Abstract. With the fast expansion and controversial impacts of short-term rental platforms such as Airbnb, many cities have called for regulating this new business model. This research aims to establish an approach to understand the impact of Airbnb (and similar services) through big data analysis and provide insights potentially useful for its regulation. The paper reveals how Airbnb is influencing Beijing’s neighbourhood housing prices through machine learning and GIS. Machine learning models are developed to analyse the relationship between Airbnb activities in a neighbourhood and prevailing housing prices. The model of the best fit is then used to analyse the neighbourhood price sensitivity in view of increasing Airbnb activities. The results show that the sensitivity is variable: there are neighbourhoods that are likely to be more price sensitive to Airbnb activities, but also neighbourhoods that are likely to be price robust. Finally, the paper gives policy recommendations for regulating short-term rental businesses based on neighbourhood’s price sensitivity.
In recent years, the successful implementation of human genome project has made people
realize that genetic, environmental and lifestyle factors should be combined together to study cancer due
to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big
data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper,
we will introduce the application of machine learning methods in handling cancer big data including the
use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.
Big Data Analysis Using Modern Statistical and Machine Learning Methods in Medicine