Multi-attribute preference modelling and regional land-use planning

2008 ◽  
Vol 65 (2) ◽  
pp. 325-335 ◽  
Author(s):  
Jayanath Ananda ◽  
Gamini Herath
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Preference Modelling ◽  
Regional Land

SOIL CAPABILITY ANALYSIS FOR REGIONAL LAND USE PLANNING — A STUDY OF THE OTTAWA URBAN FRINGE

1979 ◽  
Vol 59 (4) ◽  
pp. 363-379 ◽  
Author(s):  
J. DUMANSKI ◽  
E. C. HUFFMAN ◽  
I. B. MARSHALL
Keyword(s):  
Land Use ◽  
Regional Development ◽  
Land Use Planning ◽  
Urban Fringe ◽  
Development Plan ◽  
Capability Analysis ◽  
Suitability Map ◽  
Regional Land

A procedure is outlined for analyzing soil data for regional land use planning. Maps of basic land factor limitations are illustrated for the Ottawa urban fringe, and each map is evaluated for biological and nonbiological uses. The major land factors relative to urbanization are used along with other data to derive an urban "suitability" map. Results are compared to the regional development plan for the area.

An economic-ecological model for regional land-use planning

1986 ◽  
Vol 31 (1-4) ◽  
pp. 293-302 ◽  
Author(s):  
António S. Câmara ◽  
António P. Mano ◽  
M. Graça Martinho ◽  
M.Paula Marques ◽  
João F. Nunes ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Ecological Model ◽  
Regional Land

Climate and regional land-use planning

1984 ◽  
Vol 7 (1) ◽  
pp. 35-53 ◽  
Author(s):  
Hans Schirmer
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Regional Land

scholarly journals Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization

2021 ◽  
Vol 10 (2) ◽  
pp. 100
Author(s):  
Tingting Pan ◽  
Yu Zhang ◽  
Fenzhen Su ◽  
Vincent Lyne ◽  
Fei Cheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Novel Mutation ◽  
Spatial Optimization ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Multi Objective ◽  
Regional Land ◽  
Near Optimality

Practical efficient regional land-use planning requires planners to balance competing uses, regional policies, spatial compatibilities, and priorities across the social, economic, and ecological domains. Genetic algorithm optimization has progressed complex planning, but challenges remain in developing practical alternatives to random initialization, genetic mutations, and to pragmatically balance competing objectives. To meet these practical needs, we developed a Land use Intensity-restricted Multi-objective Spatial Optimization (LIr-MSO) model with more realistic patch size initialization, novel mutation, elite strategies, and objectives balanced via nominalizations and weightings. We tested the model for Dapeng, China where experiments compared comprehensive fitness (across conversion cost, Gross Domestic Product (GDP), ecosystem services value, compactness, and conflict degree) with three contrast experiments, in which changes were separately made in the initialization and mutation. The comprehensive model gave superior fitness compared to the contrast experiments. Iterations progressed rapidly to near-optimality, but final convergence involved much slower parent–offspring mutations. Tradeoffs between conversion cost and compactness were strongest, and conflict degree improved in part as an emergent property of the spatial social connectedness built into our algorithm. Observations of rapid iteration to near-optimality with our model can facilitate interactive simulations, not possible with current models, involving land-use planners and regional managers.

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