1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China 2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China 3. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Different urban elements may exhibit various aggregation patterns. It is of great significance to quantitatively investigate the disparity and connection among various aggregation patterns of urban elements for understanding the mechanism of urban development and supporting urban planning. Taking Wuhan city, Central China, as a case, we collected five types of urban elements, namely, impervious surface, population density, Point of Interest (POI), plot ratio, and road network, to explore their spatial distribution and characteristics of aggregation patterns. We first used Kernel Density Estimation (KDE) method to identify city centers and we found that there is one major center and seven sub-centers in Wuhan. Then we partitioned the study area by gradient analysis, and calculated the densities of urban elements. The density of urban element decreases outward from the centers, which is fitted well using "Inverse S-shaped" function. We used Concentration Degree Index (CDI) to reflect the aggregation degree of urban elements. The results indicate that the degrees of the aggregation of urban elements are: Commercial POI > population > plot ratio > road. Commercial POI and population are highly aggregated in the urban core area, while plot ratio and road are moderately aggregated in the urban core area. The spatial patterns of highly aggregated commercial POI and population are the combination of point pattern and zonal pattern, while the spatial patterns of moderately aggregated road network and plot ratio are the combination of point pattern, ring pattern and axial pattern. As for the lowly aggregated impervious surface, it shows a ring pattern. The attraction effect of city centers and vertical development promote the center-oriented agglomeration of urban elements. At the same time, the transportation lines and corridor effects of the scarce landscape resources reshape the spatial distribution pattern of urban elements.
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This paper examines the location characteristics of the newly emerged producer services in Shanghai. The research questions are: how does the location of producer services fit into Shanghai's spatial context? What are the factors shaping the spatial distribution pattern? Are the spatial distribution and its determinants similar to those observed in other cities? Analyses of data collected from field reconnaissance, interviews and secondary sources reveal that the spatial distribution of producer services in Shanghai is characterised by concentration and dispersion. The concentration of producer services in an extensive central-city core is similar to that observed in other cities. However, the wide spread of producer services over a fairly big region is unexpected. The paper adds insight to the study of producer services by contending that contextual factors such as an indigenous path of development, state intervention and market institution, determine the location of producer services in Shanghai.
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Density analysis lies at the core of studies on urban expansion; however, many methods in urban land density analysis are arbitrary and suffer from the lack of an established foundation. We observed an 鈥淚nverse S-shape Rule鈥 for urban land density that varies outward from an urban center by investigating 28 major cities in China at three time points. We proposed an inverse S-shaped function to formulate urban land density, which fit well for all of the cities in our sample using a nonlinear least squares fitting method. The parameters of the function explicitly describe the basic properties of an urban form. Based on the fitted functions, we derived an established method for the concentric partitioning of urban area and further proposed indicators to measure the urban compactness, urban expansion rate, and degree of urban sprawl. These indicators are practical for characterizing urban form and urban sprawl for either a single city or for multiple cities. A case study on major Chinese cities from 1990 to 2010 reveals that most of the cities expanded rapidly and became less compact and more dispersed during those two decades. However, most of the cities grew faster and showed more sprawl in the second decade compared to the first one. Discussions show that the model is also applicable for non-monocentric cities and possibly can be applied to many other geographical phenomena.
城市设施兴趣点(POI)在局部地理空间下往往呈现聚集型分布特征(即热点),表达该特征的核密度法(kernel density estimation)是最常用到的可视化工具。考虑到核密度方法中缺少量化统计分析,提出了一种城市设施POI分布热点探测的新方法。首先基于"距离衰减效应"计算地理单元的属性值;然后采用Getis-Ord <i>G</i><sub><i>i</i></sub><sup>*</sup>统计指数定量分析设施POI点的局部空间相关性特征。与传统基于样方法的空间自相关相比,核密度法由于顾及了地理学第一定律的区位影响,计算获得的地理单元属性值可保留空间的细节信息,热点的空间自相关分析结果可以反映设施服务影响的连续性特征。通过实际金融设施数据的自相关分析实验,表明该方法能有效提取POI基础设施在城市区域中的分布热点范围。
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Over the past two decades, metropolises in China witnessed unprecedented growth rates in urban areas. The rapid growth of urban space could lead to a series of problems. The fact that China has the largest population manifests the negative consequences of urban expansion. A rigorous and quantitative comparison of urban growth among metropolitan areas will lend basic support to policy making on regional urban development and will help city planners, economists, environmentalists, etc. This study analyzed the spatiotemporal patterns of urban expansion at different distance from city center in 27 metropolises in China using multi-temporal remotely sensed data. We use three satellite images obtained from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensor system circa 1990, 2000 and 2010 for each city to develop maps of urban areas. A concentric partitioning method was employed to define urban core area and urban fringe on the basis of built-up density calculated in a series of 1km concentric rings. Urbanization Concentricity Index was proposed to measure the degree to which residential or non-residential development is close to urban core area. Then we calculated and analyzed Urbanization Intensive Index across a series of 1km buffer zones for each city to identify the location-based spatiotemporal patterns of urbanization. UII offers