Analyze and Cluster OpenStreetMap Data

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prerequisites
basic Python • tables and their representation as CSV file • basic data science and machine learning
skills learned
convert map data to a graph representation • visualize graphs • compute and use common centrality metrics
Andras Nemeth
1 week · 6-8 hours per week · INTERMEDIATE
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In this liveProject, you’ll use LynxKite and graph data techniques to identify some of the most important geographic points in the city of Bruges. You’ll start by downloading and processing map data, and then use a simple Python program to convert it into a graph. You’ll use graph centrality metrics to quantify the importance of vertices in your graph, and determine some of Bruges’ important locations. You’ll then use the same structure to figure out the main areas of the city without using actual district data.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Andras Nemeth
András Németh is the CTO of Lynx Analytics, with significant experience (as the top technology executive on client projects) in using graphs in real-world consultancy projects. He also led the development of LynxKite, a Graph Data Science Platform in the past six years. He has an MsC in Software Engineering and an MsC in Mathematics. Before Lynx Analytics, he worked for Google where he was the tech lead on a project aiming to annotate web pages with entities from Google's Knowledge Graph.

prerequisites

This liveProject is for data scientists interested in the basic techniques of graph data science. This project is suitable for all levels of expertise, from beginners to experienced practitioners. To begin this liveProject, you will need to be familiar with:

  • Basic Python
  • Tables and their representation as CSV file
  • Basic data science and machine learning

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