4,615 research outputs found
Bridging the Gap Between Traditional Metadata and the Requirements of an Academic SDI for Interdisciplinary Research
Metadata has long been understood as a fundamental component of any Spatial Data Infrastructure, providing information relating to discovery, evaluation and use of
datasets and describing their quality. Having good metadata about a dataset is fundamental to using it correctly and to understanding the implications of issues such as missing data or incorrect attribution on the results obtained for any analysis carried out.
Traditionally, spatial data was created by expert users (e.g. national mapping agencies), who created metadata for the data. Increasingly, however, data used in spatial analysis comes from multiple sources and could be captured or used by nonexpert users â for example academic researchers â many of whom are from nonâGIS disciplinary backgrounds, not familiar with metadata and perhaps working in geographically dispersed teams. This paper examines the applicability of metadata in this academic context, using a multiânational coastal/environmental project as a case study. The work to date highlights a number of suggestions for good practice, issues and research questions relevant to Academic SDI, particularly given the increased levels of research data sharing and reuse required by UK and EU funders
Beyond data collection: Objectives and methods of research using VGI and geo-social media for disaster management
This paper investigates research using VGI and geo-social media in the disaster management
context. Relying on the method of systematic mapping, it develops a classification schema that
captures three levels of main category, focus, and intended use, and analyzes the relationships
with the employed data sources and analysis methods. It focuses the scope to the pioneering
field of disaster management, but the described approach and the developed classification
schema are easily adaptable to different application domains or future developments. The
results show that a hypothesized consolidation of research, characterized through the building
of canonical bodies of knowledge and advanced application cases with refined methodology,
has not yet happened. The majority of the studies investigate the challenges and potential
solutions of data handling, with fewer studies focusing on socio-technological issues or
advanced applications. This trend is currently showing no sign of change, highlighting that VGI
research is still very much technology-driven as opposed to theory- or application-driven. From
the results of the systematic mapping study, the authors formulate and discuss several
research objectives for future work, which could lead to a stronger, more theory-driven
treatment of the topic VGI in GIScience.Carlos Granell has been partly funded by the RamĂłn y Cajal Programme (grant number RYC-2014-16913
Improving knowledge about the risks of inappropriate uses of geospatial data by introducing a collaborative approach in the design of geospatial databases
La disponibilitĂ© accrue de lâinformation gĂ©ospatiale est, de nos jours, une rĂ©alitĂ© que plusieurs organisations, et mĂȘme le grand public, tentent de rentabiliser; la possibilitĂ© de rĂ©utilisation des jeux de donnĂ©es est dĂ©sormais une alternative envisageable par les organisations compte tenu des Ă©conomies de coĂ»ts qui en rĂ©sulteraient. La qualitĂ© de donnĂ©es de ces jeux de donnĂ©es peut ĂȘtre variable et discutable selon le contexte dâutilisation. Lâenjeu dâinadĂ©quation Ă lâutilisation de ces donnĂ©es devient dâautant plus important lorsquâil y a disparitĂ© entre les nombreuses expertises des utilisateurs finaux de la donnĂ©e gĂ©ospatiale. La gestion des risques dâusages inappropriĂ©s de lâinformation gĂ©ospatiale a fait lâobjet de plusieurs recherches au cours des quinze derniĂšres annĂ©es. Dans ce contexte, plusieurs approches ont Ă©tĂ© proposĂ©es pour traiter ces risques : parmi ces approches, certaines sont prĂ©ventives et dâautres sont plutĂŽt palliatives et gĂšrent le risque aprĂšs l'occurrence de ses consĂ©quences; nĂ©anmoins, ces approches sont souvent basĂ©es sur des initiatives ad-hoc non systĂ©miques. Ainsi, pendant le processus de conception de la base de donnĂ©es gĂ©ospatiale, lâanalyse de risque nâest pas toujours effectuĂ©e conformĂ©ment aux principes dâingĂ©nierie des exigences (Requirements Engineering) ni aux orientations et recommandations des normes et standards ISO. Dans cette thĂšse, nous Ă©mettons l'hypothĂšse quâil est possible de dĂ©finir une nouvelle approche prĂ©ventive pour lâidentification et lâanalyse des risques liĂ©s Ă des usages inappropriĂ©s de la donnĂ©e gĂ©ospatiale. Nous pensons que lâexpertise et la connaissance dĂ©tenues par les experts (i.e. experts en geoTI), ainsi que par les utilisateurs professionnels de la donnĂ©e gĂ©ospatiale dans le cadre institutionnel de leurs fonctions (i.e. experts du domaine d'application), constituent un Ă©lĂ©ment clĂ© dans lâĂ©valuation des risques liĂ©s aux usages inadĂ©quats de ladite donnĂ©e, dâoĂč lâimportance dâenrichir cette connaissance. Ainsi, nous passons en revue le processus de conception des bases de donnĂ©es gĂ©ospatiales et proposons une approche collaborative dâanalyse des exigences axĂ©e sur lâutilisateur. Dans le cadre de cette approche, lâutilisateur expert et professionnel est impliquĂ© dans un processus collaboratif favorisant lâidentification a priori des cas dâusages inappropriĂ©s. Ensuite, en passant en revue la recherche en analyse de risques, nous proposons une intĂ©gration systĂ©mique du processus dâanalyse de risque au processus de la conception de bases de donnĂ©es gĂ©ospatiales et ce, via la technique Delphi. Finalement, toujours dans le cadre dâune