A Guide to Geospatial Data Quality

Earth Sciences Sector

Welcome to the Webinar!

Eric Wright

Yvan Bédard

A Guide to Geospatial Data Quality Teleconference ID: 748 918 8

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To join the teleconference: Toll-free in Canada & US: 1-877-413-4790 Ottawa and International: 1-613-960-7514

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Goal: This webinar will inform you on the importance of geospatial data quality, the concepts underlying geospatial data quality, the fundamentals of geospatial data quality evaluation and risk management, geospatial data quality evaluation and risk management in practice, and recommendations in B2B, B2C and C2C contexts. Reminder Please put your mobile devices on mute during the presentation.

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CGDI Webinar: Guide to Geospatial Data Quality

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Registrants in Canada: 92 Registrants in Total : 113

Other registrants: • Netherlands • Sweden • Ireland • Spain • USA • Ukraine • Nigeria • Uruguay • Ecuador • Spain

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Today’s program:  13:30 Welcome and Introduction  13:50 Guide to Geospatial Data Quality  14:40 Questions and Answers (via Webex chat)  14:55 Summary and Conclusions

Introduction to GeoConnections

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GeoConnections The GeoConnections program is a national initiative, led by Natural Resources Canada, designed to facilitate access to and use of authoritative geospatial information in Canada. GeoConnections supports the integration and use of the Canadian Geospatial Data Infrastructure (CGDI). Key Program Activities:

Geospatial Strategy and Leadership – continued coordination of

geomatics activities in Canada, requiring the development and implementation of long-term national geomatics strategies and policies, in partnership with CGDI stakeholders.

Canadian Geospatial Data Infrastructure – work with the geomatics

community to advance the operational policies and standards needed to complete the CGDI and support the use of geospatial information.

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GeoConnections: Objectives  Create increased awareness of the benefits of using geospatial data and tools to achieve goals for social, economic and environmental priorities.  Facilitate the integration and use of geospatial data to support effective decision making.  Coordinate the development of national policies, standards and mechanisms and support their implementation to ensure maintenance and updating of geospatial data and compatibility with global standards.  Keep Canada at the leading edge of accessing, sharing and using geospatial information via the Internet.

The Canadian Geospatial Data Infrastructure

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What is the CGDI? 

The CGDI is an online network of resources that improves the sharing, use and integration of information tied to geographic locations in Canada.

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In essence, via collaboration, the CGDI is the convergence of policies, standards, technologies, and framework data necessary to harmonize all of Canada’s location-based information.

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Through the CGDI, Canadians can discover, access, visualize, integrate, apply and share quality location-based information. The CGDI allows citizens to gain new perspectives into social, economic, and environmental issues and make effective decisions.

CGDI Components and Guiding Principles

CGDI – Overview; CGDI Vision, Mission and Roadmap: http://geoconnections.nrcan.gc.ca/18

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Collaboration and Interoperability  Collaboration, partnerships and a common way forward between federal, provincial, territorial and regional governments; the private sector; and academia ensure interoperability for the CGDI.

 Interoperability is achieved by the convergence of framework data, policies, standards and technologies necessary to harmonize Canada’s location-based information.

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Guide to Geospatial Data Quality  Discusses why geospatial data quality is important  Reviews the concepts underlying geospatial data quality  Discusses the geospatial data quality evaluation process in details (based on ISO 19157 and ISO 19158)  Discusses the management of risks of inappropriate use of geospatial data (based on ISO 31000)  Presents detailed examples of quality evaluation and risk management tasks to be undertaken in the B2B, B2C and C2C contexts

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Guest Speaker Dr. Yvan Bédard is a consultant in research management and the Strategic/Scientific Senior Advisor of Intelli3, a private company specializing in Geo-BI and GeoData Analysis. He was a full professor and successful researcher for 28 years at Université Laval Department of Geomatics Sciences. He was the founding-director of the Centre for Research in Geomatics and a member of several strategic and scientific committees in Canada. His expertise is in GIS/Spatial Databases/Business Intelligence and Analytics as well as in the most recent spatial data quality issues. He holds a B.Sc.A. in Surveying, a M.Sc. in Geodesy, and a Ph.D. in Civil Engineering while he's been involved in computer sciences for over 35 years.

A Guide to Geospatial Data Quality

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Guide Outline  Introduction: Why is geospatial data quality important?  Background: the concepts underlying geospatial data quality  Geospatial data quality evaluation and risk management: the fundamentals  Geospatial data quality evaluation and risk management in practice  Recommendations for B2B, B2C and C2C contexts  Conclusions

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Introduction Section Overview

 The new context  Geospatial data quality:  Why?  Who should care?

 Geospatial data quality and risk management  Why this guide?

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The new context  The production, distribution and usage of geospatial data have changed dramatically in the last decade: new players  Citizens are massively consuming and producing geospatial data:    

Smartphones Navigation systems Numerous sources of digital maps and imagery Virtual globes, …

 It is the beginning of a new era… (…)

1950

1960

1970

Hardware Era

1980

1990

2000

2000

Software Era

2010

2020

(…)

Data Era

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The new context  The context is evolving… B2B B2B

B2C

C2C  The roles and responsibilities of producers, distributors and users of geospatial data and services are evolving  National geospatial data infrastructures are adapting to the new challenges of data quality evaluation, quality information communication and risk management

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Why is geospatial data quality important ?  Data are collected to meet well-defined goals and their quality is typically defined to meet these goals  To minimize costs and delays, geospatial data reuse and interoperability have become very popular  But finding data that fit an intended purpose is not easy

 In the new B2C and C2C contexts, typical geospatial data users do not understand the uncertain nature of geospatial data and take digital data for granted:  Erroneous decisions, possibly having significant social, political or economic consequences  Incidents of all sorts (injuries, material damages, 911 delays, people lost in the wild, …)

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Why is geospatial data quality important ?

Plane crash and death of 47 passengers: wrong runway map

22 injured students: erroneous vertical clearance

Death of a tourist: GPS map error (©The Globe and Mail)

Overlaying cadastral data on aerial photo, he cuts the bush fence and has a fight with his neighbor, one person at the hospital

Death of a lost skier: completeness error, missing path on map

Wrong house demolished: GPS data error

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Why is geospatial data quality important ?  Regulations and court decisions suggest that consumers must be informed about the quality of a product or service, as well as about the risks and prohibited usages:  User manuals, guarantees, warnings, …

 A recent survey in the Canadian geomatics industry indicated that (Gervais, Bédard, Larrivée, Rivest, & Roy, 2013):  70% of respondents believe users are not aware of the potential risks of using geospatial data  Respondents show strong concerns about users’ ability to manage risks  81% thought that the geospatial industry could do more to reduce users’ risks of inappropriate use of geospatial data  Users’ primary complaints concerned poor documentation and data quality 21

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Why is geospatial data quality important ?  The CGDI aims to help the geospatial community meet the data quality challenge by facilitating:     

The The The The The

comparison of data sources analysis of fitness-for-use estimation of the cost of preparing data evaluation of the risks of inappropriate data usages communication about quality and risks to users

 Data quality is a key to good decision making

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Who should care about geospatial data quality ?  In the B2B context, every player involved contributes to define data quality, from needs analysis and database design to the acquisition, integration, transformation and dissemination of geospatial data  Traditionally, B2B context (experts talk to experts):     

Completeness Logical consistency Positional accuracy Thematic accuracy Temporal quality

Metadata

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Who should care about geospatial data quality ?  Today, the new B2C and C2C contexts have emerged:  Redistribution and reuse of data (known and unknown)  Geospatial data and services have become a mass market, the general rules of quality, information and responsibility-sharing apply

 In these new B2C and C2C contexts, who should care?  Every designer and developer of systems using geospatial data (including app developers for smartphones and web-based mapping applications)  Every provider of geospatial data (including VGI volunteers)  Every expert involved in geoprocessing and disseminating representations of geospatial data (including open-data projects)  Every user of geospatial data 24

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Geospatial data quality and risk management The Complete Cycle of Geospatial Data Quality for a Spatially-Enabled Society DESCRIBING CONTEXT

Users Producers

Selecting/Producing a dataset

MONITORING REVIEWING AS NECESSARY

COMMUNICATING PROPERLY WITH ALL PLAYERS (ISO TC 145/ISO 3864-2)

DEFINING NEEDS/REQUIREMENTS (ISO 19131)

EVALUATING DATA QUALITY (ISO 19157) dataset accepted?

Yes MANAGING RISK of inappropriate use of geospatial data (ISO 31000) 25

No

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Geospatial data quality and risk management  Caring about geospatial data quality requires an evaluation of this quality  Depending upon the extent of the evaluation, it can be very complex and robust or it can remain general and show a higher level of uncertainty In the B2B context (experts serve experts)  Standardized geospatial data quality evaluation processes are well described in the ISO 19157 IS

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Geospatial data quality and risk management In the B2C context (experts serve non-experts)  Professional duty to look at risk management strategies to reduce potential negative impacts on non-expert users  Such a duty emanates from the Code of Ethics of licensed professionals, the concept of precaution, consumer protection philosophy and liability issues In the C2C context (non-experts serve non-experts)  The concepts of precaution, consumer protection philosophy and liability issues also call for risk management

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Geospatial data quality and risk management  Risk management helps to select the best strategies to minimize the risk of inappropriate use of geospatial data in situations of uncertainty:    

Uncertain Uncertain Uncertain Uncertain

data data quality usages expertise of users, ...

 Such strategies:  Raise the awareness of users and providers  Reduce the overall risks  Help recognize the responsibility of every player

 Geospatial data quality results must be well communicated to users in a language they understand AND actions performed to minimize the risks related to geospatial data usage

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Why this Guide ?  Objective: support the Canadian geospatial community into its efforts to make the spatially-enabled Society more aware of geospatial data quality with the help of international standards such as ISO 19157 (Geospatial Data Quality) and ISO 31000 (Risk Management)    

Facilitate the selection of fit-for-purpose data Facilitate interoperability Stimulate the adoption of good practices Bolster the involvement of players still hesitant about open-data initiatives, crowdsourcing, data mashups and the new era of geospatial data ubiquity

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The concepts underlying geospatial data quality Section Overview

 The inherent uncertainty of geospatial data  The perspectives of geospatial data quality:    

Internal quality External quality Perceived quality Metaquality

 The subject-matters of geospatial data quality  The management of risks associated to geospatial data quality  The dissemination of information on geospatial data quality and risks of usage  The standards supporting geospatial data quality and risk management

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The inherent uncertainty of geospatial data  The process of producing a geospatial dataset induces uncertainty: Loss of details Goal dependency Model-maker dependency Context dependency Translations between cognitive and physical models are not straightforward  Modeling and communication rules are rarely unequivocal     

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The inherent uncertainty of geospatial data  Models supporting geospatial datasets are only approximations of the real world:  Conceptual uncertainty  Is this object a “road” or a “path”? Is this a “path” or not?

 Descriptive uncertainty  Is this building quality “Standard” or “Standard +”

 Location uncertainty (in space and time)  ± 5 meters, ± 1day

 Meta-uncertainty  95% confidence error ellipses

 These four orders of uncertainty combine to generate the total uncertainty of the dataset

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The perspectives of geospatial data quality Internal quality (ISO 19157): internal characteristics of a geospatial dataset (i.e. the intrinsic properties resulting from data production methods)  Completeness: presence or absence of features, their attributes and relationships  Omission or commission

 Logical consistency: degree of adherence to logical rules of data structure, attribution and relationships  Conceptual consistency, domain consistency, format consistency, topological consistency

 Positional accuracy: accuracy of the position of features within a spatial reference system  Absolute accuracy, relative accuracy, gridded data position accuracy

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The perspectives of geospatial data quality Internal quality (ISO 19157)  Thematic accuracy: quality of the thematic attributes and of the classifications of features and their relationships  Classification correctness, non quantitative attribute correctness, quantitative attribute accuracy

 Temporal quality: quality of the temporal attributes and temporal relationships of features  Accuracy of time measurement, temporal consistency, temporal validity

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The perspectives of geospatial data quality External quality (ISO 19157)  Fitness for use: degree of agreement between data characteristics (i.e., internal quality) and the explicit and/or implicit needs of a user for a given application in a given context  Usability: based on user’s requirements, all internal quality elements of ISO 19157 may be used to evaluate usability

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The perspectives of geospatial data quality Perceived quality  Within a consumer-centered (i.e. B2C and C2C), users may have a different view of the external data quality of a geospatial dataset  Users can rate the dataset based on their perception (using a 5-star system for example) and write comments  The global perceived quality is the result of the aggregation of each individual user perception (bottom-up approach)

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The perspectives of geospatial data quality Metaquality (ISO 19157)  Information describing the quality of data quality information:  Confidence  Representativity  Homogeneity

 Metaquality helps estimating the risk related to geospatial data uncertainties

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The subject matters of geospatial data quality Dataset series level

Dataset level

Subset level

Feature type level (Adapted from Devillers, 2004)

Feature instance level Feature attribute level Attribute value level

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The subject matters of geospatial data quality  Geospatial data quality can be regarded at various granularity levels (ISO 19157 data quality scopes)  Dataset series level (e.g., the National Topographic System of Canada (NTS))  Dataset level (e.g., a specific map of the NTS)  Subset level (e.g., the subset of features included in the North-West zone)  Feature type level (e.g., the set of “roads segments” of a topographic map)  Feature instance level (e.g., road 138 on a specific topographic map)  Feature attribute level (e.g., the “Functional road class” of a road segment)  Attribute value level (e.g., code value “1” (“Freeway”) for a specific road segment)

 ISO 19157 provides methods for aggregating quality information from a single data of a single feature up to the complete dataset

The management of risks associated to geospatial data quality  Risk is about the effect of uncertainty:    

Uncertain Uncertain Uncertain Uncertain

geospatial data geospatial data quality geospatial data usages expertise of users of geospatial data

In the geospatial context

 A risk management process serves as a guide on how to avoid or manage the impacts of uncertainty  From a legal perspective, using a risk management approach is necessary to protect both the geospatial data producer and user

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The management of risks associated to geospatial data quality  Perfect quality does not exist  Overall quality involves internal + external + perceived + metaquality

 Zero risk does not exist  Risk can be reduced, rarely eliminated

 Quality and risk management are interrelated  Typically, the higher the quality, the lower the risk to manage Overall Quality

Risk to manage

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The standards supporting geospatial data quality and risk management Specific standards  ISO 19115 Geographic information – Metadata: defines the schema required for describing geographic information and services by means of metadata, including geospatial data quality  The North American Profile of ISO 19115: makes certain optional fields of ISO 19115 mandatory, supports multiple languages, code lists, …  Included in the Treasury Board of Canada Secretariat Standard on Geospatial Data  Established as a National standard by the Canadian General Standards Board

 ISO 19131 Geographic information – Data product specification: specifies requirements for the definition of geographic data products, or user requirements, based upon the concepts of other ISO 19100 International Standards, such as data quality  Producers: product specifications  Users: product requirements

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The standards supporting geospatial data quality and risk management Specific standards (cont.)  ISO 19157 Geographic information – Data quality: establishes principles for describing the quality of geographic data by:  Defining components for describing data quality  Specifying components and content structure of a register for data quality measures  Describing general procedures for evaluating the quality of geographic data  Establishing principles for reporting data quality

 ISO/TS 19158 Geographic information – Quality assurance of data supply: provides a quality assurance framework for the producer and customer in their production relationship

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The standards supporting geospatial data quality and risk management Generic standards  ISO 9000 – Quality management: family of standards that provide guidance and tools for organizations that want to ensure that their products and services consistently meet customers’ requirements and that quality is consistently improved  ISO 31000 Risk management – Principles and guidelines: provides principles, framework and a process for managing risk  IEC 31010 Risk management – Risk assessment techniques  ISO Guide 73 Risk Management - Vocabulary

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The dissemination of information on geospatial data quality and risks of usage  The traditional way to communicate quality information is the use of metadata  Designed by experts for experts (B2B context), metadata are less appropriate for other types of actors, particularly for the general public (B2C and C2C mass markets)

 The process of communicating data quality and risks must be adjusted for all audiences with new vocabularies, methods and documentation products  Since quality and risk will have different values in different usages, the information will differ for each user/usage

-> Writing proper documentation helps producers to meet their legal duty for information, advice, and warnings -> “Good data documentation and well drafted disclaimers and agreements will minimize data misuse and abuse” (National States Geographic Information Council, 2011)

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Geospatial data quality and risk management: the fundamentals Section Overview

 Geospatial data quality management  Risk management for inappropriate usage of geospatial data  Communication about geospatial data quality and risks of usage

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Geospatial data quality management concepts  Geospatial data quality management is the activity of:  Defining the required quality of needed data  Defining, implementing and controlling the necessary steps to ensure quality criteria are met  Evaluating, documenting and disseminating quality information

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Geospatial data quality management process DEFINING THE REQUIRED QUALITY

DISSEMINATING QUALITY INFORMATION

STEPS TO ENSURE QUALITY - Definition - Implementation - Control

EVALUATING

DOCUMENTING

Geospatial data quality management process in context The Complete Cycle of Geospatial Data Quality for a Spatially-Enabled Society DESCRIBING CONTEXT

Users Producers

Selecting/Producing a dataset

MONITORING REVIEWING AS NECESSARY

COMMUNICATING PROPERLY WITH ALL PLAYERS (ISO TC 145/ISO 3864-2)

DEFINING NEEDS/REQUIREMENTS (ISO 19131)

EVALUATING DATA QUALITY (ISO 19157) dataset accepted?

Yes MANAGING RISK of inappropriate use of geospatial data (ISO 31000)

No

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Geospatial data quality management concepts From a producer point of view (B2B or B2C contexts)  Geospatial data quality and risks of inappropriate use must be managed at each phase of a data product life-cycle (production or update process): Quality Management

Design

Implementation

Production

Risk Management

Delivery

Usage

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Geospatial data quality management From a producer point of view (B2B or B2C contexts)  Geospatial data quality must be managed at each phase of a data product lifecycle: New/Update

Design OR

Application schema (ISO 19109) Feature catalog (ISO 19110)

Schema for coverage geometry and functions (ISO 19123)

Product specifications (ISO 19131, referring to appropriate other ISO 19100 series standards)

(…)

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Geospatial data quality management From a producer point of view (B2B or B2C contexts)  Geospatial data quality must be managed at each phase of a data product lifecycle:

New/Update Implementation

Production Quality evaluation/control (ISO 19157)

Integrity constraints

(…) Metadata (ISO 19115, including quality information)

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Geospatial data quality management From a producer point of view (B2B or B2C contexts)  Geospatial data quality must be managed at each phase of a data product lifecycle:

New/Update Delivery

(…)

Product documentation: User manual, Warnings, and other user-centered documentation

Usage

Well-documented data or quality-aware application

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Geospatial data quality evaluation  Geospatial data quality evaluation can be defined as a process used to determine whether a geospatial data product meets the objectives with regards to:  Product specifications (Producers, internal quality)  Product requirements for a planned use (Users, “usability”, external quality or fitness-for-use)

 Can be: Formal (ISO 19157) Experts

Semi-formal

Informal (text, 5-star rating, etc.) Nonexperts

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Geospatial data quality evaluation B2B, experts

(some B2B cases, B2C, C2C)

Specify (ISO 19157)

Against specifications/ requirements (ISO 19131, used to formally describe the needs)

Data quality units Data quality measures Data quality evaluation procedures

Evaluate (ISO 19157)

Output of quality evaluation

Output of metaquality evaluation

Report (ISO 19157)

Report quality as metadata

Opt. data quality report

Informal (inspired by ISO 19157)

Informal quality evaluation

Against needs (inspired by ISO 19131)

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Risk management in context The Complete Cycle of Geospatial Data Quality for a Spatially-Enabled Society DESCRIBING CONTEXT

Users Producers

Selecting/Producing a dataset

MONITORING REVIEWING AS NECESSARY

COMMUNICATING PROPERLY WITH ALL PLAYERS (ISO TC 145/ISO 3864-2)

DEFINING NEEDS/REQUIREMENTS (ISO 19131)

EVALUATING DATA QUALITY (ISO 19157) dataset accepted?

