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地球信息科学学报 >

2017 , Vol. 19 >Issue 11: 1433 - 1441

DOI: https://doi.org/10.3724/SP.J.1047.2017.01433

地球信息科学理论与方法

城市建筑迎风面积密度矢量/栅格计算模型对比研究

  • 沈娟君 , 1 ,
  • 邱新法 , 2, * ,
  • 何永健 1 ,
  • 曾燕 3 ,
  • 李梦溪 2
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  • 1. 南京信息工程大学地理与遥感学院,南京 210044
  • 2. 南京信息工程大学应用气象学院,南京 210044
  • 3. 江苏省气候中心,南京 210009
*通讯作者：邱新法（1966-）,男,博士,教授,主要从事GIS与气象应用、城市微气候研究。E-mail:

作者简介：沈娟君（1993-）,女,硕士生,主要从事GIS与气象应用研究。E-mail:

收稿日期: 2017-05-10

  要求修回日期: 2017-08-21

  网络出版日期: 2017-11-10

基金资助

国家自然科学基金项目（41330529）

江苏省第四期“333高层次人才培养工程”科研项目（BRA2014373）

收起

Study on Comparison of Vector/Raster Calculation Model of Frontal Area Density of Urban Buildings

  • SHEN Juanjun , 1 ,
  • QIU Xinfa , 2, * ,
  • HE Yongjian 1 ,
  • ZENG Yan 3 ,
  • LI Mengxi 2
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  • 1. School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China;
  • 2. School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China;
  • 3. Climate Center of Jiangsu Province, Nanjing 210009, China
*Corresponding author: QIU Xinfa, E-mail:

Received date: 2017-05-10

  Request revised date: 2017-08-21

  Online published: 2017-11-10

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《地球信息科学学报》编辑部 所有
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摘要

城市建筑迎风面积密度（Frontal Area Density,FAD）作为重要的城市形态学参数之一,对其定量分析与制图,对城市微气候研究有着重要意义。为了找出高效可靠的方法分析城市建筑FAD的分布情况,本文以福建省晋江市为例,选取了矢量计算模型和栅格计算模型对FAD进行模拟,从计算效率、不同尺度和不同土地利用类型上,对结果进行对比分析。研究表明:计算效率上,矢量模型比栅格模型高。在城市尺度上,栅格模型与矢量模型模拟结果与宏观建筑分布特征一致,二者皆适用;在街区尺度上,栅格模型模拟结果比矢量模型更符合建筑分布规律。栅格模型的计算结果较矢量模型稳定,受分辨率影响小。在不同土地利用类型上,对于建筑分布稀疏区域2种模型皆适用;而对于建筑密集区,如商业区、城市住宅区等,栅格模型计算结果更优。

关键词： 迎风面积密度（FAD）; 建筑; 城市形态; 城市微气候; 晋江市

本文引用格式

沈娟君 , 邱新法 , 何永健 , 曾燕 , 李梦溪 . 城市建筑迎风面积密度矢量/栅格计算模型对比研究[J]. 地球信息科学学报, 2017 , 19(11) : 1433 -1441 . DOI: 10.3724/SP.J.1047.2017.01433

Abstract

Frontal Area Density (FAD) of urban buildings is one of the important parameters of urban morphology. Therefore, the quantitative analysis and its mapping play a significant role in the field of urban microclimate research. It helps climatologists and urban planners mark out the detected ventilation paths, which could improve the thermal conditions in the inner city. In order to determine an effective and reliable method of analyzing the distribution of urban FAD, we took Jinjiang city of Fujian province as an example. We selected the vector and the raster calculation model to simulate FAD. Considering computational efficiency, we analyzed the obtained results from various scales and land use types. Two computer models were developed based on GIS and geodatabase. Each calculation model has an advantage of its specific data type. Mean, maximum, minimum and standard deviation of FAD for some chosen sample areas in Qingyang subdistrict were calculated and there were significant differences between selected areas. The research shows that the vector model is more efficient than the raster model. At the urban scale, the simulations of the vector and raster models are both consistent to the distribution characteristics of buildings at the macro level. At the neighborhood scale, the results of the raster model are more in line with building distribution than the vector model. When the scale of the area is reduced, the differences between two models increase. The raster model is more stable than the vector model, and is less affected by the resolution. In different land use types, the mean FAD values in business districts and urban residential areas are higher than the others. In the raster model, the average difference between the two resolutions of urban residential area is the lowest. In the vector model, the average difference of the green space is lower than the other three land use types. Thus, the two models are applicable for sparse distribution areas, but for building dense regions, such as business districts and city residential areas, the raster model performs better.

