Network Density and the Delimitation of Urban Areas

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Network Density and the

Delimitation of Urban Areas

Article in Transactions in GIS March 2003

Impact Factor: 0.54 DOI: 10.1111/1467-9671.00139

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Giuseppe Borruso

Universit degli Studi di Trieste

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Transactions in GIS, 2003, 7(2): 177191

2003 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and350 Main Street, Malden MA 02148, USA.

Blackwell Publishing LtdOxford, UKTGISTransactions in GIS1361-1682 Blackwell Publishing Ltd 2003March 2003721000

Research PaperNetwork DensityG Borruso

Network Density and the Delimitationof Urban Areas

Giuseppe Borruso

Department of Geographical and Historical SciencesUniversity of Trieste

AbstractThis paper examines network analysis for urban areas. The research is focused on

the problem of definition and visualisation of network geography and network spaces

at different scales. The urban scale of analysis is examined and different spatial indices

are considered. The built environment of the city is considered as a reference

environment for a road network density index. The latter is implemented in order

to study the spatial interactions between network phenomena and spaces and toprovide further elements for the analysis of urban shape. The study is focused in

particular on understanding spatial patterns drawn by networks and in helping with

the delimitation of city centres. Different approaches are used to obtain the two

indices: a grid-based analysis and a spatial density estimator based on Kernel Density

Estimation. The two methodologies are analysed and compared using point data for

the urban road network junctions and street numbers as house location identifiers

in the Trieste (Italy) Municipality area. The density analysis is also used on road

network junctions data for the city of Swindon (UK) in order to test the methodology

on a different urban area.

1 Network Geography and the Geography of Networks

It is extremely difficult to provide a unique definition of network geography. Different

topologies, users, kinds, spatial distributions, services provided and representation scales

exist for different networks, together with different kinds of relations that a network

displays when realised in a geographical space. A network consists of a well-defined

geometric structure, in which relations and interactions between its basic elements are

set. Nodes and segments (or points and lines) are the basic elements that build andcharacterise it. Segments (or lines) are the means by which nodes are related and linked.

Address for correspondence:Giuseppe Borruso, Department of Geographical and Historical Sciences,University of Trieste, Piazzale Europa, 1-34127 Trieste, Italy.E-mail: [email protected]


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178 G Borruso

Blackwell Publishing Ltd. 2003

Transport and communication networks directly affect human actions from the socio-

economic point of view, contribute to the setting of spatial interaction phenomena and

play a major role in the localisation process. Networks are considered in a framework

of functional relations shaping the pattern of territory. Every localisation process involves

the idea of movement of people and goods over space. This is also a consequence of thespecialisation of different regions in the space. Such a process of specialisation creates a

reduction in the probability of two (or more) regions being similar in terms of settlement

and production patterns.

The case for habits and consumption of individuals, which contributes to the develop-

ment of inter-regional interactions, is different. There is the need for the movement of

goods across space. The pattern drawn by flows of goods entering and exiting the

involved regions describes the quality and potentials of infrastructures and contributes

in explaining the relations between the production and consumption places (Capineri

1996).

In the present study the focus is mainly on the communication networks as instru-ments of spatial interaction and organisation. Transport networks show the patterns

of spatial interaction phenomena at regional and local levels. Variations in density of

transport networks at the local level are an integral part of central-place theory. The

proportion of space occupied by transport channels increases as centres of activity are

approached, and the proportion of space occupied by roads decreases with increasing

distance from the CBD (Haggett and Chorley 1969).

2 Networks and Urban Boundaries

We can define different levels of analysis as, for example, the case of an urban net-

work. If we consider the urban level of analysis it is possible to visualise other different

network phenomena, so considering the interactions they generate and therefore the

spaces taking shape from such relations. There are different kinds of networks to be

analysed and different levels as well. The physical networks are without doubt among

the most important ones. These can include the road network and utility networks,

such as energy, water supply, sewage systems, telecommunications, etc. It is also pos-

sible to visualise some non-material networks, which remind the set of relations that

can be set between different points or nodes composing the network. These can beconnections as well as commercial, industrial, cultural, occupational agreements or

links among the locations considered. There can be particular proximity and affinity

relations among two locations, maybe distant from each other on the geographic space.

