Volunteered and crowdsourced geographic information: the OpenStreetMap project

Advancements in technology over the last two decades have changed how spatial data are created and used. In particular, in the last decade, volunteered geographic information (VGI), i.e., the crowdsourcing of geographic information, has revolutionized the spatial domain by shifting the map-making process from the hands of experts to those of any willing contributor. Started in 2004, OpenStreetMap (OSM) is the pinnacle of VGI due to the large number of volunteers involved and the volume of spatial data generated. While the original objective of OSM was to create a free map of the world, its uses have shown how the potential of such an initiative goes well beyond map-making: ranging from projects such as the Humanitarian OpenStreetMap (HOT) project, that understands itself as a bridge between the OSM community and humanitarian responders, to collaborative projects such as Mapillary, where citizens take street-level images and the system aims to automate mapping. A common trend among these projects using OSM is the fact that the community dynamic tends to create spin-off projects. Currently, we see a drive towards projects that support sustainability goals using OSM. We discuss some such applications and highlight challenges posed by this new paradigm. We also explore the most promising future uses of this increasingly popular participatory phenomenon

Volunteered and Crowdsourced Geographic Information: the OpenStreetMap Project

Advancements in technology over the last two decades have changed how spatial data are created and used. In particular, in the last decade, volunteered geographic information (VGI), i.e., the crowdsourcing of geographic information, has revolutionized the spatial domain by shifting the map-making process from the hands of experts to those of any willing contributor. Started in 2004, OpenStreetMap (OSM) is the pinnacle of VGI due to the large number of volunteers involved and the volume of spatial data generated. While the original objective of OSM was to create a free map of the world, its uses have shown how the potential of such an initiative goes well beyond map-making: ranging from projects such as the Humanitarian OpenStreetMap (HOT) project, that understands itself as a bridge between the OSM community and humanitarian responders, to collaborative projects such as Mapillary, where citizens take street-level images and the system aims to automate mapping. A common trend among these projects using OSM is the fact that the community dynamic tends to create spin-off projects. Currently, we see a drive towards projects that support sustainability goals using OSM. We discuss some such applications and highlight challenges posed by this new paradigm. We also explore the most promising future uses of this increasingly popular participatory phenomenon

Technical Guidelines to Extract and Analyze VGI from Different Platforms

An increasing number of Volunteered Geographic Information (VGI) and social media platforms have been continuously growing in size, which have provided massive georeferenced data in many forms including textual information, photographs, and geoinformation. These georeferenced data have either been actively contributed (e.g., adding data to OpenStreetMap (OSM) or Mapillary) or collected in a more passive fashion by enabling geolocation whilst using an online platform (e.g., Twitter, Instagram, or Flickr). The benefit of scraping and streaming these data in stand-alone applications is evident, however, it is difficult for many users to script and scrape the diverse types of these data. On 14 June 2016, a pre-conference workshop at the AGILE 2016 conference in Helsinki, Finland was held. The workshop was called “LINK-VGI: LINKing and analyzing VGI across different platforms”. The workshop provided an opportunity for interested researchers to share ideas and findings on cross-platform data contributions. One portion of the workshop was dedicated to a hands-on session. In this session, the basics of spatial data access through selected Application Programming Interfaces (APIs) and the extraction of summary statistics of the results were illustrated. This paper presents the content of the hands-on session including the scripts and guidelines for extracting VGI data. Researchers, planners, and interested end-users can benefit from this paper for developing their own application for any region of the world

Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection

This paper proposes a faceted information exploration model that supports coarse-grained and fine-grained focusing of geographic maps by offering a graphical representation of data attributes within interactive widgets. The proposed approach enables (i) a multi-category projection of long-lasting geographic maps, based on the proposal of efficient facets for data exploration in sparse and noisy datasets, and (ii) an interactive representation of the search context based on widgets that support data visualization, faceted exploration, category-based information hiding and transparency of results at the same time. The integration of our model with a semantic representation of geographical knowledge supports the exploration of information retrieved from heterogeneous data sources, such as Public Open Data and OpenStreetMap. We evaluated our model with users in the OnToMap collaborative Web GIS. The experimental results show that, when working on geographic maps populated with multiple data categories, it outperforms simple category-based map projection and traditional faceted search tools, such as checkboxes, in both user performance and experience

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Developing tools and models for evaluating geospatial data integration of official and VGI data sources

PhD ThesisIn recent years, systems have been developed which enable users to produce, share and update information on the web effectively and freely as User Generated Content (UGC) data (including Volunteered Geographic Information (VGI)). Data quality assessment is a major concern for supporting the accurate and efficient spatial data integration required if VGI is to be used alongside official, formal, usually governmental datasets. This thesis aims to develop tools and models for the purpose of assessing such integration possibilities. Initially, in order to undertake this task, geometrical similarity of formal and informal data was examined. Geometrical analyses were performed by developing specific programme interfaces to assess the positional, linear and polygon shape similarity among reference field survey data (FS); official datasets such as data from Ordnance Survey (OS), UK and General Directorate for Survey (GDS), Iraq agencies; and VGI information such as OpenStreetMap (OSM) datasets. A discussion of the design and implementation of these tools and interfaces is presented. A methodology has been developed to assess such positional and shape similarity by applying different metrics and standard indices such as the National Standard for Spatial Data Accuracy (NSSDA) for positional quality; techniques such as buffering overlays for linear similarity; and application of moments invariant for polygon shape similarity evaluations. The results suggested that difficulties exist for any geometrical integration of OSM data with both bench mark FS and formal datasets, but that formal data is very close to reference datasets. An investigation was carried out into contributing factors such as data sources, feature types and number of data collectors that may affect the geometrical quality of OSM data and consequently affect the integration process of OSM datasets with FS, OS and GDS. Factorial designs were undertaken in this study in order to develop and implement an experiment to discover the effect of these factors individually and the interaction between each of them. The analysis found that data source is the most significant factor that affects the geometrical quality of OSM datasets, and that there are interactions among all these factors at different levels of interaction. This work also investigated the possibility of integrating feature classification of official datasets such as data from OS and GDS geospatial data agencies, and informal datasets such as OSM information. In this context, two different models were developed. The first set of analysis included the evaluation of semantic integration of corresponding feature classifications of compared datasets. The second model was concerned with assessing the ability of XML schema matching of feature classifications of tested datasets. This initially involved a tokenization process in order to split up into single words classifications that were composed of multiple words. Subsequently, encoding feature classifications as XML schema trees was undertaken. The semantic similarity, data type similarity and structural similarity were measured between the nodes of compared schema trees. Once these three similarities had been computed, a weighted combination technique has been adopted in order to obtain the overall similarity. The findings of both sets of analysis were not encouraging as far as the possibility of effectively integrating feature classifications of VGI datasets, such as OSM information, and formal datasets, such as OS and GDS datasets, is concerned.Ministry of Higher Education and Scientific Research, Republic of Iraq

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.