Street networks in relation to landforms：Implications for fast-growing cities

In addition to socio-economic factors,major landforms may affect the city structure and urban form. Here we show that landforms have significant effects on the city shape and street patterns of the fast-growing Iranian cities of Dezful (a river) and Khorramabad (mountains and valleys),but no clear effects on the cities of Yazd and Nain. Also,where the street orientation is peaked,the Gibbs/Shannon entropy (a measure of dispersion or spread) is low,but increases as the distribution becomes more uniform because of landform constraints. The streets in the old inner parts of all the cities are,on average,shorter and denser (more streets per unit area) than the streets of the newer outer parts. The entropies of the outer parts are also greater than those of the inner parts,implying that the street-length distribution gradually becomes more dispersed or spread as the city expands. All these cities have been fast growing in the past decades,with the newer outer parts expanding rapidly. As shown here,the rapidly formed outer parts (with greater dispersion in street patterns) have significantly different textures from those of the older inner parts,indicating different functionality and growth processes. These quantitative methods for street-network analysis can be used worldwide,particularly for analysing the effects of landforms on city shape and texture.     

Exploring the relationship between street centrality and land use in Stockholm

This paper examines the relationship between different street centralities and land-use types in Stockholm. Major centrality measures of closeness, betweenness, and straightness are calculated at both global and local levels in both the primary and dual representations of the urban street network. Adaptive kernel density estimation is adopted to transform all unevenly distributed datasets to one continuous raster framework for further analysis. After computing statistical and spatial distribution of each centrality and land-use density map, we find that the density of each street centrality is highly correlated with one type of land use. Results imply that various centralities representing street properties from different aspects can capture the land development patterns of different land-use types by reflecting human activities, and are consequently important indicators to describe urban structure.     

基于街景影像多特征融合的广州市越秀区街道空间品质评估

全面准确地描述街景影像的多层次特征在基于街景影像对街道空间品质进行评估的研究中具有重要意义。以广州市越秀区为例,获取前后左右各视角街景影像中手工设计的特征（SURF特征、HOG特征）和基于深度学习的特征（语义特征）,基于单一特征和多特征融合采用支持向量机（Support Vector Machine, SVM）、随机森林（Random Forest, RF）训练各视角的评估模型。结果表明,以基于SVM建立的单特征模型为例,基于HOG特征（73.03%）、语义特征（72.28%）的模型平均精度优于SURF特征（56.00%）,基于SVM前后左右各视角模型的最优分类精度为82.8%（前）、81.7%（后）、76.6%（左）、76.6%（右）,而基于RF各视角模型的最优分类精度为82.8%（前）、85.0%（后）、78.1%（左）、70.3%（右）。前后视角的模型精度略优于左右视角。各视角最优模型均为多特征融合模型,最优模型平均分类精度和Kappa系数可达80.6%和0.62。利用街景影像评估街道空间品质时,各算法之间性能差异微弱,而特征选择及组合方式是提升精度的关键。越秀区街道空间品质存在明显空间分异,其西南部的街道空间品质亟待提升。本研究构建了基于街景影像多特征融合的大规模高精度街道空间品质测度方法,实现了对越秀区街道空间品质的评估,研究结果可为相关部门进行街道环境综合整治提供参考。     

城市街谷几何结构对街谷内流场及污染物浓度场影响的数值模拟

利用数值模拟方法研究了微尺度街道峡谷范围内街谷几何结构及街道两侧建筑物高度对称性对街谷内流场及机动车排放污染物扩散规律的影响。结果表明:当街道峡谷高宽比>2.1时,街谷内的流场结构由一个完整的垂直涡旋变为上下两个反向运动的强弱不同的垂直涡旋。各类型街谷内污染物扩散水平从强到弱依次为迎风面建筑物高度大于背风面建筑物高度的街道峡谷,迎风面建筑物高度小于背风面建筑物高度的街道峡谷;平行型街道峡谷。