Measuring Spatial Data Fitness-for-Use through Multiple Criteria Decision Making

Bibliographic Data

ID3775282
AuthorsElizabeth A Wentz (0000-0002-2881-0668, Arizona State University), Melinda Shimizu (0000-0002-4236-3024, Western Oregon University)
Year2018
Volume108
Issue4
Pages1150-1167
Publication date2018-07-04
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnals of the American Association of Geographers (JOURNAL)
Journal identifiersISSN: 2469-4452 • E-ISSN: 2469-4460
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/24694452.2017.1411246
OpenAlexW2791493325
LanguageEN
Citations received4
References cited66

This article presents a new methodology for data fitness-for-use assessment. Most current measures of data quality rely on metadata and other data producer-derived information. This creates a void of options for a user-driven assessment of data quality when metadata are sparse or unavailable, as is often the case with citizen science and volunteered geographic information. This article puts forward data fitness-for-use (DaFFU), a method that can be adapted for a wide range of data uses. Using the mathematical framework of multiple criteria decision making (MCDM), we create a method to select the best data set from multiple options using a select set of user criteria. The DaFFU methodology is demonstrated with both a simple exemplar and a detailed case study for watershed management. The simple exemplar illustrates how varying parameters and weights influence the outcome. The case study on watershed management considers four possible data sets and six data quality criteria for wetland delineation and an application toward watershed nitrogen retention, each of which has a claim on being of the “best” quality, depending on which data quality aspect the user evaluates. The DaFFU methodology allows the user to consider these data in terms of how they will be used and to use selected data quality measures. Case study results show this methodology is a robust and flexible approach to quantitatively assessing multiple data sets in terms of their intended use

Data mining · Data quality · Data science · Data set · Machine learning · Metadata · Watershed · Computer Science · Data Management and Algorithms · Engineering · Geographic Information Systems Studies · Hydrology and Watershed Management Studies · Artificial Intelligence

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