-competitiveness link is now emerging to the forefront as a study focus for industrial economics scholarship. It may seem paradoxical that the recent upsurge in literature on the proximity-competitiveness link has occurred just as the pace of generalized free-market exchange is gathering speed and bringing with it increased business mobility: "globalization allows companies to source capital, goods, and technology from anywhere and to locate operations wherever it is most cost-effective" [START_REF] Porter | On Competition[END_REF]. This encapsulates the paradox of the small world network model, which combines both local and global relational ties. The complementarity between local relations and global relations takes local systems outside the local cohesiveness box and compels consideration of their ability to tap into an extended resource base [START_REF] Watts | Collective dynamics of 'small-world' networks[END_REF].

Over and above the balancing act between these two conceptions of territorylocal one side, global the other -dubbed 'glocalization', "what is really interesting is to see how the global economy finds different ways to send roots into historically-anchored territorial structures. In short, how global constantly feeds on local" (Veltz, 2005: 13). In other words, we are witnessing a shift in the relationship between the firm and its territory, switching from a location-based strategy where space is simply the arena of business activity, to a territorial groundbase strategy where the interests of different agents (local authorities, individuals, and of course firms) converge in a territory recast as an active player [START_REF] Bramanti | From space to territory: relational development and territorial competitiveness[END_REF].

In parallel to this shift, the market vs hierarchy paradigm is being reframed with network models (Axelsson & Easton, 1992;[START_REF] Semlinger | Cooperation and competition in network governance: regional network in a globalised economy[END_REF]. Numerous studies have underscored the important role played by 'clusters' of inter-industry cooperation as a source of competitiveness [START_REF] Porter | Location, competition and economic development: Local clusters in a global economy[END_REF]. This setting, which mobilizes proximity and territorial groundbase as key principles, is the appropriate arena for tackling the issue of locational rationale, where the firm's motivations cannot be reduced solely to finding comparative advantages for factors of production.

The case of SMEs offers a rich vein of learnings when seen from the standpoint that they are characterized by a "proximity mix" (Torrès, 2004):

 Hierarchical proximity, characterized by an owner-manager who is both physically close (sharing the same address and the same work conditions as the on-salary workforce) and personally close (as they tend to personally know each individual member of staff).

 Functional proximity, visible in the absence of segregation of duties, the way the ownermanager is omnipresent and multiversatile.

 Proximity information systems, combining simple and informal internal communication with flexible, no-nonsense coordination mechanisms fronted by mutual adjustment and direct supervision.

 Temporal proximity, with a short time horizon, and where entrepreneurial vision essentially hinges on the owner-manager's own strategic vision.

 Territorial proximity, which hinges on proximity marketing in a relatively tight marketplace space, generally gauged at local/regional scale, and on a network of neighbours that the SME preferentially uses to recruit.

Looking out from this angle, what kind of process can lead SMEs sharing the same territory to co-innovate new solutions enabling them to carve out business in today's highly-globalized context-setting?

Scholarship paints the link connecting proximity and innovation

Work in the locational approach starts out by lending territory a geographic scaffold. A given territory will house different firms of different sizes operating in different sectors, each chasing their own strategic orientations. The focus here will be to address how 'close' firms can go about orchestrating cooperative effort. That said, the term 'close' should leave no room for confusion, as exemplified in proximity economics which splits closeness into two dimensions:

 Geographic proximity comes first, characterized by distance gauged either in miles or in travel-time. From this stance, then, it is spatial proximity that shapes firm location.

 Next comes organized proximitya non-spatial construct characterized by exchanges between agents and their coordinatedness.

These two dimensions do however remain tightly linked, resurfacing as core concepts in the 'proximist' approach [START_REF] Bouba-Olga | Modèles et mesures de la proximité[END_REF], which is platformed on spatial proximity but places the onus on the role of interpersonal relationships in building interorganizational cooperation. Parrilli (2009), studying Italian industrial districts, shows that although spatial proximity may well facilitate inter-firm cooperation, it is notnor can it be -the only driver. Parrilli's analysis concludes that three decisive development drivers need to combine: 'collective efficiency', 'policy inducement', and 'social embeddedness'.

