Visualization

Markers must go! At least when things concern continuous-space processes such as weather. Could anyone care less about a sensor with its exact measurement unless it is mounted exactly where the person is? Why not move beyond markers and point measurements and instead show values everywhere as surface overlays? Markers are interactive and can link to a pop-up timeline - but so are the surface overlays! This is now possible thanks to the availability of fast spatial predictors, efficient storage mechanisms and fast client side rendering.

This talk will outline an open source web application for realtime modelling and visualisation of time-varying continuous-space fields. We will discuss the technical challenges of building this application from the server side perspective of data collection, modelling and storage and from the client side perspective of visualisation and interactivity.

We will describe our approach to an integrated spatio-temporal modelling and visualization of temperatures and rainfall. The predicted temperature surfaces and rain radar data are displayed as overlays in OpenLayers using a custom layer which renders the data on the client side using the HTML5 Canvas Element. The timeline for arbitrary spatial location is built upon the Flot plotting library and is rendered the same way. These elements are backed by a server side raster cube, implemented as a georeferenced multidemensional memory mapped Numpy array. We developed this data structure specifically for storing timestamped raster data in a way that allows for very efficient spatial and temporal slicing. And for those curious ones interested in sub-raster resolutions there is always a live server-side stream oracle ready to compute predictions with MapReduce on multicore.

Spatial data traditionally has been displayed using maps and tables. Maps, though good in showing the spatial extent, are not always optimal in showing (aggregated) geo-statistical data. The human mind can easily be tricked into believing that a bigger country has a bigger share of the pie. Tables show the individual records, but still lack the overview of aggregated data, also tables are notoriously hard to read.

To view the information in an aggregated form one has to build complex queries. These are often slow and do not scale well and the user either has to interpret the resulting map or compare numbers in a table. The first being not very precise the second not very intuitive.

Within EuroGeoSource, a cross European project that allows users to identify, access, use and reuse aggregated geographical information on geo energy and mineral resources, we have come up with a new way. The users are not typical GIS experts and do not have the knowledge to build custom spatial queries. Instead based on the user requirements we have determined the most important types of aggregation (eg. by country, by deposit-type etc).

The user can search for one or more commodities. Using MapQuery and gRaphael, an SVG chart library, we than present the aggregated information in interactive (pie) charts. He can use the charts as a selection method, for instance by clicking on a single country in the 'countries chart' he will see all the occurrences of the commodity in that country on the map.

The result is a fast and intuitive way to search for aggregated data. Providing overview on the distribution of data in multiple domains and still giving access to detailed data in a traditional GIS manner.