How can I use ArcGIS Analytics for IoT

Blog Post created by DHo-esristaff Employee on Jul 1, 2019

ArcGIS Analytics for IoT is not only useful for workflows dealing with observations coming in from IoT devices and sensors, but also for other sources of real-time and big data. It provides easy ways to bring in and immediately visualize real-time information, as well as store observations over time. Analytics for IoT also enables you to build analytical processes to automate workflows and answer questions. Overall, Analytics for IoT provides many of the same capabilities and solves many of the same use cases as ArcGIS GeoEvent Serverand ArcGIS GeoAnalytics Server, but provides these capabilities as-a-service through ArcGIS Online.

ArcGIS Analytics for IoT is a good solution for a range of needs:


  • Connecting to IoT systems to visualize sensor observations
  • Geofencing areas of interest to detect spatial proximity of events
  • Increasing speed of current data processing
  • Enriching and filtering observations to focus on most interesting event data
  • Enabling data management as-a-service when data has grown to high volumes over time
  • Identifying important incidents in noisy data
  • Using spatial statistical analysis and machine learning tools for large datasets
  • Choosing cloud solutions over managing multi-machine deployments for real-time and big data

ArcGIS Analytics for IoT can be used by GIS analysts, operations officers, data scientists, and more. Specific examples of analysis include:


  • A city GIS analyst can ingest GPS data on all city vehicles like public works vehicles and snow plows to see where vehicles have travelled, areas with less coverage, and instances where vehicles exceeded the speed limit.
  • An electric utility operations officer can receive regular readings from smart meters, including indications of power outages, and automatically notify the closest field crew in the area.
  • An environmental scientist can identify times and locations of high-ozone levels across the country in a dataset of millions of static sensor reads.
  • A supply chain analyst at an oil and gas company can connect to an Automatic Identification System (AIS) data stream to monitor vessels, calculate expected arrival information, and understand when vessels enter areas of interest.