| 基于探索性空间数据分析的我国港口空间格局 |
| 投稿时间：2013-08-08 修订日期：2013-09-02 点此下载全文 |
| 引用本文：庄佳芳,余思勤.基于探索性空间数据分析的我国港口空间格局[J].上海海事大学学报,2013,34(3):66-71. |
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| 中文摘要:为研究港口群的发展对区域经济发展的影响,用探索性空间数据分析方法，对1985—2012年间我国港口空间格局及其变化进行分析.通过局部莫兰指数所作的空间自相关性分析显示，由环渤海湾、长三角、珠三角三大港口群形成我国港口空间格局，其中渤海湾港口群表现为由单个高低集聚向多个高高集聚的演变、长三角港口群是在单个高低集聚向高高集聚转变的同时出现新的高低集聚、珠三角港口群则维持相对稳定的高低集聚状态.利用ArcGIS空间分析工具获得的结果，不仅能从数值角度为港口集聚程度和集聚方式提供参考，而且可将集聚程度和集聚方式通过地图直观地展现出来. |
| 中文关键词:港口 空间格局 莫兰指数 集聚度 |
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| China port spatial pattern based on exploratory spatial data analysis |
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| Abstract:To study the effect of port cluster development on regional economy development, the exploratory spatial data analysis method is used to study China port spatial pattern and its evolution in 1985 2012. The spatial autocorrelation analysis by local Moran’s index shows that China’s port spatial pattern is formed by three big port clusters: Bohai Gulf port cluster, Yangtze River Delta port cluster and Pearl River Delta port cluster. Bohai Gulf port cluster shows the evolution from single high low concentration to multiple high high concentration; Yangtze River Delta port cluster shows that its evolution is similar to Bohai Gulf port cluster and the new high low concentration appears simultaneously; Pearl River Delta port cluster shows a relatively stable high low concentration. By the spatial analysis tool of ArcGIS, the numerical reference can be provided for the aggregation degree and aggregation mode of port, and the aggregation degree and aggregation mode of port can be shown directly by graph. |
| keywords:port spatial pattern Moran’s index degree of aggregation |
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