Advanced Geospatial Information & Intelligence Services Research

Transcription

1 Advanced Geospatial Information & Intelligence Services Research A Framework for Geospatial Uncertainty: From Concept to Communication Adam Chilton, Dr. Ed Figura, Dr. Adel Bolbol, Dr. Gobe Hobona, Prof. Mike Jackson

2 Research Programme and the Team AGIS: Advanced Geospatial Information & Intelligence Services 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 Uncertainty Visualisations for Map Portrayal

4 Questions, questions, questions How old is this information? How accurate are these coordinates? Where did the information come from? What is missing? Is the road 2 lane? All weather? Where is it safe to step? What are the chances of being spotted?

5 Different Opinions I don t want to see more confusion I m not interested in uncertainty show me the facts Whoa information overload! Of what use is it to me, to be shown uncertainty? What does this all mean? Is this CE 90 or some other measure?

6 Framework for Uncertainty ISO ISO ISO UncertML MGMP WMS SLD KML WxS ISO = International Organization for Standardization MGMP = MOD Geospatial Metadata Profile UncertML = a conceptual model and XML encoding designed for encapsulating probabilistic uncertainties WMS = Web Map Service SLD = Styled Layer Descriptor KML = Keyhole Markup Language WxS = Web Service (any of the many web services)

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 Uncertainty Model

9 Uncertainty Representation MGMP (MOD Geospatial Metadata Profile) 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 METADATA

10 class Uncertainty Fig A.4 : Data quality information Representation Metadata entity set information::md_metadata DQ_Scope + level: MD_ScopeCode + extent: EX_Extent [0..1] + leveldescription: MD_ScopeDescription [0..*] "leveldescription" is mandatory if "level" notequal 'dataset' or 'series' +dataqualityinfo 0..* DQ_DataQuality "report" or "lineage" role is mandatory if scope.dq_scope.level = 'dataset' «CodeList» DQ_Ev aluationmethodtypecode + directinternal + directexternal + indirect + scope: DQ_Scope +report 0..* +lineage 0..1 LI_Lineage DQ_ConformanceResult + specification: CI_Citation + explanation: CharacterString + pass: Boolean DQ_Element DQ_Result + 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] DQ_Quantitativ eresult + valuetype: RecordType [0..1] + valueunit: UnitOfMeasure + errorstatistic: CharacterString [0..1] + value: Record [1..*] MGMP_Quantitativ eresult + uncertaintystatistic: UncertML_Statistic [0..*]

11 Visualisations Colour Visual Variables Additions Temporal Stereotypes Labels GIS-specific Data Discovery Use changes in colour to depict changes in uncertainty Use of transparency, fuzziness, etc. to change the look of symbology as a metaphor for uncertainty Use of added features such as buffers to portray uncertainty Visualisations with a temporal aspect Exploit everyday stereotypes to portray a level of uncertainty Techniques for labels Require GIS software for presentation or creation Visualising dataset uncertainty to aid data discovery

12 Visualisations: Colour Point Line Polygon SLD GIS Google Earth Other Method of Creation Technique Thumbnail Colour Hue Intensity Saturation Traffic Light Colours 12

13 Visualisations: Visual Variables Point Line Polygon SLD GIS Google Earth Other Method of Creation Technique Thumbnail Grain Orientation X - hatch Density Sketchiness Jitter Resolution 14

14 Visualisations: Stereotypes Point Line Polygon SLD GIS Google Earth Other Method of Creation Technique Thumbnail Stars Traffic Light Icons Traffic Light Colours Emoticons 18

15 Case Study: NEO Non-Combatant Evacuation Operation Focus is planning an evacuation route and sharing with decisionmakers Look at 3 aspects: 1. Data Discovery 2. Analysis 3. Dissemination

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 Open metadata standards in support of the visualisations

17 Data Discovery: Star Rating 24

18 Data Discovery: Traffic Lights 25

19 Data Discovery: Saturation 26

20 Data Discovery: Spider Diagram 27

21 Data Discovery: Bar Chart 28

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) International Organisation for Standardisation (ISO)

23 Analysis: Quickest Route No Uncertainty With Uncertainty

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 Rendered data is Crown Copyright Ordnance Survey

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 Dissemination: KML

29 Dissemination: WMS Extruded Polygons

30 Hexagons Background map is Crown Copyright Ordnance Survey

31 Hexagons

32 Sketchiness Background map is Crown Copyright Ordnance Survey

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 Portrayal Management

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