Interactive Data Mapping Case Study
- The interactive map is designed with d3 and Google Maps API
- Pulls data from ABS traffic accident data
- Interactive map combined with data visualization
Who:
What:
When:
Why:
Geo-mapping and geospatial data visualisations are popular topics at Datalabs (yes, we’re aware of how geeky that sounds). When we speak with clients or internally we often find that ideas, data and concepts of data visualisation can be drawn back to an interactive data map and we’ve used this project to show/explore how that map data works.
We built this as a use-case for a low-impact and simple visualisation of pre-existing data and maps that could show large data sets in interactive/explorable maps. We used the Javascript library, D3, some PHP and a script to parse the data from the Australian Bureau of Statistics. This map is centred around Melbourne, Victoria, as it is the capital and population centre of Victoria.
How:
The map is interactive and the user can explore the geographic region that interests them. They can see the same data breakdown across the chosen region. This style of map is useful for showing groupings and clusters. In this example, we can see a higher instance of motor vehicle accidents around population centres and major roads (especially leading in or out of major centres). This project used Google Maps’ API so there were limited design inputs and our developer Jonas designed the charts and interface.
Melbourne is the largest grouping due to size but as you can scroll in, the user can compare smaller subsets or regions within Melbourne and others of comparable size outside Melbourne.
The Result:
The application of this map and style of data visualisation is almost limitless. In the past, we’ve used this type of map to show population growth, peak hour traffic clusters, member data for clubs and sports, infrastructure building projects and rollout, and, point of sale data.
A great-looking, usable, and scalable map that shows the massive possibilities of interactive map data visualizations.
Maps help solve big, messy data problems! Check out our Map & Cartography page for more info.
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