A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

Network-constrained bivariate clustering method for detecting urban black holes and volcanoes

ABSTRACT

Urban black holes and volcanoes are typical traffic anomalies that are useful for optimizing urban planning and maintaining public safety. It is still challenging to detect arbitrarily shaped urban black holes and volcanoes considering the network constraints with less prior knowledge. This study models urban black holes and volcanoes as bivariate spatial clusters and develops a network-constrained bivariate clustering method for detecting statistically significant urban black holes and volcanoes with irregular shapes. First, an edge-expansion strategy is proposed to construct the network-constrained neighborhoods without the time-consuming calculation of the network distance between each pair of objects. Then, a network-constrained spatial scan statistic is constructed to detect urban black holes and volcanoes, and a multidirectional optimization method is developed to identify arbitrarily shaped urban black holes and volcanoes. Finally, the statistical significance of multiscale urban black holes and volcanoes is evaluated using Monte Carlo simulation. The proposed method is compared with three state-of-the-art methods using both simulated data and Beijing taxicab spatial trajectory data. The comparison shows that the proposed method can detect urban black holes and volcanoes more accurately and completely and is useful for detecting spatiotemporal variations of traffic anomalies.     

Exploratory analysis of the interrelations between co-located boolean spatial features using network graphs

Visual data mining of spatial data is a challenging task. As exploratory analysis is fundamental, it is beneficial to explore the data using different potential visualisations. In this article, we propose and analyse network graphs as a useful visualisation tool to mine spatial data. Due to their ability to represent complex systems of relationships in a visually insightful and intuitive way, network graphs offer a rich structure that has been recognised in many fields as a powerful visual representation. However, they have not been sufficiently exploited in spatial data mining, where they have principally been used on data that come with an explicit pre-specified network graph structure. This research presents a methodology with which to infer relationship network graphs for large collections of boolean spatial features. The methodology consists of four principal stages: (1) define a co-location model, (2) select the type of co-association of interest, (3) compute statistical diagnostics for these co-associations and (4) construct and visualise a network graph of the statistic from step (3). We illustrate the potential usefulness of the methodology using an example taken from an ecological setting. Specifically, we use network graphs to understand and analyse the potential interactions between potential vector and reservoir species that enable the propagation of leishmaniasis, a disease transmitted by the bite of sandflies.     

基于局部聚类的网络Voronoi图生成方法研究

提出一种将网络约束下的Voronoi和空间聚类相结合的方法,通过构造局部的聚类分析方法对网络边进行加权,根据实际的点过程性质可以把权重定义为加权或者乘权,进行标准化后与道路段本身长度融合进行计算,依此生成网络Voronoi图,以期理解城市街道的空间特性。以武汉市江汉区为例,对城市网格管理系统产生的城市事件进行算法验证,结果表明,该方法提供了一种灵活的网络约束下的服务区域划分工具,可用于基于网络空间点过程影响下的服务区划分,也可用于系统性地定量刻画城市管理的动态特性。     

Finding community structure in spatially constrained complex networks

One feature discovered in the study of complex networks is community structure, in which vertices are gathered into several groups where more edges exist within groups than between groups. Many approaches have been developed for identifying communities; these approaches essentially segment networks based on topological structure or the attribute similarity of vertices, while few approaches consider the spatial character of the networks. Many complex networks are spatially constrained such that the vertices and edges are embedded in space. In geographical space, nearer objects are more related than distant objects. Thus, the relations among vertices are defined not only by the links connecting them but also by the distance between them. In this article, we propose a geo-distance-based method of detecting communities in spatially constrained networks to identify communities that are both highly topologically connected and spatially clustered. The algorithm is based on the fast modularity maximisation (CNM) algorithm. First, we modify the modularity to geo-modularity Q^geo by introducing an edge weight that is the inverse of the geographic distance to the power of n. Then, we propose the concept of a spatial clustering coefficient as a measure of clustering of the network to determine the power value n of the distance. The algorithm is tested with China air transport network and BrightKite social network data-sets. The segmentation of the China air transport network is similar to the seven economic regions of China. The segmentation of the BrightKite social network shows the regionality of social groups and identifies the dynamic social groups that reflect users’ location changes. The algorithm is useful in exploring the interaction and clustering properties of geographical phenomena and providing timely location-based services for a group of people.     

Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.     

基于协同区位商的北京城市职住要素空间关联

就业地和居住地是城市居民工作以及生活的主要场所,同样也是城市空间结构最重要的组成要素,写字楼与居民楼空间关联性研究对深刻理解职住关系有着至关重要的作用.利用协同区位商方法,对北京市写字楼与居民楼空间关联总体特征和局域空间关联格局进行分析.研究表明:①协同区位商作为一种测量点要素之间联系的方法,能够很好的应用到职住关系的...     

A spatial co-location mining algorithm that includes adaptive proximity improvements and distant instance references

Spatial co-location pattern mining is employed to identify a group of spatial types whose instances are frequently located in spatial proximity. Current co-location mining methods have two limitations: (1) it is difficult to set an appropriate proximity threshold to identify close instances in an unknown region, and (2) such methods neglect the effects of the distance values between instances and long-distance instance effects on pattern significance. This paper proposes a novel maximal co-location algorithm to address these problems. To remove the first constraint, the algorithm uses Voronoi diagrams to extract the most related instance pairs of different types and their normalized distances, from which two distance-separating parameters are adaptively extracted using a statistical method. To remove the second constraint, the algorithm employs a reward-based verification based on distance-separating parameters to identify the prevalent patterns. Our experiments with both synthetic data and real data from Beijing, China, demonstrate that the algorithm can identify many interesting patterns that are neglected by traditional co-location methods.     

多重流空间视角下的中国城市网络空间结构特征及组织模式

城市关系网络化是城市体系结构发展的重要趋势,不同要素流空间网络下的城市体系在空间组织上不尽相同,依托单一要素对网络进行表征的结果具有明显的局限性。本文基于企业联系、科研合作、人口迁徙和引力模拟四类要素网络,从城市节点、路径联系、社区特征等角度剖析多重流空间视角下中国城市网络的外部空间特征与差异,并探究城市间联系的内在模式规律。结果表明：(1)多维数据源视角下构建的多重城市网络在空间组织上表现出相似稳定性和多元差异性的统一。(2)一定程度上依赖于基础设施运转的人流、物流等网络受到地理距离的约束较大,相邻地区的小团体特征明显,社区结构较强;而资金流、信息流等实体性较弱的要素则突破了这种地理限制,发生联系的重点在于目标城市对于要素流的吸引力,社区结构较弱。(3)目前全国城市联系形态上具有层级性和扁平化的双重特征,据此本文基于“管道-蜂鸣”模型提出“社区蜂鸣-守门员-区域管道”城市空间组织模式,这一模式对于阐述中国的城市联系特征具有较好的适用性,可在全国识别出10个核心守门员城市和8个类型区。     

论城市空间破碎化研究

空间破碎化是城市空间演进的客观现象,表现为城市空间单元碎化、空间分割加强、空间联系弱化和空间整体功能下降,具有空间形态的封闭性、空间联系的阻隔性和空间功能的失配性等特征,是城市品质提升和可持续发展的重要制约因素,是城市空间格局与过程分析的新内容。“空间破碎化”是对“景观破碎化”的拓展和深化,有助于从更为综合整体的空间视角研究城市。“空间破碎化”研究应遵循“格局过程刻画→形成机理分析→调控模式设计”的分析框架,围绕城市空间破碎化的概念、特征、测度、机理、动态模拟和调控模式等内容,采用景观指数、空间句法、多智能体情景模拟和地学信息图谱集成的方法进行多学科融贯的综合分析,可为制定城市发展政策、促进城市发展转型、实现城市可持续发展提供理论指导和方法支撑。     

武汉都市圈城际联系通达性的测度与分析

空间- 距离- 通达性已经成为城市区域一体化联系与发展的先决条件。当前都市圈城际联 系发展与交通网络建设存在明显的不同步现象, 矛盾解决的关键是优化其路网通达性空间格局。 以武汉都市圈为实证对象, 构建最短距离矩阵, 引入时间、空间通达性模型, 建立高等级路网发育 评价指标体系, 从节点体系与网络体系两个角度, 定量分析武汉都市圈城际通达性变化及其空间 格局, 揭示出: 圈域城镇( 节点) 体系通达性空间差异明显, 与城市自身交通区位、经济发展水平密 切相关, 其空间格局与城镇等级规模呈共轭协同关系, 不同空间等级节点形成以武汉为中心的同 心圈层与“轴—辐”网络空间格局; 快速交通干道网络( 高速公路、铁路等) 是城市通达性空间演变 的重要推动力, 使其空间收敛减速, 形成显著的带状分异格局, 初步形成沪蓉东西向、京珠南北向 的两大城市经济联系交通走廊; 圈域城际交通干网发育程度偏低, 处于回路网络水平, 高等级路 网扩展潜力巨大。     

