As the saying goes – a picture says a thousand words
Because of the way the human brain works, it’s much easier for people to understand data represented in a chart or on a graph rather than hidden away in spreadsheets or reports. Data visualisation is key to making data analytics approachable and conveying analysis results effectively.
In its raw form, data can be essentially meaningless. Without the ability to compare and contrast, highlight trends and predict outcomes, the value of data remains trapped. By taking raw datasets and representing them in a visual format, analysts can unlock a wealth of insights and gain a greater understanding of any situation.
Combining the latest visualisation technologies with the huge amounts of data available from companies like HERE – a leader in mapping, navigation and location experiences – allows you to present your data on striking, interactive maps that are not only informative, but also highly engaging.
Just the other day I was reading the popular Australian music website fasterlouder.com.au and I came across an article that I found interesting. It was discussing the frustration live music fans have with bands not touring in areas outside the east coast of Australia. The original reddit post was made by Luke Penman – he drew this map to describe his frustration: Continue reading →
Recently I have been working with the Raspberry Pi kit computer to create a homemade indoor Wi-Fi Analytics solution just like momma used to make. For those of you who don’t know; Raspberry Pi is the name of a particular type of mini kit computer you can buy that runs Linux – and not a delicious type of pie!
Previously I talked about the place/suggest service which allows you to input text and get suggested results based on that input.
I’ve pulled together a guide to show you how to make a request to the suggest service, and then use the returned coordinates to place a marker on a map. I’ve used leaflet for this which is an open source solution.
Today I am going to discuss a problem that many developers face when designing a website in 2014; the rapidly expanding number of devices on which it can be accessed.
This has created a unique problem for modern web developers as we strive to create web apps that work on as many platforms as possible. The answer to this in part, is to implement a responsive layout.
Following on from Digital mapping’s dynamic makeover, where I touched on the differences between a traditional tile service process, and one which is dynamic, we also discussed how the Dynamic Tile Service can render custom styles and colours on the map tiles it produces in real-time using CartoCSS.
In this post, I’ll be talking Big Data and how the Dynamic Tile Service solves the problem of viewing large datasets on maps in web browsers.
Tiling Big Data to speed up web browser performance
The recent explosion in the popularity of ‘Big Data’ has left many people scratching their heads as to how to properly view a large dataset. Web browsers are not built to download large amounts of data all in one go – so when we try to view a large dataset on a web map, it can feel like putting a square peg in a round hole.
It doesn’t take a genius to type an address into a search field but it’s something that we all seem to get wrong! If you’re looking for a map relating to a specific address or directions as to how to get there, a simple mistype could mean you’re provided with a map of the wrong town or even the wrong state.
Knowing this is a common error, we had a need for our applications to provide meaningful responses based on user input. We needed a system where users were presented with options after typing just a few characters into the search box – these could range from suburbs and postcodes to street addresses and locations such as stores, cafes, and other places of interest. This would not only help when errors occurred but also reduces the amount of user input needed, making it a faster, more enjoyable user experience.