Journal of Geographic Information System, 2011, 3, 145-152
doi:10.4236/jgis.2011.32011 Published Online April 2011 (
Copyright © 2011 SciRes. JGIS
Peri-Urban Transformations in Agricultural Landscapes
of Perugia, Italy
Marco Vizzari
Department o f Ma n an d Territory, University of Perugia, Perugia, Italy
Received January 18, 2011; revised February 11, 2011; accepted February 15, 2011
Urban fringes represent very complex landscapes because of their proximity and mutual dependency with
cities and rural areas. These landscapes may be considered as transition entities characterized by fuzzy
boundaries. An uncontrolled development of urban sprawl and land use changes in these areas may deter-
mine negative impacts on all natural, economic and social components. Thus, urban fringes assume a
key-role in modern landscape analysis, planning and management. Landscape analysis of these interfaces, as
this study shows, can be effectively supported by GIS spatial modelling. The Settlement Density Index (SDI),
developed through GIS spatial analysis techniques, expresses punctually the territorial gradients generated by
the presence of settlements and allows the identification of the urban fringes in the two periods under inves-
tigation. These areas are then characterized and analyzed quantitatively using detailed land use data. The
comparison of the diachronic information highlights the transformations of peri-urban landscapes that appear
mainly related to the modifications of spatial configuration of urban areas and to the changes of agricultural
Keywords: Landscape Analysis, Density Analysis, Urban Fringes, GIS, Land Use Transformations,
Morphological Spatial Pattern Analysis
1. Introduction
Peri-urban areas represent very complex territorial spaces
from the economic, environmental and social viewpoints,
particularly in relation to their proximity and mutual de-
pendence with cities and rural areas [1]. Agriculture has
a strategic value for the balance and quality of the urban
environment within these tracts of land [2,3].
The concept of “peri-urban” is subject to numerous
interpretations by planners who are unable to offer un-
ambiguous criteria for the identification and territorial
delimitation of these spaces [4]. In reality, they represent
spaces with undefined boundaries [5] along the urban-
rural gradient, inside of which transitions and changes in
equilibrium and relationships can be observed [6,7].
The view of landscapes as continua and spatial gradi-
ents represents a challenge to the conventional view of
how the natural (and human) environment is organised
[8]. Urbanization can also be considered as a complex
environmental gradient that produces modifications on
the structures and functions of ecological systems with a
magnitude dependent on the steepness of the same gra-
dient [9,10].
The uncontrolled urban sprawl and transformations of
land use in peri-urban areas have strong negative impacts
on all the natural, economic and social components [11].
Thus, landscape analysis planning of these interfaces has
a significant role, not only for the quality of life of those
living in such areas, but also for the entire sustainability
of the urban and rural development [12].
Analysis of the dynamics of complex territorial sys-
tems such as peri-urban areas, an essential prerequisite
for planning, requires robust methods and advanced
technologies [13]. Geographical information systems,
through the integration of several different analytical
functions, offer an effective platform for the spatial
analysis and modelling of geographical data, thanks to
which it is possible to improve our understanding of real
world dynamics [14]. GIS spatial modelling can support
the definition and calculation of continuous indicators
allowing better assessment and interpretation of the gra-
dients characterising landscape [15]. GIS techniques al-
low improved understanding of the specific characteris-
tics of sites and the nature of the interactions between
human and natural actions in landscape configuration
In this study landscape gradients produced by settle-
ments are modelled implementing a density spatial index.
Through the analysis of the settlement indices for the two
years under investigation are detected the spatial modify-
cations of urban gradient. Moreover, specific transforma-
tions of peri-urban landscape are explored through dia-
chronic land use analysis.
2. Methods
The study area is represented by the Umbrian munici-
palities of Perugia and Corciano which encompass an
urban and productive tissue of high territorial continuity
[17] (Figure 1). The land use data used for the analysis
was extracted from the Regional Land Information Sys-
tem while the orthophoto was retrieved, via WMS, from
the National Cartographic Portal. Land use data, avail-
able for the years 1977 and 2000, currently are the only
detailed scale resources available for the land use of the
area in question (Table 1).
