An Internet connected physical indicator that visualizes Volcanic Alert Levels. The data is obtained by using the GeoNet API and is visualized using a Raspberry Pi.

What are Volcanic Levels & How are they Classified?

In New Zealand, we use a system of Volcanic Alert Levels is used to define the current status of each volcano. The alert levels range from 0 to 5. The alert levels are used to guide any appropriate response.

You can find more information about Volcanic Alert Levels at GeoNet.

Features

• Acquires volcanic alert level data from the GeoNet API.
• Hourly Updates.
• Displays alert level of volcanoes indivually using LED Bar-Graphs.
• Plots a digital graph of the alert levels using Matplotlib.

Prerequisites

Hardware:

1. Raspberry Pi Zero W or Raspberry Pi 3 B.
2. LED Bar Graph.

Software Dependencies:

1. Requests is an elegant and simple HTTP library for Python. Requests Installation & Documentation
2. Graph Plotting Library. Matplotlib 3.0

Working

API:

The GeoNet Volcano API provides us with a simple JSON feed which contains the following properties:

• volcanoID - a unique identifier for the volcano.
• volcanoTitle - the volcano title.
• level - volcanic alert level.
• activity - volcanic activity.
• hazards - most likely hazards

For this project we are only interested in volcanoID, volcanoTitle & level. The remaining features are fetched but are not used within the project.

Fetching the Data:

The raw data can be simply retrieved by reading the JSON object using the requests library & Python's built in JSON package. The raw data is updated on an hourly basis & can be found at: https://api.geonet.org.nz/volcano/val The following code is used to fetch the raw data from the API:

r = requests.get('https://api.geonet.org.nz/volcano/val')
data = r.json()

Below is an example of what the raw data looks like:

Extracting Useful Data:

We need to extract useful data such as volcanoID, volcanoTitle & level from the raw data. This useful information is encompassed within a nested dictionary. We need to figure out which nests contain the data we require. From the raw data we can see that the useful data is contained within the following nests: features& properties.

The featuresnest includes a properties nest for each volcano. The properties nest contains the volcanoID, volcanoTitle, level & other information about the volcano. The following code snippet is used to extract the useful data & groups them into individual lists:

feature = data['features']

volcanoID = []
volcanoTitle = []
level = []

for result in feature:
    properties = result['properties']

    volcanoID.append(properties['volcanoID'])
    volcanoTitle.append(properties['volcanoTitle'])
    level.append(properties['level'])

Contributing

Are you a programmer, engineer or hobbyist who has a great idea for a new feature in this project? Maybe you have a good idea for a bug fix? Feel free to grab our code & schematics from Github and tinker with it. Don't forget to smash those Star & Pull Request buttons. Contributor List

Note

We are not affiliated with GeoNet. Please be mindful & limit the number of requests made to the website so as to not overwhelm their servers. Don't forget to read their API documentation too.