LINKING 3D BUILDING MODELS, MAPS AND ENERGY-RELATED DATA IN A WEB-BASED VISUALIZATION SYSTEM

In a transformation process to become a climate-neutral city campus, universities have to deal with the sustainable concept. Since “human factor” plays a significant role in the transformation process, providing easy access to environmental data to influence building occupants’ behavior is essential. By utilizing energy-related data without spatial attribute and existing building geospatial data, data visualization in a web browser can be established for both 2D and 3D platforms. Our implementation presents a visualization of indoor sensor measurement data, where the same geospatial data can be used for both 2D and 3D visualizations even though the 3D platform needs an adjustment. Our approach results in a monitoring tool prototype based on visualization of indoor sensors measurement data, which can be accessed easily in a web browser by all building occupants

Visualization and visual analytics of geospatial data for psychological treatment

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesCurrent location-tracking solutions, along with general advances in software (e.g., development frameworks, visualization libraries) and hardware (e.g., cloud computing, mobile devices), make it increasingly easy to capture and store geospatial data to be exploited in various application areas. In this dissertation, we study the possibilities of visualization techniques and visual analytics of geospatial (user) data with the aim of helping/improving therapies in the realm of psychological health. To this aim, a web-based visualization application was created as part of a larger ecosystem of applications created by GEOTEC, including a mobile app to systematically capture user’s geospatial data (i.e., GPS coordinates), and a metrics analytical platform, which is capable of storing captured data and performing useful analysis/calculations. The visualization tool was developed to support therapists to make informed decisions pertinent to psychological illness depression interventions, by allowing them to visually inspect, compare, and analyze captured and processed data from monitored patients. Next to determining what visual elements of the visualization tool best suited the needs of the case study, a quantitative and qualitative evaluation was performed with therapists, in order to measure the resulting usefulness of the tool, find out the drawbacks for further improvement, and to generate ideas for future work and further applications in psychological health. As a result, the visualization tool was generally found to be useable (SUS score of 86.5625), useful for therapists to help during and to determine their therapy, and various useful extensions and further application areas were discovered. Based on the result, we can conclude that the tool may indeed become a beneficial mechanism for psychological intervention in real-world settings

Development of Distributed Research Center for analysis of regional climatic and environmental changes

We present an approach and first results of a collaborative project being carried out by a joint team of researchers from the Institute of Monitoring of Climatic and Ecological Systems, Russia and Earth Systems Research Center UNH, USA. Its main objective is development of a hardware and software platform prototype of a Distributed Research Center (DRC) for monitoring and projecting of regional climatic and environmental changes in the Northern extratropical areas. The DRC should provide the specialists working in climate related sciences and decision-makers with accurate and detailed climatic characteristics for the selected area and reliable and affordable tools for their in-depth statistical analysis and studies of the effects of climate change. Within the framework of the project, new approaches to cloud processing and analysis of large geospatial datasets (big geospatial data) inherent to climate change studies are developed and deployed on technical platforms of both institutions. We discuss here the state of the art in this domain, describe web based information-computational systems developed by the partners, justify the methods chosen to reach the project goal, and briefly list the results obtained so far

Visualization of and Access to CloudSat Vertical Data through Google Earth

Online tools, pioneered by the Google Earth (GE), are facilitating the way in which scientists and general public interact with geospatial data in real three dimensions. However, even in Google Earth, there is no method for depicting vertical geospatial data derived from remote sensing satellites as an orbit curtain seen from above. Here, an effective solution is proposed to automatically render the vertical atmospheric data on Google Earth. The data are first processed through the Giovanni system, then, processed to be 15-second vertical data images. A generalized COLLADA model is devised based on the 15-second vertical data profile. Using the designed COLLADA models and satellite orbit coordinates, a satellite orbit model is designed and implemented in KML format to render the vertical atmospheric data in spatial and temporal ranges vividly. The whole orbit model consists of repeated model slices. The model slices, each representing 15 seconds of vertical data, are placed on the CloudSat orbit based on the size, scale, and angle with the longitude line that are precisely and separately calculated on the fly for each slice according to the CloudSat orbit coordinates. The resulting vertical scientific data can be viewed transparently or opaquely on Google Earth. Not only is the research bridged the science and data with scientists and the general public in the most popular way, but simultaneous visualization and efficient exploration of the relationships among quantitative geospatial data, e.g. comparing the vertical data profiles with MODIS and AIRS precipitation data, becomes possible

The Development of Web3D-based Open-pit Mine Monitoring System

Large-scale open-pit mines are critical infrastructure for acquiring natural resources. However, this type of mine can experience environmental and safety problems during operations and thus requires continuous monitoring. In this study, a web three-dimensional(3D)-based monitoring system is constructed using geospatial information open platform and open-source geospatial information software which targets open-pit mines in Gangwon-do, Korea. The purpose is to develop a monitoring system of open-pit mines that enables any person to monitor the topographic and environmental changes caused by mine operations and to develop and restore the area’s ecology. Open-pit mines were classified into active or inactive mines and monitoring items and methodologies were established for each type of mine. Cesium which is a WebGL-based open-source platform was chosen as it supports dynamic data visualization and hardware-accelerated graphics related to elapsed time which is the essential factor in the monitoring. The open-pit mine monitoring system was developed based on the geospatial database which contains information required for mine monitoring as time elapses, and by developing the open-source-based system software. The geospatial information database for monitoring consists of digital imagery and terrain data and also includes vector data and the restoration plan datas. The basic geospatial information used in the monitoring includes high resolution ortho image(GSD 0.5 m or above) for all areas of the mines. This is acquired by periodically using an airborne laser scanning system and a LiDAR DEM (grid size 1m × 1 m). In addition, geospatial information data were acquired by using an UAV and terrestrial LiDAR for small-scale areas; these tools are frequently used for rapid and irregular data acquisition. The geospatial information acquired for the monitoring of the open-pit mines represents various spatial resolutions and different terrain data. The database was constructed by converging this geospatial information with the Cesium-based geospatial information open platform of the ESRI World Imagery map and with SDK World Terrain meshes. The problems that resulted from the process of fusing aerial imagery and terrain data were solved in the Cesium-based open source environment. The prototype menu for the monitoring system was designed according to the monitoring item which was determined by the type of mine. The scene of the mine and changes in terrain were controlled and analyzed using the raster function of PostGIS according to the elapsed time. Using the GeoServer, the aerial imagery, terrain and restoration information for each period were serviced using the web standard interface, and the monitoring system was completed by visualizing these elements in Cesium in 3D format according to the elapsed time. This study has established a monitoring methodology for open-pit mines according to the type of mine and proposes a method for upgrading the imagery and terrain data required for monitoring. The study also showed the possibility of developing a Web3D-based open-pit mine monitoring system that is applicable to a wide range of mashup service developments

FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

Oceans of Tomorrow sensor interoperability for in-situ ocean monitoring

The Oceans of Tomorrow (OoT) projects, funded by the European Commission’s FP7 program, are developing a new generation of sensors supporting physical, biogeochemical and biological oceanographic monitoring. The sensors range from acoustic to optical fluorometers to labs on a chip. The result is that the outputs are diverse in a variety of formats and communication methodologies. The interfaces with platforms such as floats, gliders and cable observatories are each different. Thus, sensorPeer ReviewedPostprint (author's final draft

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing