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Error-aware GIS at work: real-world applications of the Data Uncertainty Engine Gerard Heuvelink Wageningen University and Research Centre with contributions.

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Presentation on theme: "Error-aware GIS at work: real-world applications of the Data Uncertainty Engine Gerard Heuvelink Wageningen University and Research Centre with contributions."— Presentation transcript:

1 Error-aware GIS at work: real-world applications of the Data Uncertainty Engine Gerard Heuvelink Wageningen University and Research Centre with contributions from JAMES BROWN, Erik van den Berg, Rianne Bijlsma, Wies Vullings, and others

2 Slide from John Shi (Keynote ISSDQ ’07): … Implementation of the theoretical development in commercial GIS and other software … FUTURE:

3 Error-aware GIS, term introduced at ISSDQ’99

4 But the call for an error-aware GIS started much earlier

5 Peter’s vision, dream, or wishful thinking?

6 All GIS data are uncertain to some degree Measurement errors Interpolation errors Classification errors Generalisation errors Many causes, including: Consequences: Uncertainty propagation Poor decisions

7 Current GIS cannot truly handle uncertain data The Data Uncertainty Engine (DUE) aims to fill this gap, by developing a framework for:  Assessing uncertainty in data  Storing uncertain data within a database  Generating realisations of uncertain data for visualisation and use in Monte Carlo studies

8 Before presenting and illustrating DUE, we need to discuss: What is uncertainty? How can we represent it statistically? Here, we focus on uncertain spatial data, but DUE is also suitable for temporal and space-time data

9 What is uncertainty? Uncertainty arises when we are not sure about the ‘true’ state of the environment; it is an expression of confidence based on limited knowledge Uncertainty is subjective; one person can be more uncertain than another In the presence of uncertainty, we cannot identify a true ‘reality’. But perhaps we can identify all possible realities and a probability for each one Example: number of people in this room

10 Uncertainty can be described statistically with a probability distribution function (pdf)

11 Characterising uncertain spatial data Based on spatial objects that comprise one or more attributes Position of objects can be uncertain Attribute values of objects can be uncertain Uncertainty about spatially distributed attributes can be spatially correlated Positional and attribute uncertainty are represented by (possibly complex) pdfs Sampling from these pdfs (typically by using a random number generator) yields ‘possible realities’

12 Possible realities of objects with positional uncertainty Rigid object Deformable object

13 Possible realities of an uncertain spatial attribute

14 Data Uncertainty Engine: current functionality Conceptual framework for guiding an uncertainty analysis Specification of probability distribution functions (pdf) for position and attributes of spatio-temporal objects Use of expert knowledge and/or sample data to help define the pdf Specification of correlations in space and time Efficient stochastic simulation from pdfs Import and export to files and database

15 Uncertainty analysis with DUE, five stages  Loading and saving data and projects  Identifying the causes or ‘sources’ of uncertainty  Defining an uncertainty model (pdf)  Reflecting on the quality or ‘goodness’ of the model  Simulating from a pdf for visualisation and Monte Carlo uncertainty propagation studies

16 Stage 1: Load DUE objects and attributes Some objects Attributes of selected object Navigate Five stages of DUE

17 Stage 2: Help identify sources of error Source description Types of error sources

18 Stage 3: Defining a pdf Parameter values Shape functions Graph for one location Table view of parameter values

19 Stage 3 (cont’d): Defining a correlation function Parameter values Shape functions Correlation function Table view of sample data

20 Stage 4: Reflect on goodness of pdf Additional info Default categories Description of category

21 Parameter values Summary statistics Output to file Number of realisations Stage 5: Stochastic simulation and export realisations

22 Three examples of real-world applications (ongoing projects)  Uncertainty and sensitivity analysis with GeoPEARL

23 1.Uncertainty and sensitivity analysis with GeoPEARL Goal: Analyse how uncertainty in soil and pesticide properties propagates to leaching of pesticides to groundwater, assess main source of error Role of DUE: Generate 500 realisations of uncertain pesticide properties (half-life and coefficient of sorption to organic matter)

24 Define a constant in the Input stage

25 Define a (lognormal) pdf in the Model stage

26 Generate 500 realisations in the Output stage

27 Three examples of real-world applications  Uncertainty and sensitivity analysis with GeoPEARL  Dealing with uncertainty in spatial planning

28 2.Dealing with uncertainty in spatial planning Goal: Address uncertainty in spatial planning to improve monitoring and comparison of spatial plans Role of DUE: Assess and simulate positional uncertainty of breeding bird areas

29 GeO 3, Omgaan met Onzekere Objecten, Bsik RGI, Testcase Integral Zoning, Brabant province Preliminary research First concept IZ Second concept IZ Design IZ Establishing design IZ Establishment IZ

30 Defining spatial correlation of positional error in DUE

31 Breeding bird areas in subregion of province defaultsimulated

32 Three examples of real-world applications  Uncertainty and sensitivity analysis with GeoPEARL  Dealing with uncertainty in spatial planning  Handling uncertainty in integrated water basin management

33 3.Handling uncertainty in integrated water basin management Goal: Analyse the influence of input and parameter uncertainty of diffuse emissions on the average nitrogen and phosphorus concentrations of the Regge river outlet Role of DUE: Simulate spatially correlated topsoil aluminium and iron content

34 Mean and variance not constant in space

35 Three example realisations of standardised aluminium and iron content aluminium iron 312

36 Conclusions DUE takes us an important step forward towards an operational error-aware GIS It has proven useful in a variety of cases (and also in teaching): it is truly generic DUE does not replace GIS because it does not do any visualisation or spatial operations, such as overlay DUE does not do uncertainty propagation analysis (it merely generates input for a Monte Carlo analysis) It is not yet entirely bug-free The user-interfacing must be improved The functionality must be extended (e.g. handling spatially dependent categorical attributes) We definitely need funding for a follow-up project!

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38 Thank you © Wageningen UR


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