多元交通流视角下的空间级联系统比较与地理空间约束

交通运输联系是区域空间级联系统与城市体系结构研究的重要视角之一,而不同交通运输方式表达的级联体系结构亦存在一定的差异。为综合研究交通运输体系刻画的空间级联系统及克服单一交通方式的局限性,基于长途汽车、高铁和航班时刻表数据,比较分析了多元交通网络的空间级联体系结构及其表达的城市网络组织体系,并进一步揭示了地理空间的约束作用。研究发现：① 每种交通运输方式适合在一定的空间尺度和行政范围内刻画和表达城市网络体系结构与城市联系,公路客运受省域行政范围约束,高铁联系具有廊道影响效应,航空运输体现全国和区域尺度较高层次的社会经济联系。② 从旅客直达视角分析,长途汽车与高铁的城际运输市场重叠最大,近年来长途汽车的运输市场受高铁影响明显。③ 地理空间是影响陆路交通运输和组织的重要约束因子,距离衰减效应明显;结合设施空间、行政空间和管理体制的作用,长途汽车和高铁运输在空间上形成分异的社区结构;航空运输由于具有超空间连接特性,既不遵循距离衰减规律,社区结构也并不明显。     

基于路网分割的京津冀城乡空间识别及特征分析

提出一种基于路网分割应用于区域尺度快速识别城乡边界的方法。该方法利用路网与城市空间格局的强耦合联系,通过城乡斑块地理分割与精度计算确定城市斑块的最优解,识别区域城乡空间。以京津冀为例开展方法验证,分析了区域城乡空间格局、路网特征及城市用地经济效率。结果表明,该方法识别精度较高,可用于低成本、快速、高效的区域城乡空间识别;京津冀城市空间格局呈现“大京津、小河北”的差异性,路网密度特征呈现京、津、石3个显著的高密度区域,二者存在高耦合性;2015年该区域城市用地经济效益总体良好,石家庄、保定、和承德3市综合效率值欠佳,需进一步提高土地利用效率。     

城市商业型健身休闲场所空间格局及影响机理——以上海市为例

基于百度POI和美团网评分数据,分析上海市商业型健身休闲场所空间格局及影响机理.结果 显示:①上海市商业型健身休闲场所整体呈现核心-边缘格局,黄浦区、静安区、徐汇区、长宁区、普陀区和虹口区的交界处形成了核心集聚分布区.②上海市商业型健身休闲场所分布与高人口密度区、高房价地区、交通网络发达区、住宅小区、写字楼和购物中心等...     

Location patterns of urban industry in Shanghai and implications for sustainability

China’s economy has undergone rapid transition and industrial restructuring. The term “urban industry” describes a particular type of industry within Chinese cities experiencing restructuring. Given the high percentage of industrial firms that have either closed or relocated from city centres to the urban fringe and beyond, emergent global cities such as Shanghai, are implementing strategies for local economic and urban development, which involve urban industrial upgrading numerous firms in the city centre and urban fringe. This study aims to analyze the location patterns of seven urban industrial sectors within the Shanghai urban region using 2008 micro-geography data. To avoid Modifiable Areal Unit Problem (MAUP) issue, four distance-based measures including nearest neighbourhood analysis, Kernel density estimation, K-function and co-location quotient have been extensively applied to analyze and compare the concentration and co-location between the seven sectors. The results reveal disparate patterns varying with distance and interesting co-location as well. The results are as follows: the city centre and the urban fringe have the highest intensity of urban industrial firms, but the zones with 20–30 km from the city centre is a watershed for most categories; the degree of concentration varies with distance, weaker at shorter distance, increasing up to the maximum distance of 30 km and then decreasing until 50 km; for all urban industries, there are three types of patterns, mixture of clustered, random and dispersed distribution at a varied range of distances. Consequently, this paper argues that the location pattern of urban industry reflects the stage-specific industrial restructuring and spatial transformation, conditioned by sustainability objectives.     