Key words： Frontal Area Density; buildings; urban morphology; urban microclimate; Jinjiang

1 引言

城市的快速发展使得城市规模不断扩大,城市空间格局发生巨大变化。作为粗糙元素之一的建筑物是城市形态中最重要的因素,建筑的高度、宽度、朝向等对城市地表形态产生最直接的影响。一系列城市形态学参数被计算分析用来表达城市肌理形态,在这些参数中迎风面积密度是被认为与城市地表粗糙度有重要关联的参数,而粗糙度又与城市内空气流通、污染物扩散能力相关。近年来随着对城市微气候问题认识的逐渐深入,城市微气候与城市形态之间存在的密不可分的关系已经被确立[1-2]。通过定量分析研究城市形态信息,结合城市整体及局部风环境状况,对于城市气候学家、规划师探 寻城市潜在通风廊道,改善城市通风环境具有重要意义[3-4]。
关于城市建筑迎风面积密度的研究国外开始得较早。1998年,Bottema和Mestayer[5]以法国斯特拉斯堡市（2.7 km×2.2 km）为例,借由自行开发的图示化工具将三维矢量建筑按网格切分,根据每个网格内建筑体量来统计迎风面积密度,并用颜色深浅等方法图示化结果。这是最早运用计算机工具来计算城市迎风面积密度的案例。Gál和Unger[6]根据赛格德城区（25.75 km2）建筑分布特征,将矢量建筑概化后划分成不规则形状的地块,以多边形地块为单元计算整个城区的迎风面积密度。Ratti等[7]首次利用高分辨率数字高程模型（DEM）对3个欧洲城市和2个北美城市的400 m×400 m实验区域进行迎风面积密度的计算,并将结果绘制成5张“玫瑰”图进行对比。这些研究范围都限于小区域。从2006年开始,Edward等[8-10]开始以改善高密度城市香港的通风状况为目的,利用计算机和风洞实验对3个不同高度层（平台层、建筑平均高度层、城市冠层）进行试验,模拟分析了包括迎风面积密度在内的一系列城市形态学参数。针对国内大陆城市的研究目前处于起步阶段,李苑常[11]利用Rhino-Grasshopper平台选取“有效切片”对南京城区部分地区的迎风面积密度和粗糙度进行模拟和分析。少数建筑规划师在进行城市风道规划研究时对武汉和福州地区的迎风面积密度进行计算分析[12-13]。
为了找出高效可靠的方法定量分析不同尺度下城市建筑迎风面积密度的分布情况,并将模拟结果绘制成图为城市微气候研究提供参考,本文以福建省晋江市为例,分别选取矢量计算模型和栅格计算模型对迎风面积密度进行模拟,并对结果进行对比分析。因这一城市形态学参数缺少可靠的直接观测方法,本文未设定某种模拟方法的研究结果作为真实值,而是从统计数据上对2种方法的结果进行统计对比分析,找出2种方法的差异性,以求为分析它们各自的适用性提供依据。

