The distinction among urban, periurban and rural areas represents a classical example of uncertainty in land classification. Satellite images, geostatistical analysis and all kinds of spatial data are very useful in urban sprawl studies, but it is important to define precise rules in combining great amounts of data to build complex knowledge about territory. Rough Set theory may be a useful method to employ in this field. It represents a different mathematical approach to uncertainty by capturing the indiscernibility. Two different phenomena can be indiscernible in some contexts and classified in the same way when combining available information about them. This approach has been applied in a case of study, comparing the results achieved with both Map Algebra technique and Spatial Rough Set. The study case area, Potenza Province, is particularly suitable for the application of this theory, because it includes 100 municipalities with different number of inhabitants and morphologic features.
[1] M. Batty, E. Besussi, K. Maat, J. Harts, Representing Multifunctional
Cities: Density and Diversity in Space and Time. CASA Working Papers, 2003.
[2] T. C. Bailey, A. C. Gatrell, Interactive spatial data analysis, Prentice Hall, 1995.
[3] R. Camagni, M.C. Gibelli, P. Rigamonti, I costi collettivi della città dispersa, Firenze: Alinea, 2002.
[4] J.R. Eastman, "Multicriteria evaluation and GIS", in P.A. Longley, M.F. Goodchild, D.J. Maguire, D.W. Rhind, Geographic information system and science, Jonh Wiley & sons, 1999.
[5] P. Fisher, "Sorites paradox and vague geographies", in Fuzzy Sets and Systems, Vol. 113, Elsevier Science, 2000, pp. 7-18.
[6] S. Greco, B. Matarazzo, R. Slowinski, "Rough sets theory for multicriteria decision analysis" in European Journal of Operational
Research, Vol. 129, Elsevier Science Publishers, 2001, pp.1-47.
[7] M. Giaoutzi, P. Nijkamp, Decision Support Models for Sustainable Development, Aldershot: Avebury, 1993.
[8] F. Indovina, La città diffusa. Venezia: Daest, 1990.
[9] Y. Leung, Spatial analysis and planning under imprecision. Amsterdam,
The Netherlands: Elsevier Science Publishers, 1988.
[10] J. Malczewski, Gis and Multicriteria Decision Analysis, John Willey &
Sons Inc, 1999.
[11] B. Murgante, “L'uso delle tecniche di analisi spaziale per la
delimitazione delle aree periurbane del sistema insediativo della
provincia di potenza” in Archivio di studi urbani e regionali, A. XXXV,
N°81, Milano: FrancoAngeli, ISSN: 0004-0177, 2004, pp. 9-24.
[12] B. Murgante, M. Danese, "Urban versus Rural: the decrease of
agricultural areas and the development of urban zones analyzed with
spatial statistics" in International Journal of Agricultural and
Environmental Information Systems (IJAEIS), Vol.2 IGI Global,
DOI: 10.4018/jaeis.2011070102, 2011, pp. 16–28.
[13] B. Murgante, G. Las Casas, M. Danese, "Analyzing Neighbourhoods
Suitable for Urban Renewal Programs with Autocorrelation Techniques"
in J. Burian (eds.) Advances in Spatial Planning. InTech — Open
Access DOI: 10.5772/33747, 2012.
[14] B. Murgante, G. Las Casas, “G.I.S. and Fuzzy Sets for the Land
Suitability Analysis”, Lecture Note in Computer Science, Vol. 3044.
Berlin: Springer Verlag, 2004.
[15] Z. Pawlak, “Rough Sets”, in International Journal of Information &
Computer Sciences, Vol. 11, 1982, pp. 341-356.
[16] Z. Pawlak, “Rough set approach to knowledge-based decision support”,
in European Journal of Operational Research, Vol. 99, No. 1, Elsevier
Science Publishers, 1997, pp. 48-57.
[17] Z. Pawlak, “Rough Sets theory and its applications to data analysis”, in
Cybernetics and Systems An International Journal, Vol. 29, London:
Taylor and Francis, 1998, pp.661- 688.
[18] J.C. Thill, Spatial Multicriteria Decision Making and analysis,
Aldershot: Ashgate, 1999.
[19] J. Jacobs, The Economy of Cities, London: Penguin, 1969.
[20] P. Nijkamp, A. Perrels, Sustainable Cities in Europe, London:
Earthscan, 1994.
[21] B. Murgante, G. Las Casas (2004) “G.I.S. and Fuzzy Sets for the Land
Suitability Analysis”, Lecture Notes in Computer Science LNCS vol.
3044, pp. 1036-1045. Springer-Verlag, doi: 10.1007/978-3-540-24709-
8_109 .
[22] M. Cerreta, R. Mele, “A landscape complex value map: integration
among soft values and hard values in a spatial decision support”, Lecture
Notes in Computer Science, Vol. 7334, 2012, pp. 653-659.
[23] B. Manganelli, B. Murgante, “Spatial analysis and statistics for zoning
of urban areas”, in World Academy of Science, Engineering and
Technology, ser. ICUPRDIS 2012, 2012. issn: p 2010-376X e 2010-
3778 [Online]. Available: http://www.waset.org/journals/waset/v71.php
[24] Vizzari M. (2011). Spatio-temporal Analysis Using Urban-Rural
Gradient Modelling and Landscape Metrics, Lecture Notes in Computer
Science, Volume 6782, 103-118, DOI: 10.1007/978-3-642-21928-3_8.
[25] C.R. Fichera, G. Modica, M. Pollino, “GIS and Remote Sensing to
Study Urban-Rural Transformation During a Fifty-Year Period”, Lecture
Notes in Computer Science, Vol. 6782, DOI: 10.1007/978-3-642-21928-
3_17, 2011, pp. 237-252.
[26] G. Nolè, M. Danese, B. Murgante, R. Lasaponara, A. Lanorte, “Using
Spatial Autocorrelation Techniques and Multi-temporal Satellite Data
for Analyzing Urban Sprawl”, Lecture Notes in Computer Science vol.
7335, pp. 512-527. Springer-Verlag, Berlin. ISSN: 0302-9743, doi:
10.1007/978-3-642-31137-6_39, 2012.
[27] G. De Mare, T. Lenza, R. Conte, “Economic evaluations using genetic
algorithms to determine the territorial impact caused by high speed
railways”, in World Academy of Science, Engineering and Technology,
ser. ICUPRD 2012, 2012. [Online]. Available:
http://www.waset.org/journals/waset/v71.php.
[28] G. De Mare, P. Morano, A. Nestico’, “Multi-criteria spatial analysis for
the localization of production structures. Analytic hierarchy process and
geographical information systems in the case of expanding an industrial
area”, in World Academy of Science, Engineering and Technology, ser.
ICUPRD 2012, 2012, issn: p 2010-376X e 2010-3778. [Online].
Available: http://www.waset.org/journals/waset/v71.php.
[1] M. Batty, E. Besussi, K. Maat, J. Harts, Representing Multifunctional
Cities: Density and Diversity in Space and Time. CASA Working Papers, 2003.
[2] T. C. Bailey, A. C. Gatrell, Interactive spatial data analysis, Prentice Hall, 1995.
[3] R. Camagni, M.C. Gibelli, P. Rigamonti, I costi collettivi della città dispersa, Firenze: Alinea, 2002.
[4] J.R. Eastman, "Multicriteria evaluation and GIS", in P.A. Longley, M.F. Goodchild, D.J. Maguire, D.W. Rhind, Geographic information system and science, Jonh Wiley & sons, 1999.
[5] P. Fisher, "Sorites paradox and vague geographies", in Fuzzy Sets and Systems, Vol. 113, Elsevier Science, 2000, pp. 7-18.
[6] S. Greco, B. Matarazzo, R. Slowinski, "Rough sets theory for multicriteria decision analysis" in European Journal of Operational
Research, Vol. 129, Elsevier Science Publishers, 2001, pp.1-47.
[7] M. Giaoutzi, P. Nijkamp, Decision Support Models for Sustainable Development, Aldershot: Avebury, 1993.
[8] F. Indovina, La città diffusa. Venezia: Daest, 1990.
[9] Y. Leung, Spatial analysis and planning under imprecision. Amsterdam,
The Netherlands: Elsevier Science Publishers, 1988.
[10] J. Malczewski, Gis and Multicriteria Decision Analysis, John Willey &
Sons Inc, 1999.
[11] B. Murgante, “L'uso delle tecniche di analisi spaziale per la
delimitazione delle aree periurbane del sistema insediativo della
provincia di potenza” in Archivio di studi urbani e regionali, A. XXXV,
N°81, Milano: FrancoAngeli, ISSN: 0004-0177, 2004, pp. 9-24.
[12] B. Murgante, M. Danese, "Urban versus Rural: the decrease of
agricultural areas and the development of urban zones analyzed with
spatial statistics" in International Journal of Agricultural and
Environmental Information Systems (IJAEIS), Vol.2 IGI Global,
DOI: 10.4018/jaeis.2011070102, 2011, pp. 16–28.
[13] B. Murgante, G. Las Casas, M. Danese, "Analyzing Neighbourhoods
Suitable for Urban Renewal Programs with Autocorrelation Techniques"
in J. Burian (eds.) Advances in Spatial Planning. InTech — Open
Access DOI: 10.5772/33747, 2012.