基于Ripley’s K函数的南京市ATM网点空间分布模式研究

运用Ripley’s K函数的相关理论,以南京市ATM网点为研究对象,分别从平面与网络空间两种视角,在中心城区范围与主城区范围两种空间尺度上,通过单变量\begin{array}{c}K\end{array}函数法分析ATM网点的分布模式,通过双变量\begin{array}{c}K\end{array}函数法分析ATM网点与地铁站点的空间关联情况,最后对计算结果进行评价与分析。研究表明,ATM网点在南京主城区与中心城区均呈现出较强的集聚状态;在一定的距离范围内,ATM网点与地铁站点之间也有较强的依赖关系。同时,对于沿着路网分布的地理空间点状对象而言,利用网络\begin{array}{c}K\end{array}函数法进行空间点模式分析比用平面\begin{array}{c}K\end{array}函数法更加符合实际情况。     

大数据时代城市时空间行为研究方法

信息技术的快速发展带来了“大数据”时代的到来,改变了城市的空间组织和居民行为,并使得城市时空间行为研究方法面临变革。本文在总结传统城市时空间行为研究方法存在问题的基础上,对影响其变革的数据获取与处理技术进行梳理,重点从居民时空行为、城市空间及城市等级体系3个方面综述了国内外应用大数据进行城市时空间行为研究的最新进展,构建了基于大数据应用的城市时空间行为研究方法框架。本文认为,大数据时代城市时空间行为研究方法的变革主要取决于对反映居民时空行为的网络或移动信息设备数据的挖掘、处理及应用,但是还需要进一步推动相关学科间的交叉与融合,加强社交网站等网络数据在居民时空行为和城市空间研究中的应用,并指导城市规划编制与管理方法的创新。     

空间相互作用与城市关联网络演进 ——以我国20世纪90年代城际铁路客流为例

我国城市群在铁路客流空间相互作用的层次上所体现的空间分布和网络结构演化体现了城市群的重要方面。通过对1991和2000年我国200多个地级以上城市间铁路客流的经验性研究发现，城际铁路客流不但存在明显的距离衰减规律，而且各个主要城市群的铁路客流衰减变化规律及其网络结构有所不同，应用重力模型等方法不能较好地刻画这一变化。本研究借助“轴-辐”理念进一步从网络关联的角度考察了我国1990年代铁路客流变化，发现存在明显的区域重组、中心极化和空间关联升级现象，并分析这些现象所体现的城市群结构变化及其背后的原因。     

复杂网络视角下的城市人流空间概念模型与研究框架

城市内人流特征研究一直是城市地理学和城市空间研究的重要领域之一.针对已有相关研究存在仅侧重于从人口规模、密度视角分析空间分布及格局特征,而对隐藏于空间分布背后,因人流动而建立的空间交互关系关注不足的问题.本文以流空间理论、复杂网络理论、空间行为交互理论为理论基础,立足基于流、网络剖析城市内部空间中各要素间的相互作用及关...     

长春市大型零售设施空间可达性绩效测度

对传统的空间可达性测度模型进行改进,并引入营业面积,人口数量,消费者偏好等变量,对长春市大型零售设施进行研究,通过ArcGIS平台对零售设施的分布特征进行分析后表明:① 零售设施主要分布在城市三环路以内,在一环路传统商业区集聚程度最高;快消零售设施空间分布的向心性显著,呈单中心的集聚特征;耐消设施分布的离心化相对明显,呈双中心的集聚特征.② 综合,快消设施可达性绩效高值区集中在一环内,绩效指数向外递减时存在阶梯性,南北方向递减速度高于东西方向,城市南部三环路以外绩效水平最低;耐消设施绩效高值区在空间上分散分布.③ 快消设施绩效低值区是近年来长春市居住空间扩散的主要区域,零售设施的空间配置严重滞后于城市的空间拓展与人口的郊区化趋势.零售设施空间可达性绩效测度是一种关联了供需双方面因素,测量零售设施服务分配到单位消费者水平的方法.