• No results found

Advanced Geospatial Information & Intelligence Services Research

N/A
N/A
Protected

Academic year: 2021

Share "Advanced Geospatial Information & Intelligence Services Research"

Copied!
34
0
0

Loading.... (view fulltext now)

Full text

(1)

A Framework for Geospatial Uncertainty: From Concept to Communication

Adam Chilton, Dr. Ed Figura, Dr. Adel Bolbol, Dr. Gobe Hobona, Prof. Mike Jackson

Advanced Geospatial Information &

Intelligence Services Research

(2)

Research Programme and the Team

AGIS:

A

dvanced

G

eospatial

I

nformation &

I

ntelligence

S

ervices

Research

An industry led research programme funded by Dstl (Defence Science and Technology Labs) who as part of

the MOD (Ministry of Defence) provide science and technology advice into the MOD.

(3)
(4)

“Is the road 2 lane?”

“All weather?”

“What are the

chances of being

spotted?”

“Where is it safe to step?”

“How old is this information?”

“How accurate are

these coordinates?”

“Where did the

information come

from?”

“What is missing?”

(5)

Different Opinions

“I don’t want to see more confusion”

“I’m not interested in uncertainty – show me the facts”

“Whoa – information overload!”

“What does this all mean?”

“Of what use is it to me, to be shown uncertainty?”

(6)

Framework for Uncertainty

ISO 19115

ISO 19139

ISO 19157

UncertML

MGMP

WMS

SLD

KML

WxS

ISO

=

I

nternational

O

rganization for

S

tandardization

MGMP

=

M

OD

G

eospatial

M

etadata

P

rofile

UncertML

= a conceptual model and XML encoding designed for encapsulating probabilistic

uncertainties

(7)

Sources of Uncertainty

There is uncertainty in the data

from the moment it is measured...

… this propagates through

subsequent processes

… including how the data

is presented to the user

(8)
(9)

Uncertainty Representation

MGMP (

M

OD

G

eospatial

M

etadata

P

rofile)

– Data Quality Element of the profile

– Within Data Quality is an element called Quantitative

Result (i.e. a data quality analysis with a quantitative

value)

– Add a new quantitative result measurement for data

quality called: Uncertainty Statistics

(10)

Uncertainty Representation

class Fig A.4 : Data quality information

DQ_Element + nameOfMeasure: CharacterString [0..*] + measureIdentification: MD_Identifier [0..1] + measureDescription: CharacterString [0..1] + evaluationMethodType: DQ_EvaluationMethodTypeCode [0..1] + evaluationMethodDescription: CharacterString [0..1] + evaluationProcedure: CI_Citation [0..1] + dateTime: DateTime [0..*] + result: DQ_Result [1..2]

"report" or "lineage" role is mandatory if scope.DQ_Scope.level = 'dataset'

DQ_DataQuality

+ scope: DQ_Scope

LI_Lineage Metadata entity set information::MD_Metadata

«CodeList» DQ_Ev aluationMethodTypeCode + directInternal + directExternal + indirect DQ_Result "levelDescription" is

mandatory if "level" notEqual 'dataset' or 'series' DQ_Scope + level: MD_ScopeCode + extent: EX_Extent [0..1] + levelDescription: MD_ScopeDescription [0..*] DQ_Quantitativ eResult + valueType: RecordType [0..1] + valueUnit: UnitOfMeasure + errorStatistic: CharacterString [0..1] + value: Record [1..*] DQ_ConformanceResult + specification: CI_Citation + explanation: CharacterString + pass: Boolean MGMP_Quantitativ eResult + uncertaintyStatistic: UncertML_Statistic [0..*] +report 0..* +lineage 0..1 +dataQualityInfo 0..*

(11)

Visualisations

Colour

Use changes in colour to depict changes in uncertainty

Visual

Variables

Use of transparency, fuzziness, etc. to change the look of symbology as a metaphor for uncertainty

Additions

Use of added features such as buffers to portray uncertainty

Temporal

Visualisations with a temporal aspect

Stereotypes

Exploit everyday stereotypes to portray a level of uncertainty

Labels

Techniques for labels

GIS-specific

Require GIS software for presentation or creation

Data

Discovery

Visualising dataset uncertainty to aid data discovery

(12)

Visualisations: Colour

Technique

Thumbnail

P

oi

n

t

L

in

e

P

ol

ygo

n

Method of

Creation

S

L

D

G

IS

G

oo

g

le

Ea

rt

h

Oth

e

r

Colour Hue

Intensity

Saturation

Traffic Light

Colours

(13)

Visualisations: Visual Variables

Technique

Thumbnail

P oint Line P olygo n

Method of Creation

S LD GIS G oo gle E art h O the r

Grain

Orientation

X - hatch Density

Sketchiness

Jitter

Resolution

(14)

Visualisations: Stereotypes

Technique

Thumbnail

P

oint

Line

P

olygo

n

Method of Creation

S

LD

GIS

G

oo

gle

E

art

h

O

the

r

Stars

Traffic Light

Icons

Traffic Light

Colours

Emoticons

(15)

Case Study: NEO

N

on-Combatant

E

vacuation

O

peration

Focus is planning an

evacuation route and

sharing with

decision-makers

Look at 3 aspects:

1. Data Discovery

2. Analysis

(16)

1. Data Discovery

Analyst interested in using a number of data

sources

– Terrain

– Buildings

– Road networks

Questions to consider

– How appropriate is data for use in route planning?

– What limitations are there?

– What are the quality measures?

Visualisation of uncertainty through web portals

– Support user in selecting data appropriate for task in hand

– Different visual metaphors

(17)
(18)
(19)
(20)
(21)
(22)

2. Analysis

Need awareness of variations that uncertainty in

the data could introduce

– Travel speed

– Weather

– Crowds

– Road surfaces

– Number, location and characteristics of snipers

How do open standards facilitate the

understanding of uncertainty

– Open Geospatial Consortium (OGC)

(23)

Analysis: Quickest Route

(24)

Analysis: Lens of Uncertainty

Lens of Uncertainty

Displays details of

uncertainty

(25)

Analysis: Impact of Uncertainty

Time taken to reach a destination,

when uncertainty is taken into account,

can be considerably longer compared

to that with no uncertainty

This could have a significant effect

on the outcome of the operation

(26)

Analysis: Influence of Sniper Uncertainty

Routing in the presence of snipers

Uncertainty in sniper details

introduces uncertainty

-modelled by a probabilistic

viewshed

(27)

3. Dissemination

To decision makers

Planned routes

Sniper information

Via web services based on the OGC WMS standard

Cartographic styling using the OGC SLD standard

– For presentation on thin clients such as web browsers

– For presentation on desktop applications such as QGIS

(28)
(29)
(30)

Hexagons

(31)
(32)

Sketchiness

(33)

Conclusions

Conclusions

– Approaches for modelling, encoding and visualising

uncertainty can be organised into a framework to

facilitate discovery, analysis and dissemination

– The structured capture and portrayal of uncertainty can

help to improve the understanding of risk during decision

making

Further work:

– Capture of Uncertainty

– Metrication of Visualisations

Let us know if you are interested in participating in an experiment

(34)

References

Related documents