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

Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work

Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics.Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS) software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression.Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20–1.85]; cycling OR 0.43 [95% CI 0.23–0.83]), low street connectivity (OR 0.22, [0.07–0.67]; OR 0.48 [0.26–0.90]) and free car parking at work (OR 0.24 [0.10–0.59]; OR 0.55 [0.32–0.95]). Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools) close to work (OR 0.36 [0.21–0.62]) and a railway station further from home (OR 0.53 [0.30–0.93]). Covariates strongly predicted travel mode (pseudo r-squared 0.74).Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode choice, and could be addressed as part of transport policy and infrastructural interventions to promote active commuting

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

Analysing and predicting micro-location patterns of software firms

While the effects of non-geographic aggregation on inference are well studied in economics, research on geographic aggregation is rather scarce. This knowledge gap together with the use of aggregated spatial units in previous firm location studies result in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI), especially the OpenStreetMap (OSM) project, and the increasing availability of official (open) geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA). Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings demonstrate that the model yields plausible predictions and OSM data is suitable for microgeographic location analysis. Our results also show that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analysed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations

Assessing sustainable development in industrial regions towards smart built environment management using Earth observation big data

This thesis investigates the sustainability of nationwide industrial regions using Earth observation big data, from environmental and socio-economic perspectives. The research contributes to spatial methodology design and decision-making support. New spatial methods, including the robust geographical detector and the concept of geocomplexity, are proposed to demonstrate the spatial properties of industrial sustainability. The study delivers scientific decision-making advice to industry stakeholders and policymakers for the post-construction assessment and future planning phases. The research has been published in prestigious geography journals, demonstrating its success

Mining large-scale human mobility data for long-term crime prediction

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

Three Essays on Growth and Innovation of Digital Platforms

Digital platforms are complex digital technology arrangements that enable the interaction of otherwise unaffiliated organisations. This interaction often generates novel outputs and as a result digital platforms are seen as a powerful driver of digital innovation. Yet exactly how digital platforms generate innovations by facilitating interaction merits further investigation. This dissertation illustrates aspects of how platforms grow and innovate using the case of the open-geo data platform OpenStreetMap. The study draws from both quantitative as well as qualitative analysis techniques applied to highly detailed data capturing the use, design, and operation of the platform over more than ten years. A series of computationally-intensive, mixedmethods studies were conducted to utilise the full scale of available empirical material while maintaining contextual richness relevant to the case. Embedded in recent topics on digital platforms, three empirical studies are presented. Each study focuses on one aspect of growth and innovation on digital platforms. The studies specifically examine; (i) how platform operators can stimulate generativity, that is the generation of novel outputs without direct input by the operator, (ii), how the unique attributes of digital technologies enable the creation of complex ecosystems that allow for highpaced changes in a platform’s architecture even if that increases the structural complexity of a platform, and, (iii) how participants coordinate contributions to a platform’s operation when they cannot rely on stable interfaces. Collectively these studies contribute to the understanding of how platforms generate new digital innovations

Open Mapping towards Sustainable Development Goals

This collection amplifies the experiences of some of the world’s young people who are working to address SDGs using geospatial technologies and multi-national collaboration. Authors from every region of the world who have emerged as leaders in the YouthMappers movement share their perspectives and knowledge in an accessible and peer-friendly format. YouthMappers are university students who create and use open mapping for development and humanitarian purposes. Their work leverages digital innovations - both geospatial platforms and communications technologies - to answer the call for leadership to address sustainability challenges. The book conveys a sense of robust knowledge emerging from formal studies or informal academic experiences - in the first-person voices of students and recent graduates who are at the forefront of creating a new map of the world. YouthMappers use OpenStreetMap as the foundational sharing mechanism for creating data together. Authors impart the way they are learning about themselves, about each other, about the world. They are developing technology skills, and simultaneously teaching the rest of the world about the potential contributions of a highly connected generation of emerging world leaders for the SDGs. The book is timely, in that it captures a pivotal moment in the trajectory of the YouthMappers movement’s ability to share emerging expertise, and one that coincides with a pivotal moment in the geopolitical history of planet earth whose inhabitants need to hear from them. Most volumes that cover the topic of sustainability in terms of youth development are written by non-youth authors. Moreover, most are written by non-majoritarian, entrenched academic scholars. This book instead puts forward the diverse voices of students and recent graduates in countries where YouthMappers works, all over the world. Authors cover topics that range from water, agriculture, food, to waste, education, gender, climate action and disasters from their own eyes in working with data, mapping, and humanitarian action, often working across national boundaries and across continents. To inspire readers with their insights, the chapters are mapped to the United Nations 17 Sustainable Development Goals (SDGs) in ways that connect a youth agenda to a global agenda. With a preface written by Carrie Stokes, Chief Geographer and GeoCenter Director, United States Agency for International Development (USAID). This is an open access book