a measure of speed and intensity of urban expansion. The results showed that the spatial structure of these cities was closely related to national policies for regional development, demonstrating obvious regional characteristics. In general, the UCI value of the cities in western and northeastern China is higher in central and eastern China. Most cities exhibited lower UCI value in 2010 compared with 1990, showing a tendency toward dispersion. The highest UII for each city usually occurs around the urban core area and move outward over time. The UII value for second- and third-tier cities became quite small in rings more than 20 km away from city center. The area close to city center witnessed higher UII value in the first decade as compared with the second one. On the contrary, the UII value for the buffer zones away from the city center was much larger in the second decade. Urban form also correlated to the level of economic development to some extent. In the first decade, first-tier cities expanded more intensely and broadly than other cities. Some Second-and third-tier cities experienced significant urban expansion in the second decade, demonstrating a tendency towards decentralization in their development pattern. In 1990, almost all metropolises in China exhibited a trend of comprising one single large central area for the entire landscape. With the expansion of urban land and rapid new nuclei development, some developed cities exhibited a transition of urban space from being mono-centric to multi-nucleated in form, with the emergence of some sub-centers. However, some relatively less developed cities still retained their mono-centric urban space. It can be inferred that urban structure tends to transform from being monocentric to polycentric with the continuous development of economy.
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Central Business District (CBD) is the core area of urban planning and decision management. The cartographic definition and representation of CBD is of great significance in studying the urban development and its functions. In order to facilitate these processes, the Kernel Density Estimation (KDE) is a very efficient tool as it considers the decay impact of services and allows the enrichment of the information from a very simple input scatter plot to a smooth output density surface. However, most existing methods of density analysis consider geographic events in a homogeneous and isotropic space under Euclidean space representation. Considering the case that the physical movement in the urban environment is usually constrained by a street network, we propose a different method for the delimitation of CBD with network configurations. First, starting from the locations of central activities, a concentration index is presented to visualize the functional urban environment by means of a density surface, which is refined with network distances rather than Euclidean ones. Then considering the specialties of network distance computation problem, an efficient way supported by flow extension simulation is proposed. Taking Shenzhen and Guangzhou, two quite developed cities in China as two case studies, we demonstrate the easy implementation and practicability of our method in delineating CBD.
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Research concerning geographical centers of economic activity has sought to explain patterns of development and interaction in cities. This article presents a new method of defining intraurban centers within a spatial economic framework as a combination of both employment and establishment kernel-smoothed patterns. The method is applied in Phoenix, a postmodern metropolis that has grown by more than a factor of thirty between 1950 and 2005 and is one of the largest and fastest growing metropolitan areas in the United States. Centers are found to vary considerably in their sectoral composition and are grouped based on their focus of secondary, retail, or high-order emphasis. A conditional logit model is used to show how each center differentiates with regard to establishment size and sector as well as the importance of center characteristics.
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This work introduces a method aimed at identifying metropolitan sub-centres based on an interaction approach, using commuting flows. In addition, it compares the results with other approaches based on employment and/or density thresholds commonly used in the literature. Although the scientific literature and the policy debate on polycentricity are increasingly devoted to its functional dimension, empirical research aimed at identifying metropolitan sub-centres mainly relies on employment density, which refers to a more morphological approach. After discussing the concept of centre and the contemporary literature on its empirical identification, the proposed methodology is applied to the metropolitan areas of Rome and Milan. Such a method shows a better fit compared to other approaches based on job density. Results are found to be remarkably sensitive to the method applied and are dependent, to some extent, on the different concept of centre that is adopted in the characterisation of metropolitan spatial structures.
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Since cities have become more complex and some large cities are likely to be polycentric, a better understanding of cities requires a clear topology that reveals how city centers are spatially distributed and interacted with. The identification of a city center that aims to find the accurate location of the city center or delineate the city center with a precise boundary becomes vital. This work attempts to achieve this by using a new type of movement data generated from location-based social networks, whereby three different methods are deployed for clustering and compared regarding identification of city centers and delineation of their boundaries. Experiments show that city centers with precise boundaries can be identified by using the proposed approach with location-based social network data. Furthermore, the results show that the three methods for clustering have different advantages and disadvantages during the process of city center identification, and thus seem to be suitable for cities with different urban structures.
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