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June 1, 2015
Generating More Maps from Spatial Big Data

June 1, 2015 — For centuries maps have been an effective way to display location information; how things relate to one another geographically. This information can be represented in many forms: geo-referenced coordinates, addresses, place names, relative position, etc.

The most useful maps, however, are those which combine several types of information, so that the reader can quickly spot correlations.

In the digital age, it’s this ability to combine data sets which make maps a powerful information and decision-making tool; be it planning teams working out how to widen a road through a valley, environmental departments planning flood defences or simply getting from A to B in the car. However there are technical challenges to creating these new maps.

If professional map-makers have the experience to make easy-to-understand maps, they are now facing a new challenge; how to make more of them, more quickly and at lower cost. Complicating the challenge is the emergence of another kind of map-maker, often referred to as the neocartographer, with little, if any, expertise in cartography.

The rise of available data and the ease of access to it mean that neocartographers can make a map, often just for personal use, using the information they need when they need it. It’s still not easy, as it requires tracking down the data sets, often from multiple sources, integrating them, and styling them before publishing a map. GIS software, such as the open source QGIS, will help them in these tasks, but anything more complicated than overlaying layers on top of each other and styling them is hard.

One of the problems with neocartography is the visualisation at different scales which traditionally has relied on manual/human intervention to turn detailed/large scale data into intelligently simplified maps following cartographic principles. Neocartography aims to produce maps from widely-available data sources and tools, with little manual intervention and modification of the data. As a result, it has to rely on quite blunt techniques such as excluding certain data layers at smaller scales, filtering vertices and then just allowing the resulting features’ representations to overlap.

Whether the data set is large or smaller, you can rapidly and automatically generate clear and useable maps using neocartography techniques, but to achieve this you also need tools which can encode and automate traditional cartographic skills. In 1Spatial’s experience, automatic generalisation tools are needed – both to bring to cartographers the speed that they need, and give the neocartographer better control over the content of their map to increase the quality.

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