An Extended Minimum Spanning Tree method for characterizing local urban patterns

Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management.     

Automated building generalization based on urban morphology and Gestalt theory

Building generalization is a difficult operation due to the complexity of the spatial distribution of buildings and for reasons of spatial recognition. In this study, building generalization is decomposed into two steps, i.e. building grouping and generalization execution. The neighbourhood model in urban morphology provides global constraints for guiding the global partitioning of building sets on the whole map by means of roads and rivers, by which enclaves, blocks, superblocks or neighbourhoods are formed; whereas the local constraints from Gestalt principles provide criteria for the further grouping of enclaves, blocks, superblocks and/or neighbourhoods. In the grouping process, graph theory, Delaunay triangulation and the Voronoi diagram are employed as supporting techniques. After grouping, some useful information, such as the sum of the building's area, the mean separation and the standard deviation of the separation of buildings, is attached to each group. By means of the attached information, an appropriate operation is selected to generalize the corresponding groups. Indeed, the methodology described brings together a number of well-developed theories/techniques, including graph theory, Delaunay triangulation, the Voronoi diagram, urban morphology and Gestalt theory, in such a way that multiscale products can be derived.     

Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.     

3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database

Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance.     

转型期杭州城市写字楼空间分布特征及其机制

随着转型期城市现代服务业的兴起以及信息经济的发展壮大,写字楼经济逐渐成为城市经济发展和城市文明的象征,并对城市空间结构产生了重要影响。通过运用GIS空间密度分析、区位熵和空间自相关等技术方法,探讨了转型期杭州城市写字楼的空间分布特征及其演化机制,结果表明：写字楼空间容量密度分布呈由中心向外围梯度递减格局,并出现多中心融合集聚的形态;写字楼规模容量特征呈现显著的内外围差异性,内城开发密度大而强度小,外围开发强度大而密度小;而规模容量的等级结构不够合理,主要体现在第三级区块发展规模的滞后;写字楼内部企业类型中,基础性依赖行业在空间上的分布较为离散,而生产性服务业则趋于集簇式分布;写字楼内部企业存在较大的区位熵差异,即出现一定的地域专业化特征,同时,发展较为成熟的区块一般具有较高的综合服务能力,而处于发展中的区块则呈现出功能的单一性。总体上看,政治、经济和企业微观主体等各种要素,通过相互交织的综合作用机制,有效地推动了城市写字楼及其生产性服务业在空间上集聚与分散相结合的时空演化进程,其中显著表现在城市经济形态转型、城市郊区化、城市空间结构调整以及政策与政府行为等方面。     

A land-use mix allocation model considering adjacency,intensity, and proximity

ABSTRACT

Land-Use Mix (LUM) refers to the strategy of integrating complementary functions within a building or area. While LUM has become a dominant approach in urban planning, its actual benefits and vision for spatial planning remain unclear. To clarify this issue, this study discerns the spatial features of land-use patterns depending on the compatibilities among land-use categories. Accordingly, this study introduces three LUM measures – adjacency, intensity, and proximity – to identify differences in the spatial distribution of land-use categories. Based on these measures, a land-use allocation model is developed to specify spatial patterns satisfying the given compatibilities. This model is tested by applying the concept of the neighborhood unit on a case study of normative land-use patterns subject to specified compatibilities. The results describe spatial features of four compatibility sets, including a set exhibiting a compatibility conflict between the same land-use pair and LUM measures when, for example, a given land-use pair is compatible in terms of intensity but incompatible in terms of proximity. Understanding the spatial features of a normative land-use pattern that satisfies various possible compatibilities will facilitate the incorporation of the LUM approach into local planning guidance and zoning ordinances.     

A Review of: “Remote Sensing of Soils and Vegetation in the USSR”. By P. J. CURRAN,G. M. FOODY,K. YA. KONDRATYEV,V. V. KOZODEROV and P. P. FEDCHENKO. (London: Taylor & Francis, 1990.) [Pp. 200.] Price £30·00. ISBN 085066 4020.

As they increase in popularity, social media are regarded as important sources of information on geographical phenomena. Studies have also shown that people rely on social media to communicate during disasters and emergency situation, and that the exchanged messages can be used to get an insight into the situation. Spatial data mining techniques are one way to extract relevant information from social media. In this article, our aim is to contribute to this field by investigating how graph clustering can be applied to support the detection of geo-located communities in Twitter in disaster situations. For this purpose, we have enhanced the fast-greedy optimization of modularity (FGM) clustering algorithm with semantic similarity so that it can deal with the complex social graphs extracted from Twitter. Then, we have coupled the enhanced FGM with the varied density-based spatial clustering of applications with noise spatial clustering algorithm to obtain spatial clusters at different temporal snapshots. The method was experimented with a case study on typhoon Haiyan in the Philippines, and Twitter’s different interaction modes were compared to create the graph of users and to detect communities. The experiments show that communities that are relevant to identify areas where disaster-related incidents were reported can be extracted, and that the enhanced algorithm outperforms the generic one in this task.     

