The application of data mining techniques to interrogate Western Australian water catchment data sets

Current environmental challenges such as increasing dry land salinity, waterlogging, eutrophication and high nutrient runoff in south western regions of Western Australia may have both cultural and environmental implications in the near future. Advances in computer science disciplines, more specifically, data mining techniques and geographic information services provide the means to be able to conduct longitudinal climate studies to predict changes in the Water catchment areas of Western Australia. The research proposes to utilise existing spatial data mining techniques in conjunction of modern open-source geospatial tools to interpret trends in Western Australian water catchment land use. This will be achieved through the development of a innovative data mining interrogation tool that measures and validates the effectiveness of data mining methods on a sample water catchment data set from the Peel Harvey region of WA. In doing so, the current and future statistical evaluation on potential dry land salinity trends can be eluded. The interrogation tool will incorporate different modern geospatial data mining techniques to discover meaningful and useful patterns specific to current agricultural problem domain of dry land salinity. Large GIS data sets of the water catchments on Peel-Harvey region have been collected by the state government Shared Land Information Platform in conjunction with the LandGate agency. The proposed tool will provide an interface for data analysis of water catchment data sets by benchmarking measures using the chosen data mining techniques, such as: classical statistical methods, cluster analysis and principal component analysis.The outcome of research will be to establish an innovative data mining instrument tool for interrogating salinity issues in water catchment in Western Australia, which provides a user friendly interface for use by government agencies, such as Department of Agriculture and Food of Western Australia researchers and other agricultural industry stakeholders

Modelling of Spatial Big Data Analysis and Visualization

Today’s advanced survey tools open new approaches and opportunities for Geoscience researchers to create new Models, Systems and frameworks to support the lifecycle of special big data. Mobile Mapping Systems use LIDAR technology to provide efficient and accurate way to collect geographic features and its attribute from field, whichhelps city planning departments and surveyors to design and update city GIS maps with a high accuracy. It is not only about heterogenic increase in the volume of point cloud data, but also it refers to several other characteristics such as its velocity and variety. However,the vast amount of Point Cloud data gathered by Mobile Mapping Systemleads to new challenges for researches, innovation and business development to solve its five characters: Volume, Velocity, Variety, and Veracity then achievethe Value of SBD. Cloud Computing has provided a new paradigm to publish and consume new spatial models as a service plus big data utilities , services which can be utilized to overcome Point Cloud data analysis and visualization challenges. This paper presentsa model With Cloud-Based Spatial,big data Services,using spatial joinservices capabilities to relate the analysis results to its location on map,describe how Cloud Computing supports the visualizing and analyzing spatial big data and review the related scientific model’s examples

GeoLocSI – Web-Based GIS for Verification and Modification of Data Stored in Data Base

Currently there are thousand container events happening daily on more than 20 000 locations in the World. Some of these locations are big international ports and others are just little cities with not precise coordinates in the free available Data Bases (DB). Verification and validation these locations are at the same time a very important task and a challenging one. This paper describes the development of a web-based geographical information system for assisting in verifying and modifying geographical data in DB by interactive intuitive GIS technique. For the proper work of the system, first we collected geographical data for container ports from different open sources according to the known container ports’ names from our ConTraffic System. Then we stored it in a dataset in our DB and we created a map-based application which allows us to see not only the data in tabular view but also the geographical position of the ports over a map. Using this web-based application all the data can be modified quite easy, including the geographical coordinates. They can be modified directly by just typing the correct coordinates or by interactive way (drag the graphical object to the correct geographical position on the map).JRC.G.4-Maritime affair

Why Geospatial Linked Open Data for Smart Mobility?

While the concept of Smart Cities is gaining momentum around the world and government data are increasingly available and accessible on the World Wide Web, key issues remain about Open Data and data standards for smart cities. A better integration and interoperabilty of data through the World Wide Web is only possible when everyone agrees on the standards for data representation and sharing. Linked Open Data positions itself as a solution for such standardization, being a method of publishing structured data using standard Web technologies. This facilitates the interlinking between datasets, makes them readable by computers, and easily accesible on the World Wide Web. We illustrate this through the example of an evolution from a traditional Content Management System with a geoportal, to a semantic based aproach. The Traffic Safety Monitor was developed in the period of 2012-2015 to monitor the road safety and to support policy development on road safety in Flanders (the northern part of Belgium). The system is built as a Content Management System (CMS), with publication tools to present geospatial indicators on road safety (e.g. the number of accidents with cars and the number of positive alcohol tests) as Web maps using stardardized Open Geospatial Consortium Webservices. The Traffic Safety Monitor is currently further developed towards a Mobility Monitor. Here, the focus is on the development of a business process model for the semantic exchange and publication of spatial data using Linked Open Data principles targeting indicators of sustainable and smart mobility. In the future, the usability of cycling Infrastructure for vehicles such as mobility scooters, bicycle trailers etc. can be assessed using Linked Open Data. The data and metadata is published in Linked open data format, opening the door for their reuse by a wide range of (smart) applications

A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Building a context rich interface to low level sensor data

Sensor networks play an important role in our modern information society. These networks are used for a variety of activities in different domains, including traffic monitoring, environmental analysis, transport and personal health. In general, systems generate data in their own format with little or no associated semantics. As a result, data must be managed individually and significant human effort is required to analyze data and develop ad-hoc applications for different end-user requirements. The research presented here proposes a holistic and comprehensive approach to significantly reduce the human effort in analyzing networks of sensors. The goal is to facilitate any form of sensor network, enabling users to combine related semantics with sensor data, and facilitate the end-user transformation of data necessary to provide more complex query expressions, and thus meet the analytical requirements

GIS-Based Interactive Technology in Demographic Record Management and Mapping Towards Sustainable Community

Management of demographic records plays a vital role in understanding and planning for the needs of community living. However, managing conventional records requires more time, cost, and energy without accurate assurance. As there are raising concerns in assessing community information for charity purposes, safety coordination, and crime prevention, especially during emergencies; integrating demographic data with spatial information is becoming significantly important. This paper aims to establish an accessible and visible digital platform of demographic information for respective sectors, namely, the KRT, Qariah group, NGOs, and representatives of CSR projects. The objective is to digitize interactive demographic record management and mapping through GIS-based technology. This study adopts a Geographical Information System (GIS) to digitize information using web GIS integrating with the Unmanned Aerial Vehicle (UAV) technology for data acquisition in the community sampling areas; Puncak Iskandar. The proposed system which leverages the power of spatial analysis and visualization, utilizes GIS technology to store, analyse, and visualize demographic records in a geographical context. It incorporates various demographic data sources, such as census data, health records, and administrative data in managing more efficient and effective data with lower cost, energy, time, and resources. Furthermore, the GIS-based system enables the identification of spatial disparities, inequalities, and interventions in demographic characteristics. Therefore, the integration of this system into community demographic management has provided a powerful platform for accessing and coordinating population dynamics, problem-solving, and sustainable development. The implementation in Puncak Iskandar enhances the accessibility, and visualization of demographic data, enabling policy-makers, researchers, and planners to make informed decisions based on a geographical perspective. The findings demonstrate the reliability of GIS-based demographic record management in a community living towards supporting evidence-based planning, resource allocation, and policy formulation for a wide range of applications, including urban planning, public health, and social services towards a sustainable future