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: The aim of this study was to investigate how atmospheric air pollutants and meteorological conditions affected atmospheric visibility in the largest Polish agglomeration. The correlation analysis, principal component analysis (PCA) and generalized regression models (GRMs) were used to accomplish this objective. The meteorological parameters (temperature, relative humidity, precipitation, wind speed and insolation) and concentrations of the air pollutants (PM10, SO2, NO2, CO and O3) were recorded in 2004–2013. The data came from the Ursynów-SGGW, MzWarszUrsynów and Okęcie monitoring stations, located in the south of Warsaw (Poland). It was shown that the PM10 concentration was the most important parameter affecting the visibility in Warsaw. The concentration, and indirectly the visibility, was mainly affected by the pollutant emission from the flat/building heating (combustion of various fuels). It changed intensively during the research period. There were also periods in which this emission type did not have a great influence on the pollutant concentrations (mainly PM10) and visibility. In such seasons, the research revealed the influence of the traffic emission and secondary aerosol formation processes on the visibility.

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: While occurrences of wavelike motion in the stable boundary layer due to the presence of a significant restoring buoyancy force are rarely disputed, their modalities and interaction with turbulence remain a subject of active research. In this work, the characteristics of gravity waves and their impact on flow statistics, including turbulent fluxes, are presented using data collected above an Antarctic Ice sheet during an Austral Summer. Antarctica is an ideal location for exploring the characteristics of gravity waves because of persistent conditions of strong atmospheric stability in the lower troposphere. Periods dominated by wavelike motion have been identified by analysing time series measured by fast response instrumentation. The nature and characteristic of the dominant wavy motions are investigated using Fourier cross-spectral indicators. Moreover, a multi-resolution decomposition has been applied to separate gravity waves from turbulent fluctuations in case of a sufficiently defined spectral gap. Statistics computed after removing wavy disturbances highlight the large impact of gravity waves on second order turbulent quantities including turbulent flux calculations.

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: Ambient daytime and nighttime PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) and TSP (the total suspended particulates) samples were collected at two sites (named Pudong and Jinshan) in Shanghai. The concentrations of PM2.5 and TSP were lower at Pudong than at Jinshan. Higher PM2.5 and TSP concentrations were observed during daytime than nighttime for both sites. Carbonaceous aerosol and secondary sulfate were the most abundant components. Larger enrichment factor (EFs) of Zn, Pb, Cl, and S for Jinshan nighttime were observed than for other sampling periods. PM2.5 showed higher relative spatial uniformity (the coefficients of divergence, COD = 0.18) than TSP (COD = 0.23) during the sampling period. The variations of chemical components and the species ratios showed that the contributions of primary particulate emissions in Jinshan (industrial zone) were more significant than in Pudong (residential zone).