Spatial Statistics

ESRI Spatial Data Science MOOC

Assignment

Complete each section below. Within each section is a short quiz which you will complete. You do not need to submit the quiz; instead, at the end of the course we will have an “exit interview” where we will review the material (using your quiz answers as a guide).

Introduction to Spatial Data Science

Explore how spatial data, tools, and analysis techniques augment traditional data science. Understand that “spatial” means more than x,y coordinates and that place-based context reveals patterns in data that otherwise may be hidden. Start applying data engineering and visualization techniques in ArcGIS Pro and ArcGIS Notebooks.

The Spatial Approach to Predictive Analysis

Prediction is fundamental to data science. See how incorporating spatial properties into modeling workflows deepens understanding of data and adds predictive power. Learn how to apply random forest, a widely used machine learning approach, to solve problems. Train and evaluate a model, then use it to generate robust predictions.

Finding Optimal Locations Using Suitability Models

Apply widely used spatial analysis techniques to answer this universal question asked by all kinds of organizations: Where is the best location for <fill in the blank>? Perform a weighted overlay analysis that considers and ranks multiple suitability criteria. Learn how to transform data using functions to more completely represent suitability impact.

Pattern Detection and Clustering

Does a pattern have meaning or is it a product of random chance? ArcGIS includes a suite of tools to help analysts identify patterns and clusters in data and determine if they are meaningful. Learn how to apply statistical clustering methods to analyze patterns in space as well as time. Create a space-time cube, then use space-time pattern mining tools to explore spatiotemporal trends and determine where and when high and low clusters occur.

Object Detection with Deep Learning

Take a deep dive into extracting information from massive data using deep learning. Learn how to automate the process of detecting objects and identifying features from imagery. Practice preparing training sample data, then use a neural network to train an object detection model.

Communicating Results with Impact

Successful analysts and data scientists must be effective storytellers. Learn how to present a complex analysis using information products that resonate with your audience. Design and build interactive, visual stories that share the key information you want decision-makers to understand and act on.