Data Visualization

There are many different ways to visualise data, and the choice of visualisation depends on the type of data and the question that you want to answer. For example, if you want to compare the sales of two products, a bar chart would be a good choice. If you want to see how the sales of a product have changed over time, a line graph would be a better choice.

With the help of AI, we can create more sophisticated and complex visualisations that can help us to gain a deeper understanding of the data. For example, we can create 3D visualisations that show the data in a more intuitive way, or we can create animations that show how the data changes over time.

Here, we have collected the location data from the International Space Station (ISS) over the course of one day, and visualised it using a 3D globe. The data is represented by dots, and the lines represent the path of the ISS as it orbits the Earth. We used a geo reverse geocoder to convert the latitudes and longitudes into names of cities and locations.

The raw data from the ISS, available as an API. You can access the real time location here:

The created 3D model based on the data. You can access it here:

Another example is collecting live location data from Singapore's bus system and visualising it on a map. Analyzing the peaks of a given day can help public transport planners optimize the bus schedules. The data is collected in real-time using LT Data Hub, a live data platform for Singapore, and visualised using a coloured pictograms on a map.

The created maps with various days and timings on an interactive maps:

Peak times for the buses are – no surprise – around 8:30 am and 6:30 pm on weekdays.

These are just a simple example of what can be done with data visualisation. With more sophisticated AI models, we can create even more complex and informative visualisations. For example, we can create visualisations that show the data in multiple dimensions, or we can create interactive visualisations that allow the user to explore the data in more depth.

One potential use case for this technology is to visualise data from sensors in real-time. This could be used to monitor the health of a patient or to detect faults in a manufacturing process. The data from the sensors could be visualised in a 3D model of the patient or the manufacturing process, which would allow the user to quickly identify and understand the problem.

Another potential use case is simulating the flow of traffic in a city or the spread of a disease. The key to all these is a sound data and API strategy that can be used to collect and store the data. APIs are also important to allow different systems to communicate with each other, which is essential for businesses that want to offer a seamless experience to their customers.

Talk to our team about building out an API and data integration strategy!