Within a single city such non-material networks can be set among cultural or educa-

tional places or nodes, as for instance the different sites of a university or links between

museums.

In order to introduce the first of the spatial indices considered it is useful to revise

some issues related to the urban shape and its definition. The definition of the urban

shape is not a trivial matter and this is particularly true for the definition of the urban

centre. Researchers such as Burgess, Hoyt, Ullman and Harris have studied the problemof the definition of the spatial form of the city and hypothesised the existence of the

CBD and other functional areas (or sectors) surrounding the city centre (Taafe et al.

1996). An important factor for our analysis is the mutual dependency between the

spatial form of cities and the urban transport network. The built environment of a city


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Network Density 179


and the transport networks are in fact closely related. Transport network development

both influences and is influenced by the development of the (urban) built environment.

Lynch (1960) pointed out the different images of a city that individual perceptions

can produce. The identification of elements and features in the city itself, like nodes,

landmarks, edges, is strongly dependent on the experiences of individuals or group ofindividuals. As a consequence, the perceived shape and boundary of a city change

according to personal experiences.

It is therefore difficult to define consistent boundaries of a city as a whole and other

types of boundaries within the same cities. Such boundaries can be in fact related to

different and non-comparable concepts and separations. A possible diversification can

for instance involve areas such as the CBD, commercial and residential areas. In other

cases cultural or administrative areas can be perceived but not easily delimited.

Two different kinds of problem arise. On the one hand there is the problem of the

correct definition of the shape of a city, that considering the separation between urban

and not urban areas. On the other hand some areas inside the urban environment needto be defined. The city centre is considered as the most important one in the present

research. Cities are usually defined using administrative boundaries or census enumera-

tion districts and data available are usually referenced to such area features. Such official

boundaries are usually the unique homogeneous sources of city shape information. The

limits of this kind of representation are well known and cause problems in the correct

interpretation and analysis of the (urban) phenomena under examination. The carto-

graphic result depends not only on the phenomenon under investigation but also on the

pattern of the zones used in the collection of data, their shape and areas (Unwin 1981,

Dykes and Unwin 1999). Different methods have been proposed to avoid such area-related problems such as dasymetric mapping (Langford and Unwin 1994), using satel-

lite remote sensing to find the built urban land cover. Similarly, Mesev and Longley

(2000) propose the use of classified imagery as a data source for advanced urban spatial

analysis. Point data can also present useful sources for information retrieval for urban

area analysis as demonstrated by Gatrell (1994) and by Thurstain-Goodwin and Unwin

(2000). In both cases a set of discrete points, represented by the unit postcode, is used to

derive the shape of the built environment of the city and to create a continuous surface

of spatial density. Both research works use kernel density estimation (KDE) methods to

derive contour lines on this surface. Gatrell (1994) used postcodes as a starting point for

population density estimation, while Thurstain-Goodwin and Unwin (2000) consideredpostcodes and related socio-economic data for the definition of urban centres.

3 Density Indices for Urban Studies

In this study network density is used to enable definition of city centre boundaries using

point data for the junctions of the urban road network of Trieste. This dataset is used

to approximate the spatial distribution of the road network. The density analysis is

meant to highlight the presence of peaks in the distribution and to see where it is

possible to link them to the centres of urban settlements.Second, an alternative density index was constructed using point data for addresses

for the Trieste Municipality area in Italy (Figure 1). This index was used essentially to

compare the results obtained using the network density analysis and allowed a raw

estimate of the shape of the urban built environment.


