Rodolphe Devillers

Fundamentals of Spatial Data Quality

This book explains the concept of spatial data quality, a key theory for minimizing the risks of data misuse in a specific decision-making context. Drawing together chapters written by authors who are specialists in their particular field, it provides both the data producer and the data user perspectives on how to evaluate the quality of vector or raster data which are both produced and used. It also covers the key concepts in this field, such as: how to describe the quality of vector or raster data; how to enhance this quality; how to evaluate and document it, using methods such as metadata; how to communicate it to users; and how to relate it with the decision-making process. Also included is a Foreword written by Professor Michael F. Goodchild.

Towards spatial data quality information analysis tools for experts assessing the fitness for use of spatial data

Geospatial data users increasingly face the need to assess how datasets fit an intended use. However, information describing data quality is typically difficult to access and understand. Therefore, data quality is often neglected by users, leading to risks of misuse. Understanding data quality is a complex task that may involve thousands of partially related metadata. For complex cases where heterogeneous datasets have to be integrated, there is a need for tools supporting data quality analysis. This paper presents the design of such a tool that can manage heterogeneous data quality information and provide functions to support expert users in the assessment of the fitness for use of a given dataset. Combining concepts from GIS and Business Intelligence, this approach provides interactive, multi‐granularity and context‐sensitive spatial data quality indicators that help experts to build and justify their opinions. A prototype called the Multidimensional User Manual is presented to illustrate this approach.

Multidimensional Management of Geospatial Data Quality Information for its Dynamic Use Within GIS

Metadata should help users to assess the quality (fitness for use) of geospatial data, thus reducing the risk of data misuse. However, metadata presents limitations and remain largely unused. There still exists a need to provide information to users about data quality in a more meaningful way. This research aims to dynamically communicate quality information to the users in a rapid and intuitive way in order to reduce user meta-uncertainty related to geospatial data quality, and then reduce the risks of data misuses. Such a solution requires a data model able to support heterogeneous data quality information at different levels of analysis. Using a multidimensional database approach, this paper proposes a conceptual framework named the Quality Information Management Model (QIMM) relying on quality dimensions and measures. This allows a user to easily and rapidly navigate into the quality information using a Spatial On-Line Analytical Processing (SOLAP) client-tied to its GIS application. QIMM potential is illustrated by examples, and then a prototype and ways to communicate data quality to users are explored.

Thirty Years of Research on Spatial Data Quality: Achievements, Failures, and Opportunities

This article reflects on the past 30 years of academic research in the field of spatial data quality and tries to identify the main achievements, failures, and opportunities for future research. Most of this reflection results from a panel discussion that took place during the Sixth International Symposium on Spatial Data Quality (ISSDQ) in July 2009.

Marine habitat mapping in support of Marine Protected Area management in a subarctic fjord: Gilbert Bay, Labrador, Canada

This paper presents an approach that allows production of benthic substrate and habitat maps in fjord environments. This approach is used to support the management of the Gilbert Bay Marine Protected Area (MPA) in southeastern Labrador, Atlantic Canada. Multibeam sonar-derived bathymetry, seabed slope, and acoustic reflectance (backscatter) were combined using supervised classification methods and GIS with ground-truthed benthic sampling in order to derive maps of the substrates and main benthic habitats. Six acoustically distinct substrate types were identified in the fjord, and three additional substrate types without a unique acoustic signature were recognized. Ordination by multidimensional scaling and analysis of similarity generalized these to four acoustically distinct habitat types. Greatest within-habitat (alpha) diversity was found in the coralline-algae encrusted gravel habitat. Greatest between-habitat (beta) diversity was found in the management Zones 1 and 2, which have the highest level of protection. The study confirmed that the zoning plan for the MPA, which was designed to protect spawning and juvenile fish habitat for a local genetically distinct population of Atlantic cod, afforded highest levels of protection to areas with highest habitat diversity.

