A spatial cognition-based urban building clustering approach and its applications

This article presents a spatial cognition analysis technique for automated urban building clustering based on urban morphology and Gestalt theory. The proximity graph is selected to present the urban mrphology. The proximity graph considers the local adjacency among buildings, providing a large degree of freedom in object displacement and aggregation. Then, three principles of Gestalt theories, proximity, similarity, and common directions, are considered to extract potential Gestalt building clusters. Next, the Gestalt features are further characterized with seven indicators, that is, area difference, height difference, similarity difference, orientation difference, linear arrangement difference, interval difference, and oblique degree of arrangement. A support vector machine (SVM)-based approach is employed to extract the Gestalt building clusters. This approach transforms the Gestalt cluster extraction into a supervised discrimination process. The method presents a generalized approach for clustering buildings of a given street block into groups, while maintaining the spatial pattern and adjacency of buildings during the displacement operation. In applications of urban building generalization and three-dimensional (3D) urban panoramic-like view, the method presented in this article adequately preserves the spatial patterns, distributions, and arrangements of urban buildings. Moreover, the final 3D panoramic-like views ensure the accurate appearance of important features and landscapes.     

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.     

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 vector field model to handle the displacement of multiple conflicts in building generalization

In map generalization, the displacement operation attempts to resolve proximity conflicts to guarantee map legibility. Owing to the limited representation space, conflicts may occur between both the same and different features under different contexts. A successful displacement should settle multiple conflicts, suppress the generation of secondary conflicts after moving some objects, and preserve the distribution patterns. The effect of displacement can be understood as a force that pushes related objects away with properties of propagation and distance decay. This study borrows the idea of vector fields from physics discipline and establishes a vector field model to handle the displacement of multiple conflicts in building generalization. A scalar field is first constructed based on a Delaunay triangulation skeleton to partition the buildings being examined (e.g., a street block). Then, we build a vector field to conduct displacement measurements through the detection of conflicts from multiple sources. The direction and magnitude of the displacement force are computed based on an iso-line model of vector field. The experiment shows that this global method can settle multiple conflicts and preserve the spatial relations and important building patterns.     

基于点状数据的上海都市型工业空间格局研究

镶嵌于城市地域的都市型工业,有着其独特的区位特征和空间格局。基于2008年上海都市型工业微观企业数据,探讨工业郊区化背景下都市型工业空间分布、集聚特征以及行业间的空间邻近关系。研究表明：上海都市型工业企业区位选择具有中心城区与近郊区双重指向性,在距离城市中心10~20 km区间都市工业分布最密集,空间分布的非均质特征明显。空间集聚以城市内各类园区（商业楼宇、都市工业园、工业园区）为主要载体,集聚格局在行业间存在一定差异。在空间邻近关系方面,各行业间呈现出基于相同要素投入、生产关联、共同市场、政策导向的邻近布局以及基于异质排他性、环境要求的弱邻近布局特征。     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

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。     

北碚与重庆主城核心区的空间联系——基于交通和居民出行调查数据的分析

城镇体系中的空间联系研究可以分为大尺度的网络联系和小尺度的地域联系两种。以国内外研究相对较少的小尺度范围的城市空间联系为研究对象,选择处于快速城镇化进程中的重庆主城核心区与作为其卫星城之一的北碚为案例地,借用数据量需求较大、结论更符合实际的经验主义研究方法,系统分析了北碚区与重庆主城核心区间的空间联系。研究显示:北碚区与重庆主城核心区空间联系发展历程经历了4 个阶段,联系日益紧密;城市功能方面,北碚区已经从独立城市成为重庆主城核心区重要的功能组团,有别于传统的"卧城"概念;空间联系强度方面呈现出明显的距离衰减特征,并与地区的社会因素、经济发展水平等因素有密切关系;通过对二者空间联系解释的框架分析,表明城市政府的政策和规划在两地空间联系发展中起到主导作用。     

中国城市网络关联与经济增长溢出效应——基于大数据与网络分析方法的研究

深化城市网络空间特征的认识,科学测度经济增长的溢出效应,对推进新时期区域协调发展具有重要意义。本文结合城市网络和网络外部性两种研究视角,基于网络分析和空间计量方法,使用互联网大数据构建信息网络、企业网络、人口网络三种流要素城市网络,对中国336个地级以上行政单元的网络空间异质性、经济增长溢出效应进行实证分析。研究发现：中国城市网络联系具有显著的空间异质性,呈现出“中心-外围”特点,形成全国范围和省域、城市群范围两种尺度下的空间结构,表现出兼具择优选择与地理邻近的复杂特征;网络关联下城市经济增长具有显著的正向和负向溢出效应,反映出网络外部性对区域经济增长的重要影响,对比实体空间溢出效应表现出地理邻近关系下的相似性,以及跨越省级边界的差异性;对于全国范围下的网络关联矩阵,使用对称化和标准化处理更为合适,表明省域和城市群是推进区域经济协调发展的重要载体。     

Modeling the scale dependences of topological relations between lines and regions induced by reduction of attributes