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180 G Borruso


Two different methods were used to derive estimates of the spatial density. First,

a grid-based index was implemented to assess the network density in the urban area

of Trieste. This was easy to compute but affected by different levels of arbitrariness

depending on the chosen grid cell size, position and orientation. The results were thencompared to the spatial distribution of the built environment in the Trieste Municipality

area. The second method used the quadratic kernel density estimation (KDE) to obtain

smooth continuous surfaces from the same sets of point features. This provides a more

consistent analysis that overcomes the limitations of the grid-based method. In the KDE

analysis the sole source of arbitrariness is given by the choice of the bandwidth used to

sample the point dataset.

The analysis was carried out on different sub-networks derived from the graph

representing the complete urban road network of Trieste and allowed the display of

different patterns of spatial interaction. The analysis was first carried out on the complete

urban road network and successively on the main urban road network allowed for privatecar traffic (therefore not considering pedestrian and secondary streets and also dedicated

lanes) and on the main access roads to the city centre (i.e. State roads and motorways).

Figure 2 displays the full set of point data for junctions and the urban road network. It

consists of 1,513 nodes derived from the complete road network of the city of Trieste area.

Figure 1 Trieste Municipality in North Eastern Italy


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Network Density 181


Italian addresses are based on street numbers. Street number-based addresses allow

a precise location of houses or sets of apartments. Figure 3 shows the distribution of

the street numbers in Trieste Municipality. The limits of such an approach are known

and can be related to the use of unit postcodes in other national systems. Different street

numbers can in fact represent different population densities in different areas: a street

number in a small village can identify a household, while in the city centre it can

represent an entire block of many apartments. Secondly, not all of the street numbers

are used as habitations and thirdly different street numbers occupy different portions of

space. In the present case however the street number density provides us with a physical

index of the urban built environments extension and density. The second dataset wasused to compare the results obtained from the analysis on road networks junctions. A

set of 29,053 counts for the street numbers is used.

3.1 Grid Density Estimation

Following Borcherts (1961) study of Minneapolis-St. Paul, the grid square was used to

count the road junctions in order to provide an index of road network density. Instead

of measuring road density by length of road per unit, Borchert counted all the road

junctions on the map and discovered a high correlation between the road network

length and the road-junction density. He also found that network density decreasedwhen moving out from the city centre. Jones (1978) justified the use of junctions to

analyse networks. His studies on stream networks confirm the possibility of studying

linear network phenomena through the analysis of the junctions between lines. Jones

(1978) also considered the possibility of using midpoints of lines for spatial network

Figure 2 Point data for junctions and urban road network


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182 G Borruso


analysis. A linear pattern can therefore be converted into a point pattern and more easily

examined. A fine grid mesh of 100 m on side was used to sample the urban road

network of Trieste and to compute a network spatial analysis.

The use of a grid square network has been used and/or suggested in many research

works (e.g. Matti 1972, Holm 1997). The uniform size of such small network units

enables a quick co-ordination of the grid units and is ideal for statistical computation

and for providing density values without further transformation. As usual in this type

of analysis, the choice of the grid is arbitrary. The results obtained depend on the size

of the grid cell used, the orientation of the grid mesh and on the offset. A variation in

these variables can in fact produce different results in terms of spatial patterns resultingfrom the density analysis. The grid contained 8,955 square cells measuring 100 m on a

side and was superimposed on a map representing the Municipality of Trieste and its

urban road network, composed of nodes of junctions and arcs connecting these nodes.

Figure 4 shows the results. There is a range in junction density from 1 to 6 per cell,

that corresponding to a range spanning from 1 to 6 junctions per hectare. Null value

cells are not represented. Grey tones are used with the darker tones clustered in the real

urban city centre of Trieste. Darker cells also highlight the small satellite centres around

Trieste, distributed following a Northwest / Southeast orientation. A side effect of the

choice of the grid square cells is the presence of both denser and less dense cells in

areas that are expected to be homogenous, such as in the city centre, where the junctionsform a Manhattan network. In order to limit the arbitrariness in the grid-based

representation, higher values of junction density where grouped. The distribution was

therefore grouped in three classes, corresponding respectively to 1, 2 and 3 to 6 junc-

tions per hectare.

