Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China.

ABSTRACT: Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in sp

SUBMITTER: Wu C 

PROVIDER: S-EPMC5082690 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature