Due to the enormous growth of available spatial data, quality of spatial data is becoming a very important selection criterion to find the most adequate data for the intended use. Fitness for use is leading in determining quality of data. We developed a framework to assess the quality of a data set to meet specified user requirements. In this workshop, the framework will be discussed and validated against real world use cases.
Quality assessment of geospatial data: does it fit your needs?
How to deal with quality of geospatial closed, open and big data
- To clarify and present the geospatial data quality framework on fitness for use
- Receive feedback and recommendations to improve the framework and support future research
- Integrate possible outcome(s) in the framework
- Create better data quality awareness among participants, feedback for presenters on their proposed approach
- Report on the results of the workshop as an official publication
In this workshop, we present an overview of data quality and recent developments worldwide. The keynote speaker Robert Jeansoulin (Université de Paris-Est-Marne-la-Vallée), co-author of “Fundamentals of spatial data quality" [Devillers et al; 2006], will elaborate on Essentials of Data Quality and Fitness for Use. Our second invited speaker Karin Mertens (Quality Control Manager, National Geographic Institute, Belgium) will present the work of the Quality Knowledge Exchange Group (QKEN). Karin is a member of QKEN, a network established by EuroGeographics, the European National Mapping Agencies. The purpose of QKEN is to discuss data quality and quality management issues. Over 43 active participants from 25 countries and established a network of data quality experts to support EuroGeographics policy towards European data interoperability, to share knowledge amongst members; and promote experiences on quality.
Next, we will present the current geospatial data quality framework developed for communication and assessing spatial data quality at the Expertise Center for Geospatial Data Quality at Wageningen University & Research [Vullings et al, 2015; Meijer et al, ISSDQ 2015].
It is based on the principle of ‘fitness for use’ (report geospatial data quality NCG workshop 26 June 2014 (in Dutch)) and is applicable to all kinds of geospatial data varying from closed to open data, big data and sensor data, to name a few. In our view, dealing with geospatial data quality depends on the interaction between the data producer and the data consumer. This is depicted in the following picture:
In case of closed data, the producer and the consumer are known, since the consumer has to apply for the needed data. Because of this, the interaction can be complete. When dealing with open or big data, the interaction between producer and consumer is much more difficult or directed only from one side, since either the producer or the consumer is unknown.
Based on experience with geospatial quality projects (specifying criteria and auditing datasets), we defined this framework and used case studies [Meijer, ISDDQ 2015] to illustrate and specify the framework. The objective of the framework is to bridge the gap between producers and consumers. This refers to the geospatial data quality definition by improving communication at the consumer site (by specifying and elaborating the information needs), as well as on the producer site (by improving access to quality information and understanding of quality aspects of the data).
In the framework, the user as a consumer plays a central role, since the consumer and the context of the usage determine the necessary quality (fitness for use). By describing the use case of the consumer, we identify the relevant context to be the universe of discourse. The consumer often gives spatial data quality specifications within the identified Universe of Discourse of his or her application domain to his/her best knowledge. But many quality elements can be implicit and not known by the consumer. It is important to unravel the information need into criteria with the help of spatial data quality expertise. Based on this information, we define the product that is wanted by the consumer. This can vary from ‘plain’ data provisioning to automated procedures like an App suitable for providing human services. When dealing with open data, we developed generic use cases to communicate quality elements with yet unknown users with unknown use. In case of big data, additional challenges appear, because we can state here that even communication in one direction does not exist. With regard to big data and quality, we focus mainly on two of the 4 V’s, namely variability and veracity (uncertainty of data).
Related to data in Science, currently an initiative has started to establish the EU Open Science Cloud. This is part of the Digital Single Market Strategy of the European Commission. It aims at publishing the data behind the science as FAIR data: Findable, Accessible, Interoperable and Reusable, for which Metadata is key. The ‘European Open Science Cloud’ is perceived as a ‘sustainable infrastructure for the management and long-term stewardship of research data, with the final aim to accelerate discovery and innovation. Science in Europe should be supported by the infrastructure and it should make existing data infrastructures in different domains more interoperable’. As an integrated key element for Data Quality, in this workshop we will discuss the meaning of this initiative.
- R. Devillers, Robert Jeansoulin. Fundamentals of Spatial Data Quality. ISTE Publishing Company, pp. 312, 2006, 1905209568.
- Vullings, Wies et al; 2015; Spatial Data Quality: What do you mean?; AGILE 2015 – Lisbon, June 9-12, 2015
- Meijer, M et al.; 2015; Spatial data quality and a workflow tool; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-3/W3, 2015 ISPRS Geospatial Week 2015, 28 Sep – 03 Oct 2015, La Grande Motte, France
|9:30 - 12 AM Morning session||Welcome, introduction|
|Keynote: Essentials of Data Quality and Fitness for Use. Presenter: Robert Jeansoulin, Université de Paris-Est - Marne-la-Vallée, co-author of “Fundamentals of spatial data quality"|
|Work of the Quality Knowledge Exchange Network (QKEN), a network established by EuroGeographics. Presenter: Karin Mertens, Quality Control Manager, National Geographic Institute, Belgium|
|Data Quality assessment procedure as proposed by ECQSD, the Wageningen UR Expert Centre for Quality of Spatial Data. Presenter: Jandirk Bulens, Geo Information specialist at Wageningen Environmetal Research (Alterra), Netherlands|
|Introduction of the cases of the participants|
|1:30 - 4 PM Afternoon session||The Data quality from NMA/academic perspective, Presenter: Joep Crompvoets, KU Leuven, Belgium|
|Interactive session with use cases|
|Discussion, outline of a research agenda|
Call for cases
In the workshop, we would like to use real world cases. We invite you as participants to submit your case. This may be an existing case that you want to review, or a future case that you want to implement within your own organisation or environment.
A typical case could be:
Is the dataset provided by your mapping agency suited to calculate noise pollution in densely populated areas? In other words: what quality parameters are required for this use case and does the dataset comply?
Interested in participating? Register now!
For the payment registering for the workshop must be done through the AGILE website here. The fee, set by AGILE, will include the workshop facilities, coffee/tea and lunch. For workshop ‘only’ this is €110 for AGILE members and €160 for non-AGILE members. Conference attendees pay less, see the AGILE website for all other fees here.
Registration and use case submission form AGILE 2017 Workshop
Quality assessment of geospatial data: does it fit your needs?