Analyses on the Spatial Distribution Characteristics of Urban Rental Housing Supply and Demand Hotspots Based on Social Media Data

摘要

With the development of big data and the popularity of intelligent positioning systems, new opportunities for urban research and planning are brought about. Unlike traditional data acquisition, Sina Weibo check-in data, a type of social media data, its acquisition method focuses on locationbased network and big data mining. By crawling to get checkin data from Sina Weibo and Wuhan rental housing information, the paper processes and analyzes them statistically. The kernel density analysis is used to analyze the spatial distribution of hotspots, and the results are visualized to circle demand and supply hot spots in Wuhan. Then the price trend surface model is used to show the spatial change of rental housing price in the central city. This article creatively analyzes the characteristics of the rental housing crowd reflected by social media data and the spatial distribution of housing rents in Wuhan. The consistency and inconsistency shown in it can provide reference for the government to monitor the real estate rental market.