Geospatial Data Science With Python & R Training Course

Introduction:

Geospatial data science is at the heart of understanding spatial patterns, making informed decisions, and solving critical challenges in fields such as urban planning, environmental monitoring, transportation, and more. By leveraging advanced data science techniques with geospatial data, professionals can unlock powerful insights and drive innovations. This Geospatial Data Science with Python & R training course is designed to provide participants with hands-on experience in utilizing Python and R to analyze, visualize, and model spatial data.

Through practical applications, participants will gain the skills required to manipulate geospatial data, perform advanced spatial analysis, and create compelling visualizations to communicate findings. This course combines the best of both Python and R—two of the most powerful programming languages in the data science community—empowering professionals to excel in geospatial data science.

Target Audience:

This course is ideal for:

• Geospatial Analysts and GIS Professionals seeking to expand their skillset with data science tools.
• Data Scientists interested in learning how to integrate spatial data into their analyses.
• Environmental Scientists, Urban Planners, and Researchers looking to apply data science techniques to geospatial datasets.
• Software Engineers and Developers who want to build applications for geospatial data analysis.
• Anyone who is eager to learn about geospatial data science and enhance their technical proficiency in Python and R.

Course Objectives:

By the end of this course, participants will be able to:

• Master Geospatial Data Handling using Python and R: Learn how to load, process, and clean geospatial data in both Python (using libraries like GeoPandas and Shapely) and R (using libraries such as sf and raster).
• Perform Spatial Analysis: Understand key spatial analysis techniques such as buffering, overlay analysis, spatial interpolation, and more using Python and R.
• Create Geospatial Visualizations: Develop effective and insightful geospatial visualizations using Python libraries (e.g., Matplotlib, Folium) and R packages (e.g., ggplot2, tmap).
• Apply Data Science Techniques to Geospatial Data: Use machine learning algorithms and statistical models to analyze and predict trends within geospatial datasets.
• Understand Geospatial Statistics: Learn to perform spatial statistics using Python and R to identify patterns, clusters, and anomalies in geospatial data.
• Work with Big Geospatial Data: Gain the skills to handle large geospatial datasets, perform spatial operations efficiently, and manage geospatial databases.
• Integrate GIS with Data Science: Learn how to integrate GIS tools and data with Python and R for comprehensive spatial data analysis.
• Use Remote Sensing Data: Analyze remote sensing data (e.g., satellite imagery) using Python and R to extract valuable insights for environmental monitoring and more.
• Implement Geospatial Machine Learning: Develop models for spatial prediction and classification, leveraging machine learning techniques in Python and R.
• Apply Knowledge to Real-World Problems: Work on practical geospatial data science projects and case studies across industries like urban development, agriculture, and disaster management.

By integrating both Python and R, this course provides a holistic view of geospatial data science, making it an essential resource for anyone looking to enhance their skills in spatial data analysis and visualization.

This Geospatial Data Science with Python & R training course is perfect for professionals seeking to build a strong foundation in geospatial data science, enabling them to make data-driven decisions that positively impact their organization and the environment.

Duration

10 Days

Course content

Module 1: Introduction to Geospatial Data Science

• Overview of Geospatial Data Science
• Key Concepts: Spatial Data, Coordinate Systems, and Projections
• Introduction to Python and R for Geospatial Data Analysis
• Understanding GIS Data Types: Raster vs. Vector
• Setting up Python and R environments for Geospatial Analysis

Module 2: Working with Geospatial Data in Python

• Introduction to GeoPandas and Shapely
• Loading, Cleaning, and Preprocessing Spatial Data in Python
• Basic Spatial Operations: Buffer, Intersects, and Union
• Data Wrangling with GeoPandas for Analysis
• Practical Exercise: Importing and Visualizing Shapefiles

Module 3: Working with Geospatial Data in R

• Introduction to sf, raster, and sp Packages
• Loading and Cleaning Geospatial Data in R
• Coordinate Reference Systems and Transformations in R
• Working with R’s Spatial Data Structures: Raster, Polygon, Points
• Practical Exercise: Visualizing and Manipulating Spatial Data in R

Module 4: Geospatial Visualization in Python

• Visualizing Geospatial Data with Matplotlib and Folium
• Plotting Maps and Layers in Python
• Creating Interactive Maps with Folium
• Visualizing Geospatial Patterns and Trends
• Practical Exercise: Creating Custom Map Visualizations in Python