Prior to proceed to subsequent data processing, land
use polygons were converted in raster format at a resolu-
tion of 10 m, maintaining adequate polygonal shape de-
tail in accordance with the scale of the analysis [18]. The
built-up class has been subjected to extensive processing
based on morphological analysis methods aimed at seg-
menting binary patterns of settlements into mutually ex-
Figure 1. Location of the area of interest (Municipalities of
Perugia and Corciano, Umbria, Italy).
clusive categories: core, islet, loop, bridge, perforation,
edge, and branch [19] (Figure 2).
The pixels classified as bridge, edge and loop catego-
ries were eliminated because of their coincidence with
road infrastructures. In the other categories, using the
orthophoto as background, a subsequent visual selection
of settlements was conducted (urban centres, commercial
and production areas, inhabited nuclei and dispersed set-
tlements) within other areas, classified as “built-up” in
the 1977 and 2000 land use data.
Using GIS density analysis techniques, a density index
was calculated measuring the spatial incidence of settle-
ments (SDI – Settlement Density Index). GIS density
analysis takes known quantities of certain phenomena
and spreads them across the landscape based on the
quantity measured at each location and the spatial rela-
tionship of the locations of the quantities measured [20].
Unlike simple density, kernel density estimation (KDE)
produces smoother surfaces, better representing land-
Table 1. Essential metadata of the land use datasets.
Dataset Land use 1977 Land use 2000
Data source
Visual interpretation of
orthophoto and
subsequent manual
digitizing of polygons
Update of 1977
dataset using visual
interpretation and
heads-up digitizing on
digital orthophoto
Format Vector - ESRI shapefile Vector - ESRI shapefile
Map units Meters Meters
scale 1:10.000 1:10.000
Time period
content Spring 1977 Spring 2000
Rome 1940 - Gauss
Boaga Est
Rome 1940 - Gauss
Boaga Est
Figure 2. Output of MSP A procedures (adopted from Soille
& Vogt, 2009).
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scape gradients [15], since the resulting surfaces sur-
rounding each point are based on a quadratic formula
with the highest value at the centre of the surface (the
point location) tapering towards zero at the search radius
distance. In GIS-based KDE functions, varying output
raster resolutions and search radii can be defined [21].
Cell resolution, in this case also set at 10 m, was defined
according to the analysis scale [18]. Different search
radii allow analysis of the phenomena at different scales,
since a wider radius shows a more general trend over the
study area, smoothing the spatial variation of the phe-
nomenon, while a narrower radius highlights more local-
ised effects such as ‘peaks and troughs’ in the distribu-
tion [22]. A good rule is to test different search radius
values in order to examine variation in the function at
different scales [23]. In this particular application, as
suggested by Murgante et al. [4], a radius of 400 m was
adopted in the analysis of settlement density at the
working scale. Prior to calculating density, polygonal
landscape elements were converted to a mesh of points
spaced at intervals equal to the size of the cells used for
density analysis. This allowed a proper resolution to be
set, maintaining adequate polygonal shape detail in ac-
cordance with the scale of the analysis. Through this
index the territorial gradients generated by the presence
of settlements were represented as a continuous surface
in raster format (Figure s 3 and 4).
