【作者】 王琼；

【导师】 刘红星； 吴健平；

【作者基本信息】 华东师范大学 ， 地图学与地理信息系统， 2019， 硕士

【摘要】 城市绿地是城市绿化系统和城市景观的重要组成部分,具有重要的生态、社会、心理和经济功能。快速地城市化发展使得城市绿地的空间结构与布局也发生了变化。连通性作为城市绿地空间结构的重要评价指标,研究城市绿地的连通性能够更好地理解城市绿地间的相互作用和相互联系,为城市绿地规划管理提供合理的决策支持。此外,研究城市绿地连通性的动态演变不仅有利于把握城市绿地的空间变化,更对维持城市绿地系统的稳定性和优化城市生态网络具有重要的意义。现有绿地连通性研究中大多从宏观层面对大型城市绿地斑块进行分析,侧重对斑块或格局几何特征的简单分析,忽略了小型绿地斑块(例如居住区绿地、道路绿地等)对连通性的影响,且少有针对城市绿地连通性在空间上的动态变化过程的研究。本研究以上海市外环以内为研究区域,基于2008、2012、2016年绿地数据构建绿地连通图和演化图,对城市绿地连通性及其动态变化过程进行了研究。本研究提出的研究方法能够同时反映城市绿地连通性的空间布局与变化过程,可为研究城市绿地连通性的动态变化过程提供新思路,对绿化管理部门进行城市绿地空间优化和统筹绿地空间发展具有一定的参考价值。本研究主要研究内容和结论包括:(1)针对小型绿地斑块,基于图论原理,选取10 m作为距离阈值构建了以城市绿地斑块为节点、绿地斑块间的连通关系为边的绿地连通图。在连通图的基础上,利用邻近事件矩阵(Adjacency-event Martrix)统计了上海市外环以内绿地连通的空间排列特征,结果显示居住区绿地之间的空间连通频率最高。随后选取贝塔指数(β)和介数中心性指数(Betweenness Centrality,BC)作为衡量城市绿地整体连通性水平和斑块重要性程度的指标。结果表明:2008-2016年,上海市外环以内城市绿地连通性水平逐渐增大,且重要绿地斑块的数量也逐渐增多,主要包括公园绿地、单位附属绿地、居住区绿地。介数中心性较大的绿地斑块位置具有集中与均衡分布同时存在的特点,面积大的绿地斑块多,面积小的绿地斑块少。在城市绿地规划中,不仅BC值较大的大型斑块是管理的重点,小型绿地斑块由于其布局灵活的优点,也在城市绿地系统管理中具有重要的作用。(2)基于空间拓扑关系提出了7种城市绿地演化类型(包括新增、消失、扩张、收缩、分解、合并和连续),构建了以两个年份的绿地斑块为节点、以绿地演化类型为边的演化图。利用ArcScene软件,以时间为Z轴、以城市绿地为XY平面构建三维可视环境对2008、2012、2016年的城市绿地演化过程进行三维可视化,形成上海市外环以内的城市绿地演化轨迹。结合连通图与演化图提取出城市绿地的动态变化过程,分析了各个绿地演化类型的绿地斑块数量和空间分布规律。整体上,新增、分解与收缩的绿地个数分别大于消失、合并与扩张的绿地斑块数量,而且新增、消失的绿地斑块数量较大。在空间分布上,变化的城市绿地斑块位置分布较均匀,面积小的变化城市绿地斑块在研究区域的中心位置分布较多,面积大的变化城市绿地斑块位于外环以内的边缘区域较多。与2008-2012年的城市绿地变化相比,2012-2016年新增和消失的绿地斑块面积较大,特别是浦东新区南部地区。新增绿地的空间分布更加均匀,消失绿地的位置分布更加集中。(3)城市绿地连通性动态变化分析。基于连通图和演化图提出了城市绿地连通性动态变化提取的算法,提取出2008-2012、2012-2016年上海市外环以内连通图中发生新增和消失的部分。对比相邻两年份的连通性变化发现,后一时相的新增连通比前一时相的消失连通在数量上和部分地区的密集程度上更高,且连通性消失与新增的空间分布模式具有一定的相似性。(4)系统开发。基于Visual studio2012平台和C#语言开发了城市绿地连通性分析系统。该系统可以应用到更多的区域和不同的时间序列的城市绿地或其他一种、多种的土地利用类型的连通性及其变化研究。更多还原

