Fundamentals of Data Visualisation for Internet of Things (…Part 2)
Click here for “Fundamentals of Data Visualisation for Internet of Things Part 1”
One thing I have realised while going through an excel sheet full of raw IoT data is that it can make you go dizzy, even dizzier than the most boring lecture of the Quantum Physics’ Schrodinger’s Wave Equation.
As Pranav mentioned in the previous blog post, raw data collected by an Internet of Things device sensor gives you no insights and consumes time & energy to figure some valuable information.
Solution to this is the visualisation of raw data, which is not only a treat to the eyes and brain but also provides you with meaningful and actionable insights in mere seconds. I would like to share the following video by Hans Rosling’s lecture at Ted Talks, where he shares the beauty of graphs to understand statistics and debunks the myths of developing-world.
Continuing the series of blog posts started by Pranav, “Data Visualisation for IoT: Making sense out of your data”, in the upcoming blog posts, I will be covering different types of graphs and charts that one can use to visualise the data collected by the Bolt IoT cloud to get meaningful insights out of raw data along with their use-cases and story that they tell.
Nuts and Bolts of Chart Types
Histograms
A histogram is a graphical representation of continuous numerical data in form of bars where the x-axis displays ranges of data sets and the y-axis represents frequency.
The histogram is an estimate of the probability distribution of a continuous (quantitative) variable.
The above example is a data collected by a temperature sensor interfaced with the Bolt IoT platform, here you can easily visualise the temperature distribution against the frequency, which temperature range has the maximum frequency in it (50–60), etc.
Bar charts
A bar chart is a pictorial representation of grouped data with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally.
Bar graphs generally have categories on the x-axis, and numbers on the y-axis. This means that you can compare numbers between different categories. The categories need to be independent, that is changes in one of them do not affect the others.
You can see immediately that this graph gives you a clear picture of the average temperature across all months. It also tells you which months have high average temperatures, summer months like June, July, August & September. And which months are colder, i.e., the winter months, like November, December, January & February.
**A histogram and a bar chart may look similar but are actually very different in terms of what they represents and how are they used.
Pie charts
A pie chart looks like a circle (or a pie) cut up into segments. Pie charts are used to show how the whole breaks down into parts.
From the pie chart you can immediately see that the parking lot has more cars than any other vehicles, i.e., more than 50% of total vehicles parked. Motorbikes are second, with around one-quarter of the total vehicles.
Pie charts, unlike bar graphs, show dependent data.
Pie charts show percentages of a whole — your total is therefore 100% and the segments of the pie chart are proportionally sized to represent the percentage of the total.
Usually, it is not appropriate to use pie charts for more than 5 or 6 different categories. Lots of segments are difficult to visualise and such data may be better displayed on a different type of chart or graph.
Line charts
A line chart is usually used to show dependent data, and particularly trends over time. Different categories within the same parameter can be compared in a single chart forming multiple graphs.
Line graphs depict a point value for each category, which is joined in a line. You can use the data from the pie chart as a line graph too.
You can easily appreciate which time of the day has higher temperature range and which has lower; similar analysis can be made for humidity and dew levels by just having a look at the graph. Also, you can easily visualise the inverse correlation between humidity & temperature and direct correlation between dew & temperature from the graph. Line graphs are particularly useful for identifying the point in time at which a certain level of y-axis value was reached.
In the next blog, I will be sharing about some commonly used advanced graphs and their usage. Till then, if you are you looking to build an IoT product for yourself or looking for an end-to-end IoT solution for your company, then I will be glad to have a chat with you and guide you on how to go about it. I will be also happy to solve any queries you have about visualising the data of your existing IoT product. Please fill a short form at www.boltiot.com/app/consult.php and we can schedule a call.
