Geospatial Data Analytics Essential Training

Course details

The amount of spatial data—information tagged to geographic coordinates—is vast, ranging from the GPS logs that mobile phones take to online maps and navigation apps and even going as far as the images recorded by satellites orbiting around Earth. This course aims to connect this widespread type of data to daily practice by giving a comprehensive overview of the geospatial data industry. Join instructor Milan Janosov for a practical, hands-on experience exploring, visualizing, and analyzing geospatial data using several Python-based data science tools.

Learning objectives
Learn the main concepts, the different types of tools, data sources, and use cases one may come across in the field of geospatial data science.
Understand the basics of how to transform geometric information into Python objects using Shapely.
Integrate these concepts with the pandas-based data structure of Python and conduct simple analytical and visualization tasks using GeoPandas and Matplotlib.
Discover one of the most popular open geospatial data sources, OpenStreetMap, and collect, explore, and visualize different types of data using OSMnx/OverPy.
Complete a more advanced exercise combining the tools learned in the previous sections.

Concepts

0. Introduction

01 - Analyzing geospatial data
02 - What you should know

1. Theoretical Foundations

03 - What is geospatial data
04 - What is location intelligence
05 - Geospatial data sources
06 - Geospatial data use cases

2. Geometries and GeoDataFrames

07 - The basics of GeoPandas
08 - Onboarding to GeoPandas
09 - What are geometries
10 - Basic geometries
11 - Geometry operations

3. GeoPandas Deep Dive

12 - Creating your first GeoDataFrame from scratch
13 - Simple functions and computations
14 - Visualizing synthetic data with GeoPandas
15 - Visualizing sample data with GeoPandas
16 - Map projections

4. Explorative Spatial Data Analysis

17 - Acquire open geospatial data about New York City
18 - Explore the administrative boundaries of the NYC neighborhoods
19 - Combine and compare spatial datasets
20 - Enrich administrative boundaries using population information
21 - Computing local statistics
22 - Turning tabular data into geospatial
23 - Urban greenery assessment

Conclusion

24 - Continuing on with geospatial data science

Geospatial Data Analytics Essential Training

Authors

Milan Janosov, Ph.D.