Post on 01-Nov-2020
transcript
1
Lorenz Hurni
1963 Geboren in Biel/Bienne (CH)
1988 dipl. Verm.-Ing., ETH Zürich
1995 Dr. sc. techn., ETH Zürich
1994–1996 swisstopo, Bern
1996– Professor und Vorsteher,Institut für Kartografie, ETH Zürich
1996– Chefredaktor, Atlas der Schweiz
2009– Chefredaktor, Schweizer Weltatlastlas
2009– Vorsteher Departement Bau, Umwelt undGeomatik, ETH Zurich
Offene Architekturen, Raumdateninfrastrukturen und Sensornetzwerke für Anwendungen im Risikomanagement
Die EU-Projekte ORCHESTRA und SANY
Lorenz HurniInstitut für Kartografie, ETH Zürich
Kartografische Modellierung und interaktive Visualisierungen von Naturprozessen- und gefahren
Die Projekte NAHRIS, Atlas der Schweiz, ORCHESTRA , SANY und RETICAH
Lorenz HurniInstitut für Kartografie, ETH Zürich
Eidgenössische Technische HochschuleETH Zurich
Founded in 1855
15‘000 Students, 5000 Staff, 350 Professors
16 Departments16 Departments
Dept. of Civil, Environmental and Geomatic Eng.
100/50/20+20 students per year; Bologna 6+4 (3)
Curriculum in Geomatics and Planning
Institute of Cartography (IKA)
2
8
Institute of Cartography IKA
Cartography at ETH since 1855
Founded in 1925 by Eduard Imhof
Focus: topographic thematic and atlas cartography Focus: topographic, thematic and atlas cartography
30 staff members, incl. 10 PhD students
Atlas development projects: Atlas of Switzerland Swiss World Atlas
9
Cartography at ETH Zurich 1855–2009
Johannes Wild1814–1894
ETH 1855–1889
Fridolin Becker1854–1922
ETH 1887–1921
Eduard Imhof1895–1986
ETH 1922–1965
Ernst Spiess*1930
ETH 1964–1996
IKA, ca. 1954
3
2005 1938
Bietschhorn, 3934 m
Our Mission
Apply & extend cartographic high-quality visualisation to new media and thematic fields
Promote the power of cartographic methodologies to GIS users and studentsusers and students
Less (geo)data acquisition, more data selection, harmonisation, interaction and visualisation
Multithematic, multidimensional data
Multimedia Atlas Cartography
What is a map?
A map is an interpreted and symbolised image of geographical reality, representing selected features or characteristics, and is designed for use when spatial c a acte st cs, a d s des g ed o use e spat arelationships are of primary relevance.
Cartography aims at depicting spatially relevant information by means of unique graphical symbols.
Version 2008
4
Observations
Increased presence and use of maps
Fast production, low cost maps (press)
Simplification and trivialisation of map products
Cartographic rules are often disregardedg p g
Overstraining of users by classical maps
Cartographers replaced by designers and laymen, Web 2.0 (blogs)
„Everything“ is georeferenced
Projekt NAHRIS
Dealing with Natural Hazards
E-learning –Projekt im Rahmen von virtualcampus.ch
Start 2000
Operationell 2005p
Modul „Data Presentation“
Thematische Kartenerstellung im Bereich Naturgefahren
23 24
5
Uncertainty visualisation in hazard maps
Uncertainty visualization of snow avalanche intensities (uncertainty buffers of 10%)
Uncertainty visualization of annual probabilities of snow avalanches (output of Bayesian network)
Classical map production process
Motivation, aim, topic of mapping project
Data acquisition, compilation: Map editor
Data interpretation, editing; draft : Map editor
Map design (generalisation, symbolisation): Cartographerp g (g , y ) g p
Map printing/publication
Map use
Web 2.0 mapping / map production process
Motivation, aim, topic Less clear
Data compilation Various sources (professional / layman)
Data interpretation, editing, map draft Low priority
Map design Low priority, predefined symbolisationp g p y, p y
Map printing/publication Predefined channels
Map use
30
6
(Un-)structured existing data collections
Many data collections are heterogeneously or minimally structured (e.g. Internet)
Sophisticated search and data mining methods for data Sophisticated search and data mining methods for data retrieval necessary (“Stecknadel im Heuhaufen”)
High degree of uncertainty regarding data relevance
33
GIS data structures
Traditionally split up: Geometry data Thematic/semantic attributive data
Today: Multithematic/multidimensional data Topics thematic content Topics, thematic content Timestamps Spatial location as one, but not always the most important criterium
What do we need?