2 研究区概况和数据源

2.1 研究区概况

晋江市位于福建省东南沿海,泉州市东南部,北纬24°30′44″~24°54′21″,东经118°24′56″~118°41′10″。全市陆域面积649 km2,三面临海,属亚热带季风气候区,主导风向为东北风,年平均风速为2 m/s。晋江全境地形平缓,主要以平原和丘陵为主。晋江市作为第一批城镇化试点地区之一,辖6个街道、13个镇,391个行政村（社区）[14],城区位于东北部地区,包括青阳街道、梅岭街道、罗山街道等。全市建筑形态、高度多样,高层建筑所占比例较少,建筑朝向有一定规律,计算并分析其城市建筑迎风面积密度分布特征对于研究城市内风环境状况具有重要意义。

2.2 数据源

本文采用的基础地理信息数据主要包括晋江市1:500数字地形图、晋江市行政区域边界数据等。晋江市建筑分布数据是利用晋江市2014年 1:500数字地形图,提取建筑物的平面位置与建筑层数。而一般城市建筑每层楼的平均高度在3 m左右,因此将建筑层数乘以3 m得到建筑物的近似高度。将提取得到的各幅建筑物图层进行检查与拼接得到晋江全市建筑分布图。由于晋江市地形平缓,因此本文计算迎风面积密度时不考虑地形因素。在ArcGIS中建立地理数据库,根据建筑物的平面位置与高度属性信息分别建立简单纹理三维矢量建筑模型和城市数字高程模型。本文选取了城市和街区2个尺度范围对迎风面积密度进行模拟,城市级别为晋江整个城市,街区级别为晋江市青阳街道。实验区域及建筑分布情况如图1所示。
Fig. 1 The study area

图1 研究区域示意图

3 计算方法与参数确定

3.1 计算方法

城市建筑迎风面积密度FAD(Frontal Area Density)表示在一定高度增距上,某一特定风向下建筑物迎风面积与建筑物所在地块面积之比[15-16]。迎风面积密度公式为:
λ f z , θ = A ( θ ) proj ( z ) A T （1）
式中: A ( θ ) proj ( z ) 是垂直于某一风向的建筑迎风面积;是建筑所在地块面积;是选定的某一方向;是高度增距。由此可见,迎风面积密度侧重于对所选取的特定高度增距上建筑形态的描述[12],其值大小一定程度上描述了该地块附近风绕流穿过建筑的渗透率高低。它的基本特征表现在群体性和方向性上[17],即首先它是一个针对群体建筑的参量,因单一建筑周边的风环境受到自身及其他建筑的朝向、长度等多因素影响,计算单一建筑的迎风面积密度来模拟空气流通状况意义不大;其次它与风向有着直接的关系,风向不同则建筑的迎风面积不同,从而该风向下城市空间形态对城市空气流通的影响也不同。
因此,结合研究区域的风速和风向特点,利用气象数据得到研究区的风玫瑰图,计算出多个方向的迎风面积密度,并按照多个方向的风频进行加权平均,最后得到研究区的迎风面积密度。
λ f z = ∑ i = 1 n λ f z , θ × P θ , i （2）
式中:是式（1）中 λ f ( z , θ ) 的年平均; P θ , i 是年平均风速在方向的频率;n表示选取的风向个数,本文取值16。
现有的迎风面积密度的计算方法主要分为矢量计算模型和栅格计算模型。矢量计算模型原理是以三维矢量建筑数据为数据源,它是以三维空间中的点、线、面、体及其组合体来表示建筑实体空间分布的一种数据组织方式。首先借助计算机平台将建筑矢量化数据按一定大小切分形成地块,如果某一建筑正好落在地块交汇处将被切分形成多个新的建筑体。其次,在某一风向上确定垂直于该风向的投影线,获取该条投影线的两端点位置信息。然后,以每个地块为单位,通过搜索该地块下每栋建筑轮廓的节点与垂直于风向的投影线向量的关系确定每栋建筑的迎风线长度,结合建筑高度从而获得迎风面积。最后,由该地块上总迎风面积与地块面积之比得到迎风面积密度。在矢量计算模型中,迎风面积密度与建筑所在地块的面积大小有很大关系,而由于地块类型、大小不同,常规的用地面积差异很大。根据晋江市建筑特点,选取50、100、200、400 m地块大小进行测试,权衡计算精度和运算耗时,最终制图时本文选取城市尺度的地块大小为100 m×100 m,街区尺度的地块大小为50 m×50 m。
栅格计算模型的地理数据库表达方法是数字高程模型（DEM）,它是包含有高度信息的2.5维图像,图像上每个像元的灰度值都与其在表面的高度成正比。建立包含建筑物高度的城市数字高程模型（Urban DEM）,能准确地描述城市地表特征的空间起伏变化。本文所用DEM分辨率为5 m。栅格计算模型的原理:①利用标准图像处理算法获取DEM表面每个像素的单位法向量,其中法向量的确定是以该像元为中心,由米字剖分法构造三角面,通过该像元周围多个三角面的法向加权平均得到当前像元中心点的法向量[18]。②计算该像元的法向量与给定风向上水平单位向量的点积得到该像元在给定方向上的投影。由起伏特征可知,迎风状态是这2个向量夹角大于90°的情况。因此将给定方向上所有像元点积为负值的结果累加得到该方向下的迎风面积密度[7]。③利用ArcGIS空间分析将栅格图像分辨率概化至不同尺度下制图需求。在遍历栅格图像所有像元求法向量时需要分普通点、左上角点、右上角点、左下角点、右下角点、首行、首列、末行、末列共9类来考虑相邻三角面,且要按同一个方向取三角面的点。

