The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods.
Background: Dendrobii Officinalis Caulis (DC) is a well-known tonic herbal medicine worldwide and has favorable immunomodulatory activity. Various material specifications of DC are available in herbal markets, and DC is ingested by different edible methods. However, whether these specifications and edible methods are suitable or not remains unknown. Methods:In this study, we evaluated the suitability of four material specifications (fresh stem, dried stem, fengdou and powder) and three edible methods (making tea, soup and medicinal liquor) based on holistic polysaccharide marker (HPM), the major polysaccharide components in DC. First, the HPMs were extracted from the four specifications of DC by the three edible methods in different conditions. Second, qualitative and quantitative characterization of the extracted HPMs was performed using high performance gel permeation chromatography (HPGPC). Third, immunomodulatory activities of the extracted HPMs were evaluated in vivo. Results:The results showed that the HPMs were found to be quantitatively different from various specification of DC and edible methods. In vivo analysis indicated that the HPMs exerted positive effects on innate immune responses by increment in proliferation of splenocytes, secretion of IL-2 and cytotoxicity activity of NK cells. Moreover, the dosage amount of HPM should be defined as a certain range, but not the larger the better, for exerting strong immunological activities.Conclusion: According to the both chemical and biological results, fengdou by boiling with water for 4 h is the most recommended specification and edible method for DC.
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