The SDI, expressed as the km2 of surface occupied by
settlements over the km2 of the territorial surface, as-
sumes maximum values in the central portions of more
extensive urban areas, decreasing progressively as we
move towards rural areas. The local variations of the SDI
were studied in GIS environment through map algebra
and slope analysis. In the SDI surface a particular transi-
tion zone has been observed (with values of between 0.1
and 0.5) on which has been identified spatially the
peri-urban areas analyzed in this study. With the aim of
quantitatively analysing the land use forms and the rele-
vant transformations occurring over the period of time
under consideration, these areas have been characterised
by GIS overlay mapping with the corresponding land use
3. Results and Discussion
During the period under investigation has occurred a
consistent increase in urbanised areas, which, in total,
equal approx. 30% with 1190 ha. This increase has pro-
duced a territorial expansion of the peri-urban area which
equal to an area of approx. 1934 ha. It is interesting to
note how the increase in peri-urban areas is significantly
greater than that recorded for the urbanized occupied
areas. This confirms how, for obvious reasons, the terri-
torial impact of the peri-urban fringes is destined to grow
much more rapidly than the areas with settlements.
Spatial analysis of variations in SDI, obtained, using
GIS map algebra, by comparing the data pertaining to
1977 and 2000, shows precisely the changes of the
Figure 3. SDI of the years 1977 and 2000.
Figure 4. SDI of year 2000 and relative isolines. Detail in the area to the north of Perugia.
territorial gradients generated by urban sprawl occurred
in this period (Figure 5). The greatest changes in SDI are
observed in the broad recently urbanised areas, shown by
darker colouration, with variations in SDI > 0.2. Around
these areas are observed modifications, varying in extent,
resulting from the growing of settlement gradients (rep-
resented by the progressively lighter colours).
The modifications of urban gradient, generated by ur-
ban growth within study area, are also measured through
the spatial analysis of the steepness of SDI variation
(Figure 6). The greater increments, highlighted by
darker colouration, can be detected in the area of plain
around Perugia where new commercial buildings were
constructed within agricultural areas. Other relevant in-
creases of SDI slope, represented by the lighter colours,
can be observed in new residential areas located in the
western and in southern parts of the city. In these two
contexts, with reference to settlement density, the transi-
tions between urban and rural areas have become tenden-
tially sharper compared to the late 1970s. Particularly on
the plains, around the most recently urbanized areas,
increasingly sharper transitions are observed due to the
greater density of settlements and, as will be observed
later, due to the progressive loss of the traditional agri-
cultural uses. Generally, in the last decades, no attention
has been paid to the planning of new peri-urban spaces,
and residential or commercial complexes of high territo-
rial density become included within traditionally agri-
cultural areas, resulting in a high level of variation in
settlement gradients.
The significant polycentric development described
above produced an evident corresponding change in the
spatial configuration of the peri-urban areas (Figure 7).
Diachronic analysis of land use highlights clear modify-
cations in these areas within the period under considera-
tion (Table 2). Along with the cultivated areas, the urban
portions maintain a constant proportion (approx. 26%
and 64% respectively) and continue to play an essential
role in characterising the contexts under investigation.
Specifically, in relation to agricultural land use, olive
groves occupy an almost unchanged surface area (approx.
8.5%), while sowable lands are increased by around 10%
(reaching approx. 46%) at the expense of the other agri-
cultural crops such as sowable lands with trees and spe-
cialised vineyards in particular. On the one hand, this
phenomenon is linked to the progressive simplification
of agricultural systems taking place in Umbria since the
late 1950s [24], and on the other hand, to the altered spa-
tial configuration of the peri-urban areas in the two time
periods under consideration.
Indeed, as may be observed, the progressive expansion
Table 2. Main land uses in peri-urban areas and relative
variations, years 1977 and 2000.
Sup. 1977 Sup. 2000 Variation
Land use ha % ha % ha %
lands with
124814.2 615 5.8 633 50.7
Vinegards 580 6.6 331 3.1 250 43.0
Other arboral
uses 17 0.2 7 0.1 10 60.1
Olive groves761 8.7 905 8.5 14318.8
Woodlands660 7.5 949 8.9 28943.7
Built-up 231526.4 2821 26.4 50621.8
lands 310135.4 4963 46.4 186260.0
TOTAL 8757100 10691 100 193422.1
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Figure 5. Variations in settlement density within the Perugia area.