3.2 参数确定

为了确定迎风面积密度公式中高度增距Z的值,本文利用GIS中地理数据库统计了晋江市建筑高度分布直方图。如图2所示,晋江市建筑以低层楼房为主,最高建筑的高度为99 m,高度在30 m以上的建筑数量较少。依据ArcGIS中自然间断点分级法（Jenks）将晋江市所有建筑分为中低层建筑 （0~30 m）和高层建筑（31~99 m）,其中0~30 m的建筑数量占总建筑数的99.8%。因此,为了更好地适应该研究区的实际状况,本文选取的高度增距为30 m。
Fig. 2 The distribution histogram of building height in Jinjiang

图2 晋江市建筑高度分布直方图

对于不同方向迎风面积密度不同这一特点,参考晋江市气候特点,根据晋江市及其周边自动气象站点2010-2014年的数据,整理得到晋江市2010-2014年全年各风向风频数据,按照16个方向的风频进行加权平均迎风面积密度,最后得到不同尺度下晋江市迎风面积密度图。

4 结果与分析

4.1 2种模型计算效率比较

为了比较两种模型的计算效率,首先对两种模型采用的数据结构进行比较。矢量数据结构能较好地表达建筑实体的空间分布特征,数据精度高,存储冗余度低。模型计算中搜索的建筑轮廓的每个节点由一组三维坐标（X、Y、Z）组成,因此建筑节点数是矢量模型计算效率的关键部分。栅格数据的像元空间位置用行号和列号标识,数据精度由像元大小决定。为了有效还原建筑实体特征,本文选用5 m分辨率栅格数据。从模型计算时循环层数上看,矢量模型主要从地块数量和每个地块的建筑节点上循环,栅格模型主要从像元数量和按米字剖分法求像元法向量上循环。以2 km×2 km的实验单元为例,原始建筑单体为1013个,在Intel i5处理器,4 GB内存的台式计算机上进行对比实验。由2种模型的计算时间（表1）可以看出,矢量计算模型的运行时间小于栅格模型。随着计算区域的扩大,栅格模型采用的数据量将飞速增长,循环次数将远远多于矢量模型。因此,当研究区域为城市级别时,矢量模型在计算效率上更有优势。
Tab. 1 The calculation time of the two models

