| Teams |
Chen Zhiqiang, Jin Tao, Zhang Haowen,
Xu Guanhua, Lu Yanlin, Wang Jiangrui, Li Kang.
| Tutors |
Zhong Ximing, Li Kun
| Abstract |
This group project takes the central city of Xi’an as the research object.
We try to perform the real-time land price of residential sample sites by inverting the big data of house price; identify the spatial pattern of residential land price through multiple methods of cross-checking, so as to determine the more suitable floor area ratio of a certain plot; and analyze the influencing factors of residential land price from qualitative and quantitative perspectives, and adjust the proportional weights among different factors based on them.
| research |
This research project is a series of outcomes from the AI architecture course launched by Archiford and Xi’an University of Architecture and Technology. The course aims to use deep learning methods to design and advance architectural design solutions.
The problem of this project is to take the central city of Xi’an as the research object and try to invert the real-time land price of residential sample points by using the big data of house price.
The spatial pattern of residential land price is identified by cross-checking various methods to determine the appropriate plot ratio for a certain site, and the factors influencing residential land price are analyzed from both qualitative and quantitative perspectives, and the weighting of the different factors is adjusted based on this.
| Methods and Evaluations |
1 Call Gaode Map APl to get the poi geographic coordinates of Xi’an neighborhood. And sieve 5 data from each district and county by GH. There are 12 districts and counties in total, so there are 60 sets of data.
2. Call Gaode Map API to get the poi geographic coordinates of Xi’an supporting facilities (parks).
3. Deploy the coordinate points into GH to determine how many supporting facilities are within 3000 meters of the district.