Figure 6. Slope of SDI variation in percent rise.
of settlements has resulted in the spreading of certain
peri-urban areas into more intensive agricultural areas,
traditionally occupied by simple crops (Figure 8).
4. Conclusions
As the study has shown, the use of GIS-based density
indices allows modelling of settlements gradients and
quantify their transformations during time. In some areas
of the territory under investigation, a generalized in-
crease in settlement density and the expansion of the
same has altered the typical Umbrian peri-urban gradi-
ents characterised by a more gradual transition between
rural and urban environments.
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Figure 7. Development of peri-urban areas in the Perugia area between 1977 and 2000.
Figure 8. Expansion of urban fringes into agricultural areas south-east of Perugia.
Copyright © 2011 SciRes. JGIS
Diachronic analysis has effectively showed up the
spatial configuration changes for the peri-urban areas and
the transformations in land use occurring there between
1977 and 2000. Additional thematic information relating
to historic and current land use will allow expansion of
the time range and will provide further very useful in-
formation for the planning and management of these
In order to study the structural changes in these land-
scapes more thoroughly, it will be possible to include
more specific data (including land registry and census
data) relating to the agricultural and urban systems.
Moreover the use of advanced image processing meth-
odsbased on object-oriented and graph-based algorithms
[25] can allow the automatic or semi-automatic interpret-
tation of high resolution satellite images and the extrac-
tion of elements characterising these landscapes. GIS
modelling of peri-urban gradients variables and applying
evaluation methods based on multicriteria techniques and
Fuzzy logic [4,26,27].
As pointed up in this study, peri-urban landscapes play
an increasingly significant role in the planning and man
agement of the Umbrian territory. This is confirmed by
the progressive expansion of these contexts and the pro-
found transformations to which they are subject. Despite
all these transformations agriculture and related activities
continue to have a fundamental role in peri-urban land-
scape. Therefore, it is hoped that more targeted requali-
fication policies be developed, in which agricultural ac-
tivities are no longer marginal, but acquire an increase-
ingly central function.
5. References
[1] C. Tacoli, “Rural–Urban Interactions: A Guide to the
Literature,” Environment and Urbanization, Vol. 10, No.
1, 1998, pp. 147-166.
[2] European Economic and Social Committee, Peri-urban
agriculture, Opinion No. 1209, 2004.
[3] R. E. Romagna, “Lungo I Bordi”–Progetto Diriquali-
ficazione del Paesaggio Agrario di Margine, Quaderno
Tecnico, 2008.
[4] B. Murgante, L. Casas, G. A. Sansone, “A spatial rough
set for locating the periurban fringe.” In Batton-Hubert
M., Joliveau T. and Lardon S. (dir.), Rencontres inter-
nationales Géomatique et territoire, SAGEO,2007.
[5] P. A. Burrough and A. U. Frank, “Geographic Objects
with Indeterminate Boundaries,” Taylor & Francis, Lon-
don, 1996.
[6] J. Cavailhès, D. Peeters, E. Sekeris and J. Thisse, “The
Peri-Urban City: Why to Live between the Suburbs and
the Countryside,” Regional Science and Urban Econom-
ics, Vol. 34, No. 6, 2004.
[7] A. Valentini, “Il Senso del Confine – Colloquio con Piero
Zanini,” Progettare sui limition Ri-Vista Ricerche per la
progettazione del paesaggio, Università degli Studi di
Firenze, anno 4, numero, 70-74, Firenze University Press,
[8] L. M. Bridges, A. E. Crompton and J. A. Schaefer, “Land-
scapes as Gradients: The Spatial Structure of Terres-trial
Ecosystem Components in Southern Ontario,” Eco-Logical
Complexity, Vol. 4, 2007, pp. 34-41.
[9] M. J. McDonnell and S. T. A. Pickett, “Ecosystem Struc-
ture and Function along Gradients of Urbanization: An
Unexploited Opportunity for Ecology,” Ecology, Vol. 71,
No. 4, 1990, pp. 1231-1237.