Figure 3 Point data for street numbers


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Network Density 183


The map confirms the expectations of decreasing network density when moving outfrom the city centre. The city centre itself is identified by higher values of grid density.

Plate 1 (see plate section) shows the shape of the city of Trieste and surrounding villages

using the same grid but counting instead the street numbers. Isarithm lines using a

100 m contour interval are also portrayed. The City of Trieste is located under the

100 m contour line. The villages surrounding the City of Trieste lay on a plateau above

the 200 and 300 m isarithm lines and are identified by clusters of street numbers. Cells

are assigned darker tones according to the higher density values represented.

The results from both network grid density and house numbers density are com-

pared. Denser cells for both indexes are located in the core of the inhabited area. This

is particularly visible in the peripheral satellite villages and in the core of the City ofTrieste. In the southern and southeastern areas of the city density cells seem to be spread

over the land without following a well-defined pattern. For both indices causes can be

found in the nature of settlements in the southern part of Trieste that also influence the

pattern drawn by the road network. Major industrial and harbour-related facilities have

been developed in this part of the city over the years and these structures are now

interspersed with sparse houses and villages that were once not a part of the urban

settlement of Trieste. The accessibility of the industrial areas increased by means of

motorway trunks and state roads connecting them to the urban area and to the national

motorway system justifying the fuzziness in the junctions distribution.

Better results are possible after selecting only denser street number cells (values 7street numbers per hectare) as shown in Plate 2. Clusters of built areas are more evident

and they generally match with clusters in the network distribution. The comparison of

the maps shown in Figure 4 and Plate 2 highlights the effect of the presence of junc-

tions not related to town centredness (i.e. motorways) and also the presence of another

Figure 4 Trieste municipality urban road network grid density


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184 G Borruso


satellite centre in the southern part of the City of Trieste. The results are also evident in

the difference map shown in Figure 5.

3.2 Kernel Density Estimation (KDE)

KDE avoids problems related to the arbitrary choice of the grid superimposed onto the

road network junctions. The kernel consists of moving three dimensional functions that

weights events within its sphere of influence according to their distance from the point

at which the intensity is being estimated (Gatrell et al. 1996). The general form of a

kernel estimator is:

(1)

where 1(s) is the estimate of the intensity of the spatial point pattern measured at

location s, sithe observed ithevent, k( ) represents the kernel weighting function and

is the bandwidth.

For two-dimensional data the estimate of the intensity is given by:

(2)

where diis the distance between the location sand the observed event point si. The kernel

values therefore span from at the location sto zero at distance (Gatrell et al. 1996).

Figure 5 Difference map: Junctions >2/ha and Street numbers >7/ha

1( )s ks s

i

ni=

= 12

1

1( )s

d

d

ni

i

=

3

12

2

2

2

32


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Network Density 185


It presents considerable advantages in studying point patterns if compared with

other techniques, as for instance the simple observation of the point pattern and the

quadrat count analysis. The intensity of a point pattern can be estimated and rep-

resented by means of a smoothed three-dimensional continuous surface on the study

region. It is therefore possible to highlight the presence of clusters or regularity in the

distribution (first order properties). The only arbitrary variable in the KDE is repres-

ented by the bandwidth (Gatrell et al. 1996). Different bandwidths produce different

patterns. Using a wider bandwidth produces smoothing of the spatial variation of thephenomenon. On the contrary, a narrow bandwidth highlights more peaks in the dis-

tribution. Different bandwidths were tested on the junction dataset. It was observed

that bandwidths lower than 250 m produced spiky representations of the phenomenon,

providing, in extreme cases, not much more information on the distribution than the

simple observation of the point distribution. On the other hand, bandwidth values

higher than 500 m caused an excessive dilution of the spatial pattern.

Figure 6 shows the results from using the quadratic KDE with a 500 m bandwidth

on the full point dataset for road network junctions, visualised using cell junctions with

25 m spacing.

