Module 1: R Fundamentals for Geospatial Professionals

• Introduction to R and RStudio environment.
• R syntax: Variables, data types (vectors, lists, data frames).
• Control flow: Conditionals (if/else), loops (for/while).
• Functions: Defining and calling functions, scope.
• Packages: Installation, loading, and usage of R packages.
• Practical session: Writing basic R scripts for data manipulation and performing simple calculations.

Module 2: Working with Vector Data using 'sf'

• Introduction to vector data models: Points, lines, polygons.
• Understanding 'sf' (Simple Features) package for vector data.
• Reading and writing common vector formats (Shapefile, GeoJSON, KML).
• Basic vector operations: Selection, filtering, projection.
• Performing spatial joins and overlays with 'sf'.
• Practical session: Loading a shapefile into an 'sf' object, performing a spatial query, and saving the result to a new file.

Module 3: Working with Raster Data using 'terra' and 'raster'

• Introduction to raster data models: Pixels, grids, bands.
• Understanding 'terra' package for raster data processing.
• Reading and writing common raster formats (GeoTIFF, NetCDF).
• Basic raster operations: Extracting pixel values, reclassification.
• Reprojecting and resampling raster data.
• Practical session: Opening a multi-band satellite image with 'terra', extracting band values, and creating a simple image composite.

Module 4: Spatial Analysis with R

• Performing common spatial analysis operations: Buffering, union, intersection.
• Distance calculations and proximity analysis.
• Zonal statistics for raster data.
• Basic interpolation techniques (e.g., Inverse Distance Weighting).
• Introduction to spatial statistics concepts in R.
• Practical session: Performing a buffer analysis on a point layer and calculating zonal statistics for a raster dataset.

Module 5: Geospatial Data Visualization with 'ggplot2' and 'tmap'

• Creating static maps with 'ggplot2' and 'sf'.
• Customizing map aesthetics: Colors, legends, titles, labels.
• Generating interactive web maps with 'tmap' and 'leaflet'.
• Adding different base layers and overlaying vector/raster data.
• Creating animated maps and small multiples for temporal data.
• Practical session: Creating a static map of vector data and then converting it into an interactive web map using 'tmap' or 'leaflet'.

Module 6: Introduction to R for Remote Sensing Data Processing

• Overview of remote sensing data types and applications in R.
• Reading and manipulating multi-spectral imagery.
• Calculating and analyzing spectral indices (e.g., NDVI, NDWI).
• Radiometric and atmospheric corrections using R.
• Image composites and enhancements.
• Practical session: Loading a multi-spectral image, calculating NDVI, and visualizing the result.

Module 7: Image Classification in R

• Supervised image classification techniques: Random Forest, Support Vector Machines, K-Nearest Neighbors.
• Preparing training data for classification.
• Performing land cover classification using 'caret' and 'RStoolbox' packages.
• Unsupervised classification: K-Means clustering.
• Accuracy assessment of classification results (confusion matrix, Kappa coefficient).
• Practical session: Performing a supervised land cover classification on a satellite image using a popular R package.

Module 8: Time Series Analysis of Satellite Imagery with R

• Accessing and preparing multi-temporal satellite imagery for time series analysis.
• Calculating and analyzing time series of spectral indices.
• Detecting trends, seasonality, and breakpoints in time series data using 'tsibble' or 'forecast' packages.
• Applications in environmental monitoring and change detection.
• Visualizing time series data for insights.
• Practical session: Analyzing a time series of NDVI values for a specific location to identify changes or trends using R.

Module 9: Geospatial Statistics and Modeling in R

• Introduction to spatial autocorrelation (Moran's I).
• Spatial regression models (e.g., geographically weighted regression).
• Point pattern analysis in R.
• Geostatistical interpolation: Kriging and variogram modeling.
• Building spatial models for environmental prediction.
• Practical session: Calculating Moran's I for a spatial variable and performing a simple spatial regression.

