Land fragmentation is a major issue in many rural areas around the world, preventing rational agricultural production and sustainable rural development. Traditionally, land consolidation has been the primary land management approach for solving this problem. Land reallocation is recognised as the most important, complex, and time-consuming process of land consolidation. It is split into two components: land redistribution and land partitioning. In this paper, we outline a land redistribution model called LandSpaCES (Land Spatial Consolidation Expert System) which is the central module of LACONISS, a LAnd CONsolidation Integrated Support System for planning and decision making. LandSpaCES integrates GIS with an expert system (ES) and is able to generate alternative land redistributions under different scenarios. Two key system concepts are utilised: ‘No-Inference Engine Theory (NIET),’ which differentiates Land- SpaCES from conventional ES development and a parcel priority index (PPI), which constitutes the basic measure that defines the redistribution of land in terms of location. The module has been applied to a case study area in Cyprus and the results compare very favourably against an independent solution derived previously by human experts.
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LandSpaCES: A Spatial Expert System for Land Consolidation
Semantic Scholar · Environmental Science · 2011
Abstract
Land fragmentation is a major issue in many rural areas around the world, preventing rational agricultural production and sustainable rural development. Traditionally, land consolidation has been the primary land management approach for solving this problem. Land reallocation is recognised as the most important, complex, and time-consuming process of land consolidation. It is split into two components: land redistribution and land partitioning. In this paper, we outline a land redistribution model called LandSpaCES (Land Spatial Consolidation Expert System) which is the central module of LACONISS, a LAnd CONsolidation Integrated Support System for planning and decision making. LandSpaCES integrates GIS with an expert system (ES) and is able to generate alternative land redistributions under different scenarios. Two key system concepts are utilised: ‘No-Inference Engine Theory (NIET),’ which differentiates Land- SpaCES from conventional ES development and a parcel priority index (PPI), which constitutes the basic measure that defines the redistribution of land in terms of location. The module has been applied to a case study area in Cyprus and the results compare very favourably against an independent solution derived previously by human experts.