Solving land-use suitability analysis and planning problem by a hybrid meta-heuristic algorithm

The aim of Land-use Suitability Analysis and Planning Problem (LSAPP) is to identify the most suitable parcels of land for future land-uses considering several conflicting criteria. LSAPP can be modeled using a variant of a well-known combinatorial optimization problem called Quadratic Assignment Problem (QAP). In this paper, a multi-objective mathematical model is developed for LSAPP based on QAP modeling. The large-size instances of the proposed multi-objective mathematical model are difficult to solve in a reasonable CPU time using exact algorithms. So, an efficient three-phase hybrid solution procedure is proposed. In the first phase, the compensatory objectives are integrated using Analytic Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory. Then, based on the aforementioned suitability objective function and other spatial objectives and constraints, a multi-objective LSAPP is constructed. Finally, a hybrid multiple objective meta-heuristic algorithm is proposed to solve the LSAPP. The core of the proposed algorithm is based on Scatter Search while Tabu Search and Variable Neighborhood Search are also utilized. The proposed algorithm is equipped with the concepts of Pareto optimality and Veto Threshold, which improve its efficacy. The proposed algorithm is applied on a real LSAPP case study, in ‘Persian Gulf Knowledge Village’, wherein its performance is compared with a well-known evolutionary computation algorithm called Vector Evaluated Genetic Algorithm (VEGA) using comprehensive statistical analysis. A survey on time complexity of the proposed algorithm is also accomplished. The results show that MOSVNS is significantly superior to VEGA both in single and in multi-objective modes. Furthermore, analysis of time complexity of the proposed algorithm shows that it is of polynomial time and can be applied to significantly larger problems with multiple compensatory and non-compensatory objectives.     

基于二阶段禁忌算法的边坡安全模型的识别

识别岩土力学经验模型的关键是如何寻找到一个全局最优解。禁忌虎法是一种寻找全局最优解的算法。针对常规禁忌算法处理连续问题时搜索范围小，对初始点的依赖性强等不足，对算法进行了改进，并将禁忌算法的离散化过程进行了修改，提出了二阶段禁忌算法。用该算法对圆弧和楔体破坏边坡数模型中的参数进行了搜索。结果表明，该方法是一种行之有效的全局优化算法。     

“绿道-邻里”视角下建成环境对城市绿道使用的影响——以广州为例

基于“绿道-邻里”视角,采集了广州典型城市绿道上的市民使用人数与类型等属性,并运用多元线性回归模型探讨绿道周边建成环境（人口密度和土地类型混合度）与绿道使用之间的关系。结果表明：绿道周边建成环境作为绿道使用的潜在来源地和目的地,影响着城市绿道使用效果,步行和骑行可达范围内的居住区人口密度和绿道周边土地利用混合度均可作为绿道使用强度的解释因子,两者的增加均会带来绿道使用强度的提高;除此之外,周边居民区收入水平、年龄和时间等变量对城市绿道使用也会造成影响。研究结果可为城市绿道建设提供直接参考。     

Making our neighborhoods,making our selves

In this paper, we explore a “grassroots” neighborhood revitalization effort engendered at the national scale without regard to local geographies of race and class. Specifically, we examine the Harambee Great Neighborhood Initiative, convened by the well-known nonprofit Habitat for Humanity together with Milwaukee’s Local Initiatives Support Corporation, in Milwaukee, Wisconsin. Drawing from participant observation, analysis of print and digital media, volunteer surveys, and interviews with area residents and local nonprofit representatives, we demonstrate the ways in which the six-year program of planning and neighborhood development was conceived and driven by an extra-local nonprofit with significant blind spots to local and organizational politics of race and without sufficient collaboration with the Harambee community. Our analysis points to the importance of race in nonprofit governance and community revitalization efforts. Moreover, we contribute to urban geographic scholarship on nonprofit governance by examining the dynamics of privilege, inclusion, and exclusion as they relate to practices of engagement and volunteerism in nonlocal “grassroots” projects.     

大城市低收入邻里社会贫困的测度差异与成因

社会贫困是对居民的社会保障和福利的享有情况、社会参与和融合等生活状况的测度。利用中国3 大典型地域、6 个代表城市、3 类邻里的1809 份个体问卷调查数据, 本文选取13 个相关指标, 判定个体是否处于社会贫困状态, 利用回归模型探究其影响因素, 并分析社会贫困现象在不同邻里、不同城市集聚的分异特征。研究发现, 6 个城市间的社会贫困集聚程度顺序为东部、中部和西部;3 大类邻里中, 社会贫困在城中村表现最为明显, 内城居住区次之, 企业配套居住区最低。中国城市居民是否处于社会贫困状态是体制(户口类型) 和家庭(教育程度、年龄等) 因素共同作用的结果。代表体制因素的户口类型对3 类城市的社会贫困均起到影响, 东中部城市中家庭因素也有一定作用。体制因素对城中村的社会贫困起显著影响, 而家庭因素在内城居住区和企业配套居住区中作用更大。     