This estimator allows a more precise visualisation of the resulting network spatialpattern than that given by the simple grid density analysis. The centre of Trieste is

highlighted as the darker area, while lighter spots identify the satellite villages on the

surrounding plateau. There is also a less clearly defined area in the South of the city,

corresponding to the industrial area and the harbour as observed in the grid analysis.

Figure 6 Trieste municipality main urban road network and KDE on urban road network

(500 m bandwidth)
https://www.researchgate.net/publication/232128543_Spatial_Point_Pattern_Analysis_and_Its_Application_in_Geographical_Epidemiology?el=1_x_8&enrichId=rgreq-00e5b849-07b9-419a-baaa-fada6b465eb0&enrichSource=Y292ZXJQYWdlOzIxNTU3ODY4MztBUzo5OTkxMDczNzIwMzIxMkAxNDAwODMxOTg1MDU1https://www.researchgate.net/publication/232128543_Spatial_Point_Pattern_Analysis_and_Its_Application_in_Geographical_Epidemiology?el=1_x_8&enrichId=rgreq-00e5b849-07b9-419a-baaa-fada6b465eb0&enrichSource=Y292ZXJQYWdlOzIxNTU3ODY4MztBUzo5OTkxMDczNzIwMzIxMkAxNDAwODMxOTg1MDU1


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186 G Borruso


As reported by Gatrell et al. (1996) the kernel is sensitive to the choice of the

bandwidth. A smaller bandwidth of 250 m was also tested. As expected, the resulting

surface presents several peaks and seems more suitable to highlight local interaction

phenomena (Plate 3).

Local network effects are particularly visible in the city centre of Trieste and in thevillages. Clusters of intersections are evident in different zones of the urban centre thus

drawing the shapes of towns and villages centres and highlighting areas like parks,

green areas or, more generally, non-built spaces.

The different bandwidths seem to affect positively the analysis of small centres, as

it is possible to highlight patterns comparable to those found for the urban area of

Trieste. Such a result is possible even though different absolute values of network dens-

ity can be derived. It is therefore possible to reduce dramatically scale effects in this

kind of analysis. On the one hand, different size settlements can be evaluated using the

same procedure and therefore homogeneous results can be obtained. On the other hand,

regional and inter-regional effects can also be evaluated by reducing the scale and con-sidering larger areas. Network density maps can be adapted to explore scale effects by

using varying bandwidths. A simple quadratic KDE was used on both the datasets in

this initial study for a network spatial analysis of urban shape. Techniques considering

adaptive bandwidths and edge-effect corrections, as suggested by some authors (e.g.

Silverman 1986, Bracken 1994) were not considered at this stage.

The kernel density estimator was also used on the street numbers distribution. As

shown in Plate 4, it provides better visual results of the overall distribution of the built

environment presenting more distinct peaks. The kernel used had a 500 m bandwidth

and the visualisation utilized cells with a 50 m spacing. The same 500 m KDE was

performed in order to retain a homogeneous environment for comparing the indices that

were previously generated for on the two datasets.

Peaks are visible and overall the pattern is quite similar to that derived from the

grid density analysis. This index of network density can be used to provide general

information on urban shape and to justify the decreasing density of denser cells moving

out from the city centre. For certain areas of the city and villages considered (Trieste

and satellites villages) we can see a relation between the darker areas and the urban

centre. The index is less significant for some other areas of urban centres where junction

density is not well defined. In such cases high index values display the presence of several

road network links and trunks rather than a more defined urban shape. Spatial patternsof junctions are different for different kinds of settlements. Linear settlements in particu-

lar are difficult to detect using density analysis on junctions.

Interesting results arise after contouring the two density surfaces obtained and

selecting a defined set of contour lines. These represent respectively the network density

contour line delimiting 83.74% of the set of junctions (1,267/1,513 counts) and the street

number density contour line delimiting 89.16% of the set of street numbers (25,904/29,053

counts). There is a good match of the two contour lines and it is possible to highlight

areas or sub areas of both perfect matching and others where road effects intervene

and the network density is less coupled with the notion of town centredness (Figure 7).

