Resources

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.

Explore how to apply the imagery deep learning capabilities of ArcGIS to automate map production, assess damaged structures post-calamity, count trees for agriculture census, monitor land cover-change, and count features from drone imagery and full motion video. Topics covered include an introduction to artificial intelligence, machine learning, and deep learning, overview of ArcGIS Notebooks and the arcgis.learn module within ArcGIS API for Python, overview of the ArcGIS Enterprise deep learning system architecture and more. 

ArcGIS enables data preparation for modeling and is host to predictive analytical tools tuned to handle spatial data. In this webinar, you will learn how to use ArcGIS to model and explore spatial data to uncover relationships and predict spatial outcomes. The application of generalized linear regression (GLR), geographically weighted regression (GWR), and Forest-based Classification and Regression will be shown. Also, you will learn how to use geoprocessing tools for feature engineering and data preparation for predictive modeling and how ArcGIS can integrate with machine learning frameworks.