产业集群竞争优势的来源分析

产业集群作为一种极具特色的经济组织形式,具有地理上的聚集性、社会文化和环境的根植性、产业的同质性和关联性、生产经营的专业性和竞合性,这些特性很大程度上决定了其竞争优势的来源.从产业集群的概念和特性出发,分析了产业集群竞争优势的来源:集聚效应、灵活专业化生产、集群式创新、竞合机制与路径依赖的影响.     

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.     

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 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.     

旅游产业集群存在的条件及效应探讨

传统的产业集群理论和集群概念主要应用于分析制造业和高新技术产业,而对于旅游业是否存在集群一直存在争议。证明旅游集群的存在是运用集群理论研究并指导目的地发展的前提。分析旅游业具备集群存在的两个先决条件(聚集和产业联系),即旅游目的地企业有明显的空间聚集特征,且在为旅游者提供产品和服务的过程中相互协作、发生密切的产业联系;阐述旅游业具有创新性,从集群产生的效应角度证明旅游产业集群的存在;列举国外比较成功的旅游集群案例,并总结其对我国旅游产业集群发展的启示。     

Multi-objective spatially constrained clustering for regionalization with particle swarm optimization

Regionalization is an important part of the spatial analysis process, and the solution should be contiguity-constrained in each region. In general, several objectives need to be optimized in practical regionalization, such as the homogeneity of regions and the heterogeneity among regions. Therefore, multi-objective techniques are more suitable for solving regionalization problems. In this paper, we design a multi-objective particle swarm optimization algorithm for solving regionalization problems. Towards this goal, a novel particle representation for regionalization is proposed, which can be expressed in continuous space and has flexible constraints on the number of regions. In the process of optimization, a contiguous-region method is designed that satisfies the constraints and improves the efficiency. The decision solution is selected in the Pareto set based on a trade-off between the objective functions, and the number of regions can be automatically determined. The proposed method outperforms six regionalization algorithms in terms of both the number and the quality of the solutions.     

基于多重分割关联子的高分辨率遥感场景分类

高分辨率遥感影像提供了丰富的外观信息和空间结构信息,广泛应用于土地利用分类当中,源于文章领域的视觉词袋（Bag-of-Visual-Words,BoVW）模型现已成功应用于图像分类领域。传统的BoVW模型忽略了特征之间的空间布局信息和像素一致性信息,提出多重分割关联子特征,融合图像的外观信息、空间布局信息和像素一致性信息,实验表明该方法能够获取优于许多经典的遥感图像特征的性能。     

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.     

中国西部地区开发区及产业时空变化特征

通过收集中国西部12省份的714个开发区基础数据,采用最邻近距离、核密度分析法以及地理空间分析法,对1993—2015年间中国西部地区（简称西部地区）开发区的空间格局演化规律及产业分布特征展开分析。研究结果表明：（1） 西部地区开发区空间集聚特征显著,呈现出以重点城市和城市圈为依托的“多级集聚”的演变规律。（2） 西部地区开发区空间聚集程度和规模均呈现出不断增加的态势,集聚规模增长幅度大于集聚强度。（3） 西部地区国家级开发区各类型产业的集聚程度强于省级开发区,集聚规模弱于省级开发区。国家级开发区主要发展资金密集型、技术密集型产业,各类型产业分布格局呈现出单核心集聚特征;省级开发区以劳动密集型、资金密集型产业为主;各类型产业布局特征差距显著,技术密集型呈现出“大集聚、小分散”的分布格局、劳动密集型向中心城市集聚、资金密集型连片规模化发展。研究结果对于西部地区合理利用开发区来加强城市群联系,实现区域协调发展具有一定的现实意义。     