3.3 Kernel Density Analysis on Sub-networks

Subsets of the urban road network were then used in order to study the sole effect of

certain roads on the network distribution.


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Network Density 187


In the first example only the intersections belonging to the main access roads to the

City of Trieste were selected and a KDE analysis was carried out. Plate 5 shows the

results.

As expected, the pattern follows the orientation and position of the main accesses.

It is, however, worth noting that this particular analysis helps the explanation of the

fuzziness in the junction distribution in the southern part of Trieste. There is a clustering

along the southern accesses, confirming the dominance of the main accesses (motorway

trunks and state roads) on the general pattern as derived from the estimates using the

entire network.

Peaks are highlighted in this subset of the original road network. The darker areastherefore represent areas where main access routes converge and in most of these cases

they can be overlaid on a settlements centre. It is possible to consider the different

subsets and the original road network as different network ranks. Shaded areas exist

where a main access route intersects other secondary streets that are not considered. As

the junctions between the main access routes and the complete original network are

considered, it is possible to visualise areas where the main access routes enter the city

centre area, and therefore highlight the connection between the different network ranks.

An interesting consequence of this second analysis is the evidence of the road effects

on the junction distribution. As high values of the index can both represent an urban

centres boundaries and a clustering of junctions derived from the presence of motorways,this latter effect can be highlighted and its effect removed from the original analysis.

A further example shown in Plate 6 is focused on the network density calculated

over the urban road network of the city of Trieste using only the junctions falling on the

main urban network where private traffic flows. As in the previous studies a 500 m bandwidth

Figure 7 Index comparison: Selected contours for network and built environment density


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188 G Borruso


was used. The analysis is relevant in particular for the Trieste urban area, as the network

connecting the other villages is a simplified one containing only main access roads.

By comparing Plate 6 with Figure 6 and Plate 5 it is possible to highlight different

cores of city centredness. The resulting shape is therefore a direct consequence of plan-

ning policies of traffic management and of the major importance of certain roads amongthe entire network. The traffic core can be noticed. Its orientation in particular is

different from the original core derived from junctions belonging to the entire network.

The KDE at this stage also delineates access areas to the core areas of the city from the

major access roads.

From this latter analysis some considerations can be derived. It represents a further

step in this kind of network analysis as some qualitative information on the network is

considered. The analysis is not limited to the physical road network, which is relatively

static, but can be extended to the different network subsets reflecting different aspects

of network-related phenomena. Different core areas and different shapes in the spatial

patterns drawn by networks can be derived from different uses and from the differentsubsets of the same network. One example was seen above when considering private

traffic accessible roads. Bus and pedestrian networks could also be considered in order

to draw different network density maps related to public transport and people move-

ment possibilities in an urban environment.

3.4 An Extension of the Analysis: The Case of Swindon

KDE analysis on network data was also tested on point datasets for junctions for the

city of Swindon in order to compare the results obtained for the two cities. The point data

for junctions in Swindon were extracted from an OSCAR dataset (Ordnance Survey).

The results are shown in Figure 8.

The density analysis was performed used a 500 m bandwidth in order to obtain

results comparable with those for Trieste. Higher density values are visible in the city

centre where streets and roads draw a denser network. This represents the core of the

city in terms of the network spatial distribution. Other high values can be seen in the

residential areas outside the city centre and particularly in the western part of the city.

The effect of junctions between the urban road network and major access roads are also

visible, particularly in the vicinity of the roundabouts in the southeastern and south-

western parts of the city.Several observations arise from the application of the KDE methodology to a UK

city. The analysis highlights peaks in network density in the central part of the city as

expected and allows estimating the city centre shape. Residential areas in UK cities

present quite typical patterns organized around hierarchical road networks presenting

more regularity if compared to some Italian settlements, and characterised by high levels

of matching between urban land use and road network patterns. Residential areas in

Italy are less linked to urban road network structure and the arcs connecting private

houses to the main network are often private ones and therefore not considered as

belonging to the road network. In the Swindon case it is therefore easier to highlight the

strong relation between urban land use and the road network pattern. The same can besaid with respect to the road network structure when considering high-density values

corresponding to roundabouts and junctions between urban road networks and the

motorway system. In the Trieste case roundabouts are often generalised using a single

node connecting more arcs, rather than a cluster of nodes as in the Swindon case.


