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Spatial Data Science: Advance Your Analytics

£1,070.00

Discover hidden patterns, predict with confidence.

Take your analytics projects to the next level by incorporating the power of location and place-based context. This course introduces spatial statistical techniques and methods used to uncover patterns and relationships in data, unlocking insights that help organizations solve complex problems. You’ll explore a variety of scenarios and build skills with powerful ArcGIS tools used by analysts, researchers, and data scientists around the world.

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Description

2 Days
Who is this course for?
GIS Professionals
GIS Analysts
Data Scientists
Goals
Apply data engineering tools to prepare spatial data for analysis and statistical modeling, ensuring project results are built on a reliable and documented foundation.
Gain experience with exploratory spatial statistical methods and space-time analysis techniques to detect trends, clusters, hot spots, and anomalies.
Create and assess prediction models using geostatistical techniques and regression analysis.

Additional information

Venue

Virtual
Date

4th November 2026 – 5th November 2026

Topics covered:

  • Building a foundation for spatial data science: Spatial analytics and data science; Confidence in conducting spatial data science; Spatial analytics and data science case studies; types of spatial statistics; Interpreting inferential statistics; Geospatial AI.
  • Data engineering: Data Engineering view in ArcGIS Pro; evaluating data for data preparation; creating charts in the Data Engineering view; Interpreting data using charts.
  • Clustering: Heat map versus hot spots; clusters and outliers; descriptive versus inferential statistics; Analysing spatial patterns.
  • Space-time analysis: Incorporating time into your analysis; space-time analysis; emerging hot spot analysis; conducting a space-time analysis.
  • Regression analysis: What is regression? Regression equation; analysis using linear regression; exploratory regression.
  • Multiscale geographically weighted regression: How Relationships change over space; multiscale geographically weighted regression characteristics; when to use MGWR; MGWR in action
  • Presence-only prediction: What is presence-only prediction? Presence-only prediction workflow; accuracy of data inputs for prediction; Interpreting presence-only prediction output
  • Geostatistical interpolation: What is interpolation? Differentiate between regression and interpolation; geostatistical interpolation; geostatistical interpolation workflow; examine the Geostatistical Analyst Interpolation toolset; approaches to geostatistical interpolation; the geostatistical wizard

Completion of Spatial Analysis Essentials for ArcGIS or have equivalent working knowledge.

Esri will provide the following software to use during class:

  • ArcGIS Pro 3.6
  • ArcGIS Geostatistical Analyst
  • ArcGIS Spatial Analyst

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