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Location AI

DataRobot Location AI adds support for geospatial analysis across the entire AutoML workflow. These tools and techniques help users improve their modeling workflows by:

  • Natively ingesting common geospatial formats
  • Automatically recognizing geospatial coordinates in non-spatial formats
  • Allowing Exploratory Spatial Data Analysis (ESDA)
  • Enhancing model blueprints with spatially-explicit modeling tasks
  • Visualizing geospatial data using interactive maps in pre- and post-modeling
  • Gaining insights into geospatial patterns in your models

DataRobot’s typical AutoML predictive modeling workflow is:

  1. Perform exploratory data analysis EDA.
  2. Select a target feature to predict.
  3. Select a performance metric.
  4. Search for the algorithm to model the domain.

DataRobot’s Location AI enhances that workflow to capture a broad range of geospatial problems.

These sections describe:

Topic Describes...
Data ingest Work with sources of geospatial data.
ESDA Conduct Exploratory Spatial Data Analysis (ESDA) within the DataRobot environment.
Modeling Expand traditional automated feature engineering and improve model options.
Accuracy Over Space Assess model fidelity through visualizations.

Updated January 18, 2022
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