A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

Research and development in geo-information generalisation and multiple representation

a b s t r a c t This paper analyses the difficulty in fulfilling the user requirements related to geo-information generalisation. Despite the fact that this is a long-standing research topic, the results are not satisfactory and therefore there is a very active research community trying to better meet the expectations of the users, both at the side of the geo-information producers and at the side of the geo-information users. It is argued that part of the difficulties are due to the fact that the generalization problem is not specified formally enough. Therefore, currently the most important benchmark for the generalization software is the work of human cartographers doing manual generalization, supported by automated tools, and includes subjective aspects such as taste, resulting into artistic solutions. So, a very important, intermediate, research goal is formalizing the generalization problem. In addition, the expectations of the users are growing over the past years and will continue to do so in the future: faster updates propagated between different scales, ever growing size of geo-information, support for vario-scale (instead of just multiple fixed scales), integration of formal semantics and computational geometry techniques, support for 3D representations, and so on. This paper identifies the current state of the art and provides descriptions of further research and development directions in generalisation

An investigation into automated processes for generating focus maps

The use of geographic information for mobile applications such as wayfinding has increased rapidly, enabling users to view information on their current position in relation to the neighbouring environment. This is due to the ubiquity of small devices like mobile phones, coupled with location finding devices utilising global positioning system. However, such applications are still not attractive to users because of the difficulties in viewing and identifying the details of the immediate surroundings that help users to follow directions along a route. This results from a lack of presentation techniques to highlight the salient features (such as landmarks) among other unique features. Another problem is that since such applications do not provide any eye-catching distinction between information about the region of interest along the route and the background information, users are not tempted to focus and engage with wayfinding applications. Although several approaches have previously been attempted to solve these deficiencies by developing focus maps, such applications still need to be improved in order to provide users with a visually appealing presentation of information to assist them in wayfinding. The primary goal of this research is to investigate the processes involved in generating a visual representation that allows key features in an area of interest to stand out from the background in focus maps for wayfinding users. In order to achieve this, the automated processes in four key areas - spatial data structuring, spatial data enrichment, automatic map generalization and spatial data mining - have been thoroughly investigated by testing existing algorithms and tools. Having identified the gaps that need to be filled in these processes, the research has developed new algorithms and tools in each area through thorough testing and validation. Thus, a new triangulation data structure is developed to retrieve the adjacency relationship between polygon features required for data enrichment and automatic map generalization. Further, a new hierarchical clustering algorithm is developed to group polygon features under data enrichment required in the automatic generalization process. In addition, two generalization algorithms for polygon merging are developed for generating a generalized background for focus maps, and finally a decision tree algorithm - C4.5 - is customised for deriving salient features, including the development of a new framework to validate derived landmark saliency in order to improve the representation of focus maps

Using The Dbv Model To Maintain Versions Of Multi-scale Geospatial Data

Work on multi-scale issues concerning geospatial data presents countless challenges that have been long attacked by GIScience researchers. Indeed, a given real world problem must often be studied at distinct scales in order to be solved. Most implementation solutions go either towards generalization (and/or virtualization of distinct scales) or towards linking entities of interest across scales. In this context, the possibility of maintaining the history of changes at each scale is another factor to be considered. This paper presents our solution to these issues, which accommodates all previous research on handling multiple scales into a unifying framework. Our solution builds upon a specific database version model - the multiversion MVDB - which has already been successfully implemented in several geospatial scenarios, being extended here to support multi-scale research. The paper also presents our implementation of of a framework based on the model to handle and keep track of multi-scale data evolution. © 2012 Springer-Verlag.7518 LNCS284293Bédard, Y., Bernier, E., Badard, T., Multiple representation spatial databases and the concept of vuel (2007) Encyclopaedia in Geoinformatics, , Idea Group Publishing, HersheyBurghardt, D., Petzold, I., Bobzien, M., Relation modelling within multiple representation databases and generalisation services (2010) The Cartographic Journal, 47 (3), pp. 238-249Cellary, W., Jomier, G., Consistency of versions in object-oriented databases (1990) Proc. of the 16th Int. Conference on Very Large Databases, pp. 432-441. , Morgan KaufmannDeng, X., Wu, H., Li, D., Mrdb approach for geospatial data revision (2008) Proc. of SPIE, , the Int. Society for Optical EngineeringFriis-Christensen, A., Jensen, C., Object-relational management of multiply represented geographic entities Proc. 15th Int. Conference on Scientific and Statistical Database Management SSDBM (2003)Gançarski, S., Jomier, G., A framework for programming multiversion databases (2001) Data Knowl. Eng., 36, pp. 29-53Gao, H., Zhang, H., Hu, D., Tian, R., Guo, D., Multi-scale features of urban planning spatial data (2010) 18th Int. Conference on Geoinformatics, pp. 1-7Van Oosterom, P., Research and development in geo-information generalisation and multiple representation (2009) Computers, Environment and Urban Systems, 33 (5), pp. 303-310Van Oosterom, P., Stoter, J., 5D Data Modelling: Full Integration of 2D/3D Space, Time and Scale Dimensions (2010) LNCS, 6292, pp. 310-324. , Fabrikant, S.I., Reichenbacher, T., van Kreveld, M., Schlieder, C. (eds.) GIScience 2010. Springer, HeidelbergParent, C., Spaccapietra, S., Vangenot, C., Zimányi, E., Multiple representation modeling (2009) Encyclopedia of Database Systems, pp. 1844-1849. , Springer USParent, C., Spaccapietra, S., Zimányi, E., The murmur project: Modeling and querying multi-representation spatio-temporal databases (2006) Information Systems, 31 (8), pp. 733-769Ruas, A., Duchêne, C., Chapter 14 - A prototype generalisation system based on the multi-agent system paradigm (2007) Generalisation of Geographic Information, pp. 269-284. , Elsevier Science B.VSarjakoski, L.T., Chapter 2 - Conceptual models of generalisation and multiple representation (2007) Generalisation of Geographic Information, pp. 11-35. , Elsevier Science B.VSpaccapietra, S., Parent, C., Vangenot, C., GIS Databases: From Multiscale to MultiRepresentation (2000) LNCS (LNAI), 1864, pp. 57-70. , Choueiry, B.Y., Walsh, T. (eds.) SARA 2000. Springer, HeidelbergStoter, J., Visser, T., Van Oosterom, P., Quak, W., Bakker, N., A semantic-rich multi-scale information model for topography (2011) Int. Journal of Geographical Information Science, 25 (5), pp. 739-763Zhou, S., Jones, C.B., A Multirepresentation Spatial Data Model (2003) LNCS, 2750, pp. 394-411. , Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. Springer, Heidelber

Providing Multi-scale Consistency For Multi-scale Geospatial Data

We are immersed in a world in which we constantly deal (and cope) with objects and phenomena in a variety of scales in space and time. With the increase in collaborative and interdisciplinary research, there appeared a growing need for handling data in multiple scales and representations, within a single environment. The so called multi-scale environments must guarantee the manipulation of information while ensuring consistency. This paper is concerned with the challenges of managing data in multiple scales, while preserving consistency across scales. Its main contributions are the following: (a) the specification of generic, extensible multiscale integrity constraints; and (b) the implementation of a prototype based on data versioning, which supports the maintenance of these constraints. This prototype was tested using watershed data from Brazil. 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