How To Visualize IoT sensors data in Mapify

Let's explore the process of visualizing IoT sensor data with Mapify, using data from Sensor.Community, which hosts over 12,000 active sensors across 81 countries. We'll focus on temperature, humidity, and air pressure data.

Start by creating a Dataset in Mapify to store the sensor data, represented as "points." Include key variables such as sensor_id, sensor_type, manufacturer, longitude, latitude, altitude, indoor status, temperature, humidity, pressure, sea-level pressure, and timestamp. After adding these variables, save the dataset.

Next, set up a Data Feed to pull data from the API endpoint. Select "Get data from an external system" and enter the endpoint URL. It’s also important to define the schema format for the data to be sent to and recognized by Mapify. Click "Get Sample" to verify the connection and, once confirmed, save your Data Feed.

Mapify will fetch data every minute. To store this data, go to the Workflow tab and create a new workflow called "Weather Sensor Processing." Use the Data Feed you set up earlier and confirm the sample message.

Since the JSON message is an array, use the "Bulk update a Mapify Dataset" node to save all data in one go. Rename this node to "Update Sensors Dataset" and select your Sensors Dataset. Enable the “Remove existing data” option to fully replace existing data, and write an expression like "message" to handle the array.

Mapify will then ask you to map the fields, including latitude, longitude, and sensor ID. Continue mapping the other fields, converting string values to numeric as needed. Finally, save the node and workflow, and click "start" to begin the workflow.

The final step is to render this data on a map. Go to the Layers tab and create a new layer named "Weather Sensors." Set it as a real-time layer and choose the Sensors Dataset. Save the layer, and voilà, the sensor data is now available on the map!