Spatial multicriteria decision analysis to potential beekeeping assessment. Case study: Montesinho Natural Park (Portugal)

The Multicriteria decision analysis is a tool to support decision-making in the identification of areas with the utmost beekeeping potential. This paper design a GIS multicriteria approach to assess the beekeeping potential. The development of a conceptual model structure requires the participation of stakeholders and experts in that process. The spatial Multicriteria Decision Analysis (MCDA) allowed defining the potential beekeeping map. The resulting maps can be used by the beekeepers associations to easily select the more suitable areas for the apiaries location or relocation and avoid prohibited areas by legal requirements.

Code pattern analysis of object-oriented programming languages

Code patterns, including programming patterns and design patterns, are good references for programming language feature improvement and software re-engineering. However, to our knowledge, no existing research has attempted to detect code patterns based on code clone detection technology. In this study, we build upon the previous work and propose to detect and analyze code patterns from a collection of open source projects using NiPAT technology. Because design patterns are most closely associated with object-oriented languages, we choose Java and Python projects to conduct our study. The tool we use for detecting patterns is NiPAT, a pattern detecting tool originally developed for the TXL programming language based on the NiCad clone detector. We extend NiPAT for the Java and Python programming languages. Then, we try to identify all the patterns from the pattern report and classify them into several different categories. In the end of the study, we analyze all the patterns and compare the differences between Java and Python patterns.

Expenditure pattern analysis of cultural tourists at World Heritage Sites: an empirical evidence based on CHAID analysis

Nowadays, World Heritage Sites (WHS) have been facing new challenges, partially due to a different tourism consumption patterns. As it is highlighted in a considerable amount of studies, visits to these sites are almost justified by this prestigious classification and motivations are closely associated with their cultural aspects and quality of the overall environment (among others, Marujo et al, 2012). However, a diversity of tourists’ profiles have been underlined in the literature. Starting from the results obtained in a previous study about cultural tourists’ profile, conducted during the year 2009 in the city of Évora, Portugal, it is our intend to compare the results with a recent survey applied to the visitors of the same city. Recognition of Évora by UNESCO in 1986 as “World Heritage” has fostered not only the preservation of heritage but also the tourist promotion of the town. This study compares and examined tourists’ profile, regarding from the tourists’ expenditure patterns in Évora. A total of 450 surveys were distributed in 2009, and recently, in 2015, the same numbers of surveys were collected. Chi-squared Automatic Interaction Detection (CHAID) was applied to model consumer patterns of domestic and international visitors, based on socio demographic, trip characteristics, length of stay and the degree of satisfaction of pull factors. CHAID allowed find a population classification in groups that able to describe the dependent variable, average daily tourist expenditure. Results revealed different patterns of daily average expenditure amongst the years, 2009 and 2015, even if primarily results not revealed significant variations in socio-demographic and trip characteristics among the visitors’ core profile. Local authorities should be aware of this changing expensive behavior of cultural visitors and should formulate strategies accordingly. Policy and managerial recommendations are discussed.

Expenditure pattern analysis of cultural tourists at World Heritage Sites: empirical evidence based on CHAID analysis

This study investigates tourists’ expenditure patterns in the city of Évora, a world heritage site (WHS) classified by UNESCO. The use of chi-squared automatic interaction detection (CHAID) was chosen, allowing the identification of distinct segments based on expenditure patterns. Visitors’ expenditure patterns have proven to be a pertinent element for a broader understanding of visitors’ behaviour at cultural destinations. Visitors’ expenditure patterns were revealed to be increasing within years studied.

