Welcome back to the fifth installment in our Call For Code Technology mini-series, where I identify and talk about one of the six core technology areas within Call For Code. You’ll learn about that technology, how to best use it on IBM Cloud, and where to find the best resources to fuel your innovation. If you missed my other posts, make sure to check them out:
- Building Call for Code apps with IoT and Node-RED
- Building Call for Code apps with AI
- Building Call for Code apps using blockchain
- Building Call for Code apps using machine learning
First things first, if you haven’t already, accept the Call for Code challenge and join our community.
In this fifth installment, I talk about leveraging traffic and weather technologies so that you can build them into your Call for Code solution. While both traffic and weather data can serve a useful purpose when analyzed, let’s break these into two different categories and talk about them individually.
What can you do with weather data? Take a look at this blog post that details the weather feeds available from The Weather Company with easy-to-use APIs. For a limited time, these will be available to developers who are creating Call for Code applications. This is a big deal – these APIs have all kinds of excellent data, ranging from forecasts to severe weather and would be the essential resource as you’re building weather into your solution!
If you’re looking for historic climate data, check out this NOAA page for local or global summaries, broken down by length with radar imaging as well.
Are you wondering “how traffic or transportation is even relevant or related to natural disasters?” While it might initially appear unrelated, understanding the efficiency (or sometimes, a lack thereof) when it comes to transportation might help you create resources or contingency plans when there is a natural disaster.
Just like the weather datasets, such a thing also exists for traffic and transportation. You can search though data.gov for exactly what you’re looking for, but a simple search for “traffic” within city government data sets produces over 200 resources, immediately available for you to transform and do whatever you want with.
Getting started with traffic and weather data for Call for Code
If you don’t already have an IBM Cloud account, the first step is signing up, which takes less than two minutes. Just ensure that you use a valid email address because you must confirm your email address before you can create any services.
A great weather-related code pattern comes from IBMer Scott D’Angelo, where he talks about using Watson Machine Learning to predict wildfire intensity. It takes you through a step-by-step guide of creating and training a model, getting the data from NASA, and predicting wildfire intensity in a given area.
Does this topic sound familiar to you? It might! Our third place winner in our Call for Code 2018 challenge, Team Lali, created a solution around wildfire prevention, by using IoT devices, Watson Machine Learning, and AI to identify areas that are more at risk for wildfires.
On the traffic side of things, Scott D’Angelo also created a code pattern around analyzing traffic data in the city of San Francisco. This solution utilizes Watson Studio and the power of Jupyter Notebooks to bring all of this data together. You could easily adapt this code pattern to analyze other city data from the data.gov website referenced above.
Here’s a unique Call for Code idea – combine the traffic and weather ideas together to help people get out of an area when severe weather is incoming. You could use The Weather Company APIs to identify severe weather in conjunction with traffic/transportation data from a city that would find the best path out of the city when you need to get out fast.
This week we learned about traffic and weather data and how you can utilize those resources in your Call for Code submission. I also provided you with two excellent code patterns that really show some great use cases of using traffic and weather data on IBM Cloud.
I’ll be back soon with the sixth and final blog, where I’ll talk about data science and how you can leverage it into your Call for Code 2019 submission.