The scale dependences of topological relations are caused by the changes of spatial objects at different scales, which are induced by the reduction of attributes. Generally, the detailed partitions and multi-scale attributes are stored in spatial databases, while the coarse partitions are not. Consequently, the detailed topological relations can be computed and regarded as known information, while the coarse relations stay unknown. However, many applications (e.g., multi-scale spatial data query) need to deal with the topological relations at multiple scales. In this study new methods are proposed to model and derive the scale dependences of topological relations between lines and multi-scale region partitions. The scale dependences of topological relations are modeled and used to derive the relations between lines and coarse partitions from the relations about the detailed partitions. The derivation can be performed in two steps. At the first step, the topological dependences between a line and two meeting, covered and contained regions are computed and stored into composition tables, respectively. At the second step, a graph is used to represent the neighboring relations among the regions in a detailed partition. The scale dependences and detailed relations are then used to derive topological relations at the coarse level. Our methods can also be extended to handle the scale dependences of relations about disconnected regions, or the combinations of connected and disconnected regions. Because our methods use the scale dependences to derive relations at the coarse level, rather than generating coarse partition and computing the relations with geometric information, they are more efficient to support scale-dependent applications.     

Landscape metrics for three-dimensional urban building pattern recognition

Understanding how landscape pattern determines population or ecosystem dynamics is crucial for managing our landscapes. Urban areas are becoming increasingly dominant social-ecological systems, so it is important to understand patterns of urbanization. Most studies of urban landscape pattern examine land-use maps in two dimensions because the acquisition of 3-dimensional information is difficult. We used Brista software based on Quickbird images and aerial photos to interpret the height of buildings, thus incorporating a 3-dimensional approach. We estimated the feasibility and accuracy of this approach. A total of 164,345 buildings in the Liaoning central urban agglomeration of China, which included seven cities, were measured. Twelve landscape metrics were proposed or chosen to describe the urban landscape patterns in 2- and 3-dimensional scales. The ecological and social meaning of landscape metrics were analyzed with multiple correlation analysis. The results showed that classification accuracy compared with field surveys was 87.6%, which means this method for interpreting building height was acceptable. The metrics effectively reflected the urban architecture in relation to number of buildings, area, height, 3-D shape and diversity aspects. We were able to describe the urban characteristics of each city with these metrics. The metrics also captured ecological and social meanings. The proposed landscape metrics provided a new method for urban landscape analysis in three dimensions.     

Analysis of the spatial relations among point distributions on a discrete space

This paper proposes a method of analyzing spatial relations among point distributions on a discrete space. Spatial topology and proximity are discussed in an integrated framework at both local and global scales. Local relations are described by geographical representations, whereas global relations are visualized by graph representations. The latter also provide a means of classifying the point distributions. The proposed method is applied to school location planning in Japan. The results reveal the appealing properties of the method and provide empirical findings.     

Spatiotemporal analysis of rural–urban land conversion

Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural–urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi‐temporal models obtained from different time periods. The proposed model is validated using the multi‐temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi‐temporal land use change modeling and the modeling results help interpret the land use change patterns.     

Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data

Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.     

长三角城市科研合作的邻近性与自组织性

随着学者们对知识网络研究的深入,网络关联的影响因素和作用机制成为重要议题。本文基于2019—2020年Web of Science论文合作发表数据构建长三角城市科研合作网络,在空间和拓扑特征分析的基础上,采用加权指数随机图模型定量模拟了城市属性、城际关系和网络结构对合作网络的影响,揭示了科研合作中的邻近性和自组织性。研究发现：① 长三角城市科研合作网络是内外生动力共同作用的结果。就城市禀赋而言,高校数量、研发投入、人均GDP可促进城市的对外科研合作,其中高校数量的边际效应最大。② 就城际关系而言,组织邻近性的正向影响最强,同省城市合作的概率是跨省城市的3.157倍;认知邻近性每提高0.1,城市间的合作概率将是原先的1.981倍;而地理和社会邻近性的促进作用甚微,制度和文化邻近性影响为负,是择优偏好较强和方言壁垒有限的结果。③ 就网络结构而言,长三角城市科研合作具有自组织自演化性,局部星型结构和三角形结构对新合作关系的贡献为0.875和0.540,择优链接性强于传递闭合性。     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

移动定位大数据视角下的人群移动模式及城市空间结构研究进展

了解城市人群移动行为和空间结构对城市规划、交通管理、应急响应等具有重要的意义。近年来,随着信息技术(ICT)的快速发展,采集大规模、长时间序列的人群移动定位大数据变得容易,为人群移动行为研究带来了新的机遇和挑战。本文首先介绍了目前用于城市人群移动行为和空间结构研究的主要数据源及其特征,并分别从人群移动行为、城市空间结构2个方面对近3年国内外相关研究进行归纳总结。目前的研究主要从移动定位大数据中挖掘人群移动模式,理解人群移动时空规律,进一步透视城市的空间结构特征;而对城市空间结构与人群移动行为影响的研究较少。未来可通过融合多源时空数据,综合研究人群移动行为与城市空间结构之间的相互作用,发展大规模群体移动行为时空分析理论和模型,进一步深入理解人群移动行为与城市空间结构的耦合关系。