Yes MANAGING RISK of inappropriate use of geospatial data (ISO 31000)

No

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Risk management concepts  Risk management is the activity of directing and controlling what an organization does to minimize unexpected impacts on its objectives  Key principle: zero risk does not exist !  Risk management implies balancing the efforts to prevent unexpected outcomes with potential negative impacts of such outcomes for stakeholders (including consumers) Efforts

Unexpected negative impacts

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Risk management process (based on ISO 31000) DESCRIBING CONTEXT

COMMUNICATING PROPERLY TO ALL PLAYERS

ASSESSING RISK - Identification - Analysis - Evaluation

BUILDING RISK RESPONSES - Mitigation - Avoidance - Transfer/sharing - Acceptation

MONITORING + REVIEWING AS NECESSARY

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Risk management concepts From a producer point of view (B2B or B2C contexts)  Risks of inappropriate use and geospatial data quality must be managed at each phase of a data product life-cycle (production or update process): Quality Management

Design

Implementation

Production

Risk Management

Delivery

Usage

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Risk management concepts ISO 31000 (2009)

 Generic concepts and vocabulary about risk management  Approach to stimulate enterprises to develop a risk management culture and to ask the right questions at the right time (not “after” harm)  NOT a guide about good practices since they vary among domains  For all types of projects  Small, large  Individual, company, government, …  Proposes a new definition of RISK more in sync with business practices and court decisions regarding liability  Adds 3 tasks to previous version of standard:  Context establishment  Communication and consultation  Monitoring and review  NOT for certification or licensing purposes

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Concepts: ISO 31000 vocabulary comes from ISO Guide 73:2009 Risk Management - Vocabulary  Risk: “effect of uncertainty on objectives”  Effect: deviation from the expected (positive and/or negative)  Uncertainty: deficiency of information  Objectives: economic, environment, health, …

 Risk is often expressed as a combination of the positive or negative consequences of an event and their likelihood of occurrence  E.g., most surfaces of pesticide spray are overestimated when using cadastral parcels area, resulting potentially in a large proportion of undervalued concentration of pesticide and larger number of farmers meeting standards than should be. The risk of faulty decisions is high in most regions because of topography and land use. Since it is a systematic error, not a random one, there is a high risk that our environmental objectives be affected negatively (example adapted from Edoh-Alove et al, 2015)

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Risk management: Establishing the context  General context:

 Objectives: can be economic, environmental, public health, security, …  Scope: can be project, organisation, community, ...

 External context:      

Local, regional, national, international Legal and regulatory requirements Stakeholders’ perceptions Micro and macro economy Social and political environment Competition, trends, ...

 Internal context:

 Organisational culture, governance, standards, structure and strategy  Commitments, contractual relationships  SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)

 Specific context of the targeted scope:     

Risk management objectives for targeted scope Resources, time required, project management Depth, breath, inclusions, exclusions, responsibilities Methodologies Risk criteria, measures, tolerance levels, decisions to make

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Risk management: Identifying risks  Build a comprehensive list of risks that might impact (good or bad) the achievement of objectives  I.e. reasons why objectives could potentially not be reached

 Find and describe:  Sources of risk (under control or not)  Economic, social, political, natural, markets, technological, operational, human, legal, …  Known, unknown, emerging

 Impacts and cumulative effects  Possible scenarios

 People with appropriate knowledge should be involved (e.g., experts, users, support service specialists, consultants, …) N.B. the complementary IEC 31010 standard provides guidance on risk assessment techniques

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Risk management: Analysing risks  Comprehend the nature of identified risks by determining their causes, sources, consequences on objectives, likelihood to happen and interdependence    

Based on historical data and/or extrapolation and/or prediction Consider existing controls Consequences can be tangible or intangible Can be qualitative or quantitative

 Can be undertaken at various levels of detail  Comprehend the level of identified risks by combining their likelihood to happen and consequences  Indicate the level of confidence in this determination N.B. the complementary IEC 31010 standard provides guidance on risk assessment techniques

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Risk management: Evaluating risks  Comparison between the Level of risk obtained during Risk Analysis and the Risk criteria established when defining the Context     

Money Human casualties Delays Environmental disaster Etc.

 This comparison helps decision-makers to select strategies for risk treatments and their prioritization  Risk cannot be tolerated, treatment is essential  Risk can be tolerated, needs to be monitored  Risk is negligible, to be observed

 Must consider legal, regulatory and other requirements N.B. the complementary IEC 31010 standard provides guidance on risk assessment techniques

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Risk management: Treating risks  Risk treatment implies selecting and implementing one or a combination of strategies to modify a risk in order to reach accepted levels of tolerance  4 categories of strategies can be used:

 Mitigation: actions to eliminate or reduce consequences or their likelihood of occurring  Avoidance: eliminate activity to eliminate risk  Transfer/sharing: shift impact to another entity in part or entirely  Acceptance: voluntarily accept and take risk N.B. ignoring a risk = informally accepting a risk

 Several alternatives exist to treat any given risk, they differ in:  Costs  Delays  Efficiency

 Must balance efforts vs. benefits for all stakeholders with regards to the objectives set forth in the Context N.B. the complementary IEC 31010 standard provides guidance on risk assessment techniques

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Risk Management: Communicating & consulting  With internal and external stakeholders  Address issues about the contexts and interest of stakeholders, the causes and origins of identified risks, their good and bad consequences, their level, risk criteria and levels of tolerance, the treatments that already exist or that will be implemented, their monitoring and review, etc.  Examples:  Joint Committees with various expertises  Web-based forum  Paper/on-line user manual in a language understandable by target readers (Gervais, 2004)  Training  FAQ and [email protected]  Etc.

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Risk management: Monitoring & review  Regular surveillance of the risks and the success of their treatment  Detect changes in contexts  Detect emerging risks  Seek continuous improvement

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Communication about geospatial data quality and risks of usage  In a B2B (experts) context:     

Communication products and services that were identified in the B2B contract Data quality metadata (ISO 19115, ISO 19157) Initial specifications as additional information for new external expert users Depending upon the detailed context: data quality report (ISO 19157) potentially for each type of usage (to complement the metadata) Optional: user manual and other communication products and services offered in B2C and C2C contexts

 In B2C and C2C contexts: 

User manual highly recommended (Gervais, 2004), it may include:    

  

Warnings (including symbols (ISO TC 145/ISO 3864-2)) Disclaimer Recommended and non-recommended usages and much more

License Guarantee …

 A combination of communication methods as described in the Risk Treatment strategies in a later section of this presentation

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Putting theory into practice Section Overview

 Managing geospatial data quality in practice  Managing the risks of geospatial data usage in practice  Communicating about geospatial data quality and the related risks of usage in practice

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Managing geospatial data quality in practice ISO 19109:2005 Geographic information – Rules for application schema •

Conceptual modeling of features and their properties from a universe of discourse

•

Definition of application schemas

•

Use of the conceptual schema language for application schemas

•

Transition from the concepts in the conceptual model to the data types in the application schema

•

Integration of standardized schemas from other ISO geographic information standards with the application schema

ISO 19157: verify the DQ_LogicalConsistency of the schema

Example:

Road network model (extract from Levesque, Bédard, Gervais, & Devillers, 2007)

Warning symbols to highlight potential problems

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Managing geospatial data quality in practice ISO 19110:2005 Geographic information – Methodology for feature cataloguing •

Methodology for cataloguing feature types

•

Specifies how the classification of feature types is organized into a feature catalogue and presented to the users of a set of geographic data.