表1 2种模型的计算时间

计算模型 循环层数 运行时间/min
矢量模型 2 3.0
栅格模型 2 4.5

4.2 2种模型计算结果空间分布特征对比

利用上述的矢量、栅格计算模型,分别从城市、街区2个尺度模拟建筑迎风面积密度,并将结果绘制成图,用以表达不同尺度下城市建筑迎风面积密度的空间分布特征。迎风面积密度数值大小表示风通过该研究区时所受阻力的强弱情况,迎风面积密度数值越大,表示该处建筑阻挡风的能力越强,风速降低的速度越快,反之则表示风所受阻力较小,通风状况良好。不同尺度下2种模型得到的迎风面积密度值总体对比如表2所示。由表2可看出,城市尺度下2种模型得到的迎风面积密度平均值差异（0.02）比街区尺度下（0.05）小。从标准差来看,城市尺度下2种模型的结果统计值都比街区尺度下小。从最大值来看,城市尺度下两种模型的迎风面积密度最大值相近;街区尺度下2种模型的最大值存在差异,矢量模型的最大值比栅格模型的大。
Tab. 2 FAD in different scales simulated by the two models

表2 不同尺度下2种模型FAD模拟值对比

计算模型 城市尺度（晋江市） 街区尺度（青阳街道）
最小值 最大值 平均值 标准差 最小值 最大值 平均值 标准差
矢量模型 0.00 1.20 0.07 0.12 0.00 1.24 0.19 0.23
栅格模型 0.00 1.20 0.09 0.15 0.00 0.95 0.14 0.16
4.2.1 城市级别
晋江市迎风面积密度计算结果（图3）以看出,2种模型的计算结果宏观分布特征一致,红色区域即迎风面积密度数值大的地区主要分布在城市东北部的青阳街道、梅岭街道及陈埭镇,蓝绿色区域即迎风面积密度值较小的地区位于晋江中部和南部地区。以上模拟结果与晋江市主城区的实际分布情况相符,晋江市主城区位于东北部,建筑高度整体大,高度在6 m及以上的建筑占74.1%;建筑较密集,建筑密度平均值为0.423。而其余为农村地区,存在较大的空旷地域,低矮建筑占主要比例。从迎风面积密度的取值范围来看,2种模型模拟得到的最大值相近;细节对比可以看出,栅格模型计算结果中迎风面积密度低于0.45所占区域比矢量模型偏大;矢量模型计算结果中迎风面积密度高值区（图中红色区域）比栅格模型更集中凸显。结合表2看出,在城市尺度下2种模型计算得到的迎风面积密度平均值仅相差0.02。矢量和栅格2种模型得到的计算结果标准差分别为0.12、0.15。因此在城市尺度上2种模型计算结果整体一致。
Fig. 3 Distribution of FAD in Jinjiang county

图3 晋江市FAD空间分布图

4.2.2 街区级别
晋江市青阳街道迎风面积密度计算结果（图4）可以看出,2种模型的计算结果宏观分布特征总体一致,红色区域即迎风面积密度数值大的地区主要分布在青阳街道的中部和东北部,这与青阳街道高层建筑集中区的分布情况相符合。蓝色区域即迎风面积密度值较小的地区位于街道的西南部和东部地区。进一步分析得到,2种模型的计算结果仍存在一定差异,矢量模型得到的迎风面积密度的最大值比栅格模型的稍大,且红、黄色区域面积比栅格模型的大。结合表2可以看出,青阳街道下矢量模型得到的迎风面积密度平均值为0.19,栅格模型的为0.14,二者的差值（0.05）比城市尺度下大。从标准差来看,矢量模型的计算结果标准差比栅格模型的大0.07。因而,可以从空间分布图上更清楚地发现矢量模型的结果斑块分布特性明显;栅格模型得到的结果分布更细致,不同等级之间有渐变过渡。
Fig. 4 Distribution of FAD in Qingyang subdistrict

图4 青阳街道FAD空间分布图

4.3 2种模型计算结果数值差异对比

由上文可知,矢量模型和栅格模型计算得出的迎风面积密度数值规律存在差异。为了定量地分析两种模型结果的数值差异情况,对相应尺度下同一位置上2种模型的迎风面积密度模拟值进行了回归分析。图5为不同尺度下2种模型得到的迎风面积密度模拟值回归分析图,它描述了在全部采样点上不同方法计算结果的相关程度。
Fig. 5 Regression analysis of FAD simulated by the two methods