The proximity-innovation link has been heavily analyzed in the scholarship literature on 'clusters' and spin-off SME-based forms such as Italy's 'industrial districts' and, more recently, France's competitiveness clusters ("pôles de compétitivité"). The precursor work dates back a century to Alfred Marshall, but his concept was not picked up by his contemporaries. Not until Michael Porter's work did the managerial literature show signs of exploring deeper into the relationships linking business strategy and micro-environment (the forces acting on a marketplace) or linking business strategy and immediate geographic environment (through studies on topics such as the magnetic Silicon Valley clustering effect for ICT start-ups).

By building on the idea that clustering organizations that share similar operational rationales into the same territory is relevant and should be promoted through pro-active public policy planning, most cluster development analysis actually posits a logic driving the 'international division of labour' via the creation of magnetic clusters with worldwide renown. The fundamental principle is essentially that proximity-based partnerships act as a positive facilitator of business growth drivers, chiefly innovation, through:

 cross-fertilization of competencies and cooperation initiatives (especially on R&D)  mutual recognition between agents (access to capital funding, lobbying leverage, etc.)

 key competences [START_REF] Prahalad | The core competence of the corporation[END_REF]) that are easily adoptable (through universities and research centers, engineering schools, management schools)  the wider presence of what [START_REF] Marshall | Principles of Economics[END_REF][START_REF] Marshall | Industry and Trade[END_REF] dubbed 'industrial atmosphere' as a source of collective emulation.

These foundations are thought to provide the platform enabling endogenous factors like knowledge and innovation to spur territory-wide economic growth [START_REF] Vaz | Knowledge and innovation: The strings between global and local dimensions of sustainable growth[END_REF].

Our research takes a different anglean angle we see as particularly fitting at a juncture when territorial development issues have returned centrestage (especially since proximity has been thrown back into the spotlight as a factor of sustainable development), but that is still yet to attract scholarship interest. We turn away from settings where a territory is already economically active before the firm's arrival, refocusing instead on cases where firms are settled in a territory (in this instance, a rural zone) that ex ante has little economic pull and zero "industrial atmosphere".

Our aim in this focus scenario is to understand how the firm goes about developing business, how it 'builds up' proximity relationships (public-sector or private-sector) and partnerships, and how these initiatives can orchestrate or facilitate innovative approaches. Will this new angle ultimately pinpoint the linkage between innovation approaches and proximity relations?

The Naturopôle case-file: methodology and backgrounding

Research methodology

The methodology framework scaffolding this research is grounded in a single case study approach recycled here for the lead-in exploratory phase setting the scene for our research focus [START_REF] Yin | Case study research, design and methods[END_REF]. Case selection was guided by a set of standout feature-criteria geared to our research problem: a resources-based entrepreneurial approach [START_REF] Vaz | Knowledge and innovation: The strings between global and local dimensions of sustainable growth[END_REF]Barney, 2001) translating into strong self-driven business development; a 'think global, act local' approach; innovative functional and business dimensions; a rural territory groundbase far remote from policy decision-making, research institutes and training institutions; running to an endogeneously-formed small world network-type system.

Our work essentially mobilizes qualitative research tools, primarily semi-structured face-toface interviews with the key players (business leaders and local community partners such as the community council, chamber of commerce and industry, economic development agencies, and others) but backed up with numerous secondary sources of insider data (handed over by Naturopôle and its resident businesses) and independent data (student reports, press articles).

Our contributions target two audiences: first, on-the-ground practitioners looking for generic actionable local-development knowledge [START_REF] Avenier | Shaping a constructivist view of organizational design science[END_REF], who should find the fundamentals in this exploratory research; second, scholarship, by bridging our work over to research in corporate social responsibility as well as local and territorial governance.

3.2

Naturopôlekeynote background data with the local council, was founded in 1995. The original idea was to invest a business park in a spotless environment, cluster together firms operating in the human nutrition segment, and sew up the services offer for major contractor-sponsors. This backstory is a progressivelybuilding trend where contracts were increasingly signed with industrial alliance partners, who in some cases would even co-opt the firm into the upstream product engineering process.

Naturopôlethe picture today

 A thematic business park.

Naturopôle is a segment-themed business zone set in a rural environment and centred  A micro-cluster.