Consistent data!
Intuitive tools to easily access data:
Navigation: Space-Time-Topic
Data interaction, manipulation, analysis, p , y
Data visualisation
Multimedia Atlas Information Systems
Multimedia Atlas Information Systems (MAIS) are systematic, targeted collections of spatially related knowledge in electronic form, allowing a user-oriented communication for information and decision-making purposespurposes.
Differences GIS-MAIS after Schneider, 1999:
7
Main functions in a Multimedia Atlas Information System (after Ormeling 1997; Cron, 2006 and others)
General functions
Navigation functions
Didactic functions
Cartographic and visualisation functions
GIS functions
Thematic Navigation
Geo-scientific topics in the „Atlas of Switzerland – V2
40
41 42
8
43 44
45 46
47 48
9
49 50
51 52
53 54
10
55 56
57 58
59 60
11
61 62
63 64
65 66
12
67
(Un-)structured existing data collections
Many data collections are heterogeneously or minimally structured (e.g. Internet)
Sophisticated search and data mining methods for data Sophisticated search and data mining methods for data retrieval necessary (“Stecknadel im Heuhaufen”)
High degree of uncertainty regarding data relevance
ORCHESTRA and SANYEU FP6 Integrated Projects
Disasters and Risks
Public and environment have to face a large series of risks: forest fires, floods, landslides, tornadoes, storms, earthquakes, volcanic eruptions, etc.
Disasters do not necessarily respect national borders, e.g. 2003 f t fi 2003 summer forest fires
2002 floods of Central Europe, …
The number of natural disasters is increasing
Number of victims and economic losses of disasters are increasing
Scientific evidence of extreme climate events, rainfall, drought, …
Lack of interoperability for risk and environmental info.
13
ORCHESTRA
Open Architecture and Spatial Data Infrastructurefor Risk Management
ORCHESTRA aims to deliver an architecture with generic services which are useful in different risk management
li tiapplications
All services developed according to the ORCHESTRA standards will be able to interoperate with each other, making it easier to “develop once, deploy in many different situations”
AnalysisInfo CentreMaps
Archive
Control centre
ORCHESTRA Solution
Thematic data
Spatial data
Meta-informationSensors
Documents
Solution
ORCHESTRA Service Oriented Architecture
Webservices and Service-oriented Architectures
SOA: Loosely-coupled intercting software components that provide services; set of interacting services
Service: Piece of functionality made available by a service provider in order to deliver end results:
e.g. a map in the case of Cartographic Web Services
SOA: Based on concepts of interfaces and messages: Interface: Defined for all services, available for all providers and
users Messages: Described by extensive schemes and delivered through
interfaces
ORCHESTRA Architectural Approach
Architectural
ThematicServices
Simulation Management Hazard Assessment
Catalogue
Vulnerability computation
Flood modelling
ORCHESTRA Architectural Approach
ArchitecturalServices
Catalogue
Authentication
Map & Diagram
Sensor AccessFeature Access
Monitoring
SANY – Extending ORCHESTRA Architecture
14
SANY – Sensors Anywhere
SANY workflow Discovery of sensor data and
related services
Access to sensor observations from different providers
Management of sensor resources Management of sensor resources
Execution of processing services acting on sensor data
Subscription to and visualisation of sensor generated alarms
