João Moura Pires

Aggregating spatio-temporal phenomena at multiple levels of detail

Spatio-temporal data are collected at high levels of detail (LoDs). Both spatial and temporal characteristics of data can be expressed at different LoDs. Depending on the phenomenon and the analytical goal, different LoDs can be suitable for a user’s analysis since different LoDs may provide different perceptions of a phenomenon. It is crucial to model spatio-temporal phenomena having in mind that different LoDs can be useful in their analyses. We propose a granularities-based model in order to model spatio-temporal phenomena at multiple LoDs. It defines the concept of LoD and afterwards the atom generalization, granular synthesis and compressed granular syntheses set concepts to express a phenomenon at some LoD into a coarser one. This occurs in a semi-automatic way as the user just needs to define functions that create the compressed granular syntheses sets. A demonstration case was conducted applied to a real dataset about accidents in USA in which the model proposed proved to be useful to reduce the amount and complexity of data when the phenomenon is observed at coarser LoDs than the ones at which data is provided.

A granularity theory for modelling spatio-temporal phenomena at multiple levels of detail

Reasoning about spatio-temporal phenomena requires the adoption of common granularities that facilitate and enhance the comprehension of a particular phenomenon. In our day-to-day activities, spatial granules like state, province or country, and temporal granules like day, month or year, are used to index facts and to allow reasoning adopting the level of detail considered appropriate in a particular analytical context. In an era where huge amounts of spatio-temporal data are collected every day, it is crucial to model the spatio-temporal phenomena expressed in such data sets having in mind that different levels of detail can be useful in the analysis of such phenomena and that different levels of detail are related, for instance, through a spatial or temporal hierarchy. As the size and level of details of the data sets increase, the need to use multiple levels of detail that enhance our capability to achieve useful insights from data also increases. This paper presents a granularity theory devised to model spatio-temporal phenomena at different levels of detail. This granularity theory is more general than the existing granularities proposals. In fact, we relate those proposals with the presented granularity theory.

Asthma and the new anti-smoking legislation. What has changed?

January 2008 saw new legislation implanted in Portugal restricting smoking in closed public spaces. As second-hand smoke worsens asthma symptoms, it is important to understand how asthma patients feel this new legislation impacts on their well-being. A descriptive cross-sectional study was conducted in a convenience sample formed of 96 asthma patients who had Pulmonology appointments at the Pulido Valente and Santa Marta Hospitals. All data was collected from a standardised questionnaire with volunteer, anonymous and confidential answers. Epi Info software was used for statistical processing purposes, and the results analysed using the chi-squared test (alpha=5%). 67.7% of the patients polled were exposed to secondhand smoke until December 2007, with a higher percentage in those currently in work. A considerable percentage (39.6%) described positive changes (measured by improved performance of daily life activities, decrease in symptoms or lesser recourse to SOS medication), even though the majority did not recognise any improvement. 81.6% of those who noted positive changes said they were no longer exposed to second-hand smoke with the passing of the new legislation, and 65.8% stated that the smoking ban was the factor responsible for those changes. These results, only two months after the implementation of the legislative ban on smoking in public spaces, underline the importance of approving effective measures to create and enforce smoke-free environments, reducing worsening of symptoms and improving asthma patients well-being.

Asma e a nova lei do tabaco. O que mudou

January 2008 saw new legislation implanted in Portugal restricting smoking in closed public spaces. As second-hand smoke worsens asthma symptoms, it is important to understand how asthma patients feel this new legislation impacts on their well-being. n nA descriptive cross-sectional study was conducted in a convenience sample formed of 96 asthma patients who had Pulmonology appointments at the Pulido Valente and Santa Marta Hospitals. All data was collected from a standardised questionnaire with volunteer, anonymous and confidential answers. Epi InfoTM software was used for statistical processing purposes, and the results analysed using the chi-squared test (α = 5%). n n67.7% of the patients polled were exposed to second-hand smoke until December 2007, with a higher percentage in those currently in work. A considerable percentage (39.6%) described positive changes (measured by improved performance of daily life activities, decrease in symptoms or lesser recourse to SOS medication), even though the majority did not recognise any improvement. 81.6% of those who noted positive changes said they were no longer exposed to second-hand smoke with the passing of the new legislation, and 65.8% stated that the smoking ban was the factor responsible for those changes. n nThese results, only two months after the implementation of the legislative ban on smoking in public spaces, underline the importance of approving effective measures to create and enforce smoke-free environments, reducing worsening of symptoms and improving asthma patients’ well-being. n nRev Port Pneumol 2009; XV (1): 27-42

Enhancing Exploratory Analysis by Summarizing Spatiotemporal Events Across Multiple Levels of Detail