Module 10: Introduction to Google Earth Engine with R

• Overview of Google Earth Engine (GEE) platform.
• Connecting R to GEE using the 'rgee' package.
• Accessing and filtering large-scale satellite image collections (e.g., Landsat, Sentinel).
• Performing basic image processing and analysis in GEE from R.
• Exporting GEE results to R for further analysis.
• Practical session: Writing R code to filter a Sentinel-2 image collection by date and location in GEE and display the result.

Module 11: Web Mapping with R Shiny and Leaflet

• Introduction to R Shiny for building interactive web applications.
• Creating interactive web maps with 'leaflet' package in Shiny.
• Incorporating widgets and user inputs for dynamic map displays.
• Deploying Shiny applications for sharing geospatial insights.
• Integrating spatial analysis results into web maps.
• Practical session: Developing a basic Shiny application that displays a geospatial dataset and allows for interactive filtering.

Module 12: Advanced Vector Operations and Network Analysis

• Advanced geometry operations: Union, intersection, difference, symmetrical difference.
• Handling complex geometries and topology issues.
• Introduction to network analysis concepts.
• Building and analyzing simple networks (e.g., shortest path, service areas) in R.
• Applications in transportation, urban planning, and logistics.
• Practical session: Performing a shortest path analysis on a road network or calculating service areas for points of interest.

Module 13: Processing LiDAR Data with R

• Understanding LiDAR data: Point clouds, LAS format.
• Reading and manipulating LiDAR data using 'lidR' package.
• Basic point cloud processing: Filtering, normalization, outlier removal.
• Deriving Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) from LiDAR.
• Classifying ground and non-ground points.
• Practical session: Loading a LiDAR point cloud, performing noise removal, and generating a basic DTM.

Module 14: Machine Learning for Geospatial Data in R

• Introduction to machine learning concepts: Supervised vs. unsupervised learning in R.
• Feature engineering for spatial data.
• Applying regression models for spatial prediction (e.g., predicting environmental variables).
• Clustering spatial data to identify patterns.
• Validating machine learning models for geospatial applications using 'caret'.
• Practical session: Applying a regression model to predict a continuous environmental variable (e.g., temperature) based on spatial features.

Module 15: Big Geospatial Data Processing in R

• Strategies for handling large geospatial datasets in R.
• Introduction to out-of-memory processing with packages like 'terra' or 'bigmemory'.
• Parallel processing techniques for geospatial computations.
• Leveraging cloud-based storage for large raster/vector datasets.
• Optimization tips for R code performance with geospatial data.
• Practical session: Implementing a parallel processing approach for a computationally intensive geospatial task.

Module 16: Geospatial Data Quality and Validation in R

• Importance of data quality in geospatial analysis.
• Identifying common errors in spatial and attribute data programmatically.
• Implementing data validation checks in R scripts.
• Cross-validation techniques for model assessment.
• Best practices for data documentation and metadata generation in R.
• Practical session: Writing R functions to validate geospatial data for common errors (e.g., checking for invalid geometries or missing attributes).

Module 17: Developing Custom Geospatial Functions and Packages in R

• Structuring R scripts for maintainability and reusability.
• Writing modular code with functions and object-oriented programming concepts.
• Developing custom R functions for specific geospatial tasks.
• Introduction to creating R packages for sharing and distributing code.
• Version control with Git and GitHub for collaborative development.
• Practical session: Refactoring a previous script into a re-usable function and creating a simple R package structure.

Module 18: Capstone Project: End-to-End Geospatial Programming in R

• Comprehensive review of all geospatial programming concepts and libraries in R.
• Guided individual or group project work on a selected geospatial problem.
• Designing an end-to-end geospatial programming workflow from data acquisition to visualization.
• Troubleshooting and debugging R code for geospatial applications.
• Presenting project findings, code, and insights.
• Practical session: Participants work on a comprehensive capstone project, applying learned techniques to solve a real-world geospatial problem (e.g., automated land cover change detection, flood risk mapping, optimal site selection).

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588