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Centrality and Land Use: Three Case Studies on the Configurational Hypothesis

Centralité et exploitation du sol: Trois cas d’étude sur l’hypothèse de la configuration
Valerio Cutini


Cette étude constitue la continuité idéale de recherches précédentes sur la relation entre la configuration du quadrillage urbain, mouvement et localisation des activités. Toutes ces recherches ont pour but la vérification de la crédibilité de l’hypothèse qui est à la base de l’analyse de la configuration (Hillier et Hanson, 1984; Hillier, 1996a) : le rôle exercé par le quadrillage urbain comme matrice primaire à la fois au niveau de la production du mouvement et de la localisation des activités. Une telle hypothèse, si elle est démontrée, permettrait de prévoir la distribution des courants de circulation piétonne et des activités urbaines simplement sur la base de la configuration du quadrillage, sans prendre en considération la présence et la position des activités implantées. Les recherches effectuées jusqu’à présent (Cutini, 1999a ; Cutini, 1999b) ont démontré l’existence d’une corrélation étroite entre la configuration et la distribution de la circulation piétonne, en spécifiant, d’autre part, les limites d’un tel rapport. L’étude présentée décrit l’exploitation du sol au travers de l’analyse de la corrélation entre la présence des activités implantées et la distribution des index de configuration.

La recherche a concentré son attention sur les deux cas précédemment choisis et analysés; la vérification sur un troisième cas urbain, choisi pour sa signification particulière, a fourni une confirmation ultérieure des résultats obtenus. De tels résultats permettent de démontrer la fiabilité des méthodes d’analyse de la configuration (Hillier, 1996a) comme instrument de prévision de la disponibilité, de chaque partie d’une implantation, à accueillir la localisation des activités et de leur prospérité; ces même résultats permettent, de plus, de définir les limites réelles de la crédibilité d’un tel instrument. En d’autres termes, les résultats de cette recherche donnent une nouvelle définition de la notion de centralité urbaine, entendue en terme d’attractivité et en faisant abstraction de la présence et de la position des activités implantées. De plus, sur la base des résultats obtenus, la localisation des activités de monopole est mise en évidence comme une variable stratégique dans l’aménagement et la gestion du territoire urbain.

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1In classic urban geography (and in particular in classic location theory), the notion of centrality is generally defined in terms of attractiveness (Losch, 1952; Isard, 1956; Alonso, 1964; Herbert and Stevens, 1960): roughly speaking, a central urban place is said to be a place where activities seek a location, and the struggle for such a location can be seen as the ordering principle of the internal geography of towns. This is a somewhat tautological approach (that is to say, ‘a central place is a central place’), and above all, it cannot account for the factors that determine this attractiveness. These factors can be considered from several different vantage points.

  • a historicist point of view, considering centrality principally as historical heritage, and appraising the center as the main place of memory for the whole urban community.

  • an architectural point of view, appraising centrality as a morphological condition, related to the shape of urban blocks and the presence of monuments and buildings;

  • a functionalist point of view, referring centrality essentially to the presence, density and typology of the located activities and land uses.

2Yet all these different approaches to centrality (even the functionalist one) cannot account for the actual causes of the primary location of the activities in the central places, nor for the factors that frequently cause those activities to move from one place to another in search of a more favourable position.

3This is why we have now sought a different vantage point, assuming centrality as a spatial process. In fact, only such an approach can account for the frequent shifts in centrality from historic urban cores towards new development areas: no doubt these areas generally lack historical importance, architectural and morphological potential and functional prominence; still, they attract activities that seek locations in more ‘central’ areas. On the whole, it follows that economic and social entities model towns primarily by means of the relations connecting movement and urban grid. Instead of being regarded as a morphological state or a functional condition, urban centrality is then appraised as a spatial process, directly related to the continuous development of the urban grid. As soon as the grid changes as a result of any urban transformation, then even centrality itself shifts with the different distribution of attractiveness. Each urban place is hence characterised by its own centrality level, which must be considered with reference to all the other parts of the settlement, taken as a system. This is where configurational analysis comes into play.

4Our research is aimed at verifying the reliability of the methods of configurational analysis as a predictive technique concerning the distribution of the level of attractiveness in each part of an urban settlement. With this aim in view, we will now analyse the correlation between the configurational indexes and the observed activities in some selected case studies. The results of this analysis will provide the central conclusions of our research.

