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141.
Postindustrial Manufacturing in a Sunbelt Metropolis: Where Are Factories Located in Phoenix? 总被引:1,自引:0,他引:1
《Urban geography》2013,34(8):898-926
Manufacturing establishments are integral to the spatial structure of fast-growing Sunbelt metropolitan areas, but most concepts and theories of intrametropolitan location were largely developed for an earlier technological era and different spatial contexts. This article investigates the location of nine disaggregated manufacturing sectors in Phoenix, Arizona, showing varying degrees of central core concentration and metropolitan-wide clustering. Distinct sectoral co-location patterns are also evident. We interpret our results as evidence that the intrametropolitan location of postindustrial manufacturing is best understood as a series of spatial distributions with varying concentration, centralization, clustering, and other order-based characteristics. There is little evidence that randomly scattered discrete industrial zones have developed nor that spatial patterns are uniform. Enduring lock-in effects tied to transportation infrastructure are pivotal to understanding the locational distribution of manufacturing industries in metropolitan Phoenix. Results do not support a hypothesis that a positive relationship exists between establishment size and distance from sectoral mean centers. 相似文献
142.
Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient. 相似文献
143.
In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained. 相似文献
144.
Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm
Yuanyuan ZUO Zhiqun HU Shujie YUAN Jiafeng ZHENG Xiaoyan YIN Boyong LI 《大气科学进展》2022,39(12):2203-2212
A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage. 相似文献
145.
煤矿井下地球物理水害超前探测要求探测点20 m范围内不得有积水和金属物体,传统电磁法超前探测技术已不能满足要求,钻孔瞬变电磁法通过将收发装置送入掘进工作面前方的钻孔中进行探测,既远离了巷道中的各种干扰,又提高了隐蔽致灾水体的探测精度。为解决该方法对钻孔径向异常体的准确定位解释难题,通过三维正演总结了其水平分量异常响应特征,提出了异常体象限确定准则,研究了根据水平分量幅值和异常象限综合求取异常体工具面角的计算方法。将由垂直分量计算得到的每一个视电阻率视为独立异常体,基于K-means聚类算法对相应的水平分量异常曲线特征值进行二分类,实现了全数据集的视电阻率象限自动划分,结合异常工具面角算法研究得出钻孔瞬变电磁视电阻率立体成像方法。最后计算了三维数值模型的立体成像结果,对钻孔径向的小规模低阻异常体取得了良好效果。结果表明:基于K-means聚类算法的钻孔瞬变电磁视电阻率立体成像方法是地球物理与机器学习的有机结合,该方法能够为井下掘进工作面隐伏水害超前探测精细解释提供技术支撑。 相似文献
146.
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148.
从力学的角度来考虑空间聚类问题,并结合地理学基本规律提出了一种基于力学思想的空间聚类有效性评价指标(简称SCV)。实验分析表明,本文提出的评价指标能够更准确、高效地对二维地理空间数据的硬聚类结果进行有效性评价。 相似文献
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150.
In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988–2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern‐match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain ~88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll‐a. We obtained five clusters for the period 1994–1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters. 相似文献