The four Naturopôle firms operate as a micro-cluster. The team that created Naturopôle outlines the basic principle in these terms. Firms hosted at the park will evolve in the neutraceuticals niche and need to be complementary, not in competition. Core skillsets shared by these firms need to be identified to see which can be pooled. In concrete terms, the micro-cluster concept is encapsulated in the 'package solution' deal offered to * Excerpt from the media kit promoting the 1 st Naturopôle open doors event.

customers. Each firm adopts a position geared to its processual role in production (from R&D through to packaging), target market (from cosmetics to functional foods), and proprietary specialty (from source-plant to dried-form substance). Each of the four Naturopôle firms thus holds a clearly-defined position in the graph plotting these three axes (Figure 1). This means that Naturopôle can only open its doors to new arrivals if they can slot into a complementary position in the package solution currently offered by the resident firms. The candidate skillsets targeted would slot into the following activityfields: powder blending and granulation, cosmetics packing, logistics, specialized international regulatory consultancy, and any industrial or services activity harnessing skills that prove complementary to the current neutraceuticals marketplace. Naturopôle is one of three PER registered in the Allier département, with PER 'Parc Naturopôle Nutrition Santé' making it into program portfolio one as a "development project co-led by a business cluster of firms organized into a locally-driven production system, and whose core business entails using innovative technologies to employ or process plant-based raw materials for the nutraceuticals markets" * . The project actions registered revolve around five primary objectives: pool the high-added-value competencies of the four firms through R&D initiatives; sell products and secure positions on European markets; improve the all-road performance of the member-firms; build and promote the park's sustainable development record; implement territorial-scale marketing to galvanize the park's activity-theme, brand identity, and natural-health image.

In reality, the PER tag simply brought an official seal of approval to cooperative arrangements that had been around for years, especially between private-sector actors and public-sector agencies. For Philippe L., securing the PER seal is "recognition of all the work and social-centric commitment engaged by the whole team over more than a decade now. [The PER tag needs to] anchor sustainable, territory-wide economic activity that creates jobs.

[…] The 'PER' tag lends the site and its people credibility". Current mayor of Saint-Bonnet-de-Rochefort Anne-Marie D. echoes the message: "Ever since LPH was founded, we have continually fostered tight partnership relations with the firms by cooperating on ways to meet their objectives. In fact, our public-private partnership was there from the outset. We didn't have to wait for the PER".

All the actions were dealt with on schedule, as PER approval was not there to initiate cooperation between the actors but to cap a long-standing approach. Anne-Marie D. adds:

"I think we were the only ones in the department to have finished. That's because we were ready. There were teams grappling to put together PER Tourism projects that needed * Source: http://poles-excellence-rurale.diact.gouv.fr/IMG/pdf/03-Allier_fiche_illustree.pdf building wholesale from the ground up… whereas all we had to do complete the application bid was put everything down on paper in the right language. We were so far ahead on the operational front that getting the job done was just a logical extension of what we were doing". The PER experience has proven so positive across the board that the stakeholders are unanimously advocating pressing ahead with the approach, along the lines of the second-step system that government proposes to competitiveness clusters.

Naturopôlean innovational test platform

"Creating jobs in rural communities"

Philippe L.'s ambition was to start business in a rural community. His objective was to prove it was possible to launch a new venture in a rural 'area' [as the term is sometimes negativelyconnotated, Philippe L. systematically uses 'natural environment'], creating jobs to consolidate and develop vibrant community clusters. This core idea also extended to demonstrating that it was possible to garner top-flight services and business activities requiring advanced technical and technological know-how without necessarily having to relocate to the city. HR manager Francine D. stresses that "Creating jobs in rural communities is a deep-set conviction -it's not about joining a bandwagon."

At the time, the project looked no more than a reckless gamble, not just in terms of productpositioning ("they took us for tree huggers making herb tea") but also in terms of creating jobs. However, the doubters have been silenced, as the project has clearly met with success.

So much so that the Naturopôle firms are regularly showcased as a standout example, attracting local institutional representatives * , the regional press † , and even national recognition, with the President of France touring Naturopôle in 2008 ‡ .

