Spatial patterns of retail activity and street network structure in new and traditional Israeli cities

The association between spatial patterns of retail activity and the spatial configuration of street networks was examined by means of the space syntax methodology in eight Israeli cities that represent two city types, characterized by different planning approaches and urban growth: (i) new towns, which were established according to a comprehensive city plan and modern planning concepts of “tree-like” hierarchical street networks and “neighborhood units”; (ii) older cities, where street networks and the spatial patterns of retail activity were formed incrementally during their growth. Unlike in older cities, retail activity in new towns concentrates in relatively less-accessible and intermediate locations. This is indicated by a weak correlation between retail activity and the street network’s Integration and Choice centrality measures. The comparison between Israeli cities illustrates the influence of urban growth and planning approaches on the formation of retail activity and its interaction with the structure of the street network.     

A cost path and network analysis methodology to calculate distances along a complex river network in the Peruvian Amazon

Distance is a key variable in explicating environmental, social, and economic conditions and in defining spatial and temporal patterns. Prior research has primarily focused on using simple to complex algorithms for calculating distances along road networks. In contrast, few algorithms are available for distance calculations along fluvial networks which are often more erratic, divergent and transient than road networks. Fluvial transportation is relied upon worldwide, particularly in developing regions, where communities use river networks for transportation, access to natural resources and for trade and commerce. This paper presents a methodology developed for mapping complex fluvial networks for travel distance analysis. The methodology was applied in four major river basins in western Amazonia over some 35,000 km of river length and incorporating 919 communities as origins/destinations. A cost path and network analysis methodology was created using vector and raster datasets in a Geographic Information System (GIS) to assess interactions among communities and the distances traveled by river to reach district capitals, major urban centres and marketplaces. An accuracy assessment using distance values calculated from a previous study using a different methodological approach in the region as well as Google Earth Pro, found a high degree of concordance for distance calculations. Our methodology creates a very flexible approach for complex river systems that can be use to calculate river distances in an adaptive and efficient manner and that can be use in other regions of the world where rural communities must rely on rivers for transportation.     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.     

从全球生产网络到全球金融网络：理解全球-地方经济联系的新框架

全球化带来的新国际劳动分工使全球生产网络(Global Production Network, GPN)成为研究全球经济与区域发展的重要框架。与此同时,金融在现代社会经济运行中的地位日益提升,经济金融化在全球不同尺度下深刻影响着经济活动的空间分布和网络。因此,金融已经成为GPN研究不能忽略的重要环节。更重要的是,金融化带来的逻辑也日益深刻地影响到GPN的演化和运行。尽管GPN研究日益重视金融的作用,并开始研究金融业的全球生产网络,但是随着越来越多的企业通过获得国际股权投资或者在境外资本市场上市等方式融入全球资本市场,主要从生产角度理解地方经济融入全球过程的GPN难以对该现象深入分析。全球金融网络(Global Financial Network, GFN)为刻画和分析这个过程提供了新的框架。在这个分析框架中,地方经济体通过高级商业服务业(Advanced Business Service, ABS)企业与世界城市(国际金融中心)和离岸管辖区产生联系,从而融入GFN。论文介绍了GFN的基本结构,探讨了地方经济融入GFN的主要方式及其产生的影响。最后,提出GFN未来在理论和实证方面可继续深入研究的方向。     

社区生活圈公共服务设施配置的空间分异分析——以大连市沙河口区为例

城市公共服务设施的配置水平直接影响到居民的生活品质。论文基于高德地图获取的地图数据,使用城市网络分析工具(Urban Network Analysis Toolbox, UNA)对大连市沙河口区内的6类公共服务设施分布进行空间分异分析,并对区内90个社区的居民出行可达设施的数量及类别进行测度,最后对各个社区进行总体评价。结果表明：① 大连市沙河口区公共服务设施空间分布不均,北多南少,呈现出“一主多次”的“廾”字形的结构,主要分布在西安路商业区周边和各个街道的次级商业区;② 居民可达设施数量空间差异大,北多南少,各街道均有1或2个区域可达设施数量较多;③ 居民可达设施类型数量存在一定差异,70%以上的住宅楼中的居民可以获得全部6类公共服务设施;④ 各社区居民可达设施总体处于差异较小的均衡状态,但仍有个别社区存在内部差异较大的现象。设施的分布、地物的阻隔和社区的封闭程度对居民获取各类公共服务设施影响较大,建议构建“设施完善、便捷可达”的15 min社区生活圈。     

区域交通网络分形的DBM特征--交通网络Laplacian分形性质的实证研究

城镇体系在理论状态下的三角点阵格局暗示着现实中的交通网络在一定时空条件下具有ＤＢＭ模型的某些特征。从形态分析和维数对比两个方面论证了区域交通网络扔Ｌａｐｌａｃｉａｎ分形性质,认为可用ＤＢＭ模型模拟城镇体系－交通网络的形成和演化过程,从而揭示其动力学机制。     

