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Tian J.,Wuhan University | Tian J.,Key Laboratory of Digital Mapping and Land information Application Engineering | Wu X.,Wuhan University | Lin L.,Wuhan University | Ren C.,Wuhan University
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2016

The degree correlation of 50 urban street networks in their Stroke-based dual representation is measured by both Newman's assortativity coefficient and Litvak-Hofstad's assortativity coefficient. In addition, because of modified linear unit problem, the two assortativity coefficients are calculated and analyzed over different Stroke sets of a street network. Finally the relationship between degree correlation and robustness is investigated. It is found that: (1) An overwhelming majority of urban street netwos in their dual representation are disassortative and uncorrelated in terms of the degree, although the two assortativity coefficients can be inconsistent sometimes. (2) For a given street network, different Stroke sets have slight influence on the Newman's assortativity coefficient and no influence on the Litvak-Hofstad's assortativity coefficient. In general, the influence of different Stroke sets on the measures of degree correlation is minor and limited. (3) When measured by Litvak-Hofstad's assortativity coefficient, the degree correlation of urban road street network is positively correlated with its robustness. © 2016, Wuhan University All right reserved. Source


Chen Y.,Wuhan University | Chen Y.,Key Laboratory of Digital Mapping and Land information Application Engineering | Zhou Q.,Hong Kong Baptist University | Zhou Q.,Wuhan University
International Journal of Geographical Information Science | Year: 2013

A scale-adaptive digital elevation model (S-DEM) method is proposed for multi-scale terrain analysis using a single high-resolution digital elevation model (DEM) database. The motivation is to construct a DEM that is self-adaptive to a given scale of an application, rather than letting the application fit into the built-in scale of the DEM. The method is based on an adaptive compound point extraction (CPE) algorithm that extracts surface 'significant points' from a high-resolution DEM according to their degree of importance (DOI) to the scale of an application. A data structure can be established to match the demand from an application at a coarser scale. Based on the data structure, a triangulated irregular network (TIN) model can be generated to support the terrain analysis at the desired scale. The aim of the S-DEM is to support multi-scale applications in three aspects, namely, 'one database for all scales and scale-adaptive' (i.e. matching any application scale using a single high-resolution DEM), 'consistent measurement' (i.e. delivering more constant measurements of terrain parameters with changing scales), and 'skeleton preservation' (i.e. preserving basic streamlines with changing scales). Compared with the raster resampling algorithm and the maximum z-tolerance algorithm, we find that the proposed method offers better performance, providing values that meet the accuracy requirements set by DEM data standards for different scales, and producing analytical derivatives that retain terrain features with consistent measurements of terrain parameters. © 2013 Copyright Taylor and Francis Group, LLC. Source


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. Source


Wan Y.,Wuhan University | Wan Y.,Key Laboratory of Digital Mapping and Land information Application Engineering | Li L.,Wuhan University | Li L.,Key Laboratory of Digital Mapping and Land information Application Engineering | And 3 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2011

In order to quickly and accurately extract the change information of land use data, this paper provides the incremental extraction method to detect change information about land use between two different temporal versions of land use data, and automatically extracts spatial change information and attribute change information. We design and implement a system of Land Use Change Detection and Analysis (LUCDA) which could automatic extract all sorts of change information and make quantitative analysis of the change information. A practical application is used as the case to prove the ability of "timing, locating and quantifying" analysis of the system for land use planning. Source


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. Source

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