Module 5: Geospatial Visualization in R

• Introduction to ggplot2 for Geospatial Data Visualization
• Creating Static Maps with ggplot2 and tmap
• Interactive Mapping with leaflet in R
• Advanced Mapping Techniques in R
• Practical Exercise: Building Custom Geospatial Visualizations in R

Module 6: Spatial Analysis Techniques in Python

• Basic Spatial Analysis: Buffer, Intersection, and Union
• Advanced Spatial Analysis: Overlay, Clustering, and Spatial Autocorrelation
• Distance Calculations and Proximity Analysis
• Handling Geospatial Data with Large Datasets
• Practical Exercise: Spatial Analysis of Real-World Data

Module 7: Spatial Analysis Techniques in R

• Introduction to Spatial Statistics in R
• Spatial Interpolation and Kriging with R
• Analyzing Spatial Patterns and Clusters in R
• Conducting Hotspot Analysis with R
• Practical Exercise: Spatial Analysis with R’s sp and sf Packages

Module 8: Geospatial Data Science and Machine Learning in Python

• Introduction to Machine Learning with Geospatial Data
• Using Scikit-learn for Spatial Data Classification
• Spatial Regression Models in Python
• Predictive Modeling with Geospatial Data
• Practical Exercise: Geospatial Data Classification and Prediction

Module 9: Geospatial Data Science and Machine Learning in R

• Machine Learning for Geospatial Data in R
• Using caret and randomForest for Geospatial Data Classification
• Spatial Regression Models and Feature Selection
• Integrating Remote Sensing Data in Geospatial Machine Learning
• Practical Exercise: Applying Machine Learning to Geospatial Problems

Module 10: Working with Remote Sensing Data in Python

• Introduction to Remote Sensing and Satellite Imagery
• Processing Raster Data with rasterio and GDAL in Python
• Remote Sensing Image Analysis: NDVI, Classification, and Change Detection
• Visualizing Remote Sensing Data in Python
• Practical Exercise: Analyzing Satellite Imagery for Land Use/Land Cover Classification

Module 11: Working with Remote Sensing Data in R

• Remote Sensing Data in R with raster and rgdal Packages
• Performing Image Classification and NDVI Analysis in R
• Change Detection Techniques for Remote Sensing Data
• Combining Remote Sensing and Ground Truth Data for Analysis
• Practical Exercise: Remote Sensing Applications in Environmental Monitoring

Module 12: Geospatial Statistics and Spatial Econometrics in Python

• Introduction to Spatial Statistics in Python
• Measuring Spatial Autocorrelation: Moran’s I, Geary’s C
• Spatial Econometrics: Analyzing Economic and Social Data with Spatial Elements
• Conducting Spatial Regression Analysis in Python
• Practical Exercise: Applying Spatial Statistics to Geospatial Data

Module 13: Geospatial Statistics and Spatial Econometrics in R

• Introduction to Spatial Data Analysis with spdep and spatialreg Packages
• Spatial Autocorrelation and Moran’s I in R
• Conducting Spatial Econometrics with R
• Building and Interpreting Spatial Regression Models in R
• Practical Exercise: Spatial Econometrics and Statistical Modeling in R

Module 14: Handling Big Geospatial Data in Python

• Techniques for Managing Large Geospatial Datasets
• Optimizing Geospatial Data with Python’s geopandas and dask
• Geospatial Data Querying with Databases (PostGIS, SQLite)
• Distributed Geospatial Processing with Apache Spark
• Practical Exercise: Working with Big Geospatial Data in Python

Module 15: Integrating Geospatial Data Science in Python & R

• Combining Python and R for Advanced Geospatial Data Science
• Leveraging Data Science Pipelines for Geospatial Analysis
• Integrating Geospatial Data with Web and Cloud Platforms (ArcGIS Online, Google Earth Engine)
• Case Study: End-to-End Geospatial Data Science Projects
• Final Project: Applying Geospatial Data Science to a Real-World Scenario

This Geospatial Data Science with Python & R course provides a comprehensive guide for learners to effectively manipulate, analyze, visualize, and apply geospatial data science techniques using Python and R. Each module is designed to build upon the previous one, ensuring that participants gain a complete understanding of geospatial analysis from a data science perspective.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.