Hierarchical cluster analysis of groups of individuals : application to business data

3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.

Environmental data management in the Sado Estuary

Estuaries are perhaps the most threatened environments in the coastal fringe; the coincidence of high natural value and attractiveness for human use has led to conflicts between conservation and development. These conflicts occur in the Sado Estuary since its location is near the industrialised zone of Peninsula of Setúbal and at the same time, a great part of the Estuary is classified as a Natural Reserve due to its high biodiversity. These facts led us to the need of implementing a model of environmental management and quality assessment, based on methodologies that enable the assessment of the Sado Estuary quality and evaluation of the human pressures in the estuary. These methodologies are based on indicators that can better depict the state of the environment and not necessarily all that could be measured or analysed. Sediments have always been considered as an important temporary source of some compounds or a sink for other type of materials or an interface where a great diversity of biogeochemical transformations occur. For all this they are of great importance in the formulation of coastal management system. Many authors have been using sediments to monitor aquatic contamination, showing great advantages when compared to the sampling of the traditional water column. The main objective of this thesis was to develop an estuary environmental management framework applied to Sado Estuary using the DPSIR Model (EMMSado), including data collection, data processing and data analysis. The support infrastructure of EMMSado were a set of spatially contiguous and homogeneous regions of sediment structure (management units). The environmental quality of the estuary was assessed through the sediment quality assessment and integrated in a preliminary stage with the human pressure for development. Besides the earlier explained advantages, studying the quality of the estuary mainly based on the indicators and indexes of the sediment compartment also turns this methodology easier, faster and human and financial resource saving. These are essential factors to an efficient environmental management of coastal areas. Data management, visualization, processing and analysis was obtained through the combined use of indicators and indices, sampling optimization techniques, Geographical Information Systems, remote sensing, statistics for spatial data, Global Positioning Systems and best expert judgments. As a global conclusion, from the nineteen management units delineated and analyzed three showed no ecological risk (18.5 % of the study area). The areas of more concern (5.6 % of the study area) are located in the North Channel and are under strong human pressure mainly due to industrial activities. These areas have also low hydrodynamics and are, thus associated with high levels of deposition. In particular the areas near Lisnave and Eurominas industries can also accumulate the contamination coming from Águas de Moura Channel, since particles coming from that channel can settle down in that area due to residual flow. In these areas the contaminants of concern, from those analyzed, are the heavy metals and metalloids (Cd, Cu, Zn and As exceeded the PEL guidelines) and the pesticides BHC isomers, heptachlor, isodrin, DDT and metabolits, endosulfan and endrin. In the remain management units (76 % of the study area) there is a moderate impact potential of occurrence of adverse ecological effects and in some of these areas no stress agents could be identified. This emphasizes the need for further research, since unmeasured chemicals may be causing or contributing to these adverse effects. Special attention must be taken to the units with moderate impact potential of occurrence of adverse ecological effects, located inside the natural reserve. Non-point source pollution coming from agriculture and aquaculture activities also seem to contribute with important pollution load into the estuary entering from Águas de Moura Channel. This pressure is expressed in a moderate impact potential for ecological risk existent in the areas near the entrance of this Channel. Pressures may also came from Alcácer Channel although they were not quantified in this study. The management framework presented here, including all the methodological tools may be applied and tested in other estuarine ecosystems, which will also allow a comparison between estuarine ecosystems in other parts of the globe.

Spatial operators for collaborative map handling

MSC Dissertation in Computer Engineering

Spatial patterns of leprosy in a hyperendemic state in Northern Brazil, 2001-2012

