1 / 16

Visualizing large spatial/temporal data sets

Visualizing large spatial/temporal data sets. An example from the European MARS project. 15 May 2013, Hendrik Boogaard. MARS project – I ntroduction. Monitoring Agricultural ReSources (MARS) Started early nineties, operational since 2000 Main objectives:

jgibson
Télécharger la présentation

Visualizing large spatial/temporal data sets

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Visualizing large spatial/temporal data sets An example from the European MARS project 15 May 2013, Hendrik Boogaard

  2. MARS project – Introduction • Monitoring Agricultural ReSources (MARS) • Started early nineties, operational since 2000 • Main objectives: • Monitoring weather and crop conditions of current growing season (early warning, extreme events) • Forecast crop yield in objective and timely manner • In support of: • European Common Agricultural Policy on commodities & subsidies (focus on Europe, Asia) • Food aid (focus on Africa)

  3. MARS project – Introduction

  4. MARS project – Introduction • Operational services outsourced: • Provision weather data (stations, models) • Running and maintenance of agro-meteorological models for Europe, Russia and Asia (CGMS) and global crop specific soil water balances • Provision of satellite based vegetation indices and rainfall estimates • Development and maintenance of MARS-viewers

  5. MARS project – List of operational services weather monitoring based on interpolated station data pan-Europe weather monitoring based on ECMWF deterministic forecast pan-Europe and Asia weather monitoring based on ECMWF ensemble models pan-Europe crop monitoring based on interpolated station data pan-Europe crop monitoring based on ECMWF deterministic forecast pan-Europe and Asia crop monitoring based on ECMWF ensemble models pan-Europe crop yield forecast based on interpolated station data pan-Europe crop yield forecast based on ECMWF deterministic forecast pan-Europe and Asia crop yield forecast based on ECMWF ensemble models pan-Europe weather monitoring based on ECMWF deterministic forecast global crop specific drought monitoring global vegetation indices based on SPOT-VEGETATION sensor global vegetation indices based on NOAA-AVHRR sensor global vegetation indices based on METOP-AVHRR sensor pan-Europe vegetation indices based on MODIS-250m sensor pan-Europe and Horn of Africa weather and vegetation indices based on MSG-SEVIRI pan-Europe rainfall estimates based on MSG and observed rainfall Africa

  6. MARS project – Variety of data sets • Large number of themes • Different Regions Of Interest (ROIs) • Different spatial resolutions • grids, administrative regions, agro-ecological zones • Different time resolutions: day, 10-day, month, year • 9 TB of data stored in relational database (ORACLE)

  7. Viewers

  8. Viewers – Rich & flexible • Serving: • Analysts of European commission (bulletin mode) • Public e.g. universities (limited in data/features) • Online viewer to perform spatial and temporal analysis of data sets in a customized way: • Large number data sets & indicators • Flexible period definition (on-the-fly) • Flexible region definition • Analysis types: current season, anomalies, way of aggregation, similarity analysis (time series)

  9. Viewers – Key functionality • Geo-linked multiple map windows • Geo-linked graphs • Spatial layers supporting labelling, masking • Legend management • Export of data and formatted maps/graphs (PDF, PNG) • Favourite management (save current viewer windows configuration for later re-use) • Configuration of all chart layout settings • Pre-configured graph templates for analysts

  10. Viewers – Architecture & components • Client-server architecture, different components: • Client application (runs in Adobe Flash Player) • RIA developed in Adobe Flex • Application Server & WMS Server • Model Data Servers (or other apps) • Databases (data and GUI-settings)

  11. Viewers - Architecture & components

  12. Viewers – Application server & WMS Server • XML Communication between client and server • Java servlets handle all requests to secured system parts • Security check ensured at one place • Geoserver (open source) • Shape files on local hard disk of the server perform better than spatial data in the Oracle database

  13. Viewers – GUI • User interface driven by configuration settings in DB • New data / indicators / functions can be added on the fly • User interface automatically changes without coding

  14. Viewers – Model Data Servers • Respond to a request (URL) by returning data as either XML (polygon or point request) or XML + .png file (grid request) • Deliver faster thanOracle queries (through file andin-memory caching)

  15. Viewers – Examples

  16. Thanks for your attention! www.marsop.info (get access after registration)

More Related