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Geographic Data in the Meteorological Workstation NinJo

Gerhard Eymann, DWD

EGOWS Conference, Potsdam, June 2004

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Co Workers, ContributorsCo Workers, ContributorsCo Workers, ContributorsCo Workers, Contributors

DWD: data preparation, preprocessing, GeoDB operation Astrid Schöne, Carola Graute, Martin Pusack (GeoContext,

geodetic transformations), Thomas Reiniger (RasterBaseLayer, JAI)

GeoInfoDienstBw: NinJo layers, import filters Ramona Hein (GeoVector), Karl-Wilhelm Stroh (GeoRaster),

Michael Piontek (requirements, QM, manual)

Met. Service Canada (Canadien data) Norm Paulsen (+ others?)

Fa. E. Basler: development of GeoDB, layer design Jörg Benkenstein, Ewald Murra

Fa. sd&m: layer design, JAI prototype, GOF Volker Jung, Barbara Lamprecht

Fa. ask visual: 2-d visualization, GOF Gregor Schnee

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Examples with NinJoExamples with NinJoExamples with NinJoExamples with NinJo

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Examples: Landsat imageExamples: Landsat imageExamples: Landsat imageExamples: Landsat image

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MotivationMotivationMotivationMotivation

meteorological information always has a geographic context

high demands from users - with respect to existing systems - on: information content cartographic quality performance useability data scales, ranges

various kind of data vector, raster, attributes

utilization of external SW (mainly GIS)

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Meteorological RequirementsMeteorological RequirementsMeteorological RequirementsMeteorological Requirements

vector data: coastline, boundaries (political, administrative) regions (warning/climatic/forecast/flight information), water (rivers, seas, canals), cities/populated areas, roads, railroads, airports, military areas, nature reservats

raster data: orography, land usage, satellite image, topographic maps, scanned data

additional data, attributes: names (cities, countries, rivers, ...), administrative ID‘s

scale range: global ... small/regional scale (107 ... 103) automatic adaption of information content and

resolution to scale (generalization)

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Lessons from Other Systems Lessons from Other Systems Lessons from Other Systems Lessons from Other Systems

Standard GIS (Geographical Information Systems)+ thematic structure+ visual & cartographic quality+ easy data input + on-line modification of data- bad performance- special/proprietary formats- no automated/semi-autom. generalization

RDBMS extensions (following OGC, Open GIS Consortium): ESRI spatial data server: expensive, licenses required Oracle Spatial: included in Oracle enterprise versions, licenses req.

Free / GNU software products GRASS (see http://grass.itc.it/) OpenMap (see http://openmap.bbn.com/)

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History & Baseline DecisionsHistory & Baseline DecisionsHistory & Baseline DecisionsHistory & Baseline Decisions

situation < 2000: many formats, undocumented ASCII data, many locations, little cross-department information

create a data base for geography (NinJo + other applications) use Oracle Spatial for storage of geometry (2000) use Oracle Image Media Extensions for raster/image data create an interface to GIS use ESRI products (ArcView, ArcGIS) as desktop GIS

SW development by E. Basler & Partner: C/C++ API for data import (from ESRI shapefile) API for administration API for data export (special ASCII format + raw SVG) Java API for export raster/image data: GeoTIFF

no on-line access to GeoDB (performance, licenses)

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Data Data Data Data

Vector data: VMAP0 (worldwide thematic data, from NIMA) DLM1000 (national data from BKG, national cartographic institute) VG1000 (detailed administrative boundaries) special data sets (e.g. GAFOR, SWIS regions)

Raster data: GTOPO30 (global height, from USGS) GLC2000 (global land usage) Landsat images (global, Europe, Germany) Topographic Maps

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Meteorological CriteriaMeteorological CriteriaMeteorological CriteriaMeteorological Criteria

how to achieve automatic generalization? purpose: for large scales, visualize objects with high priority

and small accuracy only (and vice versa) criteria

priority of an object (e.g. river, city, road) accuracy of coordinates (geometric resolution)

data must be grouped to objects GIS don‘t do this done in preprocessing step with GIS

calculation of accuracy (for each coordinate) storage as 3rd coordinate value done with own or standard GIS algorithms problems with complicated geometries need to adapt themes with common data

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PreprocessingPreprocessingPreprocessingPreprocessing

VMAP0: huge data volume (raw ~1 Gbyte), little structure e.g. coast line ~ 1mio parts

format conversion (e.g. VPF to Shapefile) pre-processing with GIS ArcView / ArcInfo:

geometric corrections object generation priority setting (range 0 ... 5, arbitrary) priority is a feature of each object

import to Oracle Spatial GeoDB

next slide: VMAP0 raw data (coastline, 2 water themes)

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VMAP0 raw dataVMAP0 raw dataVMAP0 raw dataVMAP0 raw data

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Preprocessing (2) : AccuracyPreprocessing (2) : AccuracyPreprocessing (2) : AccuracyPreprocessing (2) : Accuracy

