Visualising Uncertain Flood Inundation Maps
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Visualising Uncertain Flood Inundation Maps. David Leedal 1 , Jeff Neal 2 , Keith Beven 1,3 and Paul Bates 2 . Lancaster Environment Centre, Lancaster University, Lancaster, UK ; School of Geographical Sciences, University of Bristol, Bristol, UK ;

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Visualising Uncertain Flood Inundation Maps

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Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

David Leedal1, Jeff Neal2, Keith Beven1,3 and Paul Bates2.

Lancaster Environment Centre, Lancaster University, Lancaster, UK;

School of Geographical Sciences, University of Bristol, Bristol, UK;

Geocentrum, Uppsala University, Uppsala, Sweden


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Outline

Demonstrate the Google uncertain flood inundation map tool

Talk about why information visualisation is important in this context

Discuss the potential impact of ‘smart operations’ such as the visualisation tool within flood risk management


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Visualisation tool components

JSON mechanism for loading data to JavaScript arrays

Python scripts for batch processing GIS data to JSON format and PNG images

Jquery sliders

Depth Exceedence selector

Point depth exceedence selector using Google charts


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Visualisation tool components

JSON mechanism for loading data to JavaScript arrays

Python scripts for batch processing GIS data to JSON format and PNG images

Jquery sliders

Depth Exceedence selector

Point depth exceedence selector using Google charts

Main control panel with intuitive user interface widgets

Drag and drop marker with interactive depth exceedence graph

The ‘show all’ probabilities option using a colour map to represent probability of exceediong the chosen depth.


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Why is information visualisation important?

  • Some useful definitions:

  • The human/floodplain system

  • ‘Objects’ or ‘blocks’

  • Operations

  • Data > information > understanding

  • Semiotic mechanism


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Examples of Objects

  • Human systems:

  • Governance

    • National

    • Regional

    • local

  • Developer

  • Environmental NGO (EA)

  • Consultant

  • Resident of floodplain


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Examples of Objects

  • Physical systems:

  • A risk zone

  • A residential property

  • A factory

  • Documents:

  • Planning proposal

  • Flood risk Assessment

    • Risk map


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Examples of Objects

  • Each object:

  • encapsulates its own data (which could be other objects)

  • can perform operations on its data

  • Can interact with other objects


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Examples of Objects

Objects represented by UML/SySML:


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Examples of Objects

When an actor in the human/floodplain system instigates a use-case:

A series of interactions between objects is instigated.

See for example the UK PPS25 document.


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Data > understanding

  • Data is not information

  • Information is not understanding

  • Information implies data in flow

  • Understanding comes about through the decoding of information by a suitable semiotic system


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Operations are high level

  • Example: a ‘resident’ object may contain the operation:

  • ‘Show me the estimated risk that my property will flood sometime in the next 100 years’

  • The operation must be designed to create understanding (combination of data and semiotic processes)


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Complicated to Complex systems

  • Well designed operations should aim to:

  • Increase the degree of understanding that actors can extract from available information

  • Increase the ease with which actors can access and exchange information

  • Increase the capacity to archive information


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Complicated to Complex systems

1890

1800

medieval


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Complicated to Complex systems

Present (OS crown (c))


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Complicated to Complex systems

Present (OS crown (c))


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Desirable properties of a complex human/floodplain system

  • To redistribute and allocate land use types to achieve:

  • Self adapting behaviour

  • Robustness

  • Decentralisation

  • Optimal seeking


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

Summary

  • The human/floodplain system is complicated but not Complex. If we can move towards a Complex system we may achieve robustness, self adaptation and efficient allocation of land use types

  • Transition to self adaptation may be hastened by the design of effective operations

    • Understanding

    • Access

    • Exchange

    • Archive

  • Probabilistic inundation mapping is key

  • The google maps visualisation tool is an attempt to design such an operation


Visualising uncertain flood inundation maps

Visualising Uncertain Flood Inundation Maps

David Leedal1, Jeff Neal2, Keith Beven1,3 and Paul Bates2.

Fig(c). The web-tool for visualising uncertain flood inundation data. The figure shows the main components of the tool including a georeferenced overlay selectable by the user via slider controls, a point-specific marker showing depth against probability of exceedence, and text areas providing word-based summaries of the chosen overlay and key technical terms.

Fig(a) Main control panel with intuitive user interface widgets

Fig(d). Drag and drop marker with interactive depth exceedence graph

Fig(b). The ‘show all’ probabilities option using a colour map to represent probability of exceediong the chosen depth.


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