Defining Fitness-for-Use for Crowdsourced Points of Interest (POI)

David Jonietz,Alexander Zipf

doi:10.3390/ijgi5090149

Abstract

(1) Background: Due to the advent of Volunteered Geographic Information (VGI), large datasets of user-generated Points of Interest (POI) are now available. As with all VGI, however, there is uncertainty concerning data quality and fitness-for-use. Currently, the task of evaluating fitness-for-use of POI is left to the data user, with no guidance framework being available which is why this research proposes a generic approach to choose appropriate measures for assessing fitness-for-use of crowdsourced POI for different tasks. (2) Methods: POI are related to the higher-level concept of geo-atoms in order to identify and distinguish their two basic functions, geo-referencing and object-referencing. Then, for each of these functions, suitable measures of positional and thematic quality are developed based on existing quality indicators. (3) Results: Typical use cases of POI are evaluated with regards to their use of the two basic functions of POI, and allocated appropriate measures for fitness-for-use. The general procedure is illustrated on a brief practical example. (4) Conclusion: This research addresses the issue of fitness-for-use of POI on a higher conceptual level by relating it to more fundamental notions of geographical information representation. The results are expected to assist users of crowdsourced POI datasets in determining an appropriate method to evaluate fitness-for-use.

Highlights

Points of Interest (POI) are zero-dimensional features which refer to specific locations or real-world entities in geographical space, such as historical sites, landmarks, public services, shops, restaurants, or bars [1]
Whereas the appropriateness of OpenStreetMap (OSM) data has been analyzed with regards to specific application tasks such as navigation [13,14], 3D-building reconstruction from building footprints [15], or bicycle-related mapping and analysis tasks [16], to the best of our knowledge, there is currently no study which explicitly evaluates the fitness-for-use of POI datasets
This is especially useful for POI datasets which, as we have argued in this paper, are on the one hand often the result of crowdsourcing and, prone to issues related to data quality, and on the other hand used for a large variety of tasks

Summary

Introduction

Points of Interest (POI) are zero-dimensional features which refer to specific locations or real-world entities in geographical space, such as historical sites, landmarks, public services, shops, restaurants, or bars [1]. Compared to traditional spatial data, as provided by commercial vendors or the authorities, Volunteered Geographic Information (VGI) is in particular need of adequate methods for data quality assessment, a fact which is due to its contributors being untrained and heterogeneous, a lack of formal specifications and the potential effects of social factors [5,6,7,8] Since in this context, quality assurance is challenging at best [6], it can be argued that the task of quality assessment has been somewhat shifted from the producer to the users of the data, who are required to evaluate its appropriateness with regards to their specific motive, or its fitness-for-use [9]. Whereas the appropriateness of OpenStreetMap (OSM) data has been analyzed with regards to specific application tasks such as navigation [13,14], 3D-building reconstruction from building footprints [15], or bicycle-related mapping and analysis tasks [16], to the best of our knowledge, there is currently no study which explicitly evaluates the fitness-for-use of POI datasets

Objectives

Methods

Discussion

Conclusion