图5 2种方法FAD模拟值回归分析图

由图5可知,在城市尺度下,样本数为74 419个,2种模型的计算结果大致相同,回归函数为 y=0.96x;在迎风面积密度数值小于0.3,即通风状况较好的情况下,栅格、矢量2种模型的样点数保持大致等量。当迎风面积密度数值在0.3-0.6时,栅格模型的结果样点数要比矢量模型的略多,对应了城市尺度下2种模型低值区空间分布特征差异。在街区尺度下,样本数为4506个,2种模型的计算结果差异比城市尺度下稍大,回归函数为y=0.81x,即整体矢量模型的结果要比栅格模型的结果偏大,且迎风面积密度数值在大于0.6时矢量模型样点数占总样点数比例比栅格模型的大。需要注意的是,街区尺度选取的青阳街道为晋江老城区,建筑相对密集,建筑高度错落有致,有利于迎风面积密度参数的准确模拟,因此虽然2种模型整体数值差异比城市尺度的大,但模拟结果仍有合理性。
综合图5可以得出:城市尺度上矢量模型与栅格模型模拟得到的结果大体一致,模型间差异小;两种模型间差异随分辨率增大而增大。在研究范围变化时栅格模型比矢量模型更能保持较稳定水平。

4.4 不同土地利用类型2种模型计算结果比较

不同土地利用类型下建筑的高度、密度、形态都不同,因而对迎风面积密度的模拟影响很大。为了对比不同土地利用类型下2种模型模拟结果,本文选取如图6的7种用地类型,每种类型选取一个样区,样区面积控制在0.4 km2左右。商业区以青阳街道北部商业中心为例,工业区选取晋江市五里工业区,城市住宅区以青阳街道的霞行社区、莲屿社区为例,农村住宅区选取永和镇中部住宅区,公共交通以晋江市汽车站附近地块为例,绿地选取晋江世纪公园,水域选取了磁灶镇水库附近。由图6可知,2种模型模拟得到的商业区和城市住宅区的平均迎风面积密度值明显大于其他地块,其次是公共交通用地和工业区,农村住宅区、绿地和水域的迎风面积密度较小。对比2种模型,矢量计算模型得到的不同土地利用类型间的平均迎风面积密度差异比栅格模型大,矢量模型得到的迎风面积密度最大值（0.52）与最小值（0.04）相差0.48,栅格模型模拟结果的最大值与最小值相差0.37。
Fig. 6 The average values of FAD simulated for different land use types at the urban scale

