Geographic Data Uncertainty
There are thousands of ways to measure the
position, shape, orientation and size of phenomena
or objects
Data uncertainty varies spatially and over time
Ambiguity of concepts (semantics,
geometry)
Poorly known resolution and precision
Lack of up-to-dateness and timeliness
Incompleteness
Low level of « processability »
…
1
Geographic Data Uncertainty
Everytime data are reused or transformed,
additional uncertainty is introduced
2
Geographic Data Uncertainty
There always remain residual uncertainty = risk
absorbed by
 data
producers
 data brokers
 Users
Uncertainty can be reduced
 Better
observation technologies and methods
 Standards
 Training

…
3
Geographic Data Uncertainty and Ethics
Typical users take digital data for granted,
assuming their quality is high and fits the
intended usage
An increasing number of incidents and
accidents result from the inappropriate use of
geospatial data
5
Geographic Data Uncertainty and Ethics
“Erroneous, inadequately documented, or
inappropriate data can have grave
consequences for individuals and the
environment.”
(AAG Geographic Information Ethics Session Description, 2009)
6
http://dataquality.scg.ulaval.ca
Geographic Data Uncertainty and Ethics
From an ethics point of view:
Poor quality data should not be used for
sensitive applications where it poses a risk of
harm
Need appropriate safeguards to avoid the
harm, and to provide effective warnings
Not enough just to anticipate intended uses
and data quality requirements. Must
anticipate the possible misuses of the system
as well
Data Uncertainty: today’s approaches
Victims’ approaches and reactions
Don’t follow
Don’t buy
Don’t
use
Never use
again…
from Bedard et al., U. of Laval
Ethical Analysis
Step 1. State problem.
Step 2. Check facts. Many problems disappear
upon closer examination, while others change
radically.
Step 3: Identify relevant factors. For example,
persons involved, laws, professional code, other
practical constraints.
Step 4: Develop list of options. Be imaginative, try
to avoid “dilemma.”
Step 5: Test options using seven tests.
Step 6: Make a choice.
Step 7: Review steps 1-6
Source: DiBiase et al under review
Data Uncertainty: Today’s Approaches
Specifications,
Quality control
Quality analysis Context-sensitive
system
warnings
Metadata
management
Spatial
Integrity constraints
Methods to select
best sources
Spatial
Database
Error-aware GIS,
Fuzzy operators
Users
...
Users
Internet
Paper map
Web services
Data
Data
collection production
Data
Diffusion
Data
Data
Selection Usage
web
services
-Training
-Manuals
-Access control
from Bedard et al., U. of Laval
Data Uncertainty: today’s approaches
Ethics-related issue
Codes of ethics influence « Good Practices »
Ex. professionals must care about individuals and
environment
« Professional misconduct » is typically set out in
regulations
Ex. Negligence, failure to report or remedy to a
danger, to protect people
In case of lawsuits, Codes of ethics have impacts
Data Uncertainty: today’s approaches
Ethics-related issue
Professional self-regulatory bodies have codes
of ethics contained in regulations
These regulations are enacted by governments
Professionals’ primary duty is to the public
welfare
Data Uncertainty: today’s approaches
Ethics-related issue
Data uncertainty issues end up in the hands of
legal systems, but they begin in the hands of
systems designers
Software engineering methods based on formal models are
recognized as the most rigorous approaches to develop quality
systems
Good practices require to understand clearly data
quality requirements and fitness-for-use
from Bedard et al., U. of Laval
Data Uncertainty: today’s approaches
Ethics-related issue
It is a duty for the expert to care about users
and to inform them about inappropriate usages
of spatial data
from Bedard et al., U. of Laval
Data Uncertainty: today’s approaches
Ethics-related issue
Involve client in every phase of a system
development method
This involvement must include decisions about
the risks related to spatial data definition,
selection, production, dissemination and potential
reuse (intended or not)
Risk-related decisions must be understood and
approved by the client
from Bedard et al., U. of Laval
C.A.R.E.F.U.L.
Computer-Assisted Risk
Evaluation For Usage
Limitation
Yvan Bédard1, Jennifer Chandler2, Rodolphe
Devillers3, Marc Gervais1
1Univ.
Laval, Geomatics
2Univ. of Ottawa, Law
3Memorial Univ. of Newfoundland, Geography
« C.A.R.E.F.U.L. »
Analysis
Operation
Formal method
+
Data modeling tool
Design
+
CAREFUL
extension
Implementation
(+ training)
traditional
knowledge
about risk
Development
CAREFUL
knowledge
about risk
« C.A.R.E.F.U.L. »
Analysis
-Identify
-Evaluate
Risk analysis
New needs
CAREFUL:
Operation
Informed and
protected users
New usages
WHAT: better risk management of
potential spatial data misuses
HOW: extending system design
methods and modeling tools to add
risk-related info
WHY: professional ethics, liability
-Indifferent
Design
-Avoid
Risk strategy -Transfer
-Control
Implementation
Development
Training, doc.