基于陆路交通网的空间通达性分析——以兰州-西宁城市区域为例

以兰州-西宁城市区域交通路网为例,基于空间句法模型、日常可达性模型和等时圈通过特征点算法,试图运用GIS技术将节点、轴线和域面结合起来对通达性进行全面阐述,多视角综合测度区域路网通达性的空间格局。研究结果表明:1.区域路网体系较为完善,路网线段集聚相对明显;线段式空间伸展格局呈"点-轴"状,轴线式空间伸展格局呈"干-枝"状,初步形成核心、过渡、外围三大等级圈层以及"X"字型路网骨架,区域交通运输与城镇发展水平表现出较强的共轭协同发展关系。2.等时圈沿国道、省道、县道扩散表现连续,沿封闭铁路、高速公路扩散出现跳跃,其等时圈空间特征多呈现"枝-叶"状。城镇日常可达性在较大程度上取决于区域中心性、周边路网发达程度和城镇密集程度,区域路网单中心极化现象比较明显,兰州市对整个区域路网的控制作用较为突出。3.区域通达度空间分布各异,核心板块破碎而外围板块连片。区域通达性差异极其显著,出现极高值区和极低值区;兰州与西宁通达度中高值区出现断裂,区域南部通达度极低值区连片成带。因此,应当构建兰州地区绕城高速和环状路网,实现路网交通的适度分流;加快兰州-西宁高等级快速交通建设,推进兰州-西宁区域一体化进程;建设兰成铁路和兰州至云南磨憨口岸高速通道,培育临夏为南部区域性中心城市。     

Generating hierarchical strokes from urban street networks based on spatial pattern recognition

Strokes are products of a higher-level aggregation of street segments that can reflect functional importance and perceptual significance that is associated with them in human spatial mental conceptualizations, which is of vital importance for network analysis, street selection, and map generalization. Street properties (e.g., street names) and angles between street segments are the two main elements used for generating street strokes according to the continuity principle of perceptual grouping into networks. However, it is difficult to automatically generate strokes with good continuity from street networks with multiple lanes such as dual carriageways or complex street junctions. This article proposes a method for generating street strokes that maintain good continuity across multiple lanes and complex street junctions. The proposed method first detects dual carriageways and complex junctions in street networks and then generates strokes according to the continuity principle of perceptual grouping. Finally, it groups the generated street strokes across the dual carriageways and complex street junctions to maintain good continuity. Moreover, the generated strokes are hierarchically ranked based on stroke length and centrality measurements. Experimental studies demonstrate the validity and effectiveness of the proposed method. The result shows that the generated street strokes maintain good continuity and reflect well the hierarchical structure of the street networks.     

Exploring space–time paths in physical and social closeness spaces: a space–time GIS approach

Exploring the evolution of people’s social interactions along with their changing physical locations can help to achieve a better understanding of the processes that generate the relationships between physical distance and social interactions, which can benefit broad fields of study related to social networks. However, few studies have examined the evolving relationships between physical movements and social closeness evolution. This is partially related to the shortage of longitudinal data in both physical locations and social interactions and the lack of an exploratory analysis environment capable of effectively investigating such a process over space and time. With the increasing availability of sociospatiotemporal data in recent years, it is now feasible to examine the relationships between physical separation and social interactions at the individual level in a space–time context. This research was intended to offer a spatiotemporal exploratory analysis approach to address this challenge. The first step was to propose the concept of a social closeness space–time path, which is an extension of the space–time path concept in time geography, to represent evolving human relationships in a social closeness space. A space–time geographical information system (GIS) prototype was then designed to support the representation and analysis of space–time paths in both physical and social closeness spaces. Finally, the effectiveness of the proposed concept and design in gaining insight into the impact of physical migration on online social closeness was demonstrated through an empirical study. The contributions of this study include an extension of the time–geographic framework from physical space to social closeness space, the development of a multirepresentation approach in a GIS to integrate an individual’s space–time paths in both physical and social closeness spaces, and an exploratory analysis of the evolving relationships between physical separation and social closeness over time.     

粒子群神经网络混合模型在农用地分等中的应用

针对常规农用地分等模型因子权重计算存在人为干扰和神经网络模型自身优化过程中易陷入局部最优的情况,该文综合了BP神经网络非线性权重数据挖掘特性和粒子群的全局优化能力,建立了农用地分等计算的粒子群神经网络混合模型(PSO-BP网络模型),并应用于广东省揭西县农用地分等计算中,发现PSO-BP网络模型能避免定级因子权重确定的人为干扰,同时具有较高的优化效率,应用效果较好。