Analysis of interaction and co-editing patterns amongst OpenStreetMap contributors

OpenStreetMap (OSM) is a very well known and popular Volunteered Geographic Information (VGI) project on the Internet. In January 2013 OSM gained its one millionth registered member. Several studies have shown that only a small percentage of these registered members carry out the large majority of the mapping and map editing work. In this article we discuss results from a social-network based analysis of seven major cities in OSM in an effort to understand if there is quantitative evidence of interaction and collaboration between OSM members in these areas. Are OSM contributors working on their own to build OSM databases in these cities or is there evidence of collaboration between OSM contributors? We find that in many cases high frequent contributors (“senior mappers”) perform very large amounts of mapping work on their own but do interact (edit/update) contributions from lower frequency contributors

A qualitative enquiry into OpenStreetMap making

Based on a case study on the OpenStreetMap community, this paper provides a contextual and embodied understanding of the user-led, user-participatory and user-generated produsage phenomenon. It employs Grounded Theory, Social Worlds Theory, and qualitative methods to illuminate and explores the produsage processes of OpenStreetMap making, and how knowledge artefacts such as maps can be collectively and collaboratively produced by a community of people, who are situated in different places around the world but engaged with the same repertoire of mapping practices. The empirical data illustrate that OpenStreetMap itself acts as a boundary object that enables actors from different social worlds to co-produce the Map through interacting with each other and negotiating the meanings of mapping, the mapping data and the Map itself. The discourses also show that unlike traditional maps that black-box cartographic knowledge and offer a single dominant perspective of cities or places, OpenStreetMap is an embodied epistemic object that embraces different world views. The paper also explores how contributors build their identities as an OpenStreetMaper alongside some other identities they have. Understanding the identity-building process helps to understand mapping as an embodied activity with emotional, cognitive and social repertoires

Proceedings of the Academic Track at State of the Map 2019 - Heidelberg (Germany), September 21-23, 2019

State of the Map featured a full day of academic talks. Building upon the motto of SotM 2019 in "Bridging the Map" the Academic Track session was aimed to provide the bridge to join together the experience, understanding, ideas, concepts and skills from different groups of researchers, academics and scientists from around the world. In particular, the Academic Track session was meant to build this bridge that connects members of the OpenStreetMap community and the academic community by providing an open passage for exchange of ideas, communication and opportunities for increased collaboration. These proceedings include 14 abstracts accepted as oral presentations and 6 abstracts presented as posters. Contributions were received from different academic fields, for example geography, remote sensing, computer and information sciences, geomatics, GIScience, the humanities and social sciences, and even from industry actors. We are particularly delighted to have included abstracts from both experienced researchers and students. Overall, it is our hope that these proceedings accurately showcase the ongoing innovation and maturity of scientific investigations and research into OpenStreetMap, showing how it as a research object converges multiple research areas together. Our aim is to show how the sum total of investigations of issues like Volunteered Geographic Information, geo-information, and geo-digital processes and representation shed light on the relations between crowds, real-world applications, technological developments, and scientific research

Exploring maintenance practices in crowd-mapping

Crowd-mapping is a form of collaborative work that empowers users to gather and share geographic knowledge. OpenStreetMap is one of the most successful examples of such paradigm, where the goal of building a global map of the world is collectively performed by over 2M contributors. Despite geographic information being intrinsically evolving, little research has so far gone into analysing maintenance practices in these domains. In this paper, we perform a preliminary exploration to quantitatively capture maintenance dynamics in geographic crowd-sourced datasets, in terms of: the extent to which different maintenance actions are taking place, the type of spatial information that is being maintained, and who engages in these practices. We apply this method to 117 countries in OSM, over one year of mapping activity. Our findings reveal that, although maintenance practices vary substantially from country to country in terms of how widespread they are, strong commonalities exist in terms of what metadata is being maintained and by whom

The Impact of Biases in the Crowdsourced Trajectories on the Output of Data Mining Processes

The emergence of the Geoweb has provided an unprecedented capacity for generating and sharing digital content by professional and non- professional participants in the form of crowdsourcing projects, such as OpenStreetMap (OSM) or Wikimapia. Despite the success of such projects, the impacts of the inherent biases within the ‘crowd’ and/or the ‘crowdsourced’ data it produces are not well explored. In this paper we examine the impact of biased trajectory data on the output of spatio-temporal data mining process. To do so, an experiment was conducted. The biases are intentionally added to the input data; i.e. the input trajectories were divided into two sets of training and control datasets but not randomly (as opposed to the data mining procedures). They are divided by time of day and week, weather conditions, contributors’ gender and spatial and temporal density of trajectory in 1km grids. The accuracy of the predictive models are then measured (both for training and control data) and biases gradually moderated to see how the accuracy of the very same model is changing with respect to the biased input data. We show that the same data mining technique yields different results in terms of the nature of the clusters and identified attributes

Quality Assessment of the Canadian OpenStreetMap Road Networks

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic review is first presented to provide details on the quality evaluation process of OSM. A case study of London, Ontario is followed as an experimental analysis of completeness, positional accuracy and attribute accuracy of the OSM street networks. Next, a national study of the Canadian OSM data assesses the overall semantic accuracy and lineage in addition to the quality measures mentioned above. Results of the quality evaluation are compared with associated OSM provenance metadata to examine potential correlations. The Canadian OSM road networks were found to have comparable accuracy with the tested commercial database (DMTI). Although statistical analysis suggests that there are no significant relations between OSM accuracy and its editing history, the study presents the complex processes behind OSM contributions possibly influenced by data import and remote mapping. The findings of this thesis can potentially guide cartographic product selection for interested parties and offer a better understanding of future quality improvement in OSM

