In periurban areas, the phenomenon of land use change is particularly intense, and it involves both natural and agricultural areas. Agricultural areas have various functions, including environmental, cultural and recreational. One of the nonproductive functions of agricultural areas is the role of agriculture in land use control. This paper analyzes the risk of land use change in a very densely popultated periurban area in Northern Italy, the Milan and Monza e Brianza area, using a model based on artificial neural networks as a weighting method and spatial correlation as a clustering method. This approach provides a useful tool for decisional support in relation to agricultural policies. The results highlight the major risks related to agricultural land consumption area, specifically that the most significant factors causing agricultural land use changes are those related to urban pressure, visualized by a geographical information systems (GIS) map. The irrelevance of existing policies designed to protect agricultural lands is also highlighted.

Agricultural Land Consumption in Periurban Areas: a Methodological Approach for Risk Assessment Using Artificial Neural Networks and Spatial Correlation in Northern Italy / C. Mazzocchi, S. Corsi, G. Sali. - In: APPLIED SPATIAL ANALYSIS AND POLICY. - ISSN 1874-463X. - 10:1(2017 Mar 01), pp. 3-20. [10.1007/s12061-015-9168-9]

Agricultural Land Consumption in Periurban Areas: a Methodological Approach for Risk Assessment Using Artificial Neural Networks and Spatial Correlation in Northern Italy

C. Mazzocchi
;
S. Corsi
Secondo
;
G. Sali
Ultimo
2017-03-01

Abstract

In periurban areas, the phenomenon of land use change is particularly intense, and it involves both natural and agricultural areas. Agricultural areas have various functions, including environmental, cultural and recreational. One of the nonproductive functions of agricultural areas is the role of agriculture in land use control. This paper analyzes the risk of land use change in a very densely popultated periurban area in Northern Italy, the Milan and Monza e Brianza area, using a model based on artificial neural networks as a weighting method and spatial correlation as a clustering method. This approach provides a useful tool for decisional support in relation to agricultural policies. The results highlight the major risks related to agricultural land consumption area, specifically that the most significant factors causing agricultural land use changes are those related to urban pressure, visualized by a geographical information systems (GIS) map. The irrelevance of existing policies designed to protect agricultural lands is also highlighted.
Artificial neural networks; Land use consumption; Metropolitan area; Periurban agriculture; Geography, Planning and Development
Settore AGR/01 - Economia ed Estimo Rurale
2015
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/432535
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