A package solution deal

The four affiliate firms forming Naturopôle harness complementary skillsets for the package solution deal that Naturopôle offers its customers, i.e. an end-to-end turnkey solution running from full product development through to distribution and back to compound extract, manufacture, physical processing and packaging. This 'package solution' is the unique proposal differentiating Naturopôle in a crowded me-too marketplace. It is also this package solution that best translates the micro-cluster concept where firms interconnect with seamless complementarity to unleash compelling synergies (see earlier, and figure 1).

The Naturopôle firms are also proactively engaged in a highly stringent quality management approach that provides their customers with vital assurances of their motivational drive and no-nonsense professionalism in a fiercely competitive international market arena. Quality certifications have already been secured (or are in the process) to formally hallmark this committed approach, including ISO 22000, ISO 9000, ISO 14001, Ecocert, and Good

Manufacturing Practice. This kind of quality policy obviously speaks to direct customers through the 'package solution' and custom-tailored orders, but it also talks to consumers through the traceability, hygiene and stability conditions that guarantee all products delivered are 100% safe. Manuel B., CEO of LPH, soundbites this market positioning strategy: "Quality in everything we do leverages the added-value we share with our customers".

Converging towards mutually-shared HRM

 Professional mobility.

Naturopôle's companies integrate professional mobility management as a core concern.

From the outside looking in, the firms operate as a micro-cluster of complementary noncompeting business functions, yet inside the cluster, their people possess key competences that could prove valuable to several other the firms under certain factor configurations.

Professional mobility is obviously a feature at Naturopôle, like any other company, but the workforce numbers involved mean that a ceiling is quickly reached. Naturopôle has responded by proposing intra-micro-cluster mobility as a solution offering perspectives for progression through a broad spectrum of channels, from promotion advancement or a position switch through to reclassification following occupational injury or medical imperatives. Progression may stem from a top-down 'pull' initiative, where a firm with a vacancy to fill informs its staff as well as the staff of its Naturopôle neighbour firms (via annual performance appraisals, noticeboard displays, or staff representatives). Progression may also be driven by a bottom-up 'push' initiative by staff voicing the need to move ahead, for person reasons (desire to change position, medical imperatives) or professional motives (career advancement perspectives). This inter-firm mobility may be fairly rare, but it remains a reality. Every year, around two staff switch firmsthe figure looks small, but given the total headcount involved, it is significant. where it can recycle today's expertise into tomorrow's high-growth markets. But innovation costs money, and Naturopôle is revenue-dependent on self-financing under the governance principle adopted and adhered to by Naturopôle directorshipsdirectorships that now have to innovate and forge partnerships to finance a share of the R&D bill. Two innovationsboth eminently strategic for Naturopôle (and understandably kept confidential)were implemented through a system that uses a core collaborative project framework to pool highly-skilled labour.

On business development projects that are not so tightly meshed into the major Naturopôle segments, the firms are looking at following the impetus of LPH into a spin-out scheme for financing innovation (possibly through the 'Young Innovative Company' tax break status for R&D expenditure-heavy SMEs) while keeping their competencies anchored to the territoryready for when fresh new start-ups set up inside Naturopôle's radius.

Geographic proximity: necessary but not sufficient

While all the firms case-studied here do share spatial proximity (as they are all localized to the same business park, at no further than a few hundred meters apart), this dimension does not come as naturally as might first be thought.

In their attempt to differentiate proximity and localization, [START_REF] Rallet | Proximity and Localization[END_REF] stressed how simply being next to someone does not automatically open up cooperation. Neighbours might ignore or even hate one another, for a host of reasons stretching from the scarcity of certain resources or a prior history of 'bad blood'. In the first scenario, a business leader clearly adopts the position that firms are always wary about pooling their human resource capital, as there is always latent competitiveness and the fear of losing good staff to a rival. In the second scenario, there are latent conflicts fuelled by previous relationships between certain participants. These observations converge on the conclusion that not only is an agglomeration of firms (i.e. firms spatially concentrates within a relatively limited geographic radius) unable to guarantee quality relational ties between co-localized organizations, but that this shared proximity can even turn into yet another barrier hampering the chances of the firms coming together and moving forward on a common project.