【Abstract】 Urban green space is an important component of urban green space system and urban landscape,with significant ecological,economic,psychological and social functions.Due to the rapid urbanization,the spatial structures and patterns of urban green spaces have changed as well.As a fundamental landscape index for describing the structure of urban green space,the connectivity of green space is of great importance for understanding the interaction and characteristics of urban green space by quantifying the connectivity of urban green space.Moreover,the research on dynamic evolution of urban green space connectivity is not only aid to deeply realize spatial changes of urban green space,but also a very importance reference and instruction for optimizing urban green space and sustaining steady of urban green space system.Most of current studies analyze the connectivity of urban green space by using large patches from macroscopic aspect,and mainly focused on simple analysis and description of the urban green space patches or patterns,while neglecting information on dynamic changes of green space pattern.This study proposed a graph-based method for investigating the spatial pattern and dynamic evolution of urban green space connectivity.Spatially speaking,the method provides a direct neighborhood representation of the green space by using adjacent relation.This enable us to identify and quantify green space patterns,such as calculate the occurrences of pairs of neighborhood green space types from adjacency-event matrix.Apart from the spatial representation,the method tracks and records green space patches through time,allowing revelations relating to what happened to them individually between snapshots.The method was applied to the region within outer ring road of Shanghai,China based on the green space data from 2008 to 2016.The proposed method holds great potential for the greening management department to optimize greening management and construct control strategy of Greenbelts.The main research contents and conclusions are summarized as follows.(1)In order to construct connectivity graphs for small urban green patches,we selected 10 m as the distance threshold to find connective neighborhoods from the green space data based on the principle of graph theory.In the connectivity graphs,urban green patches were treated as nodes while the links between adjacent neighbors were treated as edges.Then,the Adjacency-event matrix is adopted to calculate the spatial arrangements of urban green patches from the connectivity graphs.Two graph-based indices,namely,Beta Index and Betweenness Centrality,were calculated to measure the overall connectivity of urban green space and identify the important green patches.The results shown that the residential green patches have the largest spatial connections with other green patches in the study area.The results also demonstrated that the connectivity of urban green space in the study area from 2008 to 2016 has increased gradually.We also found that the important green patches are always located near the green patches who have a high Betweenness Centrality,and the spatial distribution of these important green patches is getting more and more balanced during the study period.(2)To streamline the evolution of urban green patches,seven types of spatiotemporal relations,including born,die,expansion,contraction,splitting,merging and continuation,were proposed in our study.Based on these spatio-temporal relations,an evolution graph is constructed by treating the green patches as nodes and their corresponding spatio-temporal relations as edges.By treating the timeline as the Z-axis and the urban green space as the XY plane in the ArcScene software,the threedimensional urban green space evolution graphs for 2008,2012,and 2016 are visualized,where evolution graphs can be represented as evolution trajectories.Hereafter,the dynamic changes of urban green space can be extracted from the evolution graph.Furthermore,the number and spatial distribution for each green space evolution type were analyzed.The results indicated that in our study area,the number of born is larger than that of die,the number of splitting is larger than that of merging,and the number of contraction is larger than that of expansion.Moreover,the number of green patches which involved born and die is significantly larger than other evolution types.From the spatial distribution,urban green space patches which experienced changed are distributed more evenly,with those who have small area size are mostly distributed in the center of the study area,while those have large area size are mainly distributed near the outer ring.Compared with the changes from 2008 to 2012,the number of green patches experiencing born during the period from 2012 to 2016 was decreased,especially in the southern part of the Pudong New Area.The distribution of urban green patches who has an evolution type of die has spread gradually from few areas where these green patches are concentrated to the whole study area.(3)The method for analyzing the dynamic changes of connectivity in urban green space was proposed in this study as well.By combining the connectivity graph and evolution graph together,the changes of urban green space connectivity are explicitly extracted.Specifically,the edges which involved born and die are identified between two snapshots and thus can be used to analyzed how the connectivity among urban green patches changed over years.Comparing the connectivity changes of two years,it is found that the born edges of the latter year is higher than the die of the previous year in terms of quantity and distribution density of partial area.Furthermore,there are a certain similarity spatial distribution pattern between the die and born edges.(4)System development.Based on Visual studio2012 platform and C# language,the urban green space connectivity analysis system is developed.The system can be applied to more areas and different time series for urban green space and other one or a variety of land use types of connectivity and change research.更多还原