Using ArcGIS Analytics for IoT, output observations and analysis results can be written to feature layers or stream layers for visualization in web maps.
A feature layer is a grouping of similar geographic features, for example, buildings, parcels, cities, and roads. With Analytics for IoT you can store IoT observations from sensors as features to explore visually on a map such as current vehicle locations, temperature readings, or earthquake epicenters. IoT observations represented as features can be points, lines, or polygons (areas). For more information on the types of feature layers in ArcGIS, see Feature Layers.
Since feature layers represent data as points, lines, and polygons, they can be used to visualize data in a variety of ways. For example, temperature readings can be displayed in a common blue-yellow-orange-red sequence of colors to indicate areas that are cooler or warmer. Conversely, the same temperature data could be displayed relative to a threshold that is important to your workflows such as everything below 50 degrees Fahrenheit representing temperatures of concern and temperatures above being acceptable.
Feature layer rendering can be defined in a web map. In Analytics for IoT, to visualize your collected IoT observations or analytic results, click the map icon next to the layer on the Layers page. This opens the layer in a new web map and provides many different styling options. For more information on styling your feature layers, see Change style.
When to use feature layers
Feature layers are a great way to visualize observation data, with a wide variety of styling options and query capabilities available. Feature layers are a good choice for exploring lower volumes of observation data over time, or when sensor data is not changing rapidly. When storing data in feature layers with Analytics for IoT, every feature layer has an associated map image layer that allows you to visualize high volume data as dynamic aggregations to see broader patterns more easily. For more information, see Visualize map image layers.
A stream layer is a kind of feature layer that is optimized for the visualization of real-time data. Feature layers display information that has been stored, and new information is visualized by regularly refreshing the feature layer in a web map. Stream layers display observations in the web map as soon as they are received by the server. This is effective for sensor data which changes rapidly or irregularly or for tracking workflows where assets or field crews need to be continuously monitored. In Analytics for IoT, feeds behave like a stream layer when added to a web map.
To receive real-time data immediately, a stream layer connects to an underlying stream service using HTML5 WebSockets. The stream layer is then a client to the WebSocket, and incoming data is broadcast to all connected clients as soon as it is received. Most modern web browser support WebSockets. To get more information and to test if a browser supports WebSockets, visit WebSocket.org.
When to use stream layers
Stream layers can be used when you want to display fast moving observations on a map or when you do not wish to regularly re-query your data store to display new information. Stream layers are also ephemeral, meaning the incoming data is not persisted separately from the client session. This can be useful when information is used largely as a visual aid such as monitoring weather patterns during a large sporting event.