Enhancing Data Classification Quality of Volunteered Geographic Information

Geographic data is one of the fundamental components of any Geographic Information System (GIS). Nowadays, the utility of GIS becomes part of everyday life activities, such as searching for a destination, planning a trip, looking for weather information, etc. Without a reliable data source, systems will not provide guaranteed services. In the past, geographic data was collected and processed exclusively by experts and professionals. However, the ubiquity of advanced technology results in the evolution of Volunteered Geographic Information (VGI), when the geographic data is collected and produced by the general public. These changes influence the availability of geographic data, when common people can work together to collect geographic data and produce maps. This particular trend is known as collaborative mapping. In collaborative mapping, the general public shares an online platform to collect, manipulate, and update information about geographic features. OpenStreetMap (OSM) is a prominent example of a collaborative mapping project, which aims to produce a free world map editable and accessible by anyone. During the last decade, VGI has expanded based on the power of crowdsourcing. The involvement of the public in data collection raises great concern about the resulting data quality. There exist various perspectives of geographic data quality this dissertation focuses particularly on the quality of data classification (i.e., thematic accuracy). In professional data collection, data is classified based on quantitative and/or qualitative ob- servations. According to a pre-defined classification model, which is usually constructed by experts, data is assigned to appropriate classes. In contrast, in most collaborative mapping projects data classification is mainly based on individualsa cognition. Through online platforms, contributors collect information about geographic features and trans- form their perceptions into classified entities. In VGI projects, the contributors mostly have limited experience in geography and cartography. Therefore, the acquired data may have a questionable classification quality. This dissertation investigates the challenges of data classification in VGI-based mapping projects (i.e., collaborative mapping projects). In particular, it lists the challenges relevant to the evolution of VGI as well as to the characteristics of geographic data. Furthermore, this work proposes a guiding approach to enhance the data classification quality in such projects. The proposed approach is based on the following premises (i) the availability of large amounts of data, which fosters applying machine learning techniques to extract useful knowledge, (ii) utilization of the extracted knowledge to guide contributors to appropriate data classification, (iii) the humanitarian spirit of contributors to provide precise data, when they are supported by a guidance system, and (iv) the power of crowdsourcing in data collection as well as in ensuring the data quality. This cumulative dissertation consists of five peer-reviewed publications in international conference proceedings and international journals. The publications divide the disser- tation into three parts the first part presents a comprehensive literature review about the relevant previous work of VGI quality assurance procedures (Chapter 2), the second part studies the foundations of the approach (Chapters 3-4), and the third part discusses the proposed approach and provides a validation example for implementing the approach (Chapters 5-6). Furthermore, Chapter 1 presents an overview about the research ques- tions and the adapted research methodology, while Chapter 7 concludes the findings and summarizes the contributions. The proposed approach is validated through empirical studies and an implemented web application. The findings reveal the feasibility of the proposed approach. The output shows that applying the proposed approach results in enhanced data classification quality. Furthermore, the research highlights the demands for intuitive data collection and data interpretation approaches adequate to VGI-based mapping projects. An interaction data collection approach is required to guide the contributors toward enhanced data quality, while an intuitive data interpretation approach is needed to derive more precise information from rich VGI resources

Mapping and the Citizen Sensor

Maps are a fundamental resource in a diverse array of applications ranging from everyday activities, such as route planning through the legal demarcation of space to scientific studies, such as those seeking to understand biodiversity and inform the design of nature reserves for species conservation. For a map to have value, it should provide an accurate and timely representation of the phenomenon depicted and this can be a challenge in a dynamic world. Fortunately, mapping activities have benefitted greatly from recent advances in geoinformation technologies. Satellite remote sensing, for example, now offers unparalleled data acquisition and authoritative mapping agencies have developed systems for the routine production of maps in accordance with strict standards. Until recently, much mapping activity was in the exclusive realm of authoritative agencies but technological development has also allowed the rise of the amateur mapping community. The proliferation of inexpensive and highly mobile and location aware devices together with Web 2.0 technology have fostered the emergence of the citizen as a source of data. Mapping presently benefits from vast amounts of spatial data as well as people able to provide observations of geographic phenomena, which can inform map production, revision and evaluation. The great potential of these developments is, however, often limited by concerns. The latter span issues from the nature of the citizens through the way data are collected and shared to the quality and trustworthiness of the data. This book reports on some of the key issues connected with the use of citizen sensors in mapping. It arises from a European Co-operation in Science and Technology (COST) Action, which explored issues linked to topics ranging from citizen motivation, data acquisition, data quality and the use of citizen derived data in the production of maps that rival, and sometimes surpass, maps arising from authoritative agencies

Towards an Automated Comparison of OpenStreetMap with Authoritative Road Datasets

OpenStreetMap (OSM) is an extraordinarily large and diverse spatial database of the world. Road networks are amongst the most frequently occurring spatial content within the OSM database. These road network representations are usable in many applications. However the quality of these representations can vary between locations. Comparing OSM road networks with authoritative road datasets for a given area or region is an important task in assessing OSM’s fitness for use for applications like routing and navigation. Such comparisons can be technically challenging and no software implementation exists which facilitates them easily and automatically. In this article we develop and propose a flexible methodology for comparing the geometry of OSM road network data with other road datasets. Quantitative measures for the completeness and spatial accuracy of OSM are computed, including the compatibility of OSM road data with other map databases. Our methodology provides users with significant flexibility in how they can adjust the parameterization to suit their needs. This software implementation is exclusively built on open source software and a significant degree of automation is provided for these comparisons. This software can subsequently be extended and adapted for comparison between OSM and other external road datasets