Spatial proximity is thus a necessary requisiteone achieved at Naturopôlebut turns out to be not enough. To enable projects such as the emergent flexicurity model to come to fruition, spatial proximity has to dovetail with organized proximity, as "organized proximity can be mobilized to solve […] tensions and conflicts […] through processes of cooperation and negotiation" (Rallet & Torre, 2005: 9).

The key role of organized proximity

From network to collective communityship

The Naturopôle firms network is both territorial (characterized by the geographic proximity of the clustered firms) and social (forged through collaboration between their agents). The territorialized network turns out to be far more complex that it looks, as it integrates paradoxical dimensions such as those illustrated in the parallel drawn with neighbourship. A territorial network "escapes the over-simplistic logic of a binary inclusion/exclusion model, since it has to stay open and receptive to the idea that it may extend further in the future.

While an area needs boundary-lines and fencing for it to exist, a network can only hope to survive if it has the power to extend and spread" [translation] (Lauriol et al. 2008: 98).

Research on French-model competitiveness clusters, which were created to fit an (Suire & Vicente, 2008, p.132)in other words, weak ties that promote innovation. This analysis reads communityship as being dependent on weak network ties, as both cause and consequence. For collective innovation, communityship between firmsvia their business leadersupstream of the project can set innovation in motion but is not the only factor necessary. Conversely, downstream of the project, once collective innovation has been effectively implemented, it will necessarily build communityship between the SMEs directly leading the project, and possibly also all the actors enrolled in the project, even if only indirectly involved (local government authorities, for instance).

Conclusion

The preliminary insights collected over this research look to confirm the innovative nature of the micro-cluster, not just on functional front but also, most critically, in terms of the strategic vision of the cluster itselffederated here in the Naturopôle case-file through a vision matching private-sphere (the firms) interests to public-sphere (the territory) interests.

This case-file galvanizes previous research groundwork on the potential of clusters to unlock innovation [START_REF] Porter | Location, competition and economic development: Local clusters in a global economy[END_REF]. Structures whose groundbase was initially perceived as a straightjacket to development have demonstrated agility in adapting to these weakness by initiating collaborative operational practices that, with hindsight, have proven an asset in terms of competitive differentiationa differentiation that business management literature is increasingly spotlighting as pivotal to business strategy [START_REF] Hamel | The Future of Management[END_REF][START_REF] Hamel | What Matters Now: How to Win in a World of Relentless Change, Ferocious Competition, and Unstoppable Innovation[END_REF].

Our conclusions offer early answers to the research problem tackled, surfacing linkages between innovative approaches and proximity groundbase at the cluster firms studied. The Naturopôle micro-cluster has demonstrated a "reticular self-organizing dynamic" [START_REF] Maillat | Innovation Networks and Territorial Dynamics: A Tentative Typology[END_REF]). This dynamic is grounded in Naturopôle's history and development curve, but also its ability to build a relational capital driving a virtuous spiral enabling the actors to go on to develop new innovation projects.

These first results are reported as partial findings, and certain strands warrant deeper investigation. The methodology framework scaffolding this research is a single case studyan approach that, although comfortably meeting the objectives set here, does have its limits, especially the risk of rushing to generalizable conclusions. To illustrate, the personality of the senior directors guiding the focus-organizations actually turns out to be a specific and wholly separate dimension in its own right, independently of any factors tied exclusively to the proximity issue. While this micro-cluster project has met with success, there are still question marks over the long-term survivability of this network configuration, as business growth at

Naturopôle to date has essentially revolved around the competences and drive of a handful of actors. Within this construct, Philippe L.'s leadership, backed by the committed engagement from a handful of local councillors and government authority figures, has proven decisive.

