Spatial Statistics Resources
    The latest from Esri's Spatial Statistics team


    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 Illustrated: Order the book!


    Learning Spatial Statistics is easier than you think. For anyone, from student to professional, Spatial Statistics Illustrated gives you an accessible understanding of some of the most widely used spatial statistics tools. Order the book:


    Highlighted WORKSHOPs

    Data Engineering and Visualization for Spatial Data Science

    In this workshop you'll learn how to enhance your analysis workflows using the Data Engineering view and Charts in ArcGIS Pro. 

    Spatial Statistics: Statistical Cluster Analysis

    Whenever we look at a map, we naturally organize and cluster what we see to help us make better sense of it. This workshop will explore powerful spatial statistics techniques designed to do this in space and time. 

    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. 

    Spatial Statistics: Making Predictions

    This workshop covers how prediction techniques within ArcGIS Pro can help you evaluate relationships, model phenomena, and make predictions. 

    Causal Inference Analysis

    Correlation does not imply causation. This workshop covers the Causal Inference Analysis tool to go beyond correlations and begin to understand cause-and-effect relationships in your data. 

    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. 

    Applying Spatial Data Science: A Complete Workflow

    Spatial data science is an iterative process that extends beyond the run of one tool. We'll demonstrate an analysis workflow from start to finish, providing a framework for the spatial data science process. 

    PREVIOUS WORKSHOPs

    1. Data Visualization for Spatial Analysis

    This workshop covers how data visualization techniques within ArcGIS can help you explore your data, interpret the results of analysis, and communicate findings. 

    2. From Means and Medians to Machine Learning: Spatial Statistics Basics and Innovations

    From simple methods for summarizing and describing spatial patterns to advanced machine learning clustering techniques, this workshop will introduce you to the power of spatial statistics. 

    3. Spatial Data Mining I: Essentials of Cluster Analysis

    Measuring and quantifying the patterns that we see is crucial for informed decision making. This workshop will explore the powerful spatial statistics techniques designed to quantify spatial and spatiotemporal patterns. 

    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.

    6. The Forest for the Trees: Making Predictions using Forest-based Classification and Regression

    This workshop will cover the basics of how the widely-used machine learning approach, random forest, can be used to solve complex spatial problems and make effective predictions.