沈阳市部门企业地理集聚与城市结构

基于产业空间聚集分布情况探寻城市结构特征,是当前大都市区实证研究中的聚焦点所在,但由于方法论的限制而无法真正揭示产业地理集聚之间的内在关联性。基于已有研究基础,试图通过完善潜力模型、设置距离参数、结合主成分分析法实现对产业地理集聚测度方法论的完善与发展,并选取极具代表性大都市区核心城市——沈阳市为样本单元,以2008年的经济普查部门企业数据开展实证检验。结果表明：沈阳市部门企业之间除了交通运输、仓储和邮政中心产业属于地方化经济外,其余的均为企业关联;水利、环境和公共设施管理业产业依附于制造业呈临街抑或隔街集聚,而与公共管理和组织产业之间同街道集聚;支配主角之间,存在中心CBD主宰制造业的布局,而制造业又在很大程度上影响着交通运输、仓储和邮政中心的布局;企业地理集聚形成的城市结构依然是一个明显的“单中心圈层”结构,没有表现出“去中心化”抑或多极化或分散化演变趋势。研究成果与现实情况基本吻合,侧面说明该模式对揭示城市产业地理集聚模式以及由此形成的城市结构特征具有一定的解释力。     

Spatial Modelling in Forest Ecology and Management: A Case Study. Edited by M. JANSEN,M. JUDAS and J. SABOROWSKI (Berlin: Springer Verlag, 2002). [Pp. xiii+225]. ISBN 3-540-43357-0. Price €89.95. Hardback

Urban segregation has received increasing attention in the literature due to the negative impacts that it has on urban populations. Indices of urban segregation are useful instruments for understanding the problem as well as for setting up public policies. The usefulness of spatial segregation indices depends on their ability to account for the spatial arrangement of population and to show how segregation varies across the city. This paper proposes global spatial indices of segregation that capture interaction among population groups at different scales. We also decompose the global indices to obtain local spatial indices of segregation, which enable visualization and exploration of segregation patterns. We propose the use of statistical tests to determine the significance of the indices. The proposed indices are illustrated using an artificial dataset and a case study of socio‐economic segregation in São José dos Campos (SP, Brazil).     

全国基准产业集群识别及在区域经济分析中的应用——以北京市为例

全国基准产业集群反映一国产业技术经济联系的一般特征,可以作为模板应用于区域集群分析。本研究首次识别我国基准产业集群,为我国的区域集群实证研究提供集群模板。以识别的基准产业集群为模板,本研究分析了北京市制造业集群的发展现状,并以北京市电子及通信设备制造业集群为例分析了该集群的组成结构及空间布局。分析发现,北京市目前已形成三个规模较大,并具有区域专业化优势的制造业集群:电子及通信设备制造、汽车及装备制造和电子元器件制造业集群。北京市电子及通信设备制造业集群的优势环节是通信设备制造和其他通信、电子设备制造业,其配套产业环节电子元器件和其他电气机械及器材制造业仍相对较弱。在空间上,北京市电子及通信设备制造业集群主要分布在城市近郊地区,朝阳、海淀、大兴区是集群环节配套较为完善的区域。     

美国马萨诸塞州华人空间分布自相关研究

基于美国10年一度的人口普查资料,文章采用空间自相关理论对美国马萨诸塞州华人的空间分布进行了研究。研究表明:马萨诸塞州的华人存在空间聚集现象;波士顿地区和汉普郡境内某地是华人显著聚集的区域。研究了聚集区域的分布特征并给出了相应的解释和推断。     

Measuring Global Spatial Autocorrelation with Data Reliability Information

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular SA statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, one is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation. Key Words: American Community Survey, Geary ratio, Moran’s I, permutation test, spatial Bhattacharyya coefficient.     

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。     

传染病多维度聚集性探测方法

及早发现异常健康事件的苗头是有效进行传染病早期预警的关键.现有的传染病聚集性探测仅限于时间、空间或时空维度,往往容易忽略病例个人情况从其他方面反映的信息,从而造成过度预警.论文结合蚁群聚类算法和Bayesian Gamma-Poisson 模型,提出一种全新的传染病多维度聚类探测技术.研究区麻疹爆发案例证明该技术在继承以往时空聚集性探测技术思想的基础上,考虑了病例的属性信息,能更为灵敏、准确地找出传染病聚集区域.此方法在实际工作中具有潜在的重要应用价值.     

Do foreign subsidiaries innovate and perform better in a cluster? A spatial analysis of Japanese subsidiaries in the US

The interrelationship between clustering, innovation, and performance of Japanese subsidiaries in the US is examined in this paper. First, we apply Kulldorff's spatial scan statistic to identify process innovation clustering among Japanese subsidiaries. The scale at which clustering occurs is regional rather than local, and cluster location captures the geographical environment of home-transplants from Japan. Second, we apply spatial autoregressive models to test the relationship between cluster location and innovation among Japanese subsidiaries. The results indicate that cluster location strengthens the relationship between subsidiary innovation and firm performance. Negative spatial autocorrelation shows that clustering contributes to innovation by facilitating proximate learning between less innovative laggards, and, leading innovative Japanese subsidiaries.     

BUSINESS CLUSTERS IN THE SOUTH: A CRITICAL APPRAISAL FROM INDONESIAN EVIDENCE

Advocacy of the advantages accruing to business clusters in developing economies has followed that in developed economies. With doubts emerging about the evidence for these in developed economies, it is therefore appropriate to review the experience in the developing world. A model of cluster emergence and growth has guided accounts of developing country clusters. Drawing on experience from Indonesia, doubts are raised about that model's prediction of the emergence of successful joint action in maintaining cluster advantages. Any advantages from clustering are insufficient to face development challenges arising from the globalisation of economic activity. The significance of business clusters in low income economies needs to be reviewed in the light of actual experience and the reappraisal emerging in developed or high income countries.     

Clustering of temporal event processes

A temporal point process is a sequence of points, each representing the occurrence time of an event. Each temporal point process is related to the behavior of an entity. As a result, clustering of temporal point processes can help differentiate between entities, thereby revealing patterns of behaviors. This study proposes a hierarchical cluster method for clustering temporal point processes based on the discrete Fréchet (DF) distance. The DF cluster method is divided into four steps: (1) constructing a DF similarity matrix between temporal point processes; (2) constructing a complete linkage hierarchical tree based on the DF similarity matrix; (3) clustering the point processes with a threshold determined by locating the local maxima on the curve of the pseudo-F statistic (an index which measures the separability between clusters and the compactness in clusters); and (4) identifying inner patterns for each cluster formed by a series of dense intervals, each of which contains at least one event of all processes of the cluster. The contributions of the article are: (1) the proposed DF cluster method can cluster temporal point processes into different groups and (2) more importantly, it can identify the inner pattern of each cluster. Two synthetic data sets were created to illustrate the DF distance between temporal point process clusters (the first data set) and validate the proposed DF cluster method (the second data set), respectively. An experiment and a comparison with a method based on dynamic time warping show that DF cluster successfully identifies the preconfigured patterns in the second synthetic data set. The cluster method was then applied to a population migration history data set for the Northern Plains of the United States, revealing some interesting population migration patterns.     

Detecting ethnic residential clusters using an optimisation clustering method

To understand residential clustering of contemporary immigrants and other ethnic minorities in urban areas, it is important to first identify where they are clustered. In recent years, increasing attention has been given to the use of local statistics as a tool for finding the location of racial/ethnic residential clusters. However, since many existing local statistics are primarily developed for epidemiological studies where clustering is associated with relatively rare events, its application in studies of residential segregation may not always yield satisfactory results. This article proposes an optimisation clustering method for delineating the boundaries of ethnic residential clusters. The proposed approach uses a modified greedy algorithm to find the most likely extent of clusters and employs total within-group absolute deviations as a clustering criterion. To demonstrate the effectiveness of the method, we applied it to a set of synthetic landscapes and to two empirical data sets in Auckland, New Zealand. The results show that the proposed method can detect ethnic residential clusters effectively and that it has potential for use in other disciplines as it offers an ability to detect large, arbitrarily shaped clusters.