Alpine Glaciology: An Historical Collaboration between Volunteers and Scientists and the Challenge Presented by an Integrated Approach

European Alpine glaciology has a long tradition of studies and activities, in which researchers have often relied on the field work of some specialized volunteer operators. Despite the remarkable results of this cooperation, some problems in field data harmonization and in covering the whole range of monitored glaciers are still present. Moreover, dynamics of reduction, fragmentation and decline, which in recent decades characterize Alpine glaciers, make more urgent the need to improve spatial and temporal monitoring, still maintaining adequate quality standards. Scientific field monitoring activities on Alpine glaciers run parallel to a number of initiatives by individuals and amateur associations, keepers of alternative, experiential and para-scientific knowledge of the glacial environment. Problems of harmonization, coordination, recruitment and updating can be addressed with the help of a collaborative approach—citizen science-like—in which the scientific coordination guarantees information quality and web 2.0 tools operate as mediators between expert glaciologists and non-expert contributors. This paper gives an overview of glaciological information currently produced in the European Alpine region, representing it in an organized structure, functional to the discussion. An empowering solution is then proposed, both methodological and technological, for the integration of multisource data. Its characteristics, potentials and problems are discussed

Managing Marine Mammal Observations Using a Volunteered Geographic Information Approach

Traditional methods of gathering the data needed to understand human impact on marine mammals requires extensive time and resources. To reduce the burden associated with collecting and managing marine mammal observations, a geographic information system (GIS) solution was developed using a volunteered geographic information (VGI) approach. Web and mobile applications were built for the general public to submit marine mammal observations and visualize the results. The web application also includes querying and authorized download of data. Both applications consume web services published from an ArcSDE geodatabase using ArcGIS Server 10.0

SIRENE: A Spatial Data Infrastructure to Enhance Communities' Resilience to Disaster-Related Emergency

Abstract Planning in advance to prepare for and respond to a natural hazard-induced disaster-related emergency is a key action that allows decision makers to mitigate unexpected impacts and potential damage. To further this aim, a collaborative, modular, and information and communications technology-based Spatial Data Infrastructure (SDI) called SIRENE—Sistema Informativo per la Preparazione e la Risposta alle Emergenze (Information System for Emergency Preparedness and Response) is designed and implemented to access and share, over the Internet, relevant multisource and distributed geospatial data to support decision makers in reducing disaster risks. SIRENE flexibly searches and retrieves strategic information from local and/or remote repositories to cope with different emergency phases. The system collects, queries, and analyzes geographic information provided voluntarily by observers directly in the field (volunteered geographic information (VGI) reports) to identify potentially critical environmental conditions. SIRENE can visualize and cross-validate institutional and research-based data against VGI reports, as well as provide disaster managers with a decision support system able to suggest the mode and timing of intervention, before and in the aftermath of different types of emergencies, on the basis of the available information and in agreement with the laws in force at the national and regional levels. Testing installations of SIRENE have been deployed in 18 hilly or mountain municipalities (12 located in the Italian Central Alps of northern Italy, and six in the Umbria region of central Italy), which have been affected by natural hazard-induced disasters over the past years (landslides, debris flows, floods, and wildfire) and experienced significant social and economic losses

User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks

International audienceIn this paper we propose a procedure consisting of a first collection phase of social net- work messages, a subsequent user query selection, and finally a clustering phase, de- fined by extending the density-based DBSCAN algorithm, for performing a geographic and temporal exploration of a collection of items, in order to reveal and map their latent spatio-temporal structure. Specifically, both several geo-temporal distance measures and a density-based geo-temporal clustering algorithm are proposed. The approach can be applied to social messages containing an explicit geographic and temporal location. The algorithm usage is exemplified to identify geographic regions where many geotagged Twitter messages about an event of interest have been created, possibly in the same time period in the case of non-periodic events (aperiodic events), or at regular timestamps in the case of periodic events. This allows discovering the spatio-temporal periodic and aperiodic characteristics of events occurring in specific geographic areas, and thus increasing the awareness of decision makers who are in charge of territorial planning. Several case studies are used to illustrate the proposed procedure