ISO 19157: verify the DQ_LogicalConsistency of the catalog

Example:

The CanVec+ Feature Catalog (extract)

Add a section to describe warnings http://ftp2.cits.rncan.gc.ca/pub/canvec/doc/CanVec_feature_catalogue_en.pdf

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Managing geospatial data quality in practice ISO 19123:2005 Geographic information – Schema for coverage geometry and functions •

Conceptual schema for the spatial characteristics of coverages

•

Relationship between the domain of a coverage and an associated attribute range

D igital OrthoIm agery

ISO 19157: verify the

DQ_LogicalConsistency of the schema

1..* AreaOfInteres t

1

+ interpolationType : CV_InterpolationMethod = neares tNei ghbor + domainExtent : EX_Extent + rangeType : RecordType

+collection 0..1 +evaluator

Example:

Top level classes for digital orthoimagery (from Maitra, 2004)

CV_Continuous GridCoverage

Image

+element 1..* Band

Pixel 1..* +source 1 CV_GridValues Matrix (f ro m Ele v ation )

+ gridRange : CV_GridRange + val ues : Sequence + sequencingRule : CV_SequenceRule + startSequence : CV_GridCoordinate

1 DNValue

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Managing geospatial data quality in practice ISO 19131:2007 Geographic information – Data product specifications (with ISO 19131:2007/Amd 1:2011

Requirements relating to the inclusion of an application schema and feature catalogue and the treatment of coverages in an application schema)

ISO 19157: verify the DQ_LogicalConsistency of the schema

•

Requirements for the specification of geographic data products, based upon the concepts of other ISO 19100 International Standards

•

References application schema, feature catalog or schema for coverage geometry

Section in the specifications to describe the expected quality

ISO 19157: express the expected values for all DQ_Elements

Example:

The CanVec+ Data Product Specifications (extract) http://ftp2.cits.rncan.gc.ca/pub/canvec+/doc/CanVec+_product_specifications.pdf 74

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Managing geospatial data quality in practice Integrity constraints (spatial, temporal, thematic)

ISO 19157: Integrity constraints may help in controlling all DQ_Elements

•

Intra-field (e.g., values of a numeric field must be between 0 and 1)

•

Inter-fields (e.g., if the value of the road classification attribute is “national”, then the value of the maximum speed attribute cannot be null)

•

Intra-feature (e.g., the date of an updated house assessment cannot be lower than the date of the older house assessment)

•

Inter-feature (e.g., the size of a “building” must be smaller than the size of the “parcel” it is built on)

•

Intra-feature class (e.g., “building” cannot intersect “building”)

•

Inter-feature classes (e.g., “road” cannot cross “lake”)

•

Intra-theme (e.g., “river” can connect “canal”)

•

Inter-theme (e.g., “dam” can share geometry with “road”)

Example:

Constraint repository (adapted from Normand, 1999) 75

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Managing geospatial data quality in practice ISO 19157:2013 Geographic information – Data Quality Examples: Unit 1: Dataset N, completeness of hydrants Measure 1: Number of excess items

• •

•

•

Components for describing data quality Components and content structure of a register for data quality measures General procedures for evaluating the quality of geographic data Principles for reporting data quality

Full inspection

0 excess item; pass 100% confidence

DQ_CompletenessCommission

Detailed steps 76

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Managing geospatial data quality in practice ISO 19115-1:2014 Geographic Information – Metadata – Part 1: Fundamentals ISO 19115-2: 2009 Geographic Information – Metadata – Part 2: Extensions for imagery and gridded data North American Profile of ISO 19115:2003 — Geographic Information — Metadata (NAP — Metadata) ISO/TS 19139:2007 Geographic Information – Metadata – XML schema implementation

ISO 19157: document the values for all DQ_Elements

•

Mandatory and conditional metadata sections, metadata entities, and metadata elements

•

The minimum set of metadata required to serve most metadata applications (data discovery, determining data fitness for use, data access, data transfer, and use of digital data and services)

•

Optional metadata elements to allow for a more extensive standard description of resources, if required

•

A method for extending metadata to fit specialized needs

Example:

The CanVec+ 082C metadata (extract)

Data quality elements in metadata 77

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Managing geospatial data quality in practice Proper communication typically combines several information products/services and is a direct result of the risk management strategies adopted Strongly recommended: User manual (with the content suggested by Gervais (2004) based on legal considerations):

ISO 19157: values for DQ_Elements will influence the product documentation

•

License

•

Guarantees

•

Installation

•

Product description

•

Resolution (spatial, temporal, descriptive) of the data

•

General advice

•

Functional specifications

•

Recommended uses

•

Non-recommended uses

•

Warnings and safety

•

Troubleshooting

•

Technical specifications

ISO 19115 metadata already contains part of this information, but in a technical jargon usually unintelligible for most users (see Gervais 2004 for the correspondence)

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Managing geospatial data quality in practice Examples of product documentation:

Example of a Guarantee section of a user manual (extract from Gervais, 2004)

ISO 19157: values for DQ_Elements will influence the product documentation Example of a data product User manual (Statistics Canada, 2009)

Example of a Warnings and safety section of a user manual (extract from Gervais, 2004)

Example of a Troubleshooting section of a user manual (extract from Gervais, 2004) Example of a Geospatial data quality good practices guide (Statistics Canada, 2009)

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Managing geospatial data quality in practice Warnings: ISO TC 145/ISO 3864-2 Graphical symbols • Establishes the principles for preparation, coordination and application of graphical symbols

ISO 19157: values for DQ_Elements will influence the product documentation

In Geomatics: • Numerous standards exist for map products (e.g. topographic maps, nautical charts, geology maps) • Standards exist for copyright (e.g. Creative Commons) • However, no standard exists yet for geospatial data quality analysis and risk management

Symbols and labels are powerful ways to convey the meaning of risk:    

Type of risk (danger or positive action) Level of risk Description of risk Actions to take in face of consequences

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Managing geospatial data quality in practice

ISO 19157: values for DQ_Elements will influence the product documentation

Example:

Use of symbols to facilitate the reading of a quality report (private report by Gervais, Bédard and Larrivée, 2007)

http://www.safetysign.com/help/h40/safety-header

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Managing geospatial data quality in practice Continuation of means put into place for a quality-aware delivery of the data E.g., quality-aware application • •