Improving Volunteered Geographic Information Quality Using a Tag Recommender System: The Case of OpenStreetMap

Studies have analyzed the quality of volunteered geographic information (VGI) datasets, assessing the positional accuracy of features and the semantic accuracy of the attributes. While it has been shown that VGI can, in some contexts, reach a high positional accuracy, these studies have also highlighted a large spatial heterogeneity in positional accuracy and completeness, but also concerning the semantics of the objects. Such high semantic heterogeneity of VGI datasets becomes a significant obstacle to a number of possible uses that could be made of the data. This paper proposes an approach for both improving the semantic quality and reducing the semantic heterogeneity of VGI datasets. The improvement of the semantic quality is achieved by using a tag recommender system, called OSMantic, which automatically suggests relevant tags to contributors during the editing process. Such an approach helps contributors find the most appropriate tags for a given object, hence reducing the overall dataset semantic heterogeneity. The approach was implemented into a plugin for the Java OpenStreetMap editor (JOSM) and different examples illustrate how this plugin can be used to improve the quality of VGI data. This plugin has been tested by OSM contributors and evaluated using an online questionnaire. Results of the evaluation suggest a high level of satisfaction from users and are discussed.

Interactive exploration of movement data: a case study of geovisual analytics for fishing vessel analysis

The analysis of large movement datasets is a challenging task, because of their size and spatial complexity. This paper presents an interactive geovisual analytics approach named Hybrid Spatio-Temporal Filtering that integrates filtering of multiple movement characteristics, geovisual representations of the data, and multiple coordinated views to enable analysts to focus on movement patterns that are of interest. In particular, we propose a novel technique that combines the fractal dimension and velocity of movement paths to filter out uninteresting records through an iterative signature-building process. In order to allow analysts to explore the data at different scales of the movement path length, fractal dimension estimation is performed using an adjustable moving window technique. These tools are provided in conjunction with a probability-based zonal incursion tool to visually represent when the movement nears areas of interest. The outcome is a geovisual analytics system that allows analysts to specify a hybrid filter consisting of the desired movement path complexity, the length of the paths to consider, and the velocity range that represents specific types of behaviors. This filtering of the data supports analysts in identifying movement paths that match their specified interests, resulting in a reduction in the amount of data shown to the analyst. The utility of the approach was validated through field trials, wherein fisheries enforcement officers analyzed and explored fishing vessel movement data using the prototype system. The participants responded positively to the features of the system and the support it provided for their data analysis activities. The combination of fractal dimension, velocity, and temporal filtering helped them to effectively identify subsets of data that conformed to particular behavioral patterns of interest.

Exploring geo-temporal differences using GTdiff

Many data sets exist that contain both geospatial and temporal elements, in addition to the core data that requires analysis. Within such data sets, it can be difficult to determine how the data have changed over spatial and temporal ranges. In this design study we present a system for dynamically exploring geo-temporal changes in the data. GTdiff provides a visual approach to representing differences in the data within user-defined spatial and temporal limits, illustrating when and where increases and/or decreases have occurred. The system makes extensive use of spatial and temporal filtering and binning, geo-visualization, colour encoding, and multiple coordinated views. It is highly interactive, supporting knowledge discovery through exploration and analysis of the data. A case study is presented illustrating the benefits of using GTdiff to analyze the changes in the catch data of the cod fisheries off the coast of Newfoundland, Canada from 1948 to 2006.

Comparing Selections of Environmental Variables for Ecological Studies: A Focus on Terrain Attributes

Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes–recommended in a related study and that includes a measure of relative position, slope, two measures of orientation, topographic mean and a measure of rugosity–yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.

Visually representing geo-temporal differences

Data sets that contain geospatial and temporal elements can be challenging to analyze. In particular, it can be difficult to determine how the data have changed over spatial and temporal ranges. In this poster, we present a visual approach for representing the pair-wise differences between geographically and temporally binned data. In addition to providing a novel method for visualizing such geo-temporal differences, GTdiff provides a high degree of interactivity that supports the exploration and analysis of the data.