Module 1: Introduction to Geostatistics and Spatial Data

• Defining geostatistics: Its principles, history, and applications.
• Types of spatial data: Point, line, polygon, and raster data structures.
• Understanding spatial dependence and heterogeneity.
• Geostatistical workflow: From data collection to prediction and mapping.
• Importance of spatial location in data analysis.
• Practical session: Exploring different types of spatial data in QGIS and understanding their attributes.

Module 2: Fundamentals of QGIS for Geostatistical Analysis

• Introduction to QGIS interface: Layout, tools, and plugins.
• Loading and managing spatial data in QGIS.
• Basic spatial data manipulation: Projections, coordinate systems, clipping, merging.
• Utilizing geoprocessing tools for data preparation.
• Creating and editing vector and raster layers relevant to geostatistics.
• Practical session: Setting up a QGIS project, loading various spatial data types, and performing basic data preparation.

Module 3: Exploratory Spatial Data Analysis (ESDA)

• Visualizing spatial data: Symbology, thematic mapping, histograms, box plots.
• Calculating descriptive statistics for spatial data.
• Identifying outliers and anomalous values in spatial datasets.
• Techniques for visualizing spatial patterns and trends.
• Using scatter plots and variogram clouds to explore spatial relationships.
• Practical session: Performing ESDA on a sample dataset in QGIS, generating plots and summary statistics.

Module 4: Spatial Autocorrelation and Variography

• Understanding spatial autocorrelation: Tobler's First Law of Geography.
• Measuring spatial autocorrelation: Moran's I and Geary's C statistics.
• Introduction to variograms: Definition, components (sill, range, nugget).
• Constructing experimental variograms in QGIS.
• Fitting theoretical variogram models (spherical, exponential, Gaussian).
• Practical session: Calculating Moran's I, generating an experimental variogram, and fitting a theoretical model in QGIS.

Module 5: Kriging and Spatial Interpolation

• Principles of spatial interpolation: Inverse Distance Weighting (IDW), Nearest Neighbor.
• Introduction to kriging: Ordinary Kriging, Simple Kriging.
• Assumptions and underlying theory of kriging.
• Applying kriging for spatial prediction and mapping in QGIS.
• Interpreting kriging output: Prediction maps and variance maps.
• Practical session: Performing Ordinary Kriging on a dataset in QGIS and visualizing the predicted surface and prediction variance.

Module 6: Advanced Kriging Techniques and Co-Kriging

• Universal Kriging: Accounting for trends in spatial data.
• Indicator Kriging: For categorical data or probabilities.
• Disjunctive Kriging: For non-linear relationships.
• Introduction to Co-Kriging: Using secondary variables to improve predictions.
• When and how to apply different kriging methods.
• Practical session: Applying Universal Kriging or Co-Kriging in QGIS using a dataset with a secondary variable.

Module 7: Geostatistical Simulation and Uncertainty Assessment

• Introduction to geostatistical simulation: Why simulate?
• Conditional vs. unconditional simulation.
• Sequential Gaussian Simulation and other simulation algorithms.
• Assessing uncertainty in spatial predictions.
• Generating multiple realizations and probability maps.
• Practical session: Performing geostatistical simulation in QGIS and analyzing the range of possible outcomes.

Module 8: Working with Point Patterns and Geostatistical Modeling

• Introduction to point pattern analysis: Random, clustered, and dispersed patterns.
• Measures of point pattern intensity: Kernel Density Estimation.
• Spatial point process models.
• Nearest neighbor analysis and Ripley's K function.
• Applying geostatistical models to environmental and health data.
• Practical session: Conducting Kernel Density Estimation and nearest neighbor analysis on a point dataset in QGIS.

Module 9: Spatial Regression and Geographically Weighted Regression (GWR)

• Introduction to spatial regression models.
• Understanding spatial non-stationarity.
• Principles of Geographically Weighted Regression (GWR).
• Performing GWR in QGIS and interpreting results.
• Applications of spatial regression in environmental and social sciences.
• Practical session: Running a Spatial Regression or Geographically Weighted Regression analysis in QGIS.

Module 10: Case Studies and Project Work

• Review of all geostatistical and spatial modeling concepts.
• Guided individual or group project work on a selected real-world dataset.
• Applying learned techniques to solve a practical spatial problem.
• Presentation of project results, including maps, reports, and interpretations.
• Discussion of limitations and future directions in geostatistics.
• Practical session: Completing an end-to-end geostatistical project in QGIS, from data preparation to final mapping and reporting.

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