Spatial Analysis and Geospatial Data Science With Python

Name: Spatial Analysis and Geospatial Data Science With Python
Rating: 4.3 (507 reviews)

Learn how to process and visualize geospatial data and perform spatial analysis using Python.

Created byAbdishakur Awil Hassan

Last updated 7/2020

English

English [Auto],

What you'll learn

The course introduces you to the most essential Geopython Libraries
Learn how to visualize Geospatial data in Python (static and interactive maps)
Perform Spatial Data analysis with Python
Learn how to pre-process geospatial data.
Learn the essentials of Geopandas Library, the workhorse of Geospatial data science in Python.
Perform Geocoding and Reverse Geocoding using free Open source Solutions.
Unleash the power of Locational analytics in Data science.
Reinforce your knowledge with Geospatial data science Excercises and Projects.

Course content

6 sections • 36 lectures • 2h 12m total length

1.1 Course Intro2:30
1.2 Introduction1:27
Download Data sources and Notebooks for this Module
1.3 - Jupyter Noteobok5:35
1.4 Introduction to Python6:31
1.5 Pandas Essentials - Part 14:42
Master the essentials of the Pandas library, including series and data frames, vectorized operations, data ingestion with read_csv and read_html, and descriptive statistics with info and describe.
1.6 Pandas Essentials - Part 24:59

2.1 Introduction1:43
2.2 - Reading Spatial Data2:26
Learn to read geospatial data with geopandas by loading formats like shapefiles and GeoJSON using read_file, inspect a data frame with a geometry column, and plot maps with geopandas.
2.3 - Read CSV File4:24
Read Subset data3:31
Learn to read a subset of geospatial data with pandas and geopandas by using geometry masks and row limits, filtering conflicts in Africa and inspecting the coordinate reference system.
Geodataframe & Geoseries2:56
Differentiate geo data frames and geo series in Geopandas with clear examples, showing how a geometry column defines a geo data frame or geo series, and how to plot.
Coordinate Reference System (CRS)4:40
2.7 - Assignment5:50
Practice spatial data analysis by reading shapefiles with geopandas, converting csv to a geo data frame with pandas, plotting with geopandas and matplotlib, and reprojecting to a crs before exporting.

3.1 - Introduction1:39
Explore spatial analysis in Python by performing spatial joins between latitude and longitude points and neighborhood boundaries. Apply buffer and overlay analyses, and conduct nearest neighborhood analysis to locate services.
3.2 - Spatial Join5:40
3.3 - Buffer Analysis3:57
3.4 - Overlay Analysis3:47
3.5 - Nearest Neighbourhood Analysis5:35
3.6 - Assignment6:41
Practice spatial operations with geopandas: load and plot datasets, index to Jagdalpur, apply buffer, reproject to crs epsg 2272, perform a spatial join, count incidents per neighborhood, export to geojson.

6.1 Introduction1:31
6.2 - Geocoding3:17
6.3 - Reverse Geocoding3:13
Convert coordinates to readable addresses using reverse geocoding with OpenStreetMap. Apply to a pandas DataFrame, plot results, and manage processing time with a progress bar.
6.4 - Retrieve Openstreetmap data3:53
Learn how to retrieve OpenStreetMap data in Python using the osmnx package. Download street networks and building footprints for a city like Liverpool, convert to geopandas, and visualize with folium.
6.5 - Tips to speed up Geospatial processing4:54

Requirements

Basic Understanding of Python
No GIS knowledge is required. We will give breif theoretical explanation.
You’ll need to install Anaconda and GeoPython libraries. We will provide a Guide on installation and Jupyter Notebooks

Description

Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. It is beyond creating maps and merely focusing on where things happen but instead incorporates spatial analysis and insights derived from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get introduced with Geopandas, the workhorse of Geospatial data science Python libraries.

The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science. We will be learning how to read spatial data effectively, manipulate and process spatial data, and carry out spatial operations. A large portion of the course deals with spatial operations like Buffer analysis, Spatial joins and Nearest Neighbourhood analysis. Each video contains a brief overview of the topic and a walkthrough with code examples. We conclude each section Geospatial data science assignment and project, that will help you learn more effectively.

We will also cover spatial data visualization using both Geopandasa and other interactive libraries like Folium, IpyLeaflet and Plotly Express. We cover how to make stunning Geo visualization for the most widely used map types.

The final section covers some advance features including Geocoding, reverse geocoding, accessing OpenStreetMap data in Python and some advanced tips and tricks to process large Geospatial datasets.

At the end of this course, you will be able to perform most of Geospatial data science operations in Python and also build a strong foundational knowledge in Geospatial Python.

Who this course is for:

Students who want to learn Python for Geospatial Data Science.
Students who like to take their first steps in the Geospatial data science career.
Python users who are interested in Spatial Data Science.
GIS users who are new to python and Jupyter notebooks for Geographic data analysis.

Spatial Analysis and Geospatial Data Science With Python

What you'll learn

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Course content

Introduction6 lectures • 26min

Introduction to Geopandas7 lectures • 26min

Spatial Operations6 lectures • 27min

Geospatial Data visualization (Geopandas)6 lectures • 16min

Interactive Geospatial Data visualization6 lectures • 22min

Advanced Operations5 lectures • 17min

Requirements

Description

Who this course is for: