见物见人——时空大数据支持下的存量规划方法论

City Sensing:An Inventory Planning Tool Based on Spatial-temporal Big Data

相比于传统的增量规划,存量规划中主要在产权本质、时间逻辑和空间处理尺度上有着本质的不同。因此,存量规划对现有用地现状和性质的精准刻画提出了更高的要求。作为当前传统的用地现状分析图在存量规划中存在着地块特征刻画精度有限与用地分类维度过低等不足。对用地类型的混合、同种用地类型的规模、同一地块的时间属性等用地特征,传统的现状分析图也难以进行描述。随着互联网LBS(Location-Based Service)服务的发展,越来越多LBS时空数据因其巨大的用户基数和完善的时空地理信息,受到规划师的关注。这些新的时空地理数据使得对用地功能和人口活动特征的详细刻画成为可能。使用互联网某LBS平台人口分时活动密度数据,叠加百度POI(Place of Interest),通过非监督分类和非负矩阵分解的方法,分别对北京市六环内的地块尺度、500 m网格尺度和25 m点阵尺度进行用地功能的识别与分类。通过多维度分类结果的叠加,对研究区域的用地功能、人口时空活动特征进行深入刻画,探讨通过大数据进一步辅助存量规划的用地功能研究方法。

Compared to the traditional incremental planning in China,inventory planning is different in the perspective of property,time and space.Thus,a higher demand for the depiction of existing space is needed in the inventory planning process.However,the depiction method used in incremental planning such as land use analysis map cannot fulfill this demand.With the prevalence of internet LBS（Location-Based Service） data,planners see a new opportunity to make a more detailed depiction of the existing space.This paper intends to use an LBS data of population density by hour,together with POI（Place of Interest） from Baidu.With the help of unsupervised learning algorithm,a detailed depiction of land use and population activity pattern will be presented,showing more opportunities for big data analysis in the current urban planning research.