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Using an improved spatial clustering model for evaluation of industry agglomeration

机译:使用改进的空间聚类模型评估产业集聚

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In the past researches, industrial agglomeration mainly focused on single or particular industry and less on spatial industrial structure and mutual relations. In addition, industrial cluster indeed benefits industry development. To fully control the status and characteristics of district industrial, cluster can facilitate to improve the competitive ascendancy of district industry. The related researches on industrial spatial cluster were of great significance for setting up industrial policies and promoting district economic development. In this study, an improved model that combines DBSCAN and SOM was developed for analyzing industrial cluster. Different from distance-based algorithm for industrial cluster, the proposed model can calculate spatial characteristics between firms based on DBSCAN algorithm and evaluate the similarity of attributes between firms based on SOM model. The demonstrative data sets, 25 random sampling of firms around Taichung County in central Taiwan, were analyzed for verifying the practicability of the proposed model. The analyzed results indicate that the proposed model is suitable for evaluating spatial industrial cluster. This research benefits on regional development decision-making for local government.
机译:在过去的研究中,产业集聚主要集中在单个或特定产业上,而很少关注空间产业结构和相互关系。此外,产业集群确实有利于产业发展。为了充分控制区域工业的地位和特征,集群可以促进区域工业的竞争优势。产业空间集群的相关研究对制定产业政策,促进区域经济发展具有重要意义。在这项研究中,开发了一种结合DBSCAN和SOM的改进模型来分析产业集群。与基于距离的产业集群算法不同,该模型可以基于DBSCAN算法计算企业之间的空间特征,并基于SOM模型评估企业之间的属性相似度。对台湾中部台中县周围25家企业的随机样本数据进行了分析,以验证该模型的实用性。分析结果表明,所提出的模型适用于评价空间产业集群。这项研究有益于地方政府的区域发展决策。

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