Modelling spillover effects in spatial stochastic frontier analysis

In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Contextualizing local-scale point sample data using global-scale spatial datasets: Lessons learnt from the analysis of large-scale land acquisitions

This paper examines how the geospatial accuracy of samples and sample size influence conclusions from geospatial analyses. It does so using the example of a study investigating the global phenomenon of large-scale land acquisitions and the socio-ecological characteristics of the areas they target. First, we analysed land deal datasets of varying geospatial accuracy and varying sizes and compared the results in terms of land cover, population density, and two indicators for agricultural potential: yield gap and availability of uncultivated land that is suitable for rainfed agriculture. We found that an increase in geospatial accuracy led to a substantial and greater change in conclusions about the land cover types targeted than an increase in sample size, suggesting that using a sample of higher geospatial accuracy does more to improve results than using a larger sample. The same finding emerged for population density, yield gap, and the availability of uncultivated land suitable for rainfed agriculture. Furthermore, the statistical median proved to be more consistent than the mean when comparing the descriptive statistics for datasets of different geospatial accuracy. Second, we analysed effects of geospatial accuracy on estimations regarding the potential for advancing agricultural development in target contexts. Our results show that the target contexts of the majority of land deals in our sample whose geolocation is known with a high level of accuracy contain smaller amounts of suitable, but uncultivated land than regional- and national-scale averages suggest. Consequently, the more target contexts vary within a country, the more detailed the spatial scale of analysis has to be in order to draw meaningful conclusions about the phenomena under investigation. We therefore advise against using national-scale statistics to approximate or characterize phenomena that have a local-scale impact, particularly if key indicators vary widely within a country.

The multipoint Morisita index for the analysis of spatial patterns

In many fields, the spatial clustering of sampled data points has many consequences. Therefore, several indices have been proposed to assess the level of clustering affecting datasets (e.g. the Morisita index, Ripley's Kfunction and Rényi's generalized entropy). The classical Morisita index measures how many times it is more likely to select two measurement points from the same quadrats (the data set is covered by a regular grid of changing size) than it would be in the case of a random distribution generated from a Poisson process. The multipoint version (k-Morisita) takes into account k points with k >= 2. The present research deals with a new development of the k-Morisita index for (1) monitoring network characterization and for (2) detection of patterns in monitored phenomena. From a theoretical perspective, a connection between the k-Morisita index and multifractality has also been found and highlighted on a mathematical multifractal set.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

GEOGRAPHIC-VARIATION IN APIS-CERANA-INDICA F - A SPATIAL AUTOCORRELATION ANALYSIS OF MORPHOMETRIC PATTERNS

Spatial patterns of morphometric variation in Apis cerana indica were analysed. Factor and spatial autocorrelation analyses were applied to 29 characters, measured in 17 populations in India. Correlograms showed that 15 characters are patterned geographically, and 13 of them are related to overall size. These characters are distributed as a north-south cline, probably reflecting adaptations to environmental conditions. However, the great number of characteristics without geographical pattern suggests that part of the morphometric variability is due to local stochastic divergences.

Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analysis and design of reconfigurable multimode multiport microstrip antennas for mimo systems and diversity schemes

