Explore the application of spatial data science to uncover hidden patterns and improve predictive modeling.
Spatial data science allows analysts to extract deeper insight from data using a comprehensive set of analytical methods and spatial algorithms, including machine learning and deep learning techniques. This course explores the application of spatial data science to uncover hidden patterns and improve predictive modeling. You’ll work with powerful analytical tools in Esri’s ArcGIS software and learn how to integrate popular open data science packages into your analyses.
[Unfold some info by clicking on the triangle pictogram]
Session 1: Introduction to Spatial Data Science here
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.
Session 2: The Spatial Approach to Predictive Analysis here
Prediction is fundamental to data science. See how incorporating spatial properties into mod- eling 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.
Session 3: Finding Optimal Locations Using Suitability Models here
Apply widely used spatial analysis techniques to answer this universal question asked by all kinds of organizations: Where is the best location for
Session 4: Pattern Detection and Clustering here
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.
Session 5: Object Detection with Deep Learning here
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 pre- paring training sample data, then use a neural network to train an object detection model.
Session 6: Communicating Results with Impact here
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 inter- active, visual stories that share the key information you want decision-makers to understand and act on.
Before class:
That’s it!
For attribution, please cite this work as
Warin (2020, Jan. 1). Thierry Warin, PhD: Spatial Data Science for Business. Retrieved from https://warin.ca/posts/course-spatial-data-science-for-business/
BibTeX citation
@misc{warin2020spatial,
author = {Warin, Thierry},
title = {Thierry Warin, PhD: Spatial Data Science for Business},
url = {https://warin.ca/posts/course-spatial-data-science-for-business/},
year = {2020}
}