图6 城市尺度下不同用地类型的FAD模拟平均值

对同一种土地利用类型,两种模型模拟结果虽然有差异,但差异值总体不大,在0.03-0.08,参考不同用地类型上的实际建筑情况,商业区以大型、高层建筑为主,建筑较密集,相对其他地区建筑长度、宽度要大。对于矢量计算模型,在划分建筑地块时,对位于地块交界处的建筑采取切分形成新建筑的原则,不同于栅格计算模型采用的高分辨率DEM数据,这可能是导致矢量计算模型得到的商业区的平均迎风面积密度值比栅格计算模型稍大的原因。其他用地类型上小型、低矮建筑占大多数,因此该用地类型上2种模型间差异比商业区的小。由于本文选取的公共交通用地为晋江市汽车站附近地块,位于青阳街道中心,样区选取时不仅包含公路还包括汽车站附近商铺,因此该土地利用类型上的平均迎风面积密度并没有非常小。绿地和水域用地上建筑分布非常稀疏,建筑数量较少,所以2种模型模拟得到的结果差异非常小。
在城市及街区尺度下,青阳街道的迎风面积密度被以不同分辨率形式表达,因此以青阳街道为例,对不同分辨率下该街道的4种用地类型2种计算模型模拟结果进行统计。依据青阳街道实际情况选取商业区、城市住宅区、公共交通、绿地4种类型,每个类型样区选取原则同上,统计结果如表3、4所示。
从表3、4可以看出,矢量和栅格2种模型无论在城市尺度还是街区尺度,模拟得到的商业区和城市住宅区迎风面积密度相对其他用地较高,平均值在0.35以上。4种用地类型上矢量模型的计算结果平均值都比栅格模型的大。从标准差来看,栅格模型的结果统计值比矢量模型的小,特别是商业区和城市住宅用地这2块建筑密集区,矢量模型的标准差比栅格模型的大0.04及以上。这意味着栅格模型在这类用地上的计算结果比矢量模型的差异小。在栅格模型中,2种分辨率下城市住宅区的迎风面积密度模拟结果的平均值差异最小。矢量模型中绿地的迎风面积密度值平均值差异最小,其余3类差异都较大。综合2个表格可以看出4种情况下绿地的统计结果基本相同,这可能与绿地上建筑布局稀疏有关,无论采用何种模型及分辨率都能较准确地表达该地块上迎风面积密度分布情况。
因此,由以上分析结合模拟结果的空间分布特征可知,2种模型对于不同土地利用类型的适用性不同。当研究区域建筑密集时,栅格计算模型的模拟方法要优于矢量计算模型;当研究区域的建筑布局稀疏时,2种方法皆可选用。
Tab. 3 FAD simulated for different land use types and resolutions by the vector model

表3 2种分辨率下不同用地类型的矢量模型FAD模拟值

用地类型 矢量模型（100 m） 矢量模型（50 m）
最小值 最大值 平均值 标准差 最小值 最大值 平均值 标准差
商业区 0.06 0.91 0.52 0.25 0.00 1.06 0.40 0.28
城市住宅 0.06 0.84 0.44 0.17 0.00 0.91 0.38 0.19
公共交通 0.01 0.97 0.37 0.26 0.00 1.01 0.26 0.25
绿地 0.00 0.67 0.12 0.14 0.00 0.57 0.08 0.16
Tab. 4 FAD simulated for different land use types and resolutions by the raster model

表4 2种分辨率下不同用地类型的栅格模型FAD模拟值

用地类型 栅格模型（100 m） 栅格模型（50 m）
最小值 最大值 平均值 标准差 最小值 最大值 平均值 标准差
商业区 0.08 0.89 0.44 0.21 0.00 0.76 0.37 0.18
城市住宅 0.09 0.60 0.38 0.12 0.00 0.59 0.36 0.11
公共交通 0.00 0.85 0.28 0.21 0.00 0.78 0.18 0.19
绿地 0.00 0.74 0.12 0.17 0.00 0.39 0.08 0.10

5 结论

（1）本文使用矢量计算模型和栅格计算模型对晋江市城市建筑迎风面积密度进行模拟,并从计算效率、不同尺度和不同用地类型上对结果进行对比分析,研究发现:矢量和栅格2种模型模拟得出的迎风面积密度空间分布特征整体上保持一致,与城市宏观建筑布局相符合。在城市尺度上,矢量和栅格2种模型的模拟结果数值差异较小,当研究区域尺度缩小时,差异随之增大。街区尺度上栅格模型计算结果的空间分布特征更符合实际及制图需要,模拟结果数值统计更稳定。当计算区域扩大时,2种模型的计算效率差别变得明显。城市尺度上矢量模型计算效率比栅格模型高很多。
（2）不同土地利用类型上的迎风面积密度存在较大差异。具体表现在商业区、城市住宅区上的迎风面积密度较大,其次是公共交通用地和工业区,而农村住宅区、绿地和水域上的迎风面积密度较小。2种模型对不同用地类型的适用性不同。在商业区和城市住宅区上,栅格模型的计算结果统计差异比矢量模型的小;在绿地上,2种模型得到的结果统计基本相同。
综上所述,计算效率上,矢量模型比栅格模型高。在城市尺度上,栅格模型与矢量模型模拟结果都与宏观建筑分布特征一致,二者皆适用;在街区尺度上,栅格模型模拟结果比矢量模型更符合建筑分布规律。在不同土地利用类型上,对于建筑分布稀疏区域2种模型皆适用;而对于建筑密集区,如商业区、城市住宅区等,栅格模型模拟结果更优。

The authors have declared that no competing interests exist.

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晋江市人民政府.中国晋江-晋江概况[ED/OL]. .

[ The government of Jinjiang. Jinjiang, China-General situation of Jinjiang [ED/OL]. ]

[15]
Macdonald R W, Griffiths R F, Hall D J.An improved method for the estimation of surface roughness of obstacle arrays[J]. Atmospheric Environment, 1998,32(11):1857-1864.The Lettau (1969, Journal of Applied Meteorology 8 , 828–832) relationship has been used for almost three decades by meteorologists and wind-tunnel engineers to estimate surface roughness from the geometry of regular arrays of roughness elements. However, Lettau’s relationship is limited to low roughness element densities. It does not account for the nonlinear decrease of the roughness length at high roughness densities, nor does it account for drag differences caused by different obstacle shapes or layouts. In this paper an improvement upon the Lettau relationship is derived starting from fundamental principles, based on certain assumptions about the velocity profile. The improved model includes the obstacle drag coefficient explicitly, and accounts for the peak in the z 0 versus area density curve.

DOI

[16]
Burian S J, Velugubantla S P, Brown M J.Morphological Analyses using 3D Building Databases: Salt Lake City, Utah[J]. Analyst, 2002,4(9):55-56.Abstract This report summarizes our calculations of building morphological characteristics for a 6.1 km2 area centered around the downtown of Salt Lake City, Utah. A three-dimensional building dataset, digital orthophotos, detailed land use/cover information, bald-earth topography, and roads were integrated and analyzed using a geographic information system (GIS). Building height characteristics (e.g., mean height, variance of height, height histograms) were determined for the entire study area and broken down by land use type using a dataset that contained 2891 buildings. Other parameters describing the urban morphology that were calculated include the building plan area fraction (位p), building area density (aP(z)), rooftop area density (ar(z)), frontal area index (位f), frontal area density (aF(z)), complete aspect ratio (位C), building surface area to plan area ratio (位B), and the height-to-width ratio (位S). Aerodynamic roughness length (zo) and zero-plane displacement height (zd) were calculated for the entire study area and for each land use type using standard morphometric equations and the computed urban morphological parameters. The urban morphological parameters were computed as a function of land use type, on spatial grids, and in some cases as a function of height above ground elevation. Building statistics were correlated to underlying land use using two different land use classification schemes: the seven USGS Anderson Level 2 urban land use types and a second scheme containing more detailed residential and commercial categories. Most of the morphometric parameters that we calculated were found to be similar to values computed for other cities by other researchers. The results indicate that the calculated urban morphological parameters are significantly different between different land use types. Significant differences were also noted between subcategories of residential land use. Moreover, commercial areas were found to have very different morphological characteristics as compared to other urban land use types, primarily because commercial areas have pockets of densely packed tall buildings. The findings presented herein are intended to be utilized in urban canopy parameterizations found in mesoscale meteorological and urban dispersions models.

DOI

[17]
张涛. 城市中心区风环境与空间形态耦合研究[D].南京:东南大学,2015.

[ Zhang T.A study on the coupling of the wind environment and urban spatial morphology in urban central area[D]. Nanjing: Southeast University, 2015. ]

[18]
丁宇萍,蒋球伟.地貌晕渲图的生成原理与实现[J].计算机应用与软件,2011,28(9):214-216.

[ Ding Y P, Jiang Q W.Principles and realization of Hill shading map generation[J]. Computer Applications and Software, 2011,28(9):214-216. ]

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