Las comunicaciones inalámbricas han transformado profundamente la forma en la que la gente se comunica en el día a día y es, sin lugar a dudas, una de las tecnologías de nuestro tiempo que más rápidamente evoluciona. Este rápido crecimiento implica retos enormes en la tecnología subyacente, debido y entre otros motivos, a la gran demanda de capacidad de los nuevos servicios inalámbricos. Los sistemas Multiple Input Multiple Output (MIMO) han despertado mucho interés como medio de mejorar el rendimiento global del sistema, satisfaciendo de este modo y en cierta medida los nuevo requisitos exigidos. De hecho, el papel relevante de esta tecnología en los actuales esfuerzos de estandarización internacionales pone de manifiesto esta utilidad. Los sistemas MIMO sacan provecho de los grados de libertad espaciales, disponibles a través del entorno multitrayecto, para mejorar el rendimiento de la comunicación con una destacable eficiencia espectral. Con el fin de alcanzar esta mejora en el rendimiento, la diversidad espacial y por diagrama han sido empleadas tradicionalmente para reducir la correlación entre los elementos radiantes, ya que una correlación baja es condición necesaria, si bien no suficiente, para dicha mejora. Tomando como referencia, o punto de partida, las técnicas empleadas para obtener diversidad por diagrama, esta tesis doctoral surge de la búsqueda de la obtención de diversidad por diagrama y/o multiplexación espacial a través del comportamiento multimodal de la antena microstrip, proponiendo para ello un modelo cuasi analítico original para el análisis y diseño de antenas microstrip multipuerto, multimodo y reconfigurables. Este novedoso enfoque en este campo, en vez de recurrir a simulaciones de onda completa por medio de herramientas comerciales tal y como se emplea en las publicaciones existentes, reduce significativamente el esfuerzo global de análisis y diseño, en este último caso por medio de guías de diseño generales. Con el fin de lograr el objetivo planteado y después de una revisión de los principales conceptos de los sistemas MIMO que se emplearán más adelante, se fija la atención en encontrar, implementar y verificar la corrección y exactitud de un modelo analítico que sirva de base sobre la cual añadir las mejoras necesarias para obtener las características buscadas del modelo cuasi analítico propuesto. Posteriormente y partiendo del modelo analítico base seleccionado, se exploran en profundidad y en diferentes entornos multitrayecto, las posibilidades en cuanto a rendimiento se refiere de diversidad por diagrama y multiplexación espacial, proporcionadas por el comportamiento multimodal de las antenas parche microstrip sin cargar. Puesto que cada modo de la cavidad tiene su propia frecuencia de resonancia, es necesario encontrar formas de desplazar la frecuencia de resonancia de cada modo empleado para ubicarlas en la misma banda de frecuencia, manteniendo cada modo al mismo tiempo tan independiente como sea posible. Este objetivo puede lograrse cargando adecuadamente la cavidad con cargas reactivas, o alterando la geometría del parche radiante. Por consiguiente, la atención en este punto se fija en el diseño, implementación y verificación de un modelo cuasi analítico para el análisis de antenas parche microstrip multipuerto, multimodo y cargadas que permita llevar a cabo la tarea indicada, el cuál es una de las contribuciones principales de esta tesis doctoral. Finalmente y basándose en el conocimiento adquirido a través del modelo cuasi analítico, se proporcionan y aplican guías generales para el diseño de antenas microstrip multipuerto, multimodo y reconfigurables para sistemas MIMO, con el fin de mejorar su diversidad por diagrama y/o su capacidad por medio del comportamiento multimodal de las antenas parche microstrip. Se debe destacar que el trabajo presentado en esta tesis doctoral ha dado lugar a una publicación en una revista técnica internacional de un alto factor de impacto. De igual manera, el trabajo también ha sido presentado en algunas de las más importantes conferencias internacionales en el ámbito de las antenas ABSTRACT Wireless communications have deeply transformed the way people communicate on daily basis and it is undoubtedly one of the most rapidly evolving technologies of our time. This fast growing behaviour involves huge challenges on the bearing technology, due to and among others reasons, the high demanding capacity of new wireless services. MIMO systems have given rise to considerable interest as a means to enhance the overall system performance, thus satisfying somehow the new demanding requirements. Indeed, the significant role of this technology on current international standardization efforts, highlights this usefulness. MIMO systems make profit from the spatial degrees of freedom available through the multipath scenario to improve the communication performance with a remarkable spectral efficiency. In order to achieve this performance improvement, spatial and pattern diversity have been traditionally used to decrease the correlation between antenna elements, as low correlation is a necessary but not sufficient condition. Taking as a reference, or starting point, the techniques used to achieve pattern diversity, this Philosophiae Doctor (Ph.D.) arises from the pursuit of obtaining pattern diversity and/or spatial multiplexing capabilities through the multimode microstrip behaviour, thus proposing a novel quasi analytical model for the analysis and design of reconfigurable multimode multiport microstrip antennas. This innovative approach on this field, instead of resorting to full-wave simulations through commercial tools as done in the available publications, significantly reduces the overall analysis and design effort, in this last case through comprehensive design guidelines. In order to achieve this goal and after a review of the main concepts of MIMO systems which will be followed used, the spotlight is fixed on finding, implementing and verifying the correctness and accuracy of a base quasi analytical model over which add the necessary enhancements to obtain the sought features of the quasi analytical model proposed. Afterwards and starting from the base quasi analytical model selected, the pattern diversity and spatial multiplexing performance capabilities provided by the multimode behaviour of unloaded microstrip patch antennas under different multipath environments are fully explored. As each cavity mode has its own resonant frequency, it is required to find ways to displace the resonant frequency of each used mode to place them at the same frequency band while keeping each mode as independent as possible. This objective can be accomplished with an appropriate loading of the cavity with reactive loads, or through the alteration of the geometry of the radiation patch. Thus, the focus is set at this point on the design, implementation and verification of a quasi analytical model for the analysis of loaded multimode multiport microstrip patch antennas to carry out the aforementioned task, which is one of the main contributions of this Ph.D. Finally and based on the knowledge acquired through the quasi analytical model, comprehensive guidelines to design reconfigurable multimode MIMO microstrip antennas to improve the spatial multiplexing and/or diversity system performance by means of the multimode microstrip patch antenna behaviour are given and applied. It shall be highlighted that the work presented in this Ph.D. has given rise to a publication in an international technical journal of high impact factor. Moreover, the work has also been presented at some of the most important international conferences in antenna area.

