This project was completed during the Lviv Data Science Summer School 2016 (http://cs.ucu.edu.ua/en/summerschool). The project supervisor - Dmytro Karamshuk. Geo-tagged social media datasets have recently emerged as an unprecedentedly rich source of information on the dynamics of modern cities. By providing fine-grain details about movements of million urban dwellers they can help us to understand the patterns of urban human mobility, reveal hidden patterns of spatial interactions between business activities, assist in understanding retail quality of locations and even help to plan better cities. In this project, students exploit a dataset of check-ins from Foursquare – a location-based social network – to analyze the real estate market in large cities. The goal was to use the information about geographic location of points of interests (POIs), the self-reported locations of Foursquare users and other open source datasets to devise predictors of real estate prices in a city. Several machine learning approaches to tackle this problem and exploit the land registry data for the city of London as a case study were considered during the project work.

1. Analysis of Real Estate Market
through the lenses of Geo-tagged Social
Media Data

2. Goals:
• House price class predictions (by regions)
• Important features selections
Inputs:
• Foursquare check-ins
https://figshare.com/articles/Foursquare_a
mp_Flickr_activities_in_20_cities/1584973
• House price information for London
http://landregistry.data.gov.uk/

3. Distribution of prices: Distribution of check-ins:
Analyzing data: Python
For preparing data : R/Python
Algorithm: xgboost, t-sne
Visualization: JavaScript
London map was divided by 75x75 meters grid.
Divided prices into 10 clusters (ignored errors when
region appeared in nearby cluster).
Used tools and approaches:

4. Final predicted map:
Prediction: Errors in prediction:
http://recommender.mazurik.me/

5. Feature selections
t-distributed stochastic neighbor embedding
feature importance
Accuracy (top 2 classes): 74%

6. Future work:
• Applying same model for other cities.
• Create recommendation where is better to build new pubs 
• Use people mobility as feature
• Predict price in future

7. project mentor:
Dr. Dmytro Karamshuk
Aleksey Mazurik
Vitaliy Radchenko
Igor Tymchuk
Vitalii Moshkivskyi
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