Key Laboratory of Geographic Information Systems

Wuhan, China

Key Laboratory of Geographic Information Systems

Wuhan, China
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Liu Y.,Wuhan University | Liu Y.,Key Laboratory of Geographic Information Systems | Liu Y.,Key Laboratory of Digital Mapping and Land Information Application Engineering | Peng J.,Wuhan University | And 3 more authors.
PLoS ONE | Year: 2016

Optimizing land-use allocation is important to regional sustainable development, as it promotes the social equality of public services, increases the economic benefits of land-use activities, and reduces the ecological risk of land-use planning. Most land-use optimization models allocate land-use using cell-level operations that fragment land-use patches. These models do not cooperate well with land-use planning knowledge, leading to irrational land-use patterns. This study focuses on building a heuristic land-use allocation model (PSOLA) using particle swarm optimization. The model allocates land-use with patch-level operations to avoid fragmentation. The patch-level operations include a patch-edge operator, a patch-size operator, and a patch-compactness operator that constrain the size and shape of land-use patches. The model is also integrated with knowledge-informed rules to provide auxiliary knowledge of land-use planning during optimization. The knowledge-informed rules consist of suitability, accessibility, land use policy, and stakeholders' preference. To validate the PSOLA model, a case study was performed in Gaoqiao Town in Zhejiang Province, China. The results demonstrate that the PSOLA model outperforms a basic PSO (Particle Swarm Optimization) in the terms of the social, economic, ecological, and overall benefits by 3.60%, 7.10%, 1.53% and 4.06%, respectively, which confirms the effectiveness of our improvements. Furthermore, the model has an open architecture, enabling its extension as a generic tool to support decision making in land-use planning. © 2016 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Liu Y.L.,Wuhan University | Liu Y.L.,Key Laboratory of Geographic Information Systems | Liu Y.L.,Key Laboratory of Digital Mapping and Land Information Application Engineering | Tang D.W.,Wuhan University | And 9 more authors.
International Journal of Environmental Research | Year: 2014

Land-use spatial allocation is a multi-objective collaborative spatial optimization method for rational use of the land use. Based on global search capabilities and the information feedback mechanism of ant colony optimization (ACO), a land-use spatial allocation model (ACO-LA) is proposed. FirstlyFirst, a construction graph is built for modeling the land-use spatial allocation problem. SecondlySecond, the behaviors of artificial ants are improved so that the solution can be foundobtained quickly in the searchingsearch space. Finally, the ant colony generates optimized solutions by reconciling the conflicts between different planning objectives or by setting the relative dominance of different land-use types. Our study focuses on Gaoqiao Town of Fuyang City in Zhejiang Province, China. The model maximizes the land-use suitability and spatial compactness, and minimizes the cost of changing the land use, based on a variety of constraints, e.g., the optimal land-use structure and land-use policies. The results suggest that this model can obtain an optimized land-use spatial pattern from different sets of sub-objective weights and different development scenarios. With the constraint of the land-use structure, the land-use types can be distributed more reasonably by different sets of sub-objective weights. In different development scenarios, the model encourageencourages areas of land-use types in line with the development direction, adapting to meet different development needs by setting the relative dominance of the different land-use types, Wdominance, which is added to the component selection probability Pij. © 2014 University of Tehran. All rights reserved.


He J.,Wuhan University | He J.,Key Laboratory of Geographic Information Systems | Liu Y.,Wuhan University | Liu Y.,Key Laboratory of Geographic Information Systems | And 6 more authors.
Applied Geography | Year: 2013

A variety of studies have been conducted to assess the impact of farmland preservation policies in China. Most of these studies focus on the use of pure statistical approaches to assess the policy impact. A spatially explicit modeling framework is, however, often required to better assess the policy impact and to help understand the consequence of these policies. In this article, we developed such a spatially explicit modeling framework to assess the impact of policies on arable land loss and urban sprawl through the combination of counterfactual analysis with a scenario simulation approach. Counterfactual analysis provides support to assess the impact of farmland preservation policies, while scenario simulation enables us to generate counterfactual outcomes to capture possible land use patterns without policy intervention. With support from cellular automata, farmland preservation policies are integrated into the decision-making processes of land use conversion to link policy with scenario simulation. Our case study demonstrates the potential of the proposed modeling framework for the assessment of farmland preservation policies. Experimental results indicate that farmland preservation policies play an important role in terms of reducing the rate of arable land loss and governing spontaneous urban sprawl. © 2012 Elsevier Ltd.

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