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

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)

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

MARS project – List of operational services weather monitoring based on interpolated station data Africarainfall estimates based on MSG and observed rainfall pan-Europeweather and vegetation indices based on MSG-SEVIRI pan-Europe and Horn of Africavegetation indices based on MODIS-250m sensor pan-Europevegetation indices based on METOP-AVHRR sensor globalvegetation indices based on NOAA-AVHRR sensor globalvegetation indices based on SPOT-VEGETATION sensor globalcrop specific drought monitoring globalweather monitoring based on ECMWF deterministic forecast pan-Europecrop yield forecast based on ECMWF ensemble models pan-Europe and Asiacrop yield forecast based on ECMWF deterministic forecast pan-Europecrop yield forecast based on interpolated station data pan-Europecrop monitoring based on ECMWF ensemble models pan-Europe and Asiacrop monitoring based on ECMWF deterministic forecast pan-Europecrop monitoring based on interpolated station data pan-Europeweather monitoring based on ECMWF ensemble models pan-Europe and Asiaweather monitoring based on ECMWF deterministic forecast pan-Europe

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)

Viewers

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)

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

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)

Viewers - Architecture & components

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

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

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 than Oracle queries (through file and in-memory caching)

Viewers – Examples

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