Probabilistic flood loss modelling for residential buildings

Hochwasser stellt ein großes Risiko für Wohngebäude in Europa dar, und es wird erwartet, dass das Risiko in der Zukunft aufgrund klimatischer und sozioökonomischer Veränderungen zunehmen wird. Aktuelle Hochwasserrisikomodelle basieren meist auf einfachen Wasserstands-Schadenskurven. Diese Ansätze vereinfachen die Hochwasserschadensprozesse stark, können ungenau sein und bergen große Unsicherheiten, die oft nicht quantifiziert. Die Doktorarbeit stellt die Integration neuer Daten in probabilistische, multivariable Schadensmodelle zur Verbesserung ihrer Übertragbarkeit vor. Diese neuen Datenquellen und Modellierungsansätze werden verwendet, um zukünftige Veränderung des Hochwasserrisikos für Wohngebäude in Europa abzuschätzen und Risikokomponenten zu analysieren. Die Arbeit zeigt, OpenStreetMap (OSM) Daten liefern nützliche Informationen für die Modellierung von Hochwasserschäden und ermöglichen Modelltransfers. Die Integration von aus OSM abgeleiteten Gebäudeeigenschaften und Hochwassererfahrung aus Ereignisdatenbanken in das Bayes’sche Netzwerk basierte Hochwasserschadensmodelle für den privaten Sektor (BN-FLEMOps) ermöglichte die Implementierung auf der Mesoskala. Durch Vergleiche von Schadensschätzungen mit beobachteten Schäden in mehreren Fallstudien in Europa wurde das Modell validiert und detailliert mit einem Ensemble aus 20 Schadensmodellen verglichen. In einer abschließenden Studie werden die zukünftigen Veränderungen des Risikos für Wohngebäude in Europa modelliert. Die erwarteten jährlichen Schäden bis zum Ende des 21. Jahrhunderts werden um das 10-fache ansteigen. Die Britischen Inseln und der größte Teil von Zentral-Europa müssen mit einer starken Risikozunahme rechnen. Teile Skandinaviens und des Mittelmeerraums werden dagegen ein stagnierendes oder abnehmendes Hochwasserrisiko verzeichnen. Eine Verbesserung der privaten Vorsorgemaßnahmen könnte das Hochwasserrisiko im Mittel um 15 % und in einigen europäischen Regionen um bis zu 20 % verringern.Flooding poses great risks for residential buildings in Europe and is expected to increase in the future, driven by climatic and socio-economic change. Current flood risk models rely mostly on simple stage-damage curves for flood loss estimation. This approach oversimplifies flood damage processes, can be inaccurate and harbour large uncertainties that often are not quantified and transparently communicated. This thesis presents research that integrates new data sources into probabilistic, multi-variable loss models to improve their transferability. These new data sources and approaches are used to estimate future fluvial flood risk change for residential buildings in Europe. Contributions of the three risk components, hazard, exposure, and vulnerability are analysed and compared independently and in combination. OpenStreetMap (OSM) data are identified as a valuable source of information for flood loss modelling and enables model transfers while retaining high predictive performance. Integrating OSM derived building characteristics and flood experience information from flood event databases into the Bayesian Network Flood Loss Estimation MOdel for the private sector (BN-FLEMOps) enables the spatio-temporal and scale transformation of the model. The model is validated with reported losses in multiple case studies in Europe and compared in detail with a model ensemble of 20 internationally published flood loss models. In a final study, the future flood risk changes for residential buildings in Europe are modelled. The expected annual damage will increase up to 10-fold until the end of the 21st century. Most of Central Europe and the British Isles have to expect strong risk increases. Parts of Scandinavia and the Mediterranean on the other hand will see stagnating or decreasing fluvial flood risk. Improving private precaution could reduce flood risk by 15 % on average and up to 20 % in some European regions

OSM Science - The Academic Study of the OpenStreetMap Project, Data, Contributors, Community, and Applications

This paper is an Editorial for the Special Issue titled “OpenStreetMap as a multidisciplinary nexus: perspectives, practices and procedures”. The Special Issue is largely based on the talks presented in the 2019 and 2020 editions of the Academic Track at the State of the Map conferences. As such, it represents the most pressing and relevant issues and topics considered by the academic community in relation to OpenStreetMap (OSM)—a global project and community aimed to create and maintain a free and editable database and map of the world. In this Editorial, we survey the papers included in the Special Issue, grouping them into three research perspectives: applications of OSM for studies within other disciplines, OSM data quality, and dynamics in OSM. This survey reveals that these perspectives, while being distinct, are also interrelated. This calls for the formalization of an ‘OSM science’ that will provide the conceptual grounds to advance the scientific study of OSM, not as a set of individualized efforts but as a unified approac