[10] M. J. McDonnell and A. K. Hahs, “The Use of Gra-Dient
Analysis Studies in Advancing Our Understanding of the
Ecology of Urbanising Landscapes: Current Status and
Future Directions,” Landscape Ecology, Vol. 23, No. 10,
2008, pp. 1143-1155. doi:10.1007/s10980-008-9253-4
[11] R. M. Brook and J. D. Davila, “The Peri-Urban Interface:
A Tale of Two Cities,” University of Wales and Develop-
ment Planning Unit, University College London, UK,
[12] A. Allen, “Environmental Planning and Management of
the Peri-Urban Interface: Perspectives on an Emerging
Field,” Environment and Urbanization, Vol. 15, No. 1,
2003, pp. 135-148.
[13] A. G. Wilson, “Ecological and Urban Systems Models:
Some Explorations of Similarities in the Context of
Complexity Theory,” Environment and Planning, Vol. 38,
No. 4, 2006, pp. 633- 646. doi:10.1068/a37102
[14] M. F. Goodchild, “Geographical Information Science
International,” Journal of Geographical Information Sys-
tems, Vol. 6, 1992, pp. 31-45.
[15] M. Vizzari, “Spatial Modelling of Potential Landscape
Quality,” Applied Geography, Vol. 31, No. 1, 2011, pp.
108-118. doi:10.1016/j.apgeog.2010.03.001
[16] T. Blaschke, “The Role of the Spatial Dimension within
the Framework of Sustainable Landscapes and Natural
Capital,” Landscape and Urban Planning, Vol. 75, No.
3-4, 2006, pp. 198-226.
[17] Regione Umbria, RERU–Rete Ecologica Regionale
dell'Umbria, Petruzzi Editore, Perugia, 2009.
[18] T. Hengl, “Finding the Right Pixel Size,” Computers &
Geosciences, Vol. 32, No. 9, 2006, pp. 1283-1298.
[19] P. Soille and P. Vogt, “Morphological Segmentation of
Binary Patterns,” Pattern Recognition Letters, Vol. 30,
No. 4, 2009, pp. 456-459.
[20] ESRI. ArcGIS Desktop Help, Release 9.2. Environmen-
tal Systems Research Institute, Redlands, CA, USA,
[21] M. J. Smith, M. F. Goodchild and P. A. Longley, “Geo-
spatial Analysis: A Comprehensive Guide to Principles,
Techniques and Software Tools,” Troubador Publishing,
Leicester, UK, 2007.
[22] G. Borruso, “Network Density Estimation: A GIS Ap-
proach for Analysing Point Patterns in a Network Space,”
Transactions in GIS, Vol. 12, No. 3, 2008, pp. 377-402.
[23] T. C. Bailey and A. C. Gatrell, “Interactive Spatial Data
Analysis,” Harlow, Longman, 1995.
[24] H. Desplanques, “Campagne Umbre Contributo Allo
Studio dei Paesaggi Rurali Dell’Italia Centrale,” tra-
duzione di A. MELELLI s.n.t., Quaderno della Regione
dell'Umbria, No. 10, 1975.
[25] S. Aksoy and E. Dogrusoz, “Modelling Urbanizetion
Using Spatial Building Patterns,” Proceedings of 4th
IAPR, Int. Workshop on Pattern Recognition in Remote
Sensing, Hong Kong, 2006.
[26] J. R. Eastman, , (1999) “Multi-criteria evaluation and
GIS.” In: P. A., Goodchild, M. F., Maguire, D. J., and
Rhind, D.W., Eds., Geographical Information Systems,
Longley, John Wiley and Sons, New York, pp. 493-502.
[27] J. Malczewski, “GIS and Multicriteria Decision Analy-
sis,” John Willey & Sons Inc., New York, 1999.
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