Visualizing perceived spatial data quality of 3D objects within virtual globes

Virtual globes(VGs)allow Internet users to view geographic data of heterogeneous quality created by other users.This article presents a new approach for collecting and visualizing information about the perceived quality of 3D data in VGs.It aims atimproving users’awareness of the qualityof 3D objects.Instead of relying onthe existing metadata or on formal accuracy assessments that are often impossible in practice,we propose a crowd-sourced quality recommender system based on the five-star visualization method successful in other types of Web applications.Four alternative five-star visualizations were implemented in a Google Earth-based prototype and tested through a formal user evaluation.These tests helped identifying the most effective method for a 3D environment.Results indicate that while most websites use a visualization approach that shows a‘number of stars’,this method was the least preferred by participants.Instead,participants ranked the‘number within a star’method highest as it allowed reducing the visual clutter in urban settings,suggesting that 3D environments such as VGs require different designapproachesthan2Dornon-geographicapplications.Resultsalsoconfirmed that expert and non-expert users in geographic data share similar preferences for the most and least preferred visualization methods.

Modeling data quality for risk assessment of GIS

This paper presents a methodology to determine three data quality (DQ) risk characteristics: accuracy, comprehensiveness and nonmembership. The methodology provides a set of quantitative models to confirm the information quality risks for the database of the geographical information system (GIS). Four quantitative measures are introduced to examine how the quality risks of source information affect the quality of information outputs produced using the relational algebra operations Selection, Projection, and Cubic Product. It can be used to determine how quality risks associated with diverse data sources affect the derived data. The GIS is the prime source of information on the location of cables, and detection time strongly depends on whether maps indicate the presence of cables in the construction business. Poor data quality in the GIS can contribute to increased risk or higher risk avoidance costs. A case study provides a numerical example of the calculation of the trade-offs between risk and detection costs and provides an example of the calculation of the costs of data quality. We conclude that the model contributes valuable new insight.

Spatial data trusts:an emerging governance framework for sharing spatial data

Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data governance.Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust.The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications,addressing some longstanding barriers to data sharing,such as location privacy and data sovereignty.This paper introduces and explores the concept of a‘spatial data Trust’by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data.The work identifiesfive key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in thefield,including spatial data infrastructures(SDIs),location privacy,and spatial data quality.The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.

Quality assessment of OpenStreetMap data using trajectory mining

OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attribute data inserts,deletions,and updates,can be inaccurate,incomplete,inconsistent,or vague.There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data.Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs.This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users.The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature.Using such rules,some sets of potential bugs and errors can be identified and stored for further investigations.