Representing and reasoning about changing spatial extensions of geographic features

This thesis presents a novel approach to representing and reasoning about geographic phenomena which can be interpreted based on changes affecting spatial extensions of geographic features. Of particular interest in this work are geographic features whose extensions can be described as 2-dimensional regions corresponding to portions of the earth surface under a specified projection, such as deserts, forests and oceans. The work resulted in the development of a logical framework for representing geographic events and processes. In developing such a framework, issues have been addressed regarding the relationship between these concepts and also between them and geographic features. Other crucial issues are how to define the relation between event and process types and their particular instances, and how to handle different kinds of vagueness to associate specific spatial and temporal boundaries with those instances. Of particular interest in this work is the development of a method of explicitly linking the formalism to spatio-temporal data. This requires work at multiple levels, both in consideration of how the data can be represented and in regards of how primitive elements of the logical framework can be defined. Although data can be regarded as a faithful reproduction of physical elements of the world, some conceptual elements are not always explicitly represented within data. For that reason, a logic-based approach to representing spatio-temporal geographic data was also developed and is presented in this thesis. Representing the data in a logical fashion allows implicit data to be derived by means of logical inferences, and provides a natural way of explicitly connecting the data to a semantic-based formalism. Derived data may include spatial extensions of geographic features at different times, based on existing data describing, for example, portions of the earth’s surface associated with different observable properties. Furthermore, a system has been implemented to evaluate the applicability of the proposed theory. The system takes time-stamped topographic data as an input and allows logical queries to be formulated about the data, returning textual and graphical information on geographic events, processes, and features which participate in them

Special issue on spatio-temporal theories and models for environmental, urban and social sciences: where do we stand ?

This extensive editorial of this special journal issue follows a workshop organized in conjunction with the 11th International Conference on Spatial Information Theory (COSIT 2013) in September 2013 in Scarborough, UK. The objective of this international workshop was to bring together representatives from these different disciplinary communities, and integrate academics, students, and practitioners for a one-day workshop on spatiotemporal concepts and theories. This editorial introduces the special issue, the research objectives the workshop followed and some of the main contributions as well as the theoretical achievements and research perspectives left

AN ADAPTIVE FRAMEWORK FOR REAL-TIME SPATIOTEMPORAL BIG DATA ANALYTICS

Due to advancements in and widespread usage of technologies such as smartphones, satellites, smart sensors, and social networks, collection of spatiotemporal data is growing rapidly. Such massive spatiotemporal data require appropriate techniques and technologies for their efficient analysis and processing. Analyzing massive spatiotemporal data efficiently and effectively is challenging since the data changes dynamically over space and time whereas, often, decisions followed by the analysis need to be made under real-time constraints. Compared to non-spatial data, spatiotemporal data, among other unique characteristics, are multidimensional (x, y, attributes, time) in nature, complex in structures and behaviors, and provides details at different resolutions and scales. These characteristics together make analyzing and processing massive spatiotemporal data in real time a challenging task. Resorting to high-performance computing (HPC) is a common approach for handling this computing challenge but to determine optimal solutions through data and computation analysis, appropriate analytics and computing solutions are needed. In this dissertation, we proposed a framework which is basically a platform providing spatiotemporal data-intensive analytics for data- and compute-intensive applications that require computation under real-time constraints on given computing resources. The framework is a layered structure consisting of four interrelated components (layers); three on analytics and one on adaptive computing. A graph-based approach is developed as the foundation of the analytics components which are: efficient analytics – providing acceptable solutions based on current data in the absence of historical data; predictive analytics – providing near-optimal solutions by learning from the patterns of historical data and predicting based on the learning; meta-analytics – providing optimal solutions by analyzing pattern of past data patterns; and adaptive computing that ensures appropriate analytics are applied and computation is completed in real time on available computing resources

Spatial Big Data Analytics: Classification Techniques for Earth Observation Imagery

University of Minnesota Ph.D. dissertation. August 2016. Major: Computer Science. Advisor: Shashi Shekhar. 1 computer file (PDF); xi, 120 pages.Spatial Big Data (SBD), e.g., earth observation imagery, GPS trajectories, temporally detailed road networks, etc., refers to geo-referenced data whose volume, velocity, and variety exceed the capability of current spatial computing platforms. SBD has the potential to transform our society. Vehicle GPS trajectories together with engine measurement data provide a new way to recommend environmentally friendly routes. Satellite and airborne earth observation imagery plays a crucial role in hurricane tracking, crop yield prediction, and global water management. The potential value of earth observation data is so significant that the White House recently declared that full utilization of this data is one of the nation's highest priorities. However, SBD poses significant challenges to current big data analytics. In addition to its huge dataset size (NASA collects petabytes of earth images every year), SBD exhibits four unique properties related to the nature of spatial data that must be accounted for in any data analysis. First, SBD exhibits spatial autocorrelation effects. In other words, we cannot assume that nearby samples are statistically independent. Current analytics techniques that ignore spatial autocorrelation often perform poorly such as low prediction accuracy and salt-and-pepper noise (i.e., pixels predicted as different from neighbors by mistake). Second, spatial interactions are not isotropic and vary across directions. Third, spatial dependency exists in multiple spatial scales. Finally, spatial big data exhibits heterogeneity, i.e., identical feature values may correspond to distinct class labels in different regions. Thus, learned predictive models may perform poorly in many local regions. My thesis investigates novel SBD analytics techniques to address some of these challenges. To date, I have been mostly focusing on the challenges of spatial autocorrelation and anisotropy via developing novel spatial classification models such as spatial decision trees for raster SBD (e.g., earth observation imagery). To scale up the proposed models, I developed efficient learning algorithms via computational pruning. The proposed techniques have been applied to real world remote sensing imagery for wetland mapping. I also had developed spatial ensemble learning framework to address the challenge of spatial heterogeneity, particularly the class ambiguity issues in geographical classification, i.e., samples with the same feature values belong to different classes in different spatial zones. Evaluations on three real world remote sensing datasets confirmed that proposed spatial ensemble learning outperforms current approaches such as bagging, boosting, and mixture of experts when class ambiguity exists