DATA ANALYTICS · PROJECT 02
Project 17 · Geospatial analysis
Geospatial Airbnb Analysis in London
Geospatial analysis of Airbnb prices in London using distance to the city centre, neighbourhoods and geographic neighbour metrics.
CONTEXT AND OBJECTIVE
Analyse how Airbnb listing prices in London vary with location, distance to the city centre and the context of nearby comparable listings.
DATA AND DIAGNOSIS
- Segmentation of each listing as cheap, stable or expensive within its own room type using the 25th and 75th percentiles.
- Calculation of geographic distance to Charing Cross and comparison of prices by neighbourhood and distance bands from the city centre.
- Construction of neighbourhood variables with KNN (K-Nearest Neighbors / nearest neighbours) to measure median nearby price and the local share of expensive comparable listings.
- Mapping of price, distance and local context to identify spatial concentrations and territorial patterns.
DELIVERY AND LEARNING
The analysis combines relative price segmentation, distance to the city centre and neighbourhood metrics to build a spatial reading of the market. The maps make it possible to compare neighbourhoods, detect areas with higher concentrations of expensive listings and understand how each property behaves relative to nearby listings of the same type.