918 resultados para Spatial analysis


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Objective: to identify patterns in the spatial and temporal distribution of cases of dengue fever occurring in the city of Cruzeiro, state of Sao Paulo (SP).Methods: an ecological and exploratory study was undertaken using spatial analysis tools and data from dengue cases obtained on the SinanNet. The analysis was carried out by area, using the IBGE census sector as a unit. The months of March to June 2006 and 2011 were assessed, revealing progress of the disease. TerraView 3.3.1 was used to calculate the Global Moran's I, month to month, and the Kernel estimator.Results: in the year 2006, 691 cases of dengue fever (rate of 864.2 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (TM = 0.28; p = 0.01; I-M = 0.20; p = 0.01) with higher densities in the central, north, northeast and south regions. In the year 2011, 654 cases of dengue fever (rate of 886.8 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (I, = 0.28; p = 0.01; I-M = 0.16; p = 0.05) with densities in the same regions as 2006. The Global Moran's I is a global measure of spatial autocorrelation, which indicates the degree of spatial association in the set of information from the product in relation to the average. The I varies between -1 and +1 and can be attributed to a level of significance (p-value). The positive value points to a positive or direct spatial autocorrelation.Conclusion: we were able to identify patterns in the spatial and temporal distribution of dengue cases occurring in the city of Cruzeiro, SP, and locate the census sectors where the outbreak began and how it evolved.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Saúde Coletiva - FMB

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The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatiotemporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.

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Pós-graduação em Saúde Coletiva - FMB

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This study aimed to verify the impact of inhalable particulate matter (PM10) on cancer incidence and mortality in the city of Sao Paulo, Brazil. Statistical techniques were used to investigate the relationship between PM10 on cancer incidence and mortality in selected districts. For some types of cancer (skin, lung, thyroid, larynx, and bladder) and some periods, the correlation coefficients ranged from 0.60 to 0.80 for incidence. Lung cancer mortality showed more correlations during the overall period. Spatial analysis showed that districts distant from the city center showed higher than expected relative risk, depending on the type of cancer According to the study, urban PM10 can contribute to increased incidence of some cancers and may also contribute to increased cancer mortality. The results highlight the need to adopt measures to reduce atmospheric PM10 levels and the importance of their continuous monitoring.

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Foi realizada uma análise espacial da ocorrência de leptospirose humana e canina na Supervisão de Vigilância em Saúde do Butantã, situada no município de São Paulo, no ano de 2007, associada a variáveis ambientais de risco, tais como: focos de enchente e áreas de desratização. Foram encontrados aglomerados espaciais de pontos de alagamentos em 12 setores censitários e de casos de leptospirose humana em quatro setores