Theoretical Concepts

5It is not our intention in this paper to provide a detailed description of configurational theory; readers interested in studying the subject in depth are referred to the basic literature (Hillier, 1996a; Hillier and Hanson, 1984). Nevertheless, it may be useful to present several fundamental theoretical concepts. We will limit ourselves here to the following key points:

6Configurational analysis is a complex of techniques of analysis of urban space based on the role of the urban grid as the primary element both in the generation of movement and in the location of activities. At the root of this approach is the hypothesis of the existence of the so-called ‘natural movement’ (Hillier et al., 1993), that is, that portion of the pedestrian movement that is not determined by the presence of the located activities but by the configuration of the grid itself. Natural movement may not even be the predominant portion of the totality of the pedestrian traffic (in some cases, it may only be a very small fraction); but its presence is nonetheless recognized as the primary element leading to the location of activities seeking to take advantage of a greater pedestrian flow. The located activities in turn generate a movement of attraction which, when added to the natural movement, actually has the effect of multiplying the primary effect of the grid, according to the grid’s configuration (Hillier, 1996b).

7What exactly are we referring to when we speak of the urban grid? And what do we mean by the term ‘configuration’? An urban grid can be defined as the complex of all the public spaces (that is to say, the spaces open to public transit) that can be defined within an urban settlement; of course, such a grid essentially consists of all the streets and squares, and is determined by the way blocks and buildings are lined up and grouped. According to the Space Syntax theory, defining the configuration of an urban grid means determining, for each individual part of the grid, a set of numeric values that correspond to several predefined parameters, called ‘configurational indexes’. In order to determine these values, we must first transform the grid, which is obviously continuous, into a system, that is, a discrete complex of mutually related elements. This is achieved (setting aside the presence of the activities and the actual land use) by means of the construction of an ‘axial map’, defined as the planimetric representation of the fewest and longest straight lines that connect all the convex spaces of the grid. Two fundamental relations define the system of lines obtained by this method:

  • the intersection between pairs of lines, that is, the manner in which they relate to the system: only the connected lines belong to the system, while the unconnected lines remain outside its boundaries;

  • the depth of a line with respect to another line, defined as the distance between them, topologically measured in the number of lines that divide them.

8By means of these relations, the configurational status of each line of the system can be defined (Hillier, Hanson, 1984) through the use of several selected indexes, namely the connectivity value, the control value and the integration value. We call these parameters configurational, meaning that they do not refer to the internal characteristics of an individual line, but describe the relations mutually connecting that line to all the lines in the rest of the system. The connectivity value measures the number of lines directly connected with the observed line. The control value measures the degree to which a line controls access to and from its neighbours; numerically, the control value results from the summing up of the reciprocals of the connectivity values of the neighboring lines. The integration value, which measures the mean depth of the observed line from every other line in the system, is by far the most important configurational parameter. It is generally used in preference to the dimension of the grid, and has become a standard procedure, derived from the expression (Steadman, 1983):

9I = 2 (Md – 1) / (k-2),

10where Md is the mean depth of the observed line and k is the number of lines in the whole system.

11For each line of the grid, it is possible to distinguish two kinds of integration indexes: a global integration value, which is normally computed by taking into consideration all the lines throughout the grid, and a local integration value, resulting from a consideration of only those lines that lie within a small, pre-selected (topological) area around the observed line. In the latter case, we speak of a radius 3 integration value to specify that in calculating it, we take into account only the lines that are within three steps of it. To mark the difference, overall integration is also often defined as a radius n integration value.

12On this basis, integration actually appears to be like an accessibility index, since it is considered generally and intuitively: on the whole, both terms (accessibility and integration) refer in fact to the ease of connection to a place from the surrounding territorial system, related to the spatial impedance of the grid paths. Nevertheless, a fundamental difference distinguishes the meaning of integration from the definition of accessibility given in spatial interaction modelling. Here the accessibility value in an observed point i of an urban system is generally given in an expression like the following:

13Ai = Σ΅ Ρ΅ f(δi΅)

14where P¡ represents the attractiveness potential of each j located activity and f(δiW¡) is a function of the spatial impedance δi¡ between i and ¡. Such an expression was defined in the late 50’s (Hansen, 1959) and improved (Williams, Senior, 1978; Leonardi, 1979) in more recent years.