Visualisation of sensor data on maps, charts, and tables
Application Domain
UserDomain
Sensor Applications e.g. DSS
Fusion Services
(Web) Portals
Visualisation Reporting
ng
Serv
ices
Transducer Domain
Sensor Services
Intermediate Sensor Services
Acquisition Domain
Modelssensor
data store
Furt
her
Info
rmat
ion
Pro
cess
i
Distributed Web Mapping – Where is it needed? ORCHESTRA Service Provision
WebBrowser
MapViewer
ChartViewer
Decision maker
SANY Service Provision
Service Support Environment SSE
SOSServer
e e
WMSServer
Map & DiagramServer
CatalogueServer
CatalogueClient
SOSClient
Fusion(Kriging)
WPSClient
SOSServer
SOSClient
SOSServer
SOSClient
SOSServer
SOSClient
SP4 SP4 SP5 SP6
Air Quality Meteorology Soil DisplacementAir Quality
Map and Diagram Service
Transform geographic data (V/R) and/or thematic data (Census, risk etc.) into a graphical representation using cartographic rules
Based on and enhances OGC (Open geospatial C ti ) t d dConsortium) standards:
WMS: Web Map Service
SLD: Styled Layer descriptor
SE: Symbology Encoding
MDS enables clients to send data (e.g. GML along with style) to be rendered as part of the request message for creating more complex client-side functionalities (GetMap Operation)
15
Cartographic Rules
Symbology Encoding (SE): grammar for styling map data independent of any service interface specification
Independent of data itself
Rule definitions
Three conceptual rule levels:
Generic conditions (overall, e.g. scale dependent map symbolisation)
Specific conditions (e.g. filtering, selection)
Definition of symbology
Specific rule: Spatial operators
SANY Use Case Example Dynamic mapping with cartographic rules
ORCHESTRA Use Case Example
16
Implementation –QGIS mapserver
COGEAR Information Platform Metadata
Swisstopo Data
Data of theproject partners
COGEAR Information Platform
Real-time cartography in operational hydrology Brig-Glis, 1993
17
Kartenmanipulation Zusatzfenster Kartennavigation
Statusleiste
Legenden für RasterdatenExtremwertstatistikenLive‐Bilder der StationZeitreihen, Legenden für KartenelementeLegenden für RasterdatenExtremwertstatistikenLive Bilder der StationZeitreihen, Legenden für Kartenelemente
Datenexploration & Visualisierung > Symbole
Vektor-, Rasterformat
Punkt-, Linien, Flächensymbole Punktsymbole: quantitative oder bildlich Linien: Isothermen Flächen: interpolierte Temperatur Niederschlag Flächen: interpolierte Temperatur, Niederschlag
Farbgebung Divergierend (Temperatur [°C]) Sequentiell (absolute Daten mit Nullpunkt, Seltenheit)
Precipitation: radar image
10
Fast overview with Tooltips
18
10
Comparison of stations
Different time windows
10
10
Different parameters
Automatic weather stations
Specific discharge
10
Temperature interpolation
10
19
Integration of forecasting
10 11
HQ August 2007, 23 h:Discharge and 24h-N-sums
Demonstrations
Animation of Radar Image
Animation of Discharge Map
Information vs. knowledge
«Die Unterschiede zwischen Information, Meinung und Wissen – vor allem in den Massenmedien – werden immer blasser. Die modernen Informationstechnologien wie das Internet bieten zwar eine fast vollständige Enzyklopädie an, doch diese Fülle unbearbeiteter Informationen ist nochdoch diese Fülle unbearbeiteter Informationen ist noch keineswegs Wissen. Es fehlt die Selektion und die Aufbereitung aufgrund kompetenter Sachkenntnis, die das Wissen kennzeichnen und das Begreifen erst ermöglichen.» J. Mittelstrass; nach Tages-Anzeiger-Artikel 8.10.99