https://github.com/luisalopresti/insideairbnb_trentino

This repository stores the project developed for the course “Geospatial Analysis and Representation for Data Science”, concerning the spatial analysis of AirBnB and PoI in the Autonomous Province of Trento.
https://github.com/luisalopresti/insideairbnb_trentino

Last synced: 13 days ago
JSON representation

This repository stores the project developed for the course “Geospatial Analysis and Representation for Data Science”, concerning the spatial analysis of AirBnB and PoI in the Autonomous Province of Trento.

• Host: GitHub
• URL: https://github.com/luisalopresti/insideairbnb_trentino
• Owner: luisalopresti
• Created: 2022-01-15T17:21:38.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-01-23T17:03:29.000Z (almost 3 years ago)
• Last Synced: 2024-02-12T15:16:57.782Z (11 months ago)
• Language: Jupyter Notebook
• Size: 6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Geospatial Analysis and Representation for Data Science

This repository stores the project developed for the course “Geospatial Analysis and Representation for Data Science” (University of Trento, 2021/2022).

## Task

The main purpose of this project is to study AirBnB in the province of Trento and how they distribute on the territory in relation to the main Points of Interest (PoI). The analysis has been developed according to the following steps:

1.	Identify AirBnB and PoI in the Province of Trento

2.	Highlight the areas of greatest concentration of AirBnB and of PoI

3.	Evaluate which are the cheapest AirBnB structures and define the services and touristic attractions nearby 

4.	Draw a trajectory leading from the cheapest structure to the desired touristic attraction

5.	Consider position, price, time, and desired points to visit, and draw the optimal route

6.	Verify if there exists a relation between location and AirBnB price

All the analysis and the comments can be found in the Notebooks collected in this repository.

Steps from 1 to 5 are contained in the `InsideAirBnB.ipynb` notebook, while the last point can be found inside `SpatialStatisticalAnalysis.ipynb`.

Note: this project has been developed using Colab Notebook, thus some cells are useful only in this setting.

## Data

For this project, the following data sources have been used:

-	[InsideAirBnB](http://insideairbnb.com/get-the-data.html), for the AirBnB data of Trentino 

-	[Open Data Trentino](https://dati.trentino.it/dataset/punti-di-interesse-del-trentino), for the data concerning the main points of interests in the Province 

## Main references 

-	[Course notes](https://napo.github.io/geospatial_course_unitn/)

-	[Osmnx examples](https://github.com/gboeing/osmnx-examples)

-	[Geographic Data Science with Python](https://geographicdata.science/book/intro.html)

-	[Travelling Salesperson Problem](https://mlrose.readthedocs.io/en/stable/source/tutorial2.html)