There are many spatiotemporal events with high levels of detail (LoDs) being collected in many phenomena. The LoD of analysis plays a crucial role in the user’s perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Standard practices work on a single LoD driven by the user in spite of the fact that there is no exclusive LoD to study a phenomenon. Our proposal aims to support users in carrying the inspection and comparison tasks of a phenomenon across multiple LoDs, without having to look at raw data, and to handle the spatiotemporal complexity. This paper presents a framework to build abstracts at different LoDs where five types of abstracts are proposed. The framework makes no assumption about the phenomenon, the analytical task and the phenomenon’s LoDs. The SUITE’s prototype implements the proposed framework allowing users to inspect abstracts across multiple LoDs simultaneously, helping to understand in what LoDs the phenomenon perception distinguishes itself or in what LoDs “interesting patterns” emerge.

When Granules Are not Enough in a Theory of Granularities

Several approaches have been proposed to model spatiotemporal phenomena at multiple LoDs, in particularly, under the granular computing research area, where a granularities-based model was proposed. Such model stands out from the related literature, but has two major limitations. On one hand, it has difficulties for describing regions, intervals of time, among others complex descriptions, and on the other hand, the generalization process is the same whether it is generalizing spatial, temporal or other features of a phenomenon. These problems reduce its applicability. To overcome such limitations, this paper extends the granularities-based model by introducing the granular term concept. We apply this concept to represent time instants and intervals as well as cells and raster regions. For each granular term, generalization rules are defined so that a phenomenon can be expressed from one LoD to a coarser one in an automatic way. Changing a phenomenon’s LoD can simplify granular terms, transforming for instance a time interval into a time instant or a raster region into a cell. Our contributions are shown based on a real dataset about tornadoes in the USA. The results obtained show an enhancement of application scenarios from the extended granularities-based model to its ability of providing different phenomenon’s representations in each LoD, while keeping its original strengths.

Time and space for segmenting personal photo sets

A personal collection of photos shows large variability in the depicted items, making difficult a fully automated solution to cope with sensory and semantic gaps. Emotions and non-visual contextual information can be very important to address those problems. Manual annotations are key, but their time-consuming nature alienate users from doing them. One solution is to lower the annotation effort, building solutions on top of algorithms that prepare a context separation, making possible the reuse of annotations. In this paper we present a segmentation algorithm that uses spatio-temporal information to segment personal photo collections. The algorithm is assessed in a user study, using the participants own photos. The results show users make none or few changes to the proposed segmentations, indicating an acceptance of the algorithm outcome.

Reference model and perspective schemata inference for enterprise data integration

Sharing and integrating information among multiple heterogeneous and autonomous databases has emerged as a strategic requirement in modern enterprises. We deal with this problem by proposing a declarative approach based on the creation of a reference model and perspective schemata. The former provides a common semantic, while the latter connects schemata. This paper focuses on deduction of new perspective schemata using a proposed inference mechanism. A proof-of-concept prototype, based on Logic Programming, is presented in brief.

A hybrid approach for the spatial disaggregation of socio-economic indicators

While statistical information on socio-economic activities is widely available, the data are often collected or released only at a relatively aggregated level. In these aggregated forms, the data are useful for broad-scale assessments, although we often need to disaggregate the source data in order to provide more localized estimates, and in order to analyze correlations against geophysical variables. Spatial disaggregation techniques can be used in this context, to transform data from a set of source zones into a set of target zones, with different geometry and with a higher general level of spatial resolution. Still, few previous studies in the area have attempted to leverage state-of-the-art spatial disaggregation procedures in the context of socio-economic variables, instead focusing on applications related to population modeling. In this article, we report on experiments with a hybrid spatial disaggregation technique that combines state-of-the-art regression analysis procedures with the classic methods of dasymetric mapping and pycnophylactic interpolation. The hybrid procedure was used together with population density, land coverage, nighttime satellite imagery, and OpenStreetMap road density, as ancillary data to disaggregate different types of socio-economic indicators to a high-resolution grid. Our test specifically leveraged data relative to the Portuguese territory, resulting in the production of raster datasets with a resolution of 30 arc-seconds per cell. The article discusses the spatial disaggregation methodology and the quality of the obtained results under different experimental conditions.

Severity Estimation of Stator Winding Short-Circuit Faults Using Cubist

In this paper, an approach to estimate the severity of stator winding short-circuit faults in squirrel-cage induction motors based on the Cubist model is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park’s Vector, both for current and voltage. The proposed method presents a systematic comparison between models, as well as an analysis regarding hyper-parameter tunning, where the novelty of the presented work is mainly associated with the application of data-based analysis techniques to estimate the stator winding short-circuit severity in three-phase squirrel-cage induction motors. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.