On Big, Open, Spatial (data) systems.

Had the opportunity and privilege to share some thoughts on the topic above at Rotterdam University last week and the pleasure was all mine. From the personal perspective, a presentation is a good way to get my thoughts together on the topics at hand. It does help as well when these topics are very familiar to me. On this day, it was all about present and near future developments in big, open and spatial data.

Spatial data
As the presentation took place at the School of Communication, Media and Information Technology, I did start out by explaining the spatial context of data and challenged everyone to come up with examples of non-spatial data. Also, I zoomed in a bit on the challenges of geographic data modelling. Important to mention: the real interesting part of spatial data starts when you work beyond the pins on the map. I even suggested that only with geographic analysis you’re getting your geographic data efforts paid out. It is what makes all the difference when you are working with spatial data.

Open data
Admittedly one of my favorite topics (since 2011 / e.g. Open Gov Data camp). In the Netherlands we’ve been making great progress, especially since the early days of open data. Those days were centered around local (e.g. municipal) data sets and app contests (both were and are still fun to contribute to). Since our national Kadaster has released major national datasets in the public domain, we see more organisations contribute to the open data space. A recent very notable contribution is the AHN2 – highres elevation data of the Netherlands (every 50 cm a data point, 5 cm in vertical accuracy).

There are still a few barriers to overcome for open data to breakthrough (there is a national open data taskforce at work now), but we are beginning to see the light. As 3D makes open data just a bit more special: “Before we know it, 3D (web)maps are the new normal and will wonder why we every accepted it’s 2D equivalent.” (egoquoting).

View Larger Map

Big data
In a world of interconnected sensors – human and machine alike – we will be able to know where things –and people carrying things – are, all the time. In relation to big data, I usually refer to the real-time aspect of data, the 4^th dimension if you will, and the heaps of data sources available. But not all data is meaningful or useful and it does not all lead to information. More importantly: quantity does not beat quality.

Big data is worth looking into, but it takes a different kind of understanding. I did also explain (pitch if you will) what Esri is doing to help out developers with a location platform. Geofencing functionality of the native kind, read native apps, is my personal favorite.

Got questions?
Good questions from the audience, ranging from topics about the NSA, (the future of) privacy, data mining, Facebook (will it stay? I think it will not).

The hallway discussion was about Google Glass (thought that topic is up next in this Emerging Technology series of speakers). Careful as I am in predicting any future event (just finished rereading The Singularity by Ray Kurzweil), I did predict that Google Glass will n-o-t become the tool that will find wider user adoption any time soon. The best sign for that is Google’s apparent need to demystify their own product (Techcrunch). If a product requires that much demystification, I do not foresee a first mover advantage. I would put my bet on its successor (or copycat if you will).
Looking forward to that 3D webmap of the Netherlands (and I mean the whole country, not just the urban areas). A dataproduct like that will not need any explanatory documentation.