DFG, SPP 1894/1 – Volunteered Geographic Information




A framework for measuring the fitness for purpose of OpenStreetMap data based on intrinsic quality indicators

The OpenStreetMap data in selected regions is normally evaluated by comparing with commercial or authority data, before it will be used for in a project in different application domains. However, there are three problems with this kind of so-called extrinsic quality assessment. Firstly, the results cannot directly answer the question about whether the OSM data is good enough for a certain purpose, because the extrinsic quality indicators are designed as standard to indicate an overall level of the quality. Secondly, the results may not be able to reflect the true quality of OSM data, because the existing tools (as those described by ISO) are not inclusive enough or appropriate to eloquently evaluate OSM data, because the nature of OSM, as a project of VGI, is fundamentally different to what geospatial experts have dealt with so far. Thirdly, the reference data is often not available due to contradictory licensing restrictions or high procurement costs.

In this project, we attempt to develop a framework that provides in a systematic way the methods and measures to evaluate the fitness for purpose of OSM data. A main objective is that this shall also be usable when there is no (authority) reference data for comparison is available, in order to make it applicable in a wide range of situations and extending the traditional approaches on spatial data quality evaluations based on comparison with other data sets. For this one sub-objective is to define systematically a taxonomy for different types of usage of OSM data in different application domains that includes the relevant indications and advises of: (i) what kind of information is required? (ii) what is the data quality requirements? (iii) which quality indicators can be used to evaluate the OSM data to be used? (iv) how can the quality indicators be calculated without using reference data? and (v) how creditable is the evaluation of the fitness for purpose? The framework is going to serve as procedure guidelines when using OSM data in projects in application domains such as urban planning, routing & navigation, disaster management, marketing, geocoding, map application, etc.

To achieve this, an intensive study in the history data of OSM (OSM-Full-History-Dump) will be carried out based on the existing experiences available in practices and literatures, in order to analyse the nature characteristics of OSM, derive the factors which can influence OSM data quality, and parse them hierarchically and quantitatively. On this base, existing intrinsic quality indicators will be adjusted and improved, and new intrinsic indicators will be introduced.

The main contribution of this project is the so far most exhaustive systematic identification and connection among intrinsic and extrinsic quality indicators and measurements to a large number of application domains. The framework developed in this project shall have wide applicability or be generalizable for using any kind of VGI data in the defined application domains. Moreover, the method of developing the framework can be deployed for further application domains in case similar VGI data is used.

09.09.2020 10:33
A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis among Most Cited Paper in Transactions in GIS

We are happy to share that our paper “A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis” (Barron, Neis, Zipf 2013) belongs to the top 5 most cited papers of the international journal “Transaction in GIS” (TGIS). Only recently we became aware of this. Thank you for considering this work. Certainly this paper influenced also our work [...]

04.11.2019 19:37
New DFG project: IdealVGI - Deep Learning with OSM

Recently a new DFG project proposal was accepted to the GIScience Research Group Heidelberg within the DFG priority programme VisVGI (Volunteered Geographic Information: Interpretation, Visualisation and Social Computing” [SPP 1894]). It is joint collaboration project together with Prof. Begüm Demir from TU Berlin. IDEAL-VGI: Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered [...]

04.07.2019 16:46
Program of Academic Track for State of the Map 2019 Heidelberg is online

Also the program of the “Academic Track” for ‘State of the Map‘ (SotM) as been released now. Early bird ticket prices are still available until 7th of July. See the website for all the details: 2019.stateofthemap.org Two contributions are by members of the GIScience Research Group Heidelberg: Analyzing the spatio-temporal patterns and impacts of large-scale data production events [...]

17.12.2018 17:32
Spatial conceptual compliance analysis with the OpenStreetMap History Database (OSHDB)

In a previous blog post we performed a conceptual compliance analysis between OSM data and several tagging-guidelines using the OSHDB API. The results were visualized in a line chart, comparing the different compliance ratio over several months. The following analysis focuses on a spatial representation of the conceptual compliance. It is [...]

10.10.2018 14:57
Explore the ohsome OSM History of whole Germany

Our new ohsome dashboard is another preview on what is and will be possible with our ohsome OpenStreetMap history analytics platform. Behind the scenes, we added support for the Apache Ignite big data framework and deployed an instance using the full OSM history data of whole Germany on Heidelberg University’s cloud computing infrastructure heiCLOUD. Apache Ignite is an open-source [...]

16.08.2018 09:57
OpenStreetMap Analytics Development for OpenCities Africa

Recently a consultancy and development agreement about OpenStreetMap Analytics Development has been reached with the World Bank in the context of the Open Cities Africa project and the Global Facility for Disaster Reduction and Recovery (GFDRR) Open Data for Resilience Initiative (OpenDRI). The main objective of this consultancy is to develop and implement new functionalities for OpenStreetMap [...]

23.04.2018 22:18
A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information

The growing use of crowdsourced geographic information (CGI) has prompted the employment of several methods for assessing information quality, which are aimed at addressing concerns on the lack of quality of the information provided by non‐experts. In a recently published work, we propose a taxonomy of methods for assessing the quality of CGI when no [...]

27.01.2018 14:53
ECSA Webinar on #citizenscience for monitoring urban landscape dynamics with GIScience Heidelberg

Join the next ECSA webinar on “Citizen Science for monitoring urban landscape dynamics”! The European Citizen Science Association (ECSA) will host a webinar on the topic of “Citizen Science for monitoring urban landscape dynamics”. The webinar will include two talks: Assessing urban green space in Vienna (by Gebhard Banko and Barbara Birli from the Austrian Environmental [...]

10.08.2017 08:56
HeiGIT/GIScience Heidelberg partnership with Humanitarian OpenStreetMap Team (HOT)

We are happy to hereby announce the official partnership of the HeiGIT/GIScience Research Group Heidelberg and the Humanitarian OpenStreetMap Team (HOT)! The GIScience Research Group at Heidelberg University has been supporting the use of OpenStreetMap for humanitarian and disaster management purposes already since 2008 when the first instance of the Disaster and Emergency OpenRouteService was developed [...]

28.06.2017 13:04
Are Crowdsourced Datasets Suitable for Specialized Routing Services? Case Study of OpenStreetMap for Routing of People with Limited Mobility

Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As one of the most important crowdsourced mapping platforms, in a recent study (1) we analyze the fitness for use of OpenStreetMap (OSM) database for routing and navigation [...]

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Latest Revision: 2019-06-13
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