Warnings
« C.A.R.E.F.U.L. »
Risk-related
metadata
in
Data modeling
tool
« C.A.R.E.F.U.L. »
ISO-3864-2
Symbols
for Warnings
in Data
Modeling Tool
« C.A.R.E.F.U.L. »
Risk-Related
Reporting
with the help of
Data Modeling
Tool
-user manual
-training material
-fitness-for-use report
-…
« C.A.R.E.F.U.L. »
Context-sensitive Warnings Generated from Data Modeling Tool
Canada GEOIDE Project #PIV-23
Objective: to develop innovative solutions to evaluate GI
quality and contribute to its responsible commercialization
and hence achieve an healthy protection of the public
Protection of
investment
and copyright
Privacy
Data
mashup
Selection
and usage
of GI
Geomatics
Engineering
Social
and legal
issues
Faculty
of Law
Geography
Civil
liability
Quality
of GI
Geomatics
Engineering
On the U.S. side, NSF Ethics Education
Impacts on Professional System
Designers, GIS Users
Ethics leads to protecting users against harm
Several approaches exist to reduce risks
Ethics leads to manage the risks related to
uncertain data or inappropriate uses of data
including unintended uses
CAREFUL is a new ethics-centered approach
extending formally proven software engineering
methods
Gisprofessionalethics.org contain new GIS
ethics-centered graduate curricula in progress
Gateway to the Literature
Plewe, B. The nature of uncertainty in historical
geographic information, Transactions in GIS, 6(4): 431456, 2002.
UCGIS. Uncertainty in Geographic Data and GIS-Based
Analyses, UCGIS Research Priority White Paper,
Leesburg, VA: UCGIS, 2002.
DiBiase, D., Harvey, F., Wright, D., and Goranson, C.
The GIS professional ethics project: Practical ethics
education for GIS professionals, in Unwin, D., Foote, K.,
Tate, N., and DiBiase, D. (eds.), Teaching Geographic
Information Science and Technology in Higher
Education, London: Wiley and Sons, in press, 2011.
Gateway to the Literature
Bater, C. W. and N. C. Coops (2009). "Evaluating error associated with LIDARderived DEM interpolation." Comp. Geosci 35: 289-300.
Dendoncker, N., C. Schmit, et al. (2008). "Exploring spatial data uncertainties in
land-use change scenarios." Int. J. Geog. Inf. Sci. 22(9): 1013-1030.
Xiao, N., C. A. Calder, et al. (2007). "Assessing the effect of attribute uncertainty
on the robustness of choropleth map classification." Int. J. Geog. Inf. Sci. 21(12): 121-144.
Aguilar, F. J., M. A. Aguilar, et al. (2007). "Accuracy assessment of digital
elevation models using a non-parametric approach." Int. J. Geog. Inf. Sci. 21(67): 667-686.
Zhou, Q., X. Liu, et al. (2006). "Terrain complexity and uncertainties in gridbased digital terrain analysis." Int. J. Geog. Inf. Sci. 20(10): 1137-1148.
Oksanen, J. and T. Sarjakoski (2006). "Uncovering the statistical and spatial
characteristics of fine toposcale DEM error." Int. J. Geog. Inf. Sci. 20(4): 345370.
Lindsay, J. B. (2006). "Sensitivity of channel mapping techniques to uncertainty in
digital elevation data." Int. J. Geog. Inf. Sci. 20(6): 669-692.
Henley, S. (2006). "The problem of missing data in geoscience databases." Comp.
Geosci 32: 1368-1377.
Gregory, I. N. and P. S. Ell (2006). "Error-sensitive historical GIS: Identifying areal
interpolation errors in time-series data." Int. J. Geog. Inf. Sci. 20(2): 135-152.
Bishop, T. F. A., B. Minasny, et al. (2006). "Uncertainty analysis for soil-terrain
models." Int. J. Geog. Inf. Sci. 20(2): 117-134.
Wu, J., T. H. Funk, et al. (2005). "Improving spatial accuracy of roadway networks
and geocoded addresses." Trans. GIS 9(4): 585-602.
Shi, W. Z., Q. Q. Li, et al. (2005). "Estimating the propagation error of DEM from
higher-order interpolation algorithms." Int. J. Remote Sensing 26(14): 3069-3084.
Shi, W. Z., M. Ehlers, et al. (2005). "Uncertainties in integrated remote sensing and
GIS." Int. J. Remote Sensing 26(14): 2911-2916.
Kardos, J., G. Benwell, et al. (2005). "The visualisation of uncertainty for spatially
referenced census data using hierarchical tessellations." Trans. GIS 9(1): 19-34.