地理科学进展 ›› 2013, Vol. 32 ›› Issue (11): 1612-1621.doi: 10.11820/dlkxjz.2013.11.004

• 产业经济与区域发展 • 上一篇    下一篇

沈阳市中心城区交通网络中心性及其与第三产业经济密度空间分布的关系

陈晨, 程林, 修春亮   

  1. 东北师范大学地理科学学院, 长春 130024
  • 收稿日期:2013-06-01 修回日期:2013-08-01 出版日期:2013-11-25 发布日期:2013-11-25
  • 作者简介:陈晨(1987- ),女,博士研究生,主要研究方向为人文地理学。E-mail:chenc703@nenu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41071109)。

Distribution of centrality of traffic network and its relationship with economic density of tertiary industry in Shenyang

CHEN Chen, CHENG Lin, XIU Chunliang   

  1. School of Geographical Science, Northeast Normal University, Changchun 130024, China
  • Received:2013-06-01 Revised:2013-08-01 Online:2013-11-25 Published:2013-11-25

摘要: 多中心性评价模型(Multiple Centrality Analysis,即MCA)可用于分析交通网络中心性及其与城市经济活动的关系,其所包含的邻近度、介数中心性及直达性是测度城市土地开发利用率的重要指标。本文首先测度沈阳市中心城区交通网络中心性;通过核密度估计法对交通网络中心性与第三产业经济密度进行空间插值,将两者转换为同一计算单位,测算两者相关系数,分析第三产业经济密度空间分布与交通网络中心性的空间关系及其统计学特征;其中第三产业经济密度为面域数据,需在ArcGIS 中建立渔网进行空间插值。研究结果如下:① 交通网络中心性对第三产业经济密度空间具有决定性影响,交通网络的多中心性导致了经济活动的多中心性;② 第三产业经济密度空间分布受介数中心性影响最大,直达性对第三产业经济密度空间分布影响也较大,而邻近度对第三产业经济密度分布影响较小。研究有助于整体把握沈阳市中心城区交通网络中心性空间分布状态,为城市经济活动布局提供科学依据,在城市规划理论与实践研究中具有指导意义。

关键词: 第三产业, 多中心性评价模型, 交通网络中心性, 经济密度, 沈阳市

Abstract: With the development of network science, many scholars abroad begin to focus on the research of centrality of traffic network based on MCA(Multiple Centrality Assessment) and its relationship with economic activities. Centrality of traffic network is calibrated in a MCA model composed of multiple measures such as closeness, betweenness, and straightness. MCA model is a very important indicator that measures the rate of land development and utilization, and is widely used both in the theoretical and empirical inquiries. In this paper, by using the tools developed by MIT to calculate centrality of traffic network and its relationship with economic activities precisely and efficiently, we investigated the geography of three centralities of traffic network and their correlations with economic density of tertiary industry in Shenyang City, and then applied the KDE method to both centralities of traffic network and economic density to examine the correlations between them. Since economic density is regional data based on subdistricts, we created fishnet in ArcGIS and then did spatial interpolation. The results indicated that centralities of traffic network are correlated with the spatial distribution of economic density of tertiary industry in Shenyang. Spatial distribution of economic activity density correlates highly with the betweenness of traffic network, which means that the multiple centers of the streets lead to multiple centralities of economic activities. But we found that only betweenness and straightness show clear multi-centricity. Closeness, however, just has single centrality. This also means closeness has less impact on economic activities than betweenness and straightness. The major contributions made by this research can be summarized as follows: (1) Improving overall understanding of the spatial distribution of street centralities in Shenyang, which can be one of the most powerful determinants for urban planners and designers to understand how a city works and to decide where renovation and redevelopment need to be placed, to guide economic layout. (2) The concept that central urban arterials should be conceived as the cores, not the borders, of neighborhoods has the importance of directing in the theory and practice of city planning. (3) By drawing lessons from foreign research experiences, this research can enrich the theory, methods, and practice of the street network centrality in our country. If we take into account the relative properties of different street grades and types, vehicle flow rate and capacity, one-way or two-way streets, and so on, and give them appropriate weights based on their properties, the results will be more actual and practical and can help us understand the centrality of traffic network and its relationship with economic activities more precisely. More works need be done in order to study centrality of traffic network and its relationship with economic activities more comprehensively: (1) By looking into centrality of traffic network and its relationship with every kind of economic activity, we can get clear dependent relationship between centrality of traffic network and economic activities profoundly, and better understand the different relationships between them. (2) If we can get different attributes of every traffic level and use them as weights when we do KDE analysis, the research results will be much more practical.

Key words: centrality of traffic network, economic density, Mmultiple Centrality Assessment Model, Shenyang City, tertiary industry