Udemy – Urban Analytics with Python 2024-12

Udemy – Urban Analytics with Python 2024-12 Downloadly IRSpace

Apr 18, 2025 - 17:51 Updated: Apr 21, 2025 - 17:36

0 0

Udemy – Urban Analytics with Python 2024-12

Urban Analytics with Python course. This course starts with an introduction to spatial data, including the difference between vector and raster data, while also providing a foundation for using OSM as a robust data source. As the course progresses, we guide you through setting up your Python environment and introduce essential spatial libraries such as GeoPandas and Shapely. You will start coding right away and work with geometric data types and spatial data structures. After getting familiar with the basics of Python and spatial concepts, we will focus on acquiring different types of urban data from OSM using powerful Python packages such as OSMNx and OverPy. You will learn how to collect and work with point, polygon, and graph data, from building footprints to road networks. Each step will include Python coding to ensure that you gain the technical skills needed to handle real-world spatial data tasks. Finally, you will be introduced to advanced urban analysis techniques. You will participate in practical projects, analyze road networks, build profiles, and create visualizations to explore urban areas. The course ends with a comprehensive mini-project in which you will apply all the techniques you have learned to create a liveability index for a city by combining different urban KPIs using Python.

What you will learn in this course:

Learn the basics of spatial data science, especially how to recognize and manipulate vector data in Python.
How to collect and store different types of vector data from OpenStreetMap using customizable automated methods in Python
How to use spatial analysis to quantify relevant urban features and characteristics from vector data
How to integrate and quantify different types of vector data from OpenStreetMap to extract liveability analyses of urban areas

This course is suitable for people who:

Data scientists interested in expanding their skills into the growing field of spatial and urban analytics.
GIS analysts and urban planners interested in using thematic data mining tools.