They now face the task of institutionalizing the system set-upalthough this task could well be facilitated by the welcome recognition as a 'PER' rural competency cluster, which should help cement and formalize a framework for future action and initiatives.

  exclusively on the healthcare nutrition segment. In terms of legal-administrative organization, Naturopôle is a non-profit association federating the human resource capital of the local territory. Parc Naturopôle Nutrition Santé [the French full name] is a business platform harnessing input from local leader businesses LPH, Eskiss Packaging, Nutraceutics DS and Biosphère alongside Saint-Bonnet-de-Rochefort community council, the Val de Sioule Forterre association through Leader+ territorial development funds, Montluçon-Gannat chamber of commerce and industry, the Allier département general council, and the Auvergne regional council". * It hosts four firms employing a headcount of 170 permanent staff: LPH (acronym re-translated as the 'Laboratory of Phytotherapy and Herbalism'), founded in 1987 and employing 100 staff, designs and produces plantbased food supplements; Eskiss Packaging, who joined the park in 1991, specializes in the manufacture and labelling of environmentally-friendly packaging for pharmacy, cosmetics and drugstore industry clients, and employs 21 staff; NDS (Nutraceutics Development & Services), a two-person team at the park since 2005, prototypes system solutions for processing plant-based powders and pastes; Biosphère, the latest to join in 2007, specializes in developing and producing plant extracts for the nutrition, cosmetics and pharmacy industry segments, and employs 30 staff.

Figure 1 .

 1 Figure 1. The 'package solution' offered by the Naturopôle-based micro-cluster



  schemes to tick the quality, health-hygiene-safety, and technical foundation training boxes. The project attracted buy-in from all the Naturopôle firms plus other pharmaceutical-sector businesses located in the Gannat and Vichy zones. Ultimately, the project never blossomed into its extended mutualized network format, as the two big outside pharma labs pencilled in did not ultimately cement their commitment to the approach. The initiative was thus scaled back and led at Naturopôle level. Outsourced skills pooling for R&DIn the fiercely competitive functional foods market, pharma and cosmetics giants market and distribute food supplements under their own brandnames. That said, the production process, starting upstream at ingredient selection and running through to blending and manufacturing different compound forms (dry or fluid) and on to packing and traceability, all hinges on ingredient suppliers and subcontractorsincluding the Naturopôle firms.R&D is the critical leverage for securing brand exposure in a marketplace where the key account customers are the global pharmaceutical companies. The time is clearly ripe for Naturopôle to make a move for the fast-emerging probiotics and antioxidants segments,

  Naturopôle was the brainchild of just one man, Philippe L., Pharm.D., who back in 1986 set out to create LPH [Laboratory of Phytotherapy and Herbalism] specializing in medicinal plant preparations. The originality of the project had less to do with the products (custom compounding for dispensing pharmacies) and everything to do with the conscious choice to

	3.2.1 Square one
	Rochefort municipal council of his plans to expand LPH, within the commune boundaries if
	possible, and potentially under a project to set up a lightweight business park. At that time,
	today's mayor Anne-Marie D. was on the municipal council team, but only in an assistantship
	role. Looking back, she remembers that "When they outlined that project in front of the local
	council, they said "-What on earth are you thinking? There's no way it can work, we'll end

set up the firm on his home communea small rural village (counting 656 inhabitants) deep in the Allier département, 50 miles north of Clermont-Ferrand and 30 miles west of Vichy. Philippe L's decision to start up LPH in rural Saint-Bonnet-de-Rochefort was essentially sentimental. Born and bred in the commune, into a family with strong local ties, he sums up his project in these words: "The challenge I was set was to start my own business. The challenge I set myself was to do it on my home soil".

Starting out in his own small workshop, the early days were tough. Business development hinged on diversifying production, originally based around compounding services, into manufacturing packaged capsules. His efforts led the business to open talks with industrials on stepping into physical processing. As early on as 1991, Philippe L. managed to convince one of his suppliers to join the adventure at Saint-Bonnet-de-Rochefort. This is how, after six years in business, the packaging, screenprinting and labelling specialists Eskiss Packaging upped roots and moved in.

As LPH then hit a real surge in development, Philippe L. decided to create affiliates to handle the product distribution side. It was also around this period that he informed Saint-Bonnet-deup saddled with it…" Then, two, three of us got to thinking "sure, it's risky, but if we don't grab the opportunity this time around, we won't get a second chance." The council u-turned, and green-lighted the project". The Naturopôle association, carried forward by LPH in tandem

Outstanding public-private relationships

Naturopôle, registered under the 'PER' rural competency clusters scheme, is a business park run at exclusively local commune level, enabling Philippe L., managing director of lead firm LPH, and Anne-Marie D., local mayor and President of Naturopôle, to build and develop strong synergies. This commonsense intelligence essentially stems from the cohesiveness of the visions of each partner-agent: business leaders one side of the table, local councillors the other. Anne-Marie D. delivers her viewpoint as mayor, in these terms: "For a councillor, being involved in an experience like this day-in day-out is just a fantasticand rareopportunity. This is not my home region. Philippe L. was a chance meeting. We work well together. We share the same objectives, the same motivations. Our dialogue is direct, open, no-nonsense, and that's a positive". Philippe L. has a different take, explaining he wanted to give something back to the region that raised him.

Both these figures have learnt to pull together to secure the economic development of their territory by cultivating consistent and cogent cohesiveness between the territory's brand image, the cluster's business sector, and environmentally-friendly policy. This is the message Naturopôle image management communications translate as a "locally-driven groundbase [of firms] committed to securing long-term sustainability and business development for rural zones while adopting an environmentally-responsible stance".

Proximitythe catalyst driving collaborative innovation?

This case study highlights three forms of innovation: strategic innovation, tied to the business leader and their entrepreneurship goals; functional innovation, spanning marketing (microcluster, package solution) and HR; social innovation, with heightened integration of the firm's multifaceted, multi-stakeholder environment, and qualifiable as "local sustainable development" [START_REF] Asselineau | Entreprise et territoire, architectes conjoints d'un développement local durable ?[END_REF].

At this juncture, the aim is to zero in on the linkage between inter-firm proximity and innovational ability. In other words, does the proximity factor drive innovation, and if so, which form of proximity is the key? agglomeration mindset, has actually surfaced how, in reality, it is the cluster's ability to reach beyond its geographical boundaries that makes it possible to forge new partnerships and, from there, credentialize its legitimacy and superiority [START_REF] Barabel | La dynamique de territoire et l'évolution d'un pôle de compétitivité : le cas de Cosmetic Valley[END_REF]. The paradigmatic issue, then, revolves around the collaborations that need to be built between the different agents, with the human factor becoming increasingly decisive as the frame shifts towards the "non-natural". The notion-term collaboration can thus be distilled into 'coordination' and 'cooperation' [START_REF] Camarinha-Matos | Collaborative Networks. Value creation in a knowledge society[END_REF]. Coordination becomes the project structuring and follow-through given by the project leader, making it hierarchical, compulsory, and procedures-based. Cooperation refers to the mutual readjustment that translates the readiness of the entities involved to work together in what [START_REF] Rallet | Proximity and Localization[END_REF] dubbed a "logic of similarity" based on a shared system of representations.

At Naturopôle, these two dimensions are visibly impelled by the same agent, Philippe L., who radiates the values and mindset that resonate with the others. This naturally nurtures a communityship dimension, which translates into i) a pro-flexibility outlook that systematically leans towards internal flexibility over external flexibility, and ii) the need to enculturate network-driven thinking between corporation and subcontractors by developing "a relational dynamic [that facilitates] a new breed of joint social development" (Le [START_REF] Boulaire | Entreprise-réseau et gestion des ressources humaines[END_REF]: 1141). The communityship concept highlighted in this analysis unequivocally characterizes the network configuration developed between Naturopôle member firms to facilitate innovation emergence. This communityship concept is solidly aligned to the cooperation concept detailed above as part of a shared vision.

Innovationcause and consequence of proximity cooperation?

The two constructsinnovative milieu and collaborative innovation networkhinge on the interplay between three dimensions: a cognitive dimension, materialized in a mindset geared to creating, learning and acquiring technological innovation-focused know-how; an organizational dimension, characterized by partnership-driven cooperation between the agents and the formation of innovation-oriented networks; a territorial dimension, which needs to be read as the capacity to convert outside-network connectivity into comparative advantage to ultimately increase the competitive advantage of the milieu [START_REF] Quévit | The Problem of Innovative Milieux and Territorial Structural Adjustment Policies[END_REF].

The other network dimension with relevance to our analysis is social networking. Explored by scholarship via studies on high-tech clusters or via small-world network theory [START_REF] Watts | Collective dynamics of 'small-world' networks[END_REF]), Granovetter's social network theory (1973), when re-applied at territory scale, highlights the "interplay and porousness of disjointed yet complementary communities"


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