15In interaction models, accessibility thus coincides with gravitational potential, each activity aiming at obtaining a location at its highest place of value (Leonardi, 1978). In the configurational theory, on the other hand, the attractiveness potential of each portion of a settlement is assigned according to the way all the elements in the grid become mutually connected, which even influences the location of activities. Hence, while accessibility depends on the presence, consistency and position of the located activities, integration is computed taking into account only the configuration of the grid, that is, the spatial relations connecting its individual elements. In other words, it is recognized that both of these elements (accessibility and integration) shape urban land use by means of the production of territorial inequalities; and in both cases those inequalities are understood to be due to differences in ease of connection of the several parts of the settlement. But while in spatial interaction modelling, ease of connection is considered

16with reference to the presence of the located activities, configurational theory appraises attractiveness on the mere basis of the spatial relations connecting all the elements of the grid. Therefore integration can be interpreted, roughly but significantly, as a ‘pure accessibility’ index unrelated to existing land use.

17With reference to the problem of the prediction of pedestrian flows in towns, our research to date (Cutini, 1999a) has proved the existence of a significant correlation between the configurational indexes and the rates of pedestrian movement. The corresponding relation was typically exponential, and we interpreted this as the outcome of the multiplier effect caused by the attracted activities working, in their turn, as movement attractors.

18The index that the resulting movement rates correlated to most closely was the local (radius 3) integration value, while the correlation of movement rates versus both connectivity and control value was less significant. We were also obliged to observe (Cutini, 1999b) that this correlation, though far stronger than we had expected, lessened dramatically when tested across the whole urban grid. This effect was apparently caused by the presence of strong global attractors, which had been located in the grid without any reference to its configuration. These results seem substantially to confirm the hypotheses of the configurational approach. The configurational theory, in fact, does not deny the principles of the gravitational approach to urban systems: each individual movement is actually the result of the interaction between an origin and a destination; nevertheless, in the hypothesis that all the origins and destinations are uniformly distributed throughout the grid, the resulting total movement can be regarded as a through movement, an overall function of the configuration of the grid. In other words, we can assert that, other things being equal, the trend of configurational indexes in the lines of the grid can reliably reproduce the distribution of movement on the grid as a whole. Yet in most actual cases, other things are in fact not equal over the whole grid: the demographic density, the presence of amenities and facilities, the presence of strong attractors, mostly monopolistic, are usually not uniform in the grid, but only within small sections of it. The consequent lack of uniformity therefore distorts the correspondence between movements and configuration when the analysis is extended over the whole grid.

19This discovery led to the idea of bringing land use into the picture, with the aim of verifying the existence and strength of a correlation between the presence of activities and configuration. Only if activities were actually proved to be strongly related to configurational indexes could we assert that their presence and respective locations were influenced by the grid; such an influence would establish the configuration of the grid as the primary cause of the production of inequalities with regard to attractiveness. Analysis of the grid would then make it possible to estimate the centrality level of each part of the settlement.

20The method sketched above was applied to our selected case studies: two Tuscan towns, Grosseto and Orbetello, those we had studied previously with reference to the prediction of pedestrian movement. A further urban case, the ancient Tuscan town of Volterra, selected for its significance from several points of view, was then subjected to the same analysis, the final outcome of which confirmed our previous results.

The Case Studies

21Our research was first applied to the case studies previously selected, the Tuscan towns of Grosseto and Orbetello.

Our First Case study: Grosseto

22The axial maps of the two urban grids had already been constructed; these consisted of about 800 lines, in the case of Grosseto (the larger settlement, with about 70,000 inhabitants), and around 150 lines for Orbetello (15,000 inhabitants). The subsequent processing of the axial maps by means of Axman Software (Cutini, 1999a; Cutini, 1999b) provided for each individual line for each of the two cases a complete set of the values of all the configurational indexes. Axman also provides a chromatic representation of the trend of all configurational indexes in the lines of the grid; in particular, the trend of integration (both global and local) is described with warm colours (up to orange and red) for the most integrated lines, and cold colours (beginning with violet and blue) for those with the poorest integration values. (figures 1 and 2)

