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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)

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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

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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

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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

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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

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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

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(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

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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:

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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

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(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.

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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

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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

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Serv

ices

Transducer Domain

Sensor Services

Intermediate Sensor Services

Acquisition Domain

Modelssensor

data store

Furt

her

Info

rmat

ion

Pro

cess

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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)

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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

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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

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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

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Fast overview with Tooltips

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Comparison of stations

Different time windows

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Different parameters

Automatic weather stations

Specific discharge

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Temperature interpolation

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Integration of forecasting

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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