长江中游城市群社团结构演化及其邻近机制 ——基于生产性服务企业网络分析

刻画长江中游城市群空间结构演化特征,通过QAP分析定量揭示其影响机制。结果显示：长江中游城市群存在3个分别以武汉、长沙和南昌为核心、以省界为界限的城市社团,社团结构存在显著的异质性特征。城市群层面上,地理邻近、文化邻近和行政邻近均对城市群社团结构产生显著影响,行政邻近中的省级行政邻近影响最大;交流技术进步、金融资源集聚与扩散对社团内外联系也产生显著影响。分析社团层面,发现此时地理邻近在各社团内部结网互动中起决定性作用,且不同影响因素在3个社团中的影响效应存在一定的差异性。     

Rapid three-dimensional urban model production using bilayered displacement mapping

The depiction and navigation of large-scale urban landscapes are limited by the great cost of traditional computer-aided design (CAD) models for large urban environment in terms of both the labor of data entry and the runtime computational expense. This article presents a hybrid modeling approach that enables rapid urban model production from legacy spatial data. Our scheme fills the gap between the low geometry models, such as photo-textured digital terrains, and high geometry models, such as true three-dimensional CAD models. To achieve optimal performance in modeling and rendering, we employ bilayered displacement mapping consisting of global displacement mapping (GDM) for terrains and local displacement mapping (LDM) for buildings. The LDM is performed only within image processing so that the complexity of the models depends only on the area of an urban model. We present a use case of rapid urban model production to compare our approach with the traditional polygonal urban models of a widely used geo-browser.     

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.     

一种新的城市景观扩张过程测度方法：多阶邻接度指数

提出多阶邻接度指数（Multi-order Adjacency Index, MAI）,应用多阶缓冲区完善对新旧斑块间空间关系的识别,从而更为详细地揭示城市扩张过程特征。MAI的延伸公式可供自下而上从宏观分析城市整体的扩张程度及空间变化过程。选取武汉市作为研究区,基于4期遥感影像（1995、2000、2005和2010年）划分3个时段应用MAI分析武汉市城市景观格局的演变特征。结果表明,MAI能够详细地反映出新增城市斑块在空间上有更加离散的分布趋势,新增斑块的扩张程度逐渐加深,建成区的边界不断向外延展。比较MAI与LEI（Landscape Expansion Index, LEI）的特征差异,指出MAI能够更加详尽地反映新旧斑块间的空间位置关系。     

城市建筑空间设计的犯罪防控效应

建筑空间设计包括外部空间设计与内部空间设计,前者设计建筑物之间的空间组合形态,后者设计建筑内部的空间组合形态。合理的建筑物空间设计是创造可防控空间的有效手段,是消除建筑空间盲区的有力措施,也是实施城市犯罪空间防控的重要途径。建筑外部空间组合设计发挥防控效应有两个工作层面,一是将建筑单体组合成群体,二是将建筑群体组合成组团。建筑内部空间组合设计发挥防控效应包括住宅建筑内部空间设计、公共设施建筑内部空间设计、高层建筑与智能化建筑内部空间设计。     

Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

Detecting clusters over intercity transportation networks using K-shortest paths and hierarchical clustering: a case study of mainland China

Intercity transportation infrastructures and services determine the depth and breadth of the spatial interactions among cities within an urban agglomeration, and have profound impacts on the spatial structure of the urban agglomeration. To evaluate whether the public intercity ground transportation infrastructures and services (i.e. passenger trains and long-distance buses) can support the integration and development of urban agglomerations, we propose a method for ‘transportation cluster’ detection (TCD), which has three unique features: (1) the K-shortest paths are used to quantify the proximity between cities, which is more in line with people’s travel behaviors; (2) a dendrogram is obtained through hierarchical clustering to reveal the structural hierarchies of transportation clusters; and (3) the integration of geo-modularity and hierarchical clustering assures high strength of division of transportation networks. The proposed TCD method was applied to the network of passenger trains, the network of long-distance buses, and the combined network of both in mainland China, respectively. By comparing the resultant transportation clusters with the urban agglomerations delineated by the Chinese government, cities that have weak transportation connections with other cities within an urban agglomeration were identified, and such findings could help devise transportation planning to better support the integrated development of urban agglomerations.     

基于ERGM模型的江苏省城市网络紧凑性形成机理研究

城市网络紧凑性指区域紧凑在城市网络空间结构上的呈现形态,城市网络组织语境下,指各级城市在网络空间中的协同互补关系。为了探究此关系的影响因素,论文选取多元流网络拓扑结构中的节点和连接边属性指标,运用一级模糊综合评价法测算网络紧凑度,并构建网络紧凑度的空间关联图谱,引入指数随机图模型(exponential random graph model,ERGM)定量分析江苏省城市网络紧凑性的形成机理。研究发现,江苏省网络紧凑性具有显著的三角闭合结构,局部紧凑水平的提升并不依赖第三方城市的组织协调;网络紧凑性表现出较强的规模导向,经济规模越高的城市之间越倾向于形成紧凑关系;网络紧凑性的形成受益于多城市的空间集聚,但江苏省区域分割带来的要素流动限制,制约了城市间紧凑关系的建立,从而网络紧凑性在区域层面呈现出畸形集聚现象。     

重化工业集群式创新机制与空间响应研究

本文从集群权力分层视角出发,以德国化学工业和汽车、光伏重工产业为研究对象,采用多种空间计量模型分析方法,研究重化工业创新模式及其空间组织形态变化。理论研究结果表明,重化工业分布的弱地理邻近与空间尺度扩大是基于其产业技术特征和创新合作特点的“必然”：由于集群技术权力分层导致其创新外部性发生根本性改变,地理邻近非再必需,创新合作空间尺度由集群向区域层面拓展。德国实证研究分析揭示：重化工业空间布局呈显著弱地理邻近分布效应;其空间集聚形态已非传统意义上的产业集群,而是依托更大空间尺度的产业集聚带;重工与化工产业有着基于对综合性知识和共性技术扩散共同需求的区位指向;其核心企业因对创新合作者的高度选择权而呈现“孤岛效应”布局。研究指出,重视重化工业技术创新垂直扩散能力的培育、建立多生产环节的共性技术联盟、重视大工业区区域创新协同合作将有助于促进中国重化工业创新升级和空间布局优化。     

沿黄三大城市群城市网络结构演化特征与驱动因素

基于2000—2020年全行业企业总部-分支关系大数据,融合社会网络分析、多维邻近、空间分析等方法,从节点类型、层级关联、社区划分及影响因素4个方面揭示了沿黄三大城市群城市网络空间格局、时空演化及其驱动因素。研究发现：(1)城市网络节点强度空间极化显著并具有层级性。山东半岛城市群网络结构发育最成熟,一、二层级城市占比高,中原城市群次之,关中平原城市群最低。(2)城市网络轴线及其层级特征显著,网络关联程度东强西弱,并随时间演化而逐步增强。形成以青岛、济南、郑州、西安为中心的星形放射状骨架,但城市群之间横向跨界联系较弱。(3)青岛、济南、郑州、西安的中心度-中介中心度形成“高-高”组合,是城市群网络的“中枢”和“桥梁”。(4)城市网络社区受边界阻隔效应、邻域效应和外溢效应影响,总体形成“三大一小”的社区结构和“先集聚、后融合”的演变趋势。(5)地理邻近、制度邻近、社会邻近对城市网络发育具有正向作用,产业邻近发挥负向抑制作用。     

A trajectory clustering approach based on decision graph and data field for detecting hotspots

Spatial clustering can be used to discover hotspots in trajectory data. A trajectory clustering approach based on decision graph and data field is proposed as an effective method to select parameters for clustering, to determine the number of clusters, and to identify cluster centers. Synthetic data and real-world taxi trajectory data are utilized to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can automatically determine the parameters for clustering as well as perform efficiently in trajectory clustering. Hotspots are identified and visualized during different times of a single day and at the same times on different days. The dynamic patterns of hotspots can be used to identify crowded areas and events, which are crucial for urban transportation planning and management.     

A three-step approach of simplifying 3D buildings modeled by CityGML

CityGML (City Geography Markup Language), the OGC (Open Open Geospatial Consortium) standard on three-dimensional (3D) city modeling, is widely used in an increasing number of applications, because it models a city with rich geometrical and semantic information. The underlying building model differentiates four consecutive levels of detail (LoDs). Nowadays, most city buildings are reconstructed in LoD3, while few landmarks in LoD4. For visualization or other purposes, buildings in LoD2 or LoD1 need to be derived from LoD3 models. But CityGML does not indicate methods for the automatic derivation of the different LoDs. This article presents an approach for deriving LoD2 buildings from LoD3 models which are essentially the exterior shells of buildings without opening objects. This approach treats different semantic components of a building separately with the aim to preserve the characteristics of ground plan, roof, and wall structures as far as possible. The process is composed of three steps: simplifying wall elements, generalizing roof structures, and then reconstructing the 3D building by intersecting the wall and roof polygons. The first step simplifies ground plan with wall elements projected onto the ground. A new algorithm is developed to handle not only simple structures like parallel and rectangle shapes but also complicated structures such as non-parallel, non-rectangular shapes and long narrow angles. The algorithm for generalizing roof structure is based on the same principles; however, the calculation has to be conducted in 3D space. Moreover, the simplified polygons of roof structure are further merged and typified depending on the spatial relations between two neighboring polygons. In the third step, generalized 3D buildings are reconstructed by increasing walls in height and intersecting with roof structures. The approach has been implemented and tested on a number of 3D buildings. The experiments have verified that the 3D building can be efficiently generalized, while the characteristics of wall and roof structure can be well preserved after the simplification.