Quantifying Of Urban Scene Perceptions And Spatio-temporal Analysis Of Human Activities

With the rapid development of urbanization,tough attention has been paid to urban livability.Street plays an important role in physical activity and urban life,which have a particularly profound influence on the experience of physical activity and health.Understanding and quantifying the human perceptions of locale environment have long been of hot to a wide variety of fields.Previous studies are subject to limited samples and inefficient means of data processing,which is hard to measure human perceptions of locale environment for a large-scale urban area.Currently,some new ideas for human scene perceptions come into being with the increasing availability of street view and cutting-edge computer vision.Based on Baidu Street View image covering the Shenzhen city,the classification of 12 road scene categories is achieved under the help of SegNet that is a semantics segmentation framework,such as sky,building,tree and so on.Subsequently,the diversity theory of Landscape Ecology is employed to measure the variety of street scene,namely,Richness Index,Shannon-Weaver Index and Simpson Index for street view.Besides,Green View Index,the Openness and Enclose of vision are proposed to illustrate visual quality of street space.In addition,the motorization of street,the radio of car,the radio of pedestrian and bike are taken as the representative elements.Finally,diversity of street's function,density of typical POI and road density are extracted from Points of Interest(POI)and road networks provided by Open Street Map(OSM).Consequently,a model for scene perceptions is provided on the basis of four indicators mentioned above,aiming to quantify human perceptions for street scene.In the meanwhile,the spatio-temporal distribution of human activities in Shenzhen is mapping by integrating Tencent user density over two weeks.Furthermore,in consideration of spatial and temporal non-stationarity of data,Geographically and Temporally Weighted Regression(GTWR)is used to explore the relationships between human perceptions for street scene and the spatio-temporal distribution characteristics of urban population.The results show that the model of scene perceptions could partly quantify human perceptions for street scene,and the spatio-temporal distribution of urban population is influenced by various factors that are heterogeneous over space and time.Compared with traditional methods of employing the proportion of segmentation elements,this study arms researchers with more objective measures of human perceptions for street scene,assisting researchers with understanding the underlying urban structure and revealing the impacts of urban function.