ISO 19157: values for DQ_Elements will dictate how the product should be used

Examples: Dataset is rated using a number of stars

Displays warnings to users according to the data they consult or the operations they conduct on the data Based on metadata

Example of a context-sensitive warning of inconsistency after a query in a quality-aware application (Gervais et al., 2009). The warning contains 3 parts recommended by ISO: level of risk, nature of problem, action to solve problem

Example of VQI using a 5star rating system (from (Koistinen, 2015))

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Geospatial data quality evaluation B2B, experts

(some B2B cases, B2C, C2C)

Specify (ISO 19157)

Against specifications/ requirements (ISO 19131, used to formally describe the needs)

Data quality units Data quality measures Data quality evaluation procedures

Evaluate (ISO 19157)

Output of quality evaluation

Output of metaquality evaluation

Report (ISO 19157)

Report quality as metadata

Opt. data quality report

Informal (inspired by ISO 19157)

Informal quality evaluation

Against needs (inspired by ISO 19131)

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Evaluating geospatial data quality in practice ISO 19157:2013 Geographic information – Data quality – Data quality units Data quality unit = 1 scope + N data quality elements •

•

Examples:

Quality unit 1: MD_Scope: dataset DQ_Elements: DQ_LogicalConsistency, DQ_Completeness Quality unit 2: MD_Scope: feature type (hydrant) DQ_Element: DQ_QuantitativeAttributeAccuracy See 19157:2013 for more examples

Scope (MD_Scope): specifies the extent, spatial and/or temporal, and/or common characteristic(s) that identify the data on which data quality is to be evaluated Data quality elements (DQ_Element) from ISO 19157:2013):

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Evaluating geospatial data quality in practice ISO 19157:2013 Geographic information – Data quality – Data quality measures A data quality element should refer to one measure only, by means of a measure reference (DQ_MeasureReference) • • •

measureIdentification nameOfMeasure measureDescription

• •

List of standard measures provided in ISO 19157 New measures can be created

Examples (ISO 19157, annex D): For DQ_CompletenessCommission: Excess item Number of excess items Rate of excess items Number of duplicate feature instances See 19157:2013 for more examples

For DQ_CompletenessOmission: Missing item Number of missing items Rate of missing items

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Evaluating geospatial data quality in practice ISO 19157:2013 Geographic information – Data quality – Data quality evaluation procedures Data evaluation procedure = 1,N data evaluation methods Data quality evaluation methods (DQ_EvaluationMethod) can be divided into two main classes: direct and indirect • •

• • •

Direct evaluation methods compare the data with internal and/or external reference information Indirect evaluation methods infer or estimate data quality using information on the data such as lineage DQ_FullInspection DQ_SampleBasedInspection DQ_IndirectEvaluation

Example: Area guided non-random sampling method X X X X X X X X X X X X X X X X X See 19157:2013 for more examples

X X X

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Evaluating geospatial data quality in practice ISO 19157:2013 Geographic information – Data quality – Output of quality evaluation At least one data quality result (DQ_Result) is provided for each data quality element • • •

DQ_QuantitativeResult DQ_ConformanceResult DQ_DescriptiveResult

Examples:

DQ_QuantitativeResult (DQ_CompletenessCommission), Number of excess items: 3

DQ_ConformanceResult

(DQ_CompletenessCommission), Number of excess items: pass

DQ_DescriptiveResult (DQ_LogicalConsistency), Conceptual schema compliance: “The rules of the CanVec+ conceptual schema are all recorded and validated in the source database containing the CanVec+ product. This approach ensures the conceptual consistency between the conceptual schema and the CanVec+ product.” (from CanVec+ 082C metadata (extract)) See 19157:2013 for more examples

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Evaluating geospatial data quality in practice ISO 19157:2013 Geographic information – Data quality – Output of metaquality evaluation Metaquality elements are a set of quantitative and qualitative statements about a quality evaluation and its result: • •

Examples (from ISO 19157): DQ_Confidence

•

DQ_Confidence: trustworthiness of a data quality result DQ_Representativity: degree to which the sample used has produced a result which is representative of the data within the data quality scope DQ_Homogeneity: expected or tested uniformity of the results obtained for a data quality evaluation

Standard deviation or a confidence interval on a given confidence level.

DQ_Representativity All the geographic zones and concerned time periods are covered and the population is sufficiently large

DQ_Homogeneity

Comparison of the evaluation results of several segments of a global data set expressed using root mean square errors See 19157:2013 for more examples

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Evaluating geospatial data quality in practice ISO 19157:2013 Geographic information – Data quality – Report quality as metadata Data quality is reported as metadata in compliance with Clause 7, Clause 10, Annex C, ISO 19115-1:2014 and ISO 19115-2:2009

Example:

The CanVec+ 082C metadata (extract)

See 19157:2013 for more examples

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Evaluating geospatial data quality in practice ISO 19157:2013 Geographic information – Data quality – Data quality report In order to provide more details or to present the information in an easier to understand format than reported as metadata, a standalone quality report may additionally be created Usually one report per usage/user

Examples:

•

The structure of the report is free.

Standalone data quality report: commission per feature class (extract from ISO 19157:2013)

Summarized feature class quality evaluation (adapted from a private report by Gervais, Bédard and Larrivée, 2007) See 19157:2013 for more examples

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Evaluating geospatial data quality in practice Informal data quality evaluation

Examples: Use of a 5-star rating system to rate the representation of buildings in a virtual globe environment (from (Jones, 2011)) Use of a 5-star rating system in a SDI environment (from (Koistinen, 2015))

•

For producers: in B2B and B2C contexts, less formal processes to evaluate and report data quality should be inspired by the ISO 19157 approach

•

For consumers in the B2C and C2C context: various means are used, unknowingly following ISO 19157 rationale in a less rigorous manner. They typically fit their measurement method with their quality representation method.

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Risk management process (based on ISO 31000) DESCRIBING CONTEXT

COMMUNICATING PROPERLY TO ALL PLAYERS

ASSESSING RISK - Identification - Analysis - Evaluation

BUILDING RISK RESPONSES - Mitigation - Avoidance - Transfer/sharing - Acceptation

MONITORING + REVIEWING AS NECESSARY

Managing the risks of geospatial data usage in practice DESCRIBING CONTEXT

ISO 31000:2009 Risk Management – Describing the risk management context • • • •

General context External context Internal context Specific context of the targeted scope

Understand how the planned activity fits into the wider organization and market/society and the organization’s approach to risk management, in order to scope the risk management strategy

Example:

There are 3 other publicly available sources of data similar to ours. Their cost and overall accuracy is similar to ours and clients have difficulty to choose the one that best fit their needs. The evolution of the market is uncertain as well as the actions of the competition. By implementing a new data quality and risk management strategy, it will help to improve significantly the communication with potential clients and help them understand how our data can fulfill their geospatial needs. A potential client who knows better is reassured and has more chances of becoming client. Our objective is to increase our market share by 10% within 2 years. If we implement the strategy, the costs and risks are ... If we don't, the risks are...

