Analyze and Cluster OpenStreetMap Data

This project is part of the Hands-on Graph Data Science with LynxKite bundle.
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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liveProject This project is part of the Hands-on Graph Data Science with LynxKite bundle. liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save: $10 (33%)
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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you’ll learn the basics of graph data structures and how to define relationships between data.

  • Convert map data to a graph representation
  • Visualize graphs
  • Compute and use common centrality metrics
  • Simple graph clustering techniques

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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