Feature Engineering for Spatial Data Analysis

09:00-17:00, January 25

Workshop / Overview

In this workshop, participants will be able to understand what are the types of data processing they can do in order to extract better knowledge out of their spatial data.

We will introduce tools for visualizing, extracting features and feature engineering and explain the logic and steps needed in order to create them. The techniques will be presented in the context of a simple machine learning model for predicting taxi tariffs and illustrate how we can improve it with these new features.

Participants will be able to learn:

  • Visualise spatial data for exploratory data analysis
  • Calculate distances
  • Extract routes, route distances and estimated durations
  • Extract city features based on open source tools, e.g. Open Street Maps
  • Data processing performance
  • Spatial indices in Python
  • Ideas for traffic estimation
  • Approaches and potentials for GPS analysis and how they can help you in terms of feature engineering.

We will utilise cloud-based Jupyter notebooks and participants will be able to take the code with them so they can apply for their next project.

Workshop / Outcome

Participants are expected to learn the main techniques on spatial data analysis and be able to use the tools presented on the workshop for their next projects.

Workshop / Difficulty

Beginner level

Workshop / Prerequisites

  • Basic to intermediate knowledge in Python
  • Desired - Docker installed
  • Own laptop

Track / Co-organizers

Caio Miyashiro

Data Scientist, Free Now

Selim Onat

Data Scientist, Free Now

Eva Jaumann

Data Scientist, Free Now

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