转型期城市低收入邻里的类型、特征和产生机制:以南京市为例

本文以南京市为例,检查转型期城市贫困人口的空间分布特征,并着重分析贫困人口集中的低收入邻里的类型、特征及其产生机制。研究表明,转型期我国城市贫困人口的空间分布表现为在邻里或社区层面上的集中。城市贫困人口在邻里层次上的集聚,导致三种类型低收入邻里的产生,包括老城衰退邻里、退化的工人新村和农民工集聚区(城中村)。分析表明,这些低收入邻里的产生源于国家导向的城市发展政策和国家福利住房供应制度,并在住房市场化和房地产导向的城市发展过程中得以强化。基于南京市的实证分析和实地调查,城市贫困人口的空间分布状况和低收入邻里的特征被检查,低收入邻里的产生机制得以验证。     

The broken promises of social mix: the case of the Cabrini Green/Near North area in Chicago

Public policies of social mixing have been enacted as the reversal of what segregation and concentrated poverty are presumed to have produced: intensified social problems (i.e., “neighborhood effects”). In addition, the pervasive discourses of diversity have provided more support for the idea of social mixing. Studies on planned and unplanned diverse neighborhoods have shown how certain diverse patterns can emerge and endure over time. Yet these studies have failed to explain how such demographic diversity becomes integration. In this article, I draw on a multidimensional perspective of socio-spatial integration to present a qualitative case study of the Cabrini Green/Near North area in Chicago—a neighborhood with a long history of segregation and recent socially engineered diversity. The case shows how contentious this new coexistence has been, and how segregation has been shifting its mechanisms of enforcement from housing to other spheres of life. I conclude with reflections on four dimensions of socio-spatial integration, and on the troubling policy and theoretical implications of the “social mix” paradigm.     

基于邻域扩展量化法的城市边界识别

城市空间分析的基本条件是可靠的测度,而城市的基本测度是规模。客观定义城市边界是有效确定城市规模的技术前提。近年来国内外学者提出几种城市边界识别的方法,其中能够定量反映城市内部实体空间组织关系的多采用矢量图像。但这些矢量数据的获取十分困难,且实时性差。因此,本文借鉴前人的研究成果,基于邻域扩展量化和标度思想,提出一种应用于遥感栅格图像上的城市边界识别方法。该方法的本质是一种空间邻域融合法,通过改变像元邻域的作用范围,可以得到不同的空间集群数目;借助搜索范围与集群数目的标度关系确定一个客观的半径,据此可以利用GIS技术确定城市边界。将该方法应用于北京地区多个年份的遥感图像,发现了像元的有效邻域作用范围。此方法以栅格图像为基础,数据实时性好并且获取容易,计算过程简便,在未来的城市边界研究过程中,可望与现有的方法相互补充。     

无人机模糊图像自动检测方法

图像模糊程度是图像评价的一个关键指标参数,它影响着图像的质量和信息。特别对于无人机拍摄的图像,如果利用模糊的图像参与计算,会造成很大的误差甚至出现颠覆性的结果。因此对于图像模糊的检测显得至关重要。传统的图像模糊检测方法大都基于人工检测、且有合格的参考图像参与评价过程,方法耗时、费力,无法用于大量无人机拍摄图像的分析。因此本文基于Sobel边缘检测原理,利用4个方向的Sobel算子,寻找图像中每个Sobel边缘点的模糊邻域,并构建模糊邻域宽度值的计算准则,由此来计算出整幅图像的平均模糊邻域宽度值,并将这个计算结果作为检测模糊的直接依据。同时考虑到无人机拍摄图像的特点,将其按照拍摄时间顺序排列,依次将相邻图像互为参考,通过对比互为参考图像的模糊邻域宽度值的变化情况,将宽度值突变的图像确定为模糊图像。据此对无人机拍摄的所有图像进行模糊检测。最终通过7组2322张 图像进行自动检测发现151张图像模糊,通过人工检测发现158张图像模糊,平均检测率95.57%。该检测方法具有较强的 适用性。     

Maximum Reduced Proper Motion method: Detection of new nearby ultracool dwarfs

In this paper, we describe how to use the Maximum Reduced Proper Motion method (Phan‐Bao et al. 2003) to detect 57 nearby L and late‐M dwarfs (d_phot ≤ 30 pc), 36 of them newly discovered. Spectroscopic observations of 43 of the 57 ultracool dwarfs were previously reported in Martín et al. (2010). These ultracool dwarfs were identified by color criteria in ∼5000 square degrees of the DENIS database and then further selected by the method for spectroscopic follow‐up to determine their spectral types and spectroscopic distances. We also report here our newly measured proper motions of these ultracool dwarfs from multi‐epoch images found in public archives (ALADIN, DSS, 2MASS, DENIS), with at least three distinct epochs and time baselines of 2 to 46 years (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)