[14] B. Murgante, G. Las Casas, “G.I.S. and Fuzzy Sets for the Land
Suitability Analysis”, Lecture Note in Computer Science, Vol. 3044.
Berlin: Springer Verlag, 2004.
[15] Z. Pawlak, “Rough Sets”, in International Journal of Information &
Computer Sciences, Vol. 11, 1982, pp. 341-356.
[16] Z. Pawlak, “Rough set approach to knowledge-based decision support”,
in European Journal of Operational Research, Vol. 99, No. 1, Elsevier
Science Publishers, 1997, pp. 48-57.
[17] Z. Pawlak, “Rough Sets theory and its applications to data analysis”, in
Cybernetics and Systems An International Journal, Vol. 29, London:
Taylor and Francis, 1998, pp.661- 688.
[18] J.C. Thill, Spatial Multicriteria Decision Making and analysis,
Aldershot: Ashgate, 1999.
[19] J. Jacobs, The Economy of Cities, London: Penguin, 1969.
[20] P. Nijkamp, A. Perrels, Sustainable Cities in Europe, London:
Earthscan, 1994.
[21] B. Murgante, G. Las Casas (2004) “G.I.S. and Fuzzy Sets for the Land
Suitability Analysis”, Lecture Notes in Computer Science LNCS vol.
3044, pp. 1036-1045. Springer-Verlag, doi: 10.1007/978-3-540-24709-
8_109 .
[22] M. Cerreta, R. Mele, “A landscape complex value map: integration
among soft values and hard values in a spatial decision support”, Lecture
Notes in Computer Science, Vol. 7334, 2012, pp. 653-659.
[23] B. Manganelli, B. Murgante, “Spatial analysis and statistics for zoning
of urban areas”, in World Academy of Science, Engineering and
Technology, ser. ICUPRDIS 2012, 2012. issn: p 2010-376X e 2010-
3778 [Online]. Available: http://www.waset.org/journals/waset/v71.php
[24] Vizzari M. (2011). Spatio-temporal Analysis Using Urban-Rural
Gradient Modelling and Landscape Metrics, Lecture Notes in Computer
Science, Volume 6782, 103-118, DOI: 10.1007/978-3-642-21928-3_8.
[25] C.R. Fichera, G. Modica, M. Pollino, “GIS and Remote Sensing to
Study Urban-Rural Transformation During a Fifty-Year Period”, Lecture
Notes in Computer Science, Vol. 6782, DOI: 10.1007/978-3-642-21928-
3_17, 2011, pp. 237-252.
[26] G. Nolè, M. Danese, B. Murgante, R. Lasaponara, A. Lanorte, “Using
Spatial Autocorrelation Techniques and Multi-temporal Satellite Data
for Analyzing Urban Sprawl”, Lecture Notes in Computer Science vol.
7335, pp. 512-527. Springer-Verlag, Berlin. ISSN: 0302-9743, doi:
10.1007/978-3-642-31137-6_39, 2012.
[27] G. De Mare, T. Lenza, R. Conte, “Economic evaluations using genetic
algorithms to determine the territorial impact caused by high speed
railways”, in World Academy of Science, Engineering and Technology,
ser. ICUPRD 2012, 2012. [Online]. Available:
http://www.waset.org/journals/waset/v71.php.
[28] G. De Mare, P. Morano, A. Nestico’, “Multi-criteria spatial analysis for
the localization of production structures. Analytic hierarchy process and
geographical information systems in the case of expanding an industrial
area”, in World Academy of Science, Engineering and Technology, ser.
ICUPRD 2012, 2012, issn: p 2010-376X e 2010-3778. [Online].
Available: http://www.waset.org/journals/waset/v71.php.
@article{"International Journal of Information, Control and Computer Sciences:57034", author = "Benedetto Manganelli and Beniamino Murgante", title = "Analyzing Periurban Fringe with Rough Set", abstract = "The distinction among urban, periurban and rural areas represents a classical example of uncertainty in land classification. Satellite images, geostatistical analysis and all kinds of spatial data are very useful in urban sprawl studies, but it is important to define precise rules in combining great amounts of data to build complex knowledge about territory. Rough Set theory may be a useful method to employ in this field. It represents a different mathematical approach to uncertainty by capturing the indiscernibility. Two different phenomena can be indiscernible in some contexts and classified in the same way when combining available information about them. This approach has been applied in a case of study, comparing the results achieved with both Map Algebra technique and Spatial Rough Set. The study case area, Potenza Province, is particularly suitable for the application of this theory, because it includes 100 municipalities with different number of inhabitants and morphologic features.
", keywords = "Land Classification, Map Algebra, Periurban Fringe, Rough Set, Urban Planning, Urban Sprawl.", volume = "6", number = "11", pages = "1359-7", }