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Managing the risks of geospatial data usage in practice ASSESSING RISK - Identification Identification - Analysis - Evaluation

Example:

List of potential risks of inappropriate use of geospatial data related to features/attributes in a geospatial dataset (extract from Grira, 2014)

ISO 31000:2009 Risk Management – Identifying risks for the users of data

This step will generate a comprehensive list of risks of inappropriate use of geospatial data Conducted using: • • • • Feature / Attribute

Cultivated parcel Floodplain

Pesticide spread area

Analysis of existing documentation (e.g., specifications, contracts, and task flow charts) Interviews Brainstorming sessions Collaborative approach with users, … Identified Risks

R-1: the user may think that the whole region delimited by the cadastral boundaries was cultivated. Some areas may not be cultivated, such as woodland, rocky button or areas near cadastral boundaries. R-2: Floodplains are vague data. The user would think that the provided boundaries are accurate whereas they are fuzzy and large boundaries are not represented as such on the map. R-3: the areas where the pesticide is spread have large and fuzzy boundaries and uncertain location within the plot (because of the techniques and the methods of pesticide spreading). The user could think that the area is accurate whereas positional accuracy is not considered in the area calculation. Note: the uncertainty for R-3 is related to the pesticide spreading zone, i.e. its boundaries and its location. However, the uncertainty for R-1 is related to the plot (its boundaries) where the spreading zone is located.

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Managing the risks of geospatial data usage in practice ASSESSING RISK - Identification - analysis Analysis - Evaluation

Example:

Analysis of potential risks of inappropriate use of geospatial data (extract from Grira, 2014)

Feature / Attribute

ISO 31000:2009 Risk Management – Analyzing risks Causes and sources of risks Consequences Likelihood of occurring

• • •

Analysis of lessons learned from previous projects Simulation methods Probabilistic analysis, …

Conducted using:

Identified Risks

Cultivated parcel

R-1

Floodplain

R-2

Pesticide spread area

• • •

R-3

Impact of Risk

Probability of Occurrence

Strong overestimation of the ratio quantity of pesticide / Medium hectare (high) Strong underestimation of the quantity of pesticides that might Medium be present in water (high) Strong underestimation of the quantity of pesticides that might Medium be present in water (high)

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Managing the risks of geospatial data usage in practice ISO 31000:2009 Risk Management – Evaluating risks

ASSESSING RISK - Identification - Analysis - evaluation Evaluation

Example:

Evaluation of potential risks of inappropriate use of geospatial data (extract from Grira, 2014)

Feature / Attribute

Prioritize risks according to level of tolerance Will help to select strategies for risk treatments Must consider legal, regulatory and other requirements

•

Ranking matrix

Conducted using:

Identified Risks

Cultivated parcel

R-1

Floodplain

R-2

Pesticide spread area

• • •

R-3

Impact of Risk

Probability of Occurrence

Strong overestimation of the ratio quantity of Medium pesticide / hectare (high) Strong underestimation of the quantity of pesticides Medium that might be present in water (high) Strong underestimation of the quantity of pesticides that might be present in Medium water (high)

Overall Risk Evaluation

High

Medium

Medium

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Managing the risks of geospatial data usage in practice BUILDING RISK RESPONSES - mitigation Mitigation - Avoidance - Transfer/sharing - Acceptation

Examples: • • • •

• • •

ISO 31000:2009 Risk Management – Mitigating risks • •

Strategies that modify actions to eliminate or reduce consequences or their likelihood of occurring May have significant impact on costs and additional efforts

Improve database design/dataset structure Improve the quality control of the dataset (e.g., add integrity constraints) Use standards (e.g., for data quality and risk management interoperability) Properly inform users in a language they understand (highly recommended) • Provide a user manual • Offer a 1-800 help line or [email protected] • List target usages and non-recommended usages • Provide a Guide of good practices • Train users, … Conduct tests on the dataset (users) Compare with another dataset (users) (…)

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Managing the risks of geospatial data usage in practice BUILDING RISK RESPONSES - mitigation Mitigation - Avoidance - Transfer/sharing - Acceptation Communicating risks of inappropriate use of geospatial data at the design phase (from Levesque et al., 2007)

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ISO 31000:2009 Risk Management – Mitigating risks Examples: Communicating risks of inappropriate use of geospatial data at the usage phase (from Gervais et al., 2009)

Web-based forum (from Grira, 2014)

Managing the risks of geospatial data usage in practice BUILDING RISK RESPONSES - Mitigation - Avoidance Avoidance - Transfer/sharing - Acceptation

Examples: • • • • •

ISO 31000:2009 Risk Management – Avoiding risks •

Strategies that eliminate the activity to eliminate risk

Stop distributing or using the dataset or a part of Eliminate a category of users Eliminate a data provider Explicitly and clearly forbid a given usage (…)

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Managing the risks of geospatial data usage in practice BUILDING RISK RESPONSES - Mitigation - Avoidance -Transfer/sharing Transfer - Acceptation

Examples: • • •

• • • •

ISO 31000:2009 Risk Management – Transferring and sharing risks •

Strategies that move to, or share a risk with, other parties in order to reduce it or to eliminate it

Buy an insurance Obtain the dataset from a broker who can give advice related to its use Use a dataset with a guarantee that explains clearly risk sharing (who is responsible of what) • The content of a guarantee for geospatial products is described in a paper by Plante and Gervais to be published in Geomatica in 2015. Have the dataset evaluated by an expert Replace a B2C strategy with a B2B strategy for your business by contracting a data broker who will offer the B2C strategy Have the data quality evaluated by an external expert for the new usages (…)

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Managing the risks of geospatial data usage in practice BUILDING RISK RESPONSES - Mitigation - Avoidance - Transfer/sharing Acceptation - Acceptation

Example: •

ISO 31000:2009 Risk Management – Accepting risks •

Strategy that voluntarily accept the risks

Use the dataset no matter what the risks are and do nothing about it, i.e. take the risk

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Managing the risks of geospatial data usage in practice COMMUNICATING PROPERLY TO ALL PLAYERS

ISO 31000:2009 Risk Management – Communicating risks •

•

• •

• • • •

Communicating with the persons in charge of offering data/services, the persons in charge of the data production, and the users Developing the information products identified in the risk treatment strategies, especially: • B2C + C2C: paper or on-line user manuals with previously recommended content • B2B: embed risk-related information within data quality metadata and report Using a vocabulary adapted to the various audiences Properly informing users: distribute the information products; keep users informed of new context elements, new quality controls, new quality evaluations, new risk management strategies implemented, new usages, new restrictions, new good practices, etc. Promoting joint committees with various expertises Supporting Frequently Asked Questions (FAQ), 1-800 info lines, [email protected] Offering training, webinars (…)