Accuracy calculation in principle possible during import to Oracle GeoDB done indepently for each theme identical coordinates in different themes must have identical

accuracy!

development of algorithm & calculation of accuracy outside GeoDB with exported data consideration of distance between points consideration of gradient test of Douglas-Peucker algorithm

aim 1: near-logarithmic distribution ~1% lowest accuracy 0, 2% accu 1, ... 50% accu 9

aim 2: maintain geometric correctness

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Preprocessing: Accuracy trapsPreprocessing: Accuracy trapsPreprocessing: Accuracy trapsPreprocessing: Accuracy traps

Clockwise: accu 9, accu 6, accu 4, accu 2 (old example, water polygon )

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Preprocessing: AdaptionPreprocessing: AdaptionPreprocessing: AdaptionPreprocessing: Adaption

set accuracy values of identical coordinates to same value own algorithm/program done mutually with all themes of common data set accu to smallest possible value consequence: deterioration of statistics

geometric inconsistencies in data

preprocessing of well-attributed data (e.g. DLM1000) allows semi-automated object generation (Avenue scripts)

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Import to NinJoImport to NinJoImport to NinJoImport to NinJo

large data volume of GeoDB Export Format: VMAP0 themes ~ 220 MB, DLM1000 ~ 40 MB SVG available since 2002, but also very large SVG does not contain yet priority and accuracy

NinJo needs fast access spatial tiling (e.g. VMAP0 30 degree) binary format storage of level-of-detail (LOD) data indepently (cumulativ)

conversion program / import filter required (GID Bw) Java stand-alone program with GUI editing XML configs

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VPF ESRI Export

ESRI Shapefile

ESRI ArcView :- object generation- priority setting- geometry- aggregation ESRI Shapefile

GeoDB Import

GeoDB C/C++ API

GeoDB:Oracle 8.i +Spatial +Image Media Ext.

step 1

step 2

Import to NinJo, step 1 & 2Import to NinJo, step 1 & 2Import to NinJo, step 1 & 2Import to NinJo, step 1 & 2

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Inport to NinJo, step 2 & 3Inport to NinJo, step 2 & 3Inport to NinJo, step 2 & 3Inport to NinJo, step 2 & 3

Export(C/C++ or Java / JNI)

Export-Format (GDB)

SVG Format(preview only)

accuracy (recalculation)

adaption (themes 1,2,3,.) Export Format (GDB)

NinJo import service

internalformatLOD 0-2

internalformatLOD 3-8

internalformatLOD 9

Export Format (GDB)

end step 2

step 3

config / set-up GVL

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Preprocessing Raster DataPreprocessing Raster DataPreprocessing Raster DataPreprocessing Raster Data

format satisfying all requirements: GeoTIFF contains geographic information as special, standardized tags tiling inherently possible storage of various resolutions as pages

data are stored unprojected (lat-lon) import program (GID Bw)

creates tiled, multi-page GeoTIFF e.g. GTOPO30 (8 Bit): ~ 90 Mbyte

all data (vector, raster) are stored on NinJo client

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Visualization Visualization Visualization Visualization

vector data: GOF (Graphics Object Factory) clipping requires exact geometry (polygones)

raster data use of Java Advanced Imaging (JAI)? prototype showed feasibilty and performance hardware accelerated on Intel architecture used for all raster / image data (Sat, Radar)

automatic selection of data-base: VMAP0, Canada, Germany, ...

geodetic transformations are done on-the-fly! raster data transformation: use JAI warping method

(transform coarse grid exactly, interpolate)

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Visualization settingsVisualization settingsVisualization settingsVisualization settings

criteria priority, accuracy pre-defined as a function of scale (XML config files)

on-line selection of themes (left)

rigth: setting of priority, accuracy, data-base set color,

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Priority settingsPriority settingsPriority settingsPriority settings

prio is set automatically depending on the scale

accu is also set autom. prio of each theme may

be adjusted independently

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Attribute settingsAttribute settingsAttribute settingsAttribute settings

select theme choose color set fill attribute set line attribute

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Visualization of PrioritiesVisualization of PrioritiesVisualization of PrioritiesVisualization of Priorities

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Visualization of AccuraciesVisualization of AccuraciesVisualization of AccuraciesVisualization of Accuracies

above: accu 0,upper right: accu 5,

right: accu 9

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Canadien dataCanadien dataCanadien dataCanadien data

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SummarySummarySummarySummary

performance goals met for raster & vector data

automatic generalization works

preprocessing of vector data is very much work

accuracy calculation and adaption work (not completely satisfactory)

configuration of NinJo complicated, not finished

users have different / controversary approaches/demands

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Other applicationsOther applicationsOther applicationsOther applications

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OutlookOutlookOutlookOutlook

integration of attributes (names, geographic/admin. ID‘s) pick mechanisms, operations with polygons (warning) additional European data (e.g. European Global Map) additional data from partners new german DLM1000 in preparation full support of SVG? support of GML (Geographic Markup Language)? NinJo [batch] products need geo-reference

(e.g. for Web Mapping Service)