Density and spatial pattern of the neuropathological changes in the cerebellum in the prion disease variant Creutzfeldt-Jakob disease (vCJD)

The objective of this chapter is to quantify the neuropathology of the cerebellar cortex in cases of the prion disease variant Creutzfeldt-Jakob disease (vCJD). Hence, sequential sections of the cerebellum of 15 cases of vCJD were stained with H/E, or immunolabelled with a monoclonal antibody 12F10 against prion protein (PrP) and studied using quantitative techniques and spatial pattern analysis. A significant loss of Purkinje cells was evident in all cases. Densities of the vacuolation and the protease resistant form of prion protein (PrPSc) in the form of diffuse and florid plaques were greater in the granule cell layer (GL) than the molecular layer (ML). In the ML, vacuoles and PrPSc plaques, occurred in clusters which were regularly distributed along the folia, larger clusters of vacuoles and diffuse plaques being present in the GL. There was a negative spatial correlation between the vacuoles and the surviving Purkinje cells in the ML and a positive spatial correlation between the clusters of vacuoles and the diffuse PrPSc plaques in the ML and GL in five and six cases respectively. A canonical variate analysis (CVA) suggested a negative correlation between the densities of the vacuolation in the GL and the diffuse PrPSc plaques in the ML. The data suggest: 1) all laminae of the cerebellar cortex were affected by the pathology of vCJD, the GL more severely than the ML, 2) the pathology was topographically distributed especially in the Purkinje cell layer and GL, 3) pathological spread may occur in relation to a loop of anatomical projections connecting the cerebellum, thalamus, cerebral cortex, and pons, and 4) there are differences in the pathology of the cerebellum in vCJD compared with the M/M1 subtype of sporadic CJD (sCJD).

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Onion (Allium cepa) is one of the most cultivated and consumed vegetables in Brazil and its importance is due to the large laborforce involved. One of the main pests that affect this crop is the Onion Thrips (Thrips tabaci), but the spatial distribution of this insect, although important, has not been considered in crop management recommendations, experimental planning or sampling procedures. Our purpose here is to consider statistical tools to detect and model spatial patterns of the occurrence of the onion thrips. In order to characterize the spatial distribution pattern of the Onion Thrips a survey was carried out to record the number of insects in each development phase on onion plant leaves, on different dates and sample locations, in four rural properties with neighboring farms under different infestation levels and planting methods. The Mantel randomization test proved to be a useful tool to test for spatial correlation which, when detected, was described by a mixed spatial Poisson model with a geostatistical random component and parameters allowing for a characterization of the spatial pattern, as well as the production of prediction maps of susceptibility to levels of infestation throughout the area.

Cognitive functioning and efficacy of cognitive intervention in Multiple Sclerosis

Tese de Doutoramento em Psicologia Clínica / Psicologia