An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

A regression method for real-time video quality evaluation

No-Reference (NR) metrics provide a mechanism to assess video quality in an ever-growing wireless network. Their low computational complexity and functional characteristics make them the primary choice when it comes to realtime content management and mobile streaming control. Unfortunately, common NR metrics suer from poor accuracy, particularly in network-impaired video streams. In this work, we introduce a regression-based video quality metric that is simple enough for real-time computation on thin clients, and comparably as accurate as state-of-the-art Full-Reference (FR) metrics, which are functionally and computationally inviable in real-time streaming. We benchmark our metric against the FR metric VQM (Video Quality Metric), finding a very strong correlation factor

Methods in machine learning for probabilistic modelling of environment, with applications in meteorology and geology

Earth scientists increasingly deal with ‘big data’. Where once we may have struggled to obtain a handful of relevant measurements, we now often have data being collected from multiple sources, on the ground, in the air, and from space. These observations are accumulating at a rate that far outpaces our ability to make sense of them using traditional methods with limited scalability (e.g., mental modelling, or trial-and-error improvement of process based models). The revolution in machine learning offers a new paradigm for modelling the environment: rather than focusing on tweaking every aspect of models developed from the top down based largely on prior knowledge, we now have the capability to instead set up more abstract machine learning systems that can ‘do the tweaking for us’ in order to learn models from the bottom up that can be considered optimal in terms of how well they agree with our (rapidly increasing number of) observations of reality, while still being guided by our prior beliefs. In this thesis, with the help of spatial, temporal, and spatio-temporal examples in meteorology and geology, I present methods for probabilistic modelling of environmental variables using machine learning, and explore the considerations involved in developing and adopting these technologies, as well as the potential benefits they stand to bring, which include improved knowledge-acquisition and decision-making. In each application, the common theme is that we would like to learn predictive distributions for the variables of interest that are well-calibrated and as sharp as possible (i.e., to provide answers that are as precise as possible while remaining honest about their uncertainty). Achieving this requires the adoption of statistical approaches, but the volume and complexity of data available mean that scalability is an important factor — we can only realise the value of available data if it can be successfully incorporated into our models.Engineering and Physical Sciences Research Council (EPSRC

A comprehensive review on the design and optimization of surface water quality monitoring networks

This is the final version. Available from Elsevier via the DOI in this record. The surface water quality monitoring network (WQMN) is crucial for effective water environment management. How to design an optimal monitoring network is an important scientific and engineering problem that presents a special challenge in the smart city era. This comprehensive review provides a timely and systematic overview and analysis on quantitative design approaches. Bibliometric analysis shows the chronological pattern, journal distribution, authorship, citation and country pattern. Administration types of water bodies and design methods are classified. The flexibility characteristics of four types of direct design methods and optimization objectives are systematically summarized, and conclusions are drawn from experiences with WQMN parameters, station locations, and sampling frequency and water quality indicators. This paper concludes by identifying four main future directions that should be pursued by the research community. This review sheds light on how to better design and construct WQMNs.Key-Area Research and Development Program of Guangdong ProvinceNational Natural Science Foundation of ChinaInnovation Project of Universities in Guangdong Province-Natural Scienc

Geocomputational approaches for the analysis of Next-Generation Sequencing (NGS) and multi-scale data in landscape genomics

The application of geocomputation to the field of landscape genomics (Manel et al. 2010) permits to carry out demanding computational tasks that recently emerged because of the advent of large Next-Generation Sequencing data. When investigating the genetic mechanisms of evolution in spatially distributed plants or animals, geocomputation also proves to be useful to process many association models (gene x environment) in a multi-scale context

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V

A Tutorial on Geographic Information Systems: A Ten-year Update

This tutorial provides a foundation on geographic information systems (GIS) as they relate to and are part of the IS body of knowledge. The tutorial serves as a ten-year update on an earlier CAIS tutorial (Pick, 2004). During the decade, GIS has expanded with wider and deeper range of applications in government and industry, widespread consumer use, and an emerging importance in business schools and for IS. In this paper, we provide background information on the key ideas and concepts of GIS, spatial analysis, and latest trends and on the status and opportunities for incorporating GIS, spatial analysis, and locational decision making into IS research and in teaching in business and IS curricula

A PREDICTIVE USER BEHAVIOUR ANALYTIC MODEL FOR INSIDER THREATS IN CYBERSPACE

Insider threat in cyberspace is a recurring problem since the user activities in a cyber network are often unpredictable. Most existing solutions are not flexible and adaptable to detect sudden change in user’s behaviour in streaming data, which led to a high false alarm rates and low detection rates. In this study, a model that is capable of adapting to the changing pattern in structured cyberspace data streams in order to detect malicious insider activities in cyberspace was proposed. The Computer Emergency Response Team (CERT) dataset was used as the data source in this study. Extracted features from the dataset were normalized using Min-Max normalization. Standard scaler techniques and mutual information gain technique were used to determine the best features for classification. A hybrid detection model was formulated using the synergism of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models. Model simulation was performed using python programming language. Performance evaluation was carried out by assessing and comparing the performance of the proposed model with a selected existing model using accuracy, precision and sensitivity as performance metrics. The result of the simulation showed that the developed model has an increase of 1.48% of detection accuracy, 4.21% of precision and 1.25% sensitivity over the existing model. This indicated that the developed hybrid approach was able to learn from sequences of user actions in a time and frequency domain and improves the detection rate of insider threats in cyberspace