Four-dimensional web mapping and environmental data mashups

1. Four-dimensional web mapping and environmental data mashups Jon Blower Reading e-Science Centre Environmental Systems Science Centre University of Reading United Kingdom

2. 4-D web mapping • A number of mapping systems are now available on the web • Increasing number of OGC WMS-compliant servers • Most mapping software assumes static, two-dimensional data (x-y) • Environmental science data is dynamic and four-dimensional (x-y-z-t) • Other dimensions possible, e.g. spectral band • OGC WMS supports 4(plus)-D data • But not very many servers or clients support the full spec

3. NetCDF (Common Data Form) • Increasingly becoming the de facto standard for many types of environmental data • Highly suitable for 4-D data • Augmented by Climate and Forecast (CF) metadata standards • BIG plus for interoperability! • Use if at all possible • You’ll benefit from lots of tools that understand CF-NetCDF • We have written a WMS for exposing images of data from 4D NetCDF files • ...and a geo-website interface to the WMS. • Designed to be easy for third parties to pick up and use. • Can also read data via OPeNDAP

4. Godiva2 WMS WMS Could use images from many other WMSs DATA DATA metadata (XML) images (PNG) WMS = OGC-compliant Web Map Service Web server HTML, Javascript Web server and WMS could be co-located

5. The Godiva2 website NetCDF data converted to images on the fly and served through a WMS. Displayed in a draggable, zoomable map (OpenLayers) http://lovejoy.nerc-essc.ac.uk:8080/ncWMS/godiva2.html

6. Selection of depth Depth mapped onto ELEVATION dimension of WMS

7. Selection of time (range) WMS supports the TIME dimension Selection of a time range leads to generation of an animation

8. Finding the data value at a point Click on the data layer, data value is shown Uses GetFeatureInfo part of WMS specification

9. Timeseries plots If a time range is selected, can create a timeseries plot at a point Uses GetFeatureInfo with a time range and “format=image/png”

10. Viewing in Google Earth • Godiva2 website contains link to load currently-visible data in Google Earth • Our WMS outputs in KMZ format • Can then view data alongside other KML datasets • Can view animations of data • No problem with map projections! • Although overlays still look funny near the poles… • Can’t interact with the data as much as is possible on the website

11. Lessons learned • We had to make some extensions to WMS to serve our needs • SCALE parameter for colour scale changes • Piecemeal serving of metadata (whole Capabilities document can grow very large) • Key technical challenge is mapping (lon,lat) to (i,j) efficiently • Made difficult by exotic coordinate ref systems (e.g. NEMO tripolar) • Slow response would kill user inactivity • Not many WMS servers and clients implement the WMS spec fully and correctly (or efficiently) • We are feeding back our findings to the community • Community would benefit from a good quality web-based 4-D WMS interface • This should be a community development effort • Any volunteers to join in?

12. Environmental data mashups

13. What is a mashup? • Taking two or more “things” that were independently produced and putting them together such that the resulting whole is greater than the sum of the parts • Requires adherence to common standards • Helped by simple interfaces and formats • WMS, GeoRSS, KML… • RESTful Web Services more suitable than SOAPy ones • (HTTP GET is much more mashup-friendly than HTTP POST) • But you can’t do everything with simple systems • E.g. use a mashup to explore or generate a hypothesis, then a more sophisticated system to do complex analysis

14. Mashup 1: Ocean science DAMOCLES (Arctic ice) NetCDF NetCDF Java program KML WMS KML KMZ Google Earth ARGO float data DRAKKAR model data (NEMO code) Useful for checking model results against assimilated observations to look for anomalies

15. Pop-up shows data from ARGO float

16. Mashup 2: Hurricane Katrina SST data from Met Office FOAM model Output from TRACK program plain text NetCDF NetCDF Python script WMS WMS KMZ KMZ KMZ Google Earth vorticity data from ECMWF reanalysis Can check positioning of storm tracks and view effect of storms on ocean (e.g. cooling of sea surface as Katrina passes)

17. Ocean surface cools as Katrina passes

18. Conclusions • Combining and visualizing disparate geospatial datasets is becoming ever easier • Relies on standards-compliance • Data format (e.g. NetCDF) • Metadata conventions (e.g CF) • Service interfaces (WMS, WCS, WFS etc) • Two levels of data access: • Quick look (WMS, KML, GeoRSS) – easy but limited • Full access (WCS, WFS) – harder but richer • Standards still in flux but now is a good time to join in!