ALBERT

Description: The worldwide Sensor Web comprises observation data from diverse sources. Each data provider may process and assess datasets differently before making them available online. This information is often invisible to end users. Therefore, publishing observation data with quality descriptions is vital as it helps users to assess the suitability of data for their applications. It is also important to capture contextual information concerning data quality such as provenance to trace back incorrect data to its origins. In the Open Geospatial Consortium (OGC)’s Sensor Web Enablement (SWE) framework, there is no sufficiently and practically applicable approach how these aspects can be systematically represented and made accessible. This paper presents Q-SOS—an extension of the OGC’s Sensor Observation Service (SOS) that supports retrieval of observation data together with quality descriptions. These descriptions are represented in an observation data model covering various aspects of data quality assessment. The service and the data model have been developed based on open standards and open source tools, and are productively being used to share observation data from the TERENO observatory infrastructure. We discuss the advantages of deploying the presented solutions from data provider and consumer viewpoints. Enhancements applied to the related open-source developments are also introduced.

Description: Recent disasters, such as the 2010 Haiti earthquake, have drawn attention to the potential role of citizens as active information producers. By using location-aware devices such as smartphones to collect geographic information in the form of geo-tagged text, photos, or videos, and sharing this information through online social media, such as Twitter, citizens create Volunteered Geographic Information (VGI). To effectively use this information for disaster management, we developed a VGI framework for the discovery of VGI. This framework consists of four components: (i) a VGI brokering module to provide a standard service interface to retrieve VGI from multiple resources based on spatial, temporal, and semantic parameters; (ii) a VGI quality control component, which employs semantic filtering and cross-referencing techniques to evaluate VGI; (iii) a VGI publisher module, which uses a service-based delivery mechanism to disseminate VGI, and (iv) a VGI discovery component to locate, browse, and query metadata about available VGI datasets. In a case study we employed a FOSS (Free and Open Source Software) strategy, open standards/specifications, and free/open data to show the utility of the framework. We demonstrate that the framework can facilitate data discovery for disaster management. The addition of quality metrics and a single aggregated source of relevant crisis VGI will allow users to make informed policy choices that could save lives, meet basic humanitarian needs earlier, and perhaps limit environmental and economic damage.

Description: As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. A Landsat satellite sensor which is a remote sensing platform, with its ability to analyze global data, rapidly present itself as being an invaluable tool for studying the growth of urban areas. In this study, we present the virtual geo-library as the geovisualization tools to provide the analytical studies of the urbanization process in Malang City, East Java, Indonesia, using images derived from Landsat sensor family (1989 to 2014). We provide a dynamic geovisualization through virtual geo-library, where users could understand and get valuable scientific information (e.g., urban area changes and land use transformation in higher land). This system is also equipped with the tools to enable users to create automatic cartographic maps and print the results out as a digital pdf format file.

Description: Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.

Description: Building Information Models (e.g., IFC) and virtual 3D city models (e.g., CityGML) are revolutionising the way we manage information about our cities. However, the main focus of these models is on the physical and functional characteristics of urban properties and facilities, which neglects the legal and ownership aspects. In contrast, cadastral data models, such as the Land Administration Domain Model (LADM), have been developed for legal information management purposes and model legal objects such as ownership boundaries without providing correspondence to the object’s physical attributes. Integration of legal and physical objects in the virtual 3D city and cadastral models would maximise their utility and flexibility to support different applications that require an integrated resource of both legal and physical information, such as urban space management and land development processes. The aim of this paper is to propose a data model that supports both legal and physical information of urban environments. The methodology to develop this data model is to extend the core cadastral data model and integrate urban features into the data model. The outcome of the research can be utilised to extend the current data models to increases their usability for different applications that require both legal and physical information.

Description: Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel behavior and activity analysis. Illustrating this situation, recently, a number of studies attempted to use LBSN data (e.g., Foursquare check-ins) to investigate patterns of human travel and activity. Of particular note is that compared to other individual-level characteristics of users, such as age, profession, education, income and so forth, gender is relatively highly available in the profiles of Foursquare users. Moreover, considering gender differences in travel and activity analysis is a popular research topic and is helpful in better understanding the changes in women’s roles in family, labor force participation, society and so forth. Therefore, this paper empirically investigates how gender influences the travel and activity patterns of active local Foursquare users in New York City. Empirical investigations of gender differences in travel and activity patterns are conducted at both the individual and aggregate level. The empirical results reveal that there are gender differences in the travel and activity patterns of active local users in New York City at both the individual and aggregate level. Finally, the results of the empirical study and the extent to which LBSN data can be exploited to produce travel diary data are discussed.

Description: The integration of user-generated content made in a collaborative environment is being increasingly considered a valuable input to reference maps, even from official map agencies such as USGS and Ordnance Survey. In Brazil, decades of lack of investment has resulted in a topographic map coverage that is both outdated and unequally distributed throughout the territory. This paper aims to analyze the spatial distribution of updates of OpenStreetMap in rural and urban areas in the country to understand the patterns of user updates and its correlation with other economic and developmental variables. This analysis will contribute to generating the knowledge needed in order to consider the use of this data as part of a reference layer of the National Spatial Database Infrastructure as well to design strategies to encourage user action in specific areas.

Description: When humanitarian workers embark on learning and dialogue for linking geoinformation to disaster management, the activities they confront are usually more difficult than interesting. How to accelerate the acquisition and deployment of skills and tools for spatial data collection and analysis, given the increasingly unmanageable workload of humanitarians? How to engage practitioners in experiencing the value and limitations of newly available tools? This paper offers an innovative approach to immerse disaster managers in geoinformation: participatory games that enable stakeholders to experience playable system dynamic models linking geoinformation, decisions and consequences in a way that is both serious and fun. A conceptual framework outlines the foundations of experiential learning through gameplay, with clear connections to a well-established risk management framework. Two case studies illustrate this approach: one involving flood management in the Zambezi river in southern Africa through the game UpRiver (in both physical and digital versions), and another pertaining to World Bank training on open data for resilience that combines applied improvisation activities with the need to understand and deploy software tools like Open Street Map and InaSAFE to manage school investments and schoolchildren evacuation in a simulated flood scenario for the city of La Plata, Argentina.

Description: This study modeled the urban growth in the Greater Cairo Region (GCR), one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC) maps (1984, 2003 and 2014) were produced from satellite images by using Support Vector Machines (SVM). Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change) and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modeled to predict the future scenarios for year 2025 using Land Change Modeler (LCM) embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.

Description: This paper presents an integrated framework for exploratory multi-scale spatio-temporal feature extraction and clustering of spatio-temporal data. The framework combines the multi-scale spatio-temporal decomposition, feature identification, feature enhancing and clustering in a unified process. The original data are firstly reorganized as multi-signal time series, and then decomposed by the multi-signal wavelet. Exploratory data analysis methods, such as histograms, are used for feature identification and enhancing. The spatio-temporal evolution process of the multi-scale features can then be tracked by the feature clusters based on the data adaptive Fuzzy C-Means Cluster. The approach was tested with the global 0.25° satellite altimeter data over a period of 21 years from 1993 to 2013. The tracking of the multi-scale spatio-temporal evolution characteristics of the 1997–98 strong El Niño were used as validation. The results show that our method can clearly reveal and track the spatio-temporal distribution and evolution of complex geographical phenomena. Our approach is efficient for global scale data analysis, and can be used to explore the multi-scale pattern of spatio-temporal processes.