Geo-habilitación de Gestores de Contenidos: CMSMap

El mundo del software está cambiando. El desarrollo de Internet y las conexiones de datos hacen que las personas estén conectadas prácticamente en cualquier lugar. La madurez de determinadas tecnologías y el cambio del perfil de los usuarios de consumidor a generador de contenidos son algunos de los pilares de este cambio. Los Content Management Systems (CMS) son plataformas que proporcionan la base para poder generar webs colaborativas de forma sencilla y sin necesidad de tener excesivos conocimientos previos y son responsables de buena parte de este desarrollo. Una de las posibilidades que todavía no se han explotado suficientemente en estos sistemas es la georreferenciación de contenidos. De esta forma, aparece una nueva categoría de enlaces semánticos en base a las relaciones espaciales. En el actual estado de la técnica, se puede aprovechar la potencia de las bases de datos espaciales para manejar contenidos georreferenciados y sus relaciones espaciales, pero prácticamente ningún CMS lo aprovecha. Este proyecto se centra en desarrollar un módulo para el CMS Drupal que proporcione un soporte verdaderamente espacial y una interfaz gráfica en forma de mapa, mediante las que se puedan georreferenciar los contenidos. El módulo es independiente del proveedor de cartografía, ya que se utiliza la librería Open Source de abstracción de mapas IDELab Mapstraction Interactive. De esta forma se aúna la independencia tecnológica con la gestión verdaderamente espacial de los contenidos

Apoio geoespacial a uma operação NEO

No âmbito das obrigações que o Estado Português tem em garantir a segurança dos seus cidadãos, é efetuada, em países ou regiões onde há comunidades nacionais, uma avaliação quanto ao risco de vida para os cidadãos nacionais que aí residam ou aí se encontrem, entendendo-se, à luz do direito internacional consuetudinário, que é legítima a eventual execução de intervenção militar de extração de nacionais não combatentes dessas zonas de risco. Este trabalho pretende contribuir para uma reflexão sobre o apoio geoespacial a uma operação de extração de cidadãos nacionais não combatentes, que se denomina NEO (non-combatant evacuation operation). Dada a importância do conhecimento holístico do ambiente operacional para os comandantes militares, os Sistemas de Informação Geográfica desempenham um papel fundamental em termos da análise, contextualização e visualização da informação geoespacial, sendo um precioso sistema de apoio à decisão. A tomada de decisão é efetuada com os contributos de várias áreas de conhecimento, sendo fundamental que o planeamento seja efetuado com base na mesma informação geoespacial, evitando a existência de uma multitude de dados geoespaciais nem sempre coerentes, atualizados e acessíveis a todos os que deles necessitam, pretendendo-se com este trabalho fornecer um contributo para resolver este problema. Aborda-se também a escassez dos dados geográficos nas zonas em que este tipo de operações se poderá desenrolar, a pertinência e a adequabilidade de utilização de dados espaciais abertos, os modelos de dados, bem como a forma como a informação pode ser disponibilizada.

Visualisation of quality information for geospatial and remote sensing data:providing the GIS community with the decision support tools for geospatial dataset quality evaluation

The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. The research presented here therefore focused on defining and developing a GEO label – a decision support mechanism to assist data users in efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for use. This thesis thus presents six phases of research and development conducted to: (a) identify the informational aspects upon which users rely when assessing geospatial dataset quality and trustworthiness; (2) elicit initial user views on the GEO label role in supporting dataset comparison and selection; (3) evaluate prototype label visualisations; (4) develop a Web service to support GEO label generation; (5) develop a prototype GEO label-based dataset discovery and intercomparison decision support tool; and (6) evaluate the prototype tool in a controlled human-subject study. The results of the studies revealed, and subsequently confirmed, eight geospatial data informational aspects that were considered important by users when evaluating geospatial dataset quality and trustworthiness, namely: producer information, producer comments, lineage information, compliance with standards, quantitative quality information, user feedback, expert reviews, and citations information. Following an iterative user-centred design (UCD) approach, it was established that the GEO label should visually summarise availability and allow interrogation of these key informational aspects. A Web service was developed to support generation of dynamic GEO label representations and integrated into a number of real-world GIS applications. The service was also utilised in the development of the GEO LINC tool – a GEO label-based dataset discovery and intercomparison decision support tool. The results of the final evaluation study indicated that (a) the GEO label effectively communicates the availability of dataset quality and trustworthiness information and (b) GEO LINC successfully facilitates ‘at a glance’ dataset intercomparison and fitness for purpose-based dataset selection.

INNOVATIVE HUMAN-COMPUTER INTERACTIONS TO SUPPORT COGNITIVE COLLABORATIVE GEOSPATIAL ENVIRONMENTS

In the current world geospatial information is being demanded in almost real time, which requires the speed at which this data is processed and made available to the user to be at an all-time high. In order to keep up with this ever increasing speed, analysts must find ways to increase their productivity. At the same time the demand for new analysts is high, and current methods of training are long and can be costly. Through the use of human computer interactions and basic networking systems, this paper explores new ways to increase efficiency in data processing and analyst training.

Web GIS to support irrigation management: a prototype for SAGRA network, Alentejo Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Exploratory geospatial data analysis using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Geospatial data harmonization from regional level to european level: a usa case in forest fire data

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Adding SOS Core & transactional profiles support to a web thin client

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Geospatial technologies for public participation: better decisions for smarter cities?

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Analyzing Geospatial patterns of syrian refugee flows in southeastern Turkey by use of remote sensing and complementary data

Crisis-affected communities and global organizations for international aid are becoming increasingly digital as consequence geotechnology popularity. Humanitarian sector changed in profound ways by adopting new technical approach to obtain information from area with difficult geographical or political access. Since 2011, turkey is hosting a growing number of Syrian refugees along southeastern region. Turkish policy of hosting them in camps and the difficulty created by governors to international aid group expeditions to get information, made such international organizations to investigate and adopt other approach in order to obtain information needed. They intensified its remote sensing approach. However, the majority of studies used very high-resolution satellite imagery (VHRSI). The study area is extensive and the temporal resolution of VHRSI is low, besides it is infeasible only using these sensors as unique approach for the whole area. The focus of this research, aims to investigate the potentialities of mid-resolution imagery (here only Landsat) to obtain information from region in crisis (here, southeastern Turkey) through a new web-based platform called Google Earth Engine (GEE). Hereby it is also intended to verify GEE currently reliability once the Application Programming Interface (API) is still in beta version. The finds here shows that the basic functions are trustworthy. Results pointed out that Landsat can recognize change in the spectral resolution clearly only for the first settlement. The ongoing modifications vary for each case. Overall, Landsat demonstrated high limitations, but need more investigations and may be used, with restriction, as a support of VHRSI.

Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

IowAccess Project 8 : statewide geographic information systems (GIS) support, 1998

This is the final report of the of IowAccess Project 8, which designed and implemented a geospatial data infrastructure for Iowa, including a formalized coordination body, a coordination staff, and enhanced data clearing house, and a statewide GIS training and education effort.