ABSTRACT OBJECTIVE To describe the spatial patterns of leprosy in the Brazilian state of Tocantins. METHODS This study was based on morbidity data obtained from the Sistema de Informações de Agravos de Notificação (SINAN – Brazilian Notifiable Diseases Information System), of the Ministry of Health. All new leprosy cases in individuals residing in the state of Tocantins, between 2001 and 2012, were included. In addition to the description of general disease indicators, a descriptive spatial analysis, empirical Bayesian analysis and spatial dependence analysis were performed by means of global and local Moran’s indexes. RESULTS A total of 14,542 new cases were recorded during the period under study. Based on the annual case detection rate, 77.0% of the municipalities were classified as hyperendemic (> 40 cases/100,000 inhabitants). Regarding the annual case detection rate in < 15 years-olds, 65.4% of the municipalities were hyperendemic (10.0 to 19.9 cases/100,000 inhabitants); 26.6% had a detection rate of grade 2 disability cases between 5.0 and 9.9 cases/100,000 inhabitants. There was a geographical overlap of clusters of municipalities with high detection rates in hyperendemic areas. Clusters with high disease risk (global Moran’s index: 0.51; p < 0.001), ongoing transmission (0.47; p < 0.001) and late diagnosis (0.44; p < 0.001) were identified mainly in the central-north and southwestern regions of Tocantins. CONCLUSIONS We identified high-risk clusters for transmission and late diagnosis of leprosy in the Brazilian state of Tocantins. Surveillance and control measures should be prioritized in these high-risk municipalities.

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

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.

Analysis and simulation of social unrest in Europe: towards understanding social unrest in Europe

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

Walking Through the Revolution: A Spatial Reading of Literary Echoes

This paper presents an embryo of a literary guide on the Carnation Revolution to be explored for educational historical excursions other than leisure and tourism. We propose a historical trail through the centre of Lisbon, city of the Carnation Revolution, called Walk through the Revolution. The trail aims to reinforce collective memory about the major events that occurred in the early moments leading to the coup. The trail is made up by nine places of rememberance, for which literary excerpts are suggested and which are supported by a digital research procedure. A set of seven fixed and observer-independent categories are used to analyse the literary contents of 23 literary works published up to 2013. These literary works refer to events that happened between the eve of April 25 and May 1, 1974. At the same time, literary descriptions are explored using a spatial approach in order to define the literary geography of the most iconic military actions and popular demonstrations that occurred in Lisbon and the surroundings. The literary geography and the cartography of the historical events are then compared. Data analysis and visualization benefit from the use of standardised and quantitative methods, including basic statistics and geographic information systems.

A spatial multicriteria model for urban accessibility mapping

Accessibility is nowadays an important issue for the development of cities. It is seen as a priority in order toguarantee equal access to fundamental rights, to improve the quality of life of citizens and to ensure that everyone, regardless of age, mobility or ability, have equal access to all the resources and benefits cities have to offer. Consequently, factors closely related to the accessibility have gained a higher relevance for identifying and assessing the location of urban facilities. The main goal of the paper is to present an accessibility evaluation model applied in Santarém, in Brazil, a city located midway between the larger cities of Belem and Manaus. The research instruments, sampling method and data analysis proposed for mapping urban accessibility are described. Daily activities were used to identify and group key destinations. The model was implemented within a geographic information system and integrates the individualâ s perspective through the definition of each key destination weight, reflecting their significance for daily activities in the urban area. Accessibility to key destinations was mapped over 24 districts of the city of Santarém. The results of this model application can support city administration decision-making for new investments in order to improve urban quality of life.

Educação para a sustentabilidade: O uso de Sistemas de Informação Geográfica Participativos como instrumento de participação de crianças e adolescentes na construção de sociedades mais sustentáveis

Tese de Doutoramento em Ciências da Educação (Área de Conhecimento: Educação ambiental e para a Sustentabilidade)

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Analysing the impact of public capital stock using the NEG wage equation: a panel data approach

This paper examines the relationship between the level of public infrastructure and the level of productivity using panel data for the Spanish provinces over the period 1984-2004, a period which is particularly relevant due to the substantial changes occurring in the Spanish economy at that time. The underlying model used for the data analysis is based on the wage equation, which is one of a handful of simultaneous equations which when satisfied correspond to the short-run equilibrium of New Economic Geography theory. This is estimated using a spatial panel model with fixed time and province effects, so that unmodelled space and time constant sources of heterogeneity are eliminated. The model assumes that productivity depends on the level of educational attainment and the public capital stock endowment of each province. The results show that although changes in productivity are positively associated with changes in public investment within the same province, there is a negative relationship between productivity changes and changes in public investment in other regions.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.