Spatial Analysis with ArcGIS Pro
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Geospatial & GISLevel: AdvancedCertification: Available

Spatial Analysis with ArcGIS Pro

Unlock advanced spatial modeling, geoprocessing automation, spatial statistics, raster change detection, machine learning image classification, and surface interpolation.

Duration

10 Working Days + 1 Week Capstone

Format

Virtual only

Tuition

₦300,000

Credential

PFA Advanced Spatial Analyst Certificate of Competency

Official Course Brief & Prospectus

Spatial Analysis with ArcGIS Pro

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Spatial Analysis with ArcGIS Pro Official Course Brief

Programme Overview

Spatial Analysis with ArcGIS Pro equips analysts, agronomists, and researchers with advanced analytical capabilities. Go beyond basic mapping to uncover hidden patterns, forecast crop yield variations, perform land suitability modeling, automate workflows with ModelBuilder and Python, and classify multi-spectral remote sensing imagery using machine learning algorithms.

Course Highlights

Multi-criteria suitability modeling for agricultural zoning and commercial farm siting
Surface interpolation workflows using IDW, Ordinary Kriging, and Spline modeling
Spatial statistics: Hot Spot Analysis (Getis-Ord Gi*), cluster analysis, and spatial autocorrelation
Automating complex geoprocessing chains with ModelBuilder visual pipelines
Machine learning image classification (Random Forest, Maximum Likelihood) for satellite/drone imagery
Multi-temporal raster change detection for crop canopy and forest vegetation monitoring
Syllabus & Modules

What You Will Learn

12 Modules

Module 1: Advanced Analytical Foundations

  • The spatial problem-solving framework and hypothesis formulation
  • Configuring raster and vector geoprocessing analysis environments
  • Understanding spatial resolution, cell size alignment, and snap rasters

Module 2: Proximity & Distance Modeling

  • Euclidean distance, direction, and allocation surfaces
  • Cost distance, least-cost pathway modeling for agricultural corridors
  • Multi-ring buffer zones and service area delineations

Module 3: Advanced Spatial Overlay & Fuzzy Logic

  • Weighted overlay vs weighted sum techniques
  • Applying fuzzy logic and continuous membership functions
  • Combining physical, soil, and meteorological layers for decision models

Module 4: Automating Workflows with ModelBuilder

  • Designing automated geoprocessing workflows with ModelBuilder
  • Using model variables, preconditions, and iterators (batch processing)
  • Exporting visual models to Python (arcpy) automation scripts

Module 5: Spatial Interpolation & Surface Modeling

  • Deterministic methods: Inverse Distance Weighted (IDW) and Spline
  • Geostatistical methods: Semivariogram modeling and Ordinary Kriging
  • Generating soil nutrient (N-P-K) and moisture gradient surfaces from field samples

Module 6: Spatial Statistics & Pattern Analysis

  • Testing spatial randomness with Global Moran's I autocorrelation
  • Hot Spot Analysis (Getis-Ord Gi*) for disease and pest infestation mapping
  • Anselin Local Moran's I for cluster and outlier identification

Module 7: Multi-Criteria Land & Crop Suitability Modeling

  • Establishing evaluation criteria and standardized scoring scales
  • Weighting factors using Analytical Hierarchy Process (AHP)
  • Producing commercial crop suitability zone maps for irrigation planning

Module 8: Raster Differencing & Change Detection

  • Multi-temporal raster alignment and radiometric normalization
  • Vegetation index differencing (NDVI delta) to detect crop degradation
  • Computing volume change, biomass shifts, and canopy growth rates

Module 9: Principles of Satellite & Drone Image Classification

  • Spectral signatures of soil, water, healthy vegetation, and stressed crops
  • Unsupervised classification using ISO Cluster techniques
  • Collecting, evaluating, and managing representative training samples

Module 10: Machine Learning Classification Workflows

  • Executing Random Forest (Trees) and Support Vector Machine (SVM) classifiers
  • Maximum Likelihood classification for multispectral drone imagery
  • Segmenting imagery with Object-Based Image Analysis (OBIA)

Module 11: Classifier Accuracy Assessment & Validation

  • Creating stratified random ground-truth validation points
  • Generating and interpreting confusion matrices and overall accuracy
  • Calculating Kappa coefficients and post-classification boundary smoothing

Module 12: Advanced Capstone Project

  • Executing an end-to-end commercial agricultural suitability or crop damage audit
  • Synthesizing results into executive technical reports and web GIS summaries
  • Presenting capstone findings to industry panel review

Who Is This Programme For?

This curriculum is engineered for professionals and learners seeking practical, applied competence:

Senior GIS analysts and spatial data scientists
Precision agriculture specialists and crop yield modelers
Urban, environmental, and disaster risk assessment professionals
Researchers seeking statistical rigor in geospatial problem solving

Learning Outcomes & Career Advantage

Develop predictive multi-criteria suitability models for land evaluation and investment
Perform statistically rigorous hot-spot and cluster analysis on agricultural datasets
Automate repetitive enterprise spatial processing routines with visual models
Classify high-resolution remote sensing imagery into high-accuracy land cover maps

Tuition / Rate

₦300,000

Enrolment Open
Course Format:Virtual only
Program Duration:10 Working Days + 1 Week Capstone
Competency Level:Advanced
Certification:PFA Advanced Spatial Analyst Certificate of Competency
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Spatial Analysis with ArcGIS Pro

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Certificate of Competency awarded on completion
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