Training on Geospatial Data Analysis with R
Master geospatial data analysis with R. Learn to analyze spatial data, create maps, and extract valuable insights from geographic information.
Next intake
20 Jul 2026 · Nakuru
Duration
5 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Advanced
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
This course provides a comprehensive introduction to analyzing geospatial data using R, a powerful open-source statistical programming language. Participants will learn to handle, visualize, and analyze spatial data through hands-on exercises and real-world case studies. The course covers the fundamentals of geospatial data manipulation, spatial statistics, and visualization techniques, empowering participants to perform sophisticated spatial analyses and generate insightful visualizations.
Course Duration
5 Days
Who Should Attend
- Geospatial analysts
- Data scientists and researchers
- Urban planners
- Environmental scientists
- GIS professionals
- Anyone interested in spatial data analysis using R
What you'll walk away with
By the end of this course, participants will be able to:
- Understand the fundamentals of geospatial data and its types.
- Gain proficiency in using R and relevant packages for spatial data analysis.
- Learn techniques for spatial data manipulation, including data import, cleaning, and transformation.
- Develop skills in visualizing geospatial data to effectively communicate insights.
- Apply spatial statistical methods to analyze spatial patterns and relationships.
What we cover, module by module
Module 1: Introduction to Geospatial Data and R
- Overview of geospatial data types and formats (raster, vector, etc.)
- Introduction to R for spatial analysis
- Installing and configuring R packages for spatial analysis (e.g., sf, sp, rgdal)
Module 2: Spatial Data Manipulation
- Importing and exporting geospatial data (shapefiles, GeoJSON, etc.)
- Data cleaning and transformation techniques
- Working with coordinate reference systems and projections
Module 3: Spatial Data Visualization
- Creating maps with base R and ggplot2
- Customizing maps with layers, themes, and labels
- Visualizing spatial data distributions and patterns
Module 4: Spatial Statistical Analysis
- Introduction to spatial statistics concepts (e.g., spatial autocorrelation, kernel density estimation)
- Performing spatial clustering and hotspot analysis
- Conducting spatial regression analysis
Module 5: Advanced Topics and Case Studies
- Integrating geospatial data with other data types (e.g., time series, socioeconomic data)
- Advanced visualization techniques (interactive maps, 3D visualization)
- Case studies and practical applications in various fields (urban planning, environmental monitoring, etc.)
Where the change lands
Organisational Impact
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Strengthens the organisation’s capacity to interpret and apply geospatial data for strategic decision-making.
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Enhances efficiency in projects related to urban planning, environmental management, and resource allocation.
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Provides cost-effective solutions by leveraging open-source R for advanced geospatial analytics.
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Improves the ability to generate actionable insights from spatial datasets, supporting evidence-based policies.
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Builds a skilled workforce capable of driving innovation through geospatial technologies.
Personal Impact
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Equips participants with practical skills in geospatial data handling, visualization, and analysis using R.
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Expands career opportunities in GIS, data science, urban planning, and environmental research.
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Enhances technical confidence through hands-on exercises and real-world case applications.
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Develops the ability to translate raw geospatial data into meaningful insights and impactful visualizations.
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Provides a strong foundation in open-source analytics, empowering participants to work independently and innovatively.
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
- NakuruNext
20 Jul → 24 Jul·In-Person
Book this intake - Kigali
20 Jul → 24 Jul·In-Person
Book this intake - Accra
20 Jul → 24 Jul·In-Person
Book this intake - Kisumu
27 Jul → 31 Jul·In-Person
Book this intake - Johannesburg
27 Jul → 31 Jul·In-Person
Book this intake - Dakar
27 Jul → 31 Jul·In-Person
Book this intake
Common questions.
Still not sure? Send us a note and a facilitator will get back to you within a business day.
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Course finder
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For corporate teams
Training 10+ professionals?
We deliver Training on Geospatial Data Analysis with R in-house at your offices, at a venue we arrange, or fully virtual. Customise the curriculum against your KPIs, and get a bespoke price for the cohort size you need.