基于空间句法和LBS大数据的合肥市人口分布空间格局研究

针对目前城市人口分布研究现状,在传统的空间句法模型基础上,将LBS大数据技术有机引入到城市人口分布研究中,从而构建了理论分布和实际分布有机结合的城市人口分布研究新思路。合肥市中心城区的案例研究显示：空间句法模型与LBS大数据分析所得到的人口集聚区域在空间上并不完全一致;根据空间句法模型和LBS大数据分析结果的综合对比,合肥市中心城区被划分为高密度、中密度和低密度3类人口分区,同时提出了不同密度分区的人口分布发展建议。研究表明,LBS大数据的适时、动态特点能弥补传统数据的不足,其与空间句法模型有机结合将能为城市人口分布研究提供更加精确高效的工具与方法。

Space Pattern of Urban Population Distribution Based on Space Syntax and LBS Big Data

The space pattern of urban population distribution is a classical research topic of urban science and urban planning. In terms of the current research situation of urban population distribution, the LBS big data technology which is considered as a new method and tool to observe the urban spatial and temporal characteristics is introduced into the research of urban population distribution based on the traditional space syntax model. Then, a new idea of urban population distribution research with the integration of theoretical distribution and actual distribution is established. The case study in the central urban area of Hefei City shows that: the spatial clustering areas obtained respectively by space syntax model and LBS big data analyses are different in space. According to the comprehensive comparison of space syntax model and LBS big data analysis, the central urban area of Hefei City is divided into 3 types of population distribution including high density, medium density and low density. The high density zoning consists of the old town, Shushan district and Baohe district. The medium density zoning includes Binhu district, Luyang district and High-tech area. Meanwhile, the low density zoning consists of economic developing area and Yaohai district. Finally, the suggestions of population distribution development in different density partitions are proposed. The research shows that the timely and dynamic characteristics of LBS big data can make up for the shortcomings of traditional data and greatly broaden the source and timeliness of basic data. Obviously, this will enhance the accuracy of the study. And, more importantly, it will provide more accurate and efficient tools and methods combined with the classical space syntax model for the study of urban population distribution. In addition, it is hoped that this research can make some exploration and reference for expanding the practical application field of LBS big data.