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4 Conclusions

This paper has examined the issues related to the visualisation and analysis of network

phenomena at the local urban scale. This is a particular case of a broader analysis of

networks at different scales or levels of analysis. The focus was on the investigation of

network effects at the local level and possible links with other levels of analysis. The

examination of networks should consider both networks as a set of spatial phenomena

and the other spatial objects that are linked and influenced by the presence of the

network. In the present stage of the research purely physical elements were considered, as

the road network as the main locusof the analysis and the street numbers as the sourcefor comparative indices of urban boundaries and the definition of the urban centre.

A network density index was derived and used to explore different dynamics taking

place in the urban environment at different network levels. The index was derived

following two different methodologies. First, a grid-based density was considered and

plotted, and a more refined analysis was then performed that relied on Kernel Density

Estimation (KDE).

The results obtained are quite similar at certain scales of representation and ana-

lysis. The grid-based method allows the visualisation of different phenomena using the cell

as a homogeneous means of representation and allows an easy comparison between

different phenomena. The KDE is a more refined technique and allows a better visualisa-tion of the phenomena under examination that is less dependent on size, position and

orientation of the grid chosen. This kind of estimator allows the scale of the analysis to

vary with the dimension of the phenomenon under examination. It is in fact possible to

study different spatial phenomena, as medium-sized cities (Trieste in this particular case)

Figure 8 Swindon urban road network and KDE (500 m bandwidth): Junctions derived from

OSCAR data (Ordnance Survey )


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190 G Borruso


and small sized ones (villages or other cities) by simply changing a kernels bandwidth and

choosing the appropriate classes of surface representation. Similarly, different analyses

are possible by using different networks. The index derived using the urban road density

on the network considered as a whole gave good results for town centredness analyses.

Such an index was also compared with an ancillary index of the urban built environ-ment derived from street numbers density. Other networks subsets like the urban road

network for private traffic highlighted the true shape of the drivers city while the

analysis of main access roads can help in the retrieval of point/areas of access to town

centres. By choosing different subsets of a road network, it is possible to highlight dif-

ferent cores in the city and therefore the density analysis can help in the examination of

urban areas from the different network users perspectives and in deriving network spaces.

When considering other cities it is possible to confirm the results obtained in terms

of city centre shape and a general correspondence between inhabited areas and high

network density values. The results however depend partly on the original data used and

partly on the planning policies in the different countries and on the characteristics ofhousing. Road network datasets do not always contain information on network arcs

connecting private houses to the road network, thus weakening the general hypothesis

of high network values corresponding to inhabited areas.

Several other limitations in the methodology that was adopted can also be noted.

These are related to the strictly physical nature of the original network on which the

analysis was carried out. The full physical street and road network of a city is in fact

unlikely to change considerably in the short term and therefore only results limited to

the existing network can be derived. Some additional qualitative information was how-

ever introduced when subsets of the original networks were considered, these reflecting

more realistic situations such as the drivers network. This approach highlighted vari-

ations in the boundaries of the city centres and also the existence of different core areas

of the city depending on the different characteristics of the networks considered. No

information can be derived on other more complex spatial phenomena that characterise

the city itself. The true extension of areas like the CBD and residential and industrial

zones cannot in fact be derived from the sole distribution of networks junctions.

Finally, the density analysis on junctions does not allow solutions in highlighting

core areas in inhabited centres, usually small ones, which are located along a road trunk

as is the case of some centres surrounding the Trieste urban area. Further development

of this kind of research will therefore consider such aspects.

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