Fig. 1 – Distribution of global (radius = n) integration value in the whole grid of Grosseto

Fig. 1 – Distribution of global (radius = n) integration value in the whole grid of Grosseto

Fig. 2 – Distribution of local (radius = 3) integration value in the whole grid of Grosseto

23We then indicated the presence of activities in the two settlements. Beginning with Grosseto, we selected a sample of 100 lines, well distributed throughout the grid, from the centre to the edge. The observed activities were then classified according to their characteristics, in six basic categories: primary commercial activities (food shops, bar, tobacconists, chemists’ shops, etc.), secondary commercial activities (clothes, books, computer stores, restaurants, etc.), financial activities (banks, insurance companies, etc.), professional and crafts activities, political and administrative activities such as public offices, cultural and religious activities (schools, libraries, museums and exhibitions, churches, etc.). In addition, we made a further distinction, between monopolistic activities and those operating in a free market.

24Since the topological approach involves the appraisal of each line of the urban grid as a single path unit, it was necessary to standardise the consistency of the observed activities, using as a measure the number of activities per 25 metres, in order to leave out of consideration the actual length of each line. Of course we admit that this—that is, proceeding from the assumption that the observed activities are uniformly distributed along the lines—is somewhat artificial. If all the activities were highly concentrated along a limited section of the line (or, on the other hand, if there were strong inequalities in their distribution) we would not be able to record the resulting difference. However, since the topological approach of configurational analysis assigns to each line a set of configurational values (whatever its length), we may safely consider the above assumption acceptable for our purposes.

25We therefore began to focus on the correlation between the configurational indexes (resulting from the automatic processing of the axial map) and the present activities (resulting from direct observation).

26The analysis by linear and non-linear regression of the correlation of the total number of observed activities against the global integration (radius n) index over the whole grid yielded disappointing results, with a dramatically poor coefficient of determination

27R2 1 = 0,051. Equally poor results were obtained when the activities were broken down into the various categories: none of them turned out to be significantly correlated with the same configurational value. We then took into account the local (radius 3) integration value, instead of the global one, and there the results improved: we had a coefficient of determination R2 = 0,164 for all the activities taken together, and similar results (R2 1 = 0,145, R2 2 = 0,178, R2 3 = 0,134, R2 4 = 0,144, R2 5 = 0,0016, R2 6 = 0,0005) for their several categories taken one by one. It is readily apparent that free market activities appear better correlated with integration than monopolistic ones. Nevertheless, though higher than the previous results, all these coefficients still remain well below an acceptable standard of significance.

28Bearing in mind the results of our previous research (Cutini, 1999a), our next step was to break down the analysed system from the whole urban grid into sub-systems of several limited lines, which we then defined on the basis of the same available configurational indexes: the criterion for the definition was the presence within each sub-system of a strong local integrator (that is, a line with a local integration value above the 90 percentile), and of all the observed lines lying within a 3-step topological radius from it. We thus obtained 6 sub-systems, respectively composed of 26, 15, 10, 13, 16 and 20 lines. Obviously their respective delimitations correspond roughly to specific urban sub-areas, even though the topological approach may at times prevent a close correspondence between the resulting sub-systems and the actual neighborhoods.

29The analysis of the correlation of the observed activities with the integration (local, of course) value finally provided excellent results, which can actually be considered highly significant: R2 1 = 0,601, R2 2 = 0,693, R2 3 = 0,704, R2 4 = 0,716, R2 5 = 0,611 and R2 6 = 0,134 (see the diagrams in the appendix).

30We then focused on the relation between the local integration index and the only non-monopolistic activities; the results confirmed the correctness of our previous conclusions, providing the following correlation coefficients with reference to the several selected sub-systems: R2 1 = 0,718, R2 2 = 0,757, R2 3 = 0,729, R2 4 = 0,717, R2 5 = 0,613 and R2 6 = 0,116 (see the diagrams in the appendix). All these results are summarized in the table below:

nr. sub-system

total activities

free-market activities



















Table 1 – Values of the coefficient of determination R² referred to the correlation activities versus local integration in the 6 sub-systems of the grid of Grosseto

31Two aspects appear worth highlighting:

  • Although in some cases the distances are minimal, we can nonetheless quickly see that in all cases the position of the free market activities reproduces the distribution of configurational values more closely than the position of the monopolistic activities: the presence of activities operating in a monopolistic market, on the contrary, appears likely to distort the correspondence between configuration and land use within each sub-system. This is particularly evident in the first two sub-systems, which correspond respectively to the historic center and the railway station area, where the presence of monopolistic activities (public administration, public offices, utilities, cultural, political and religious activities) is far greater than in the peripheral areas.