Developing Data Extraction and Dynamic Data Visualization (Styling) Modules for Web GIS Risk Assessment System (WGRAS)

Interactive web-GIS tools play an important role in determining disaster risk assessment which ultimately result in reduction of unexpected damages, cost and saves millions of lives. Disaster management practitioners largely benefited information at their disposal about location where possible incidents are eminent, anticipate the impact and project possible outcomes to help mitigate and organize proper response. It is also important to note that, accurate and timely information is critical for coherent coordination in response to disasters. All the above can be achieved through proper data collection combined with computer assisted modelling, analysis, production and timely dissemination of spatial information. This Master’s thesis aims to extend features of Web GIS for Risk Assessment (WGRAS) project conducted at the Department of Physical Geography and Ecosystem Science at Lund University. The work includes development of tools for geospatial data acquisition and extraction from freely available external open non-commercial sources and dynamic, user-oriented map Visualization allowing user-defined symbolization and coloring resulting flexible visual portrayal of geospatial data in the web environment. In this regard, solutions are driven based upon open source, open data and implementation strictly complies with open web standard protocols and web services. As a result, WGRAS is furnished with easy and user driven raw geo-spatial data extracts for an area of interest from OpenStreetMap (OSM). Thus, data is automatically stored for later use for different spatial modelling and analysis. The second most important contribution of this thesis is the feature developed to solve visualization of geographic information through a map server where maps are generated with a pre-defined style that limits user’s visual needs. Visualization module enables dynamic definition of style (symbolization and coloring) data which assist non-GIS expert to produce instant and meaningful presentation of maps to the end user. Overall, the work in this practical thesis adds value to disaster management and analysis in terms of easy provision of data and enabling clear dissection of disaster prone areas using effective visualization mechanism.Interactive web-GIS tools play an important role in determining disaster risk assessment which ultimately result in reduction of unexpected damages, cost and saves millions of lives. Disaster management practitioners largely benefited information at their disposal about location where possible incidents are eminent, anticipate the impact and project possible outcomes to help mitigate and organized response. It is also important to note that, accurate and timely information is critical for coherent coordination in response to disasters. This can be achieved through proper data collection combined with computer assisted modelling, analysis, production and timely dissemination of spatial information. This Master’s thesis aims to extend features of Web GIS for Risk Assessment (WGRAS) project conducted at the Department of Physical Geography and Ecosystem Science at Lund University. Modules are developed to enable easy integration of geospatial data extraction from freely available sources which are open to use and non-commercial. Implementation is facilitated with intuitive user interface which allows extracts for an area by location name(s) or area defined by two latitude and two longitude values. The other major contribution of the study focuses on visualization of geographic information in the web environment. Currently, map servers use pre-defined styling mechanism which virtually doesn’t satisfy user’s visual needs. This module enable dynamic and user-oriented map visualization allowing non-GIS experts to define (symbolization and colouring) and produce instant and meaningful presentation of maps to the end user. As recommendation, visualization of geographic data in the web environment should further be examined, especially the map servers in use should integrate powerful and meaningful dynamic styling on top existing pre-defined style. In conclusion, this thesis adds value for disaster management and analysis in terms of easy provision of geographic data and enabling clear dissection of disaster prone areas using effective visualization mechanism

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

International audienceSocial media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20-29th June 2016. Highlights • Prototype real-time social geodata pipeline for flood events and demonstration dataset • National-scale flood warnings/river levels set 'at-risk areas' in Twitter API queries • Monitoring multiple locations (without keywords) retrieved current, geotagged tweets • Novel application of word embeddings in flooding context identified relevant tweets • Pipeline extracts tweets to visualise using open-source libraries (SciKit Learn/Gensim) Keywords Flood management; Twitter; volunteered geographic information; natural language processing; word embeddings; social geodata. Hardware required: Intel i3 or mid-performance PC with multicore processor and SSD main drive, 8Gb memory recommended. Software required: Python and library dependencies specified in Appendix A1.2.1, (viii) environment.yml Software availability: All source code can be found at GitHub public repositorie

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

Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web

The increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view

Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web

The increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view. ii