…

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Managing the risks of geospatial data usage in practice COMMUNICATING PROPERLY TO ALL PLAYERS

Examples:

Quality metadata (the CanVec+ 082C metadata (extract)) Standalone data quality report: commission per feature class (extract from ISO 19157:2013)

ISO 31000:2009 Risk Management – Communicating risks

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Managing the risks of geospatial data usage in practice MONITORING + REVIEWING AS NECESSARY

ISO 31000:2009 Risk Management – Monitoring and reviewing risks • • • • • •

Example:

To oversee and systematically evaluate, using metrics, the effectiveness of actions taken To update the initial list of identified risks and their characteristics To gather information useful for the development or the update of risk response strategies To review some aspects related to the planning process of risk management as a whole To collect feedback about quality (e.g., via web-based forum or 5-star VQI Volunteered Quality Information) To implement new restrictions, add integrity constraints, implement new training, build/update a register for quality and risk management, …

Risk register (adapted from Grira, 2014) Feature / Attribute

Identified Risks

Cultivated parcel

R-1

Floodplain

R-2

Impact of Risk

Strong overestimation of the ratio quantity of pesticide / hectare (high) Strong underestimation of the quantity of pesticides that might be present in water (high)

Owner

Probability of Occurrence

Project manager

Medium

Project manager

Medium

Overall Risk Evaluation

Response

Status

High

Mitigate (…)

(date) open

Medium

Mitigate (…)

(date) open

Action items

Followup with users Followup with users

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105

Recommendations Section Overview

 Recommendations in a Business-to-Business context  Recommendations in a Business-to-Consumer context  Recommendations in a Consumer-to-Consumer context

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Recommendations in a B2B context  Challenge: more formal data quality evaluation and reporting  Recommendations:  Facilitate communication between experts about data quality by adopting a common language   

ISO 19157 ISO 19115-1 (including quality metadata) Synthesized quality reports, aggregated quality information, automated Q&A advisory system

 Foster more efficient geospatial data reuse and interoperability by adopting a common frame of reference regarding quality (i.e. set of concepts)  

ISO 19157 ISO 19158

 Facilitate contractual agreements by adopting this common frame of reference and common language  Decrease the risks of inappropriate use of geospatial data using risk management concepts and strategies 

ISO 31000

 Encourage high data quality with strong quality assurance procedures and quality controls (i.e. improve metaquality)    

ISO 9000 ISO 19157 ISO 19158 Quality auditing, certification, user accreditation

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Recommendations in a B2B context  Recommendations (cont.):  Develop a Guide of Good Practices to visually represent geospatial data quality (quality maps, quality radars, quality tables, quality warning symbols, etc.)  Develop a Guide of Good Practices to mitigate the risks of geospatial data use  Further clarify the roles and responsibilities between contracting parties by including quality guarantees  Gradually implement good practices with the help of geospatial data quality experts  Develop a series of quality-related products and services such as quality guarantee, quality certificate, quality audit, quality control and quality assurance mechanisms, accreditation of quality experts

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Recommendations in a B2C context  Challenge: managing risks for a better protection of consumers (and providers)  Recommendations:  Facilitate geospatial data selection based on users’ needs (cf. external quality) by providing:  Lists of recommended and non-recommended uses  1-800 free line or [email protected]

 Contribute to the advancement of the geospatial community and spatiallyenabled society by offering:  User manuals written in a language understandable by the target users  See slide 78 for suggested content  Emphasize on clear advices and warnings (use symbols, cf. ISO TC 145/ISO 3864-2)  Real guarantees (as for any other product or service in a mature market)  Guides of Good Practices  Synthesized quality reports and aggregated quality information

 Stimulate quality analysis and users’ awareness with:  Web-based participatory VQI (Volunteered Quality Information) (e.g., 5-stars + comments)  Web-based users forums  Web-based or in-person training

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Recommendations in a B2C context  Recommendations (cont.):  Reduce the uncertainty related to certain law-related topics by investing into studies to:     

Understand the new trends and rights regarding privacy, data ownership, copyright, data vs. service Further develop the concept of guarantee (see Plante and Gervais, 2015) Further clarify the responsibility of non-experts VGI data contributors Clarify responsibilities when geospatial services and data cross borders Stimulate legal interoperability of geospatial data (see Uhlir, 2013)

 Rapidly make the move towards becoming a mature mass-market  Specialized training, innovation, collaboration

 Improve metadata (i.e., easier to use, new types of quality-centered and riskcentered metadata written for the end-users)  Increase geospatial data providers’ and users’ awareness of potential risks of inappropriate usage of geospatial data by gathering, on a crowdsourced web site, examples of damages

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Recommendations in a C2C context  Challenge: increasing awareness  Recommendations:  Rapidly inform developers of public-oriented web-based systems and smartphone apps about their duty and potential liability with regards to geospatial data quality  Facilitate geospatial data selection based on users’ needs (cf. external quality) by providing:  Lists of recommended and non-recommended uses  1-800 free line or [email protected]  User manuals written in a language understandable by the target users  See slide 78 for suggested content  Emphasize on clear advices and warnings (use symbols, cf. ISO TC 145/ISO 3864-2)  Guides of Good Practices  Training and collaboration for the providing C (e.g., when publishing data mashups)

 Reduce the uncertainty related to certain law-related topics by investing into studies to:  Understand the new trends and rights regarding privacy, data ownership, copyright  Further clarify the responsibility of data contributors, integrators and distributors  Clarify responsibilities when geospatial services and data cross borders

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Conclusions  As geospatial data is increasingly being produced and (re)used by new types of actors, the question of geospatial data quality is becoming a major concern  The objective of this guide was to support the Canadian geospatial community into its efforts to make the spatially-enabled society more aware of geospatial data quality  Based on international standards such as ISO 19157 (Geospatial data quality) and ISO 31000 (Risk management), this guide presented:  The concepts underlying geospatial data quality  The management of geospatial data quality  The geospatial data quality evaluation process in details (based on ISO 19157)  The management of risks of inappropriate use of geospatial data (based on ISO 31000)  Detailed examples of quality evaluation and risk management tasks to be undertaken in the B2B, B2C and C2C contexts

Question and Answer Session

How to find the CGDI Resource Centre

114

http://www.nrcan.gc.ca/earth-sciences/geomatics/canadasspatial-data-infrastructure/8906

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Thank you! Mr. Eric Wright Geomatics Engineer, CCEO/GeoConnections [email protected]

Dr. Yvan Bédard Senior Scientific and Strategic Advisor, Intelli3 Inc. [email protected] To access copies of resources discussed today, please visit: geoconnections.nrcan.gc.ca

QUESTIONS?