approche collaborative, un rĂ©fĂ©rentiel ontologique de risque est proposĂ© pour enrichir les connaissances sur les risques et pour diffuser cette connaissance aux concepteurs et utilisateurs finaux. Lâapproche est implantĂ©e sous une plateforme web pour mettre en Ćuvre les concepts et montrer sa faisabilitĂ©.Nowadays, the increased availability of geospatial information is a reality that many organizations, and even the general public, are trying to transform to a financial benefit. The reusability of datasets is now a viable alternative that may help organizations to achieve cost savings. The quality of these datasets may vary depending on the usage context. The issue of geospatial data misuse becomes even more important because of the disparity between the different expertises of the geospatial data end-users. Managing the risks of geospatial data misuse has been the subject of several studies over the past fifteen years. In this context, several approaches have been proposed to address these risks, namely preventive approaches and palliative approaches. However, these approaches are often based on ad-hoc initiatives. Thus, during the design process of the geospatial database, risk analysis is not always carried out in accordance neither with the principles/guidelines of requirements engineering nor with the recommendations of ISO standards. In this thesis, we suppose that it is possible to define a preventive approach for the identification and analysis of risks associated to inappropriate use of geospatial data. We believe that the expertise and knowledge held by experts and users of geospatial data are key elements for the assessment of risks of geospatial data misuse of this data. Hence, it becomes important to enrich that knowledge. Thus, we review the geospatial data design process and propose a collaborative and user-centric approach for requirements analysis. Under this approach, the user is involved in a collaborative process that helps provide an a priori identification of inappropriate use of the underlying data. Then, by reviewing research in the domain of risk analysis, we propose to systematically integrate risk analysis â using the Delphi technique â through the design of geospatial databases. Finally, still in the context of a collaborative approach, an ontological risk repository is proposed to enrich the knowledge about the risks of data misuse and to disseminate this knowledge to the design team, developers and end-users. The approach is then implemented using a web platform in order to demonstrate its feasibility and to get the concepts working within a concrete prototype
Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the âforestâ of data, and to miss the âtreesâ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole âforestâ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement
Place and city: merging our affective and social spatial dimension in the (smart) platial city
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsWe are living in (smart) cities that hold social-oriented promises but currently, most
of these cities disregard the humans. Although some alternatives are appearing such
as smart citizen-centric approaches, there is a lack of how promoting truly appealing
perspectives toward a common good or better social synergies. Thereby, smart cities,
with their associated Information and Communication Technology tools, are offering
new possibilities, but, unfortunately, citizens are not fully exploiting the opportunities
to empower themselves because, among other reasons, they are not aware of their
common spatialities. Currently, we are not able to operationalize the spatial humanurban
interactions regarding citizensâ cognitions, feelings and behaviors towards city
places (i.e., sense of place) and meaningful geographic human relationships (i.e., social
capital). Both concepts are significant as resources for an alternative landscape
based on human perception and organization of social interactions fostered through
the geographic place(s). In this research, we highlight the need to understand and
operationalize social concepts spatial dimension for a better understanding of a smart
citizen-centric approach which is mainly dependent on our capability to understand
platial urban dynamics. We conceptualized a (spatial) conceptual framework for sense
of place and social capital at the individual level to study their spatial relationship in
the urban context. We developed a web map-based survey based on the literature to
spatialize, characterize and measure sense of place, social capital and civic engagement.
Using the spatial data collected, we validated our framework and demonstrated the
importance to encompass the spatial dimension of social concepts (i.e., sense of place
and social capital) as pivotal aspect (1) to understand the platial urban dynamics; (2)
to provide useful social-spatial data to city processes (e.g. civic engagement); and (3)
to reveal the potential to include them in social theory and structural equation models.
Furthermore, we highlighted the crucial role of Geographic Information Science (GISc)
techniques to gather the spatial dimension of those social concepts. Although in this research we focus on the spatial relationship between sense of place and social capital
on civic engagement, the possibilities to relate our framework and methodology to other
city based-notions can bring to light new platial urban dynamics. This research wants to
open up the agenda for further research into exploratory place-based geography studies
and, simultaneously, sets up a common social ground to build other socially-oriented
conceptualizations or applications on top of it
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