Whenever we look at a map, we inherently start turning that map into information by finding patterns, assessing trends, or making decisions. Spatial statistics in ArcGIS empowers you to answer questions confidently and make important decisions using more than simple visual analysis.
We have compiled some of our favorite resources including our latest Esri User Conference presentations, hands-on tutorials, and everything you need to get started using Spatial Statistics.
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Spatial Statistics: Machine Learning Based Clustering
Machine Learning (ML) is a set of data-driven algorithms that play a critical role in spatial problem solving in a wide range of areas, including spatial pattern detection. This workshop covers techniques in Spatial Statistics that leverage machine learning methods to perform cluster analysis.
Spatial Statistics: Analyzing Space Time Data
The space-time cube is a powerful data structure that enables you to apply statistical, machine learning, and visualization techniques to your space-time data. In this workshop we'll walk through the basics of preparing your data and aggregating it into a space-time cube.
Creating Indices: Combining Variables to Make Better Decisions
This workshop focuses on the Calculate Composite Index tool. Indices are ubiquitous and consequential, but this seemingly simple analysis is not without challenges. The workshop guides you through each stage of the composite index workflow in detail, from designing an index to interpreting and evaluating the results.
Geostatistical Analyst: Concepts and Applications of Kriging
This workshop will provide a clear, practical foundation of the most widely-used interpolation method in GIS: kriging. Attendees will learn about best practices for applying these concepts, assumptions of the methods, and how to put the results into practice.
4. Spatial Data Mining II: A Deep Dive Into Space-Time Analysis
Space and time are inseparable, and integrating the temporal aspect of your data into your spatial analysis leads to powerful discoveries. This workshop builds on the methods discussed in Spatial Data Mining I by presenting advanced techniques for analyzing your data in the context of both space and time.
5. Beyond Where: Modeling Spatial Relationships and Making Predictions
This workshop covers techniques for modeling our spatial data to uncover relationships and predict spatial outcomes. Concepts covered include Exploratory Regression, Generalized Linear Regression, Geographically Weighted Regression, and Local Bivariate Relationships.