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This repo contains the code to work with the Hierarchical Point-Of-Interest Kernels.

Requirements

  • Install a Python Virtual Environment using python>=3.3. Then install the required packages from the provided requirements.txt
pip install -r requirements.txt
  • To run the experiments you will need HPK. We implemented HPK as an additional kernel in Kelp, a java machine learning framework focusing on kernel machines for structured data. HPK can be installed with the provided kelp-classify.jar file.

You will also need `GeoL, The Geospatial Library developed for running generic data cleaning and pre-processing tasks when using geographic data with Machine Learning.

Project Structure

The project is organised as follows:

  • notebooks = This folder contains the notebooks used to prepare and run the experiments. There are three notebooks: two for pre-processing respectively the Urban Atlas and Foursquare datasets, and a third one to run the experiments which should be run last.

  • scripts = Simple Python executables to perform generic operations (e.g. prepare the folder structure etc...)

  • data = Test and Train datasets available for all the analysed cities.

A note on the datasets:

Due to legal constrains, we could not provide the data as they are. Both Urban Atlas and Foursquare require in fact a registration and forbid a straightforward re-use.

  • Urban Atlas can be freely downloaded through its portal.
  • Foursquare data can be retrieved using GeoL's crawler. It requires an appropriate API key.