  • Moreover, we can see that the examined correlation remains weak in only one case (sub-system no. 6)—and indeed this discrepancy even serves to confirm the effectiveness of the results: in fact, while the other 5 sub-systems correspond to older, typically multifunctional areas that have grown up spontaneously, sub-system no. 6 actually corresponds to a recent development area, urbanised since the 70s by means of a zoning plan characterized by a strong functional division between (a few) commercial districts and (many) residential streets. Shops and offices were simply not allowed to take their place in those streets, which eliminated the possibility of a spontaneous choice of the most favourable location for each activity.

32Unfortunately these results, though considerably higher than the standard of significance, were depreciated by the actual low rate of consistency of the data (from 10 to 26). In order to verify the meaning of such results in a more consistent (and therefore significant) data base, we then altered the scale of our work, focusing on an analysis of the correlation between land use and configuration in a single urban sub-area. For our new case study, we chose sub-system 1, which gravitates towards the local integrator currently named Corso Carducci, the main street of the ancient town; this area roughly corresponds to the historic center of Grosseto, still encircled within its ancient town walls. This choice involved taking into account all the lines (namely 56) lying within sub-system 1; of course, we had to carry out the direct observation of the presence and consistency of all the activities (broken down as in the studies so far) in each of them.

33In order to reduce the effect of single-line land use (which is likely to be affected by local and incidental elements), we divided the observed 56 lines into 7 groups of 8 lines per group, rounded off according to the local integration value (beginning with the group of strong integrators and moving towards the group containing the most segregated lines). Finally, we checked the correlation of those 7 integration values against the respective total consistency of the activities along the corresponding lines. In this case, the results were excellent: the relation was proved to be exponential, with a coefficient of determination R2 = 0,946. And, as usual, the analysis regarding only the non-monopolistic activities provided an even higher result: R2 = 0,966 (see the diagrams in the appendix).

Our Second Case Study: Orbetello

34We then applied all aspects of these procedures to our second case study, the town of Orbetello. We constructed the axial map corresponding to the town’s urban grid and processed it by means of the same Axman Software; on the basis of the resulting configurational indexes (figures 3 and 4), we then defined its sub-systems, choosing to focus on the one that corresponded to the town’s historic centre. At the same time, we carried out direct observations of the current activities along the 70 lines belonging to that sub-system, broken down into the categories specified previously. The correlation of the local integration index and the observed presence of activities in the lines of the historic center, though not seriously disappointing, was not very strong (R2 = 0,640 in the case of the total number of activities, and 0.651 taking into account only the free-market ones). Nevertheless, these rather low values can easily be explained as the effect of the fortuitous presence (or absence) of activities in many single lines that were likely to be affected by contingent or local causes. Therefore, as we had done for the previous case, we attempted to clear out these contingent (and not generalizable) elements by breaking down all the lines into 7 groups of 10 lines and studying the correlation of the mean local integration value of each group with the total number of activities in each group (per length unit, of course). In this case we obtained excellent results, as the exponential relation was proved by a coefficient of determination R2 = 0.829 with all the observed activities, and even R2 = 0.919 if we excluded the monopolistic ones (see the diagrams in the appendix).

Figure 3 – Distribution of global (radius = n) integration value in the whole grid of Orbetello

Figure 3 – Distribution of global (radius = n) integration value in the whole grid of Orbetello

Figure 4 – Distribution of local (radius = 3) integration value in the whole grid of Orbetello

A Further Test: Volterra

35A further case study was then added, in order to verify this very clear result by applying the method to a particularly significant urban settlement. We chose the town of Volterra, an ancient Tuscan town with a present population of around 10,000 inhabitants and characterised by the prominence of the historic center, still encircled within medieval town walls, and far larger and more populous that its recent surroundings. In this case the axial map was constructed using 140 lines, which had all been observed with regard to the presence of activities. The study of the correlation between local integration (figure 5) and the density of activities in the 140 lines provided an exponential relation with a coefficient of determination R2 = 0,455. As above, we broke down all the lines into 7 groups of 20 lines according to 7 different levels of integration; we then analysed the correlation of the seven resulting pairs of data. The results in this case were even more surprising, providing an exponential relation characterized by a coefficient of determination R2 = 0,986 (the coefficient of correlation was R = 0,993), which can, of course, be regarded as almost perfect.

Figure 5 – Distribution of local (radius = 3) integration value in the historic centre of Volterra

Graph showing total activities versus local integration in Volterra’s historic centre (7 groups of 20 lines)

Discussing the Results

36A number of observations can be made concerning the results of the research to date:

  • A correlation between the presence of activities and the grid configuration does exist, and is stronger than we expected;

  • The form of this correlation is clearly exponential;

  • The configurational index activities are best correlated with the local integration;

  • The correlation, though very strong within small sub-systems of the grid (which correspond to areas defined by the lines gravitating towards a strong local integrator), weakens when analysed over the whole settlement;

  • The correlation, though very strong in areas that have grown up spontaneously, weakens in areas resulting from development zoning plans; The correlation of configurational indexes is stronger with the presence of free-market activities than with monopolistic ones.

37Such results can be easily interpreted.

38The exponential form of the correlation seems to account for the way multiplier effect activities work out: they are attracted by the configuration of the grid towards the most favourable position they can achieve, but their location itself, in turn, attracts other activities.

39Centrality, defined in terms of attractiveness for activities, is hence proved to be appraised as a function of configuration. Each part of the settlement, in fact, is characterised by its own centrality level, which seems to depend on a configurational parameter (the global integration value of the strong local integrator of the sub-system) and on several other (non-configurational) elements, namely:

  • the presence of strong overall attractors (that is, prominent activities with a market radius extended over the whole grid);

  • the demographic density of the area;

  • the presence of specific utilities and amenities.

40All these elements seem to work together to determine the coefficients K and W of the expression

41y = K exp Wx

42(where x is the local integration value and y the density of activities in each line), which has been proved to reproduce within limited urban sub-areas the relation connecting configuration and land use. That is to say, all these elements working together determine the way configuration influences land use in each individual sub-system.

43For instance, in the case of Grosseto, we had the following values for these coefficients:






















Table 2 – Values of the Constants K and W in the 6 Sub-Systems of the Grid of Grosseto

44It can also be argued that the plot of activities versus integration, appearing simply as a wide cloud of scattered dots (fig. 6a) can otherwise be easily (and usefully) seen as the aggregation of several (six in the case of Grosseto) groups of points, closely correlated around their respective sub-system’s exponential curve fig. 6b).

Figure 6a: Plot of total activities against local integration at Grosseto

Figure 6a: Plot of total activities against local integration at Grosseto

45In other words, activities and integration seem to be strongly correlated over the whole grid by means of the expression we have already seen. Nevertheless, the coefficients K and W, which specify the changes in correlation while moving on the grid, and their value, can be defined within each of the sub-systems on the basis of configurational and gravitational elements alone.

46This kind of description, moreover, makes it possible to define and to clearly distinguish the actual level of centrality of the several sub-systems of an urban settlement. That level, for each sub-system, is represented by its respective regression curve: the higher the position of the curve appears in the diagram, the stronger the resulting attractiveness of the corresponding sub-system. In the case of Grosseto, for instance, it is easy to notice the high (indeed, it is the highest) attractiveness level of the historic centre, substantially coinciding with sub-system 1, with respect to all the other parts of the settlement, and especially to the most peripheral ones, lying at the bottom of the diagram.

47On the other hand, each single curve reproduces the distribution of centrality within the corresponding sub-system, according to a clear exponential trend, described by the expression we have already seen.

48Working on small sections of an urban settlement, we can thus argue the possibility of predicting the distribution of attractiveness in all their respective lines, taking into account only the grid configuration, and setting aside the presence and actual position of the located activities; or, in other words, we can assert, in the same hypothesis, the reliability of configurational analysis as a predictive tool regarding thecapacity of those lines to create activities and enable them to flourish.

49Of course, this dynamics refers only to activities that are free to move, and to choose their own position on the grid; it weakens if they are compelled to accept the location choice of the planner. In the latter case, which is generally likely to run out in the short term, a located activity can do nothing but try to succeed in flourishing, in growing (and often in merely surviving) despite its specific location, if that location is not favourable enough to enable it to cope with the market competition. In the long run, overly rigid and unfavourable regulations are usually adapted to the requirements of the activities.

50Again, this dynamics refers in particular to activities that must contend with market competition, while monopolistic activities can choose their location on the grid without taking its configuration into account.

51On the other hand, with specific reference to monopolistic activities, two aspects appear worth highlighting:

  • A non-configurational location of a monopolistic activity involves extra cost, which will of course not be borne by the activity itself in the form of lower profits, but will be absorbed by the users in terms of increased transport costs;

  • Because monopolistic activities are not constrained by the configuration of the grid, the matter of their location can be regarded as a strategic variable in town development planning and management: in fact, their location can be used to enhance the attractiveness of urban areas that lack configurational centrality. In order to describe this effect, we can say that the presence of monopolistic activities actually modifies the attractiveness-integration curve: more specifically, their presence increases the K coefficient value, raising the local curve on the diagram, and thereby enhancing the centrality level of the particular sub-system. Because of this, monopolistic activities can be used to change the position on the diagram of the attractiveness curve of a local system in order to modify its centrality level.

52In this connection, two possible results are worth mentioning as typical and as occurring frequently:

  • urban decentralisation plans aimed at relieving blocked central areas: in this case, the shifting of monopolistic activities can helpfully support the flourishing of free-market activities in segregated areas;

  • the decline in attractiveness of the ancient urban cores and the shifting of centrality towards new development areas: in this case, the preservation of the monopolistic activities can help to ensure the survival of the other activities in the old towns, as well as the survival and preservation of the ancient centres themselves.


53As concerns our research target, the density of activities was proved to be clearly related to the grid configuration. In fact, the resulting trend of local integration closely reproduces the distribution of urban activities within each sub-system, while the global integration value influences the attractiveness level of the several sub-systems.

54This being the case, we may therefore consider configurational analysis to be an excellent tool when applied to the understanding of the internal geography of towns, with specific regard to the distribution of land uses.

55We can also assert the reliability of the methods of configurational analysis as a predictive technique concerning the distribution of the levels of attractiveness throughout an urban settlement, with reference to any planned transformation of its grid. Nevertheless, in this case, caution is advisable. The configuration of the urban grid, in fact, determines attractiveness towards free-market activities, which aim at taking advantage of a greater flow-through of traffic; on the other hand, all the monopolistic

56activities can be located without any reference to the configuration of the grid and to the trend described by the distribution of the configurational indexes. Yet monopolistic activities do, in their turn, produce movement, and therefore also determine attraction towards all kinds of activities.

57In other words, centrality, appraised in terms of attractiveness, seems to be determined both by the configuration of the grid, and by the presence of the located activities, in particular the monopolistic activities, which, because they are not subject to the constraints of free-market competition, are likely to play an important role: they are not attracted by the configuration of the grid, but at the same time, they still produce attraction.

58On the one hand, therefore, the presence of monopolistic activities distorts the close correspondence between configuration and land use, making it difficult to predict the actual distribution of attractiveness in an urban scale.

59On the other hand, however, their intrinsic nature as ‘non-attracted attractors’ enables us to see them as a highly useful resource for modifying (either enhancing or reducing) the attractiveness level determined by the grid configuration of any selected section of an urban settlement, and therefore the centrality of that particular section.

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Table des illustrations

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Titre Fig. 1 – Distribution of global (radius = n) integration value in the whole grid of Grosseto
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Titre Figure 3 – Distribution of global (radius = n) integration value in the whole grid of Orbetello
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Titre Figure 6a: Plot of total activities against local integration at Grosseto
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Valerio Cutini, « Centrality and Land Use: Three Case Studies on the Configurational Hypothesis », Cybergeo: European Journal of Geography [En ligne], Systèmes, Modélisation, Géostatistiques, document 188, mis en ligne le 26 mars 2001, consulté le 24 mai 2022. URL : ; DOI :

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Valerio Cutini

valerio.cutini@ing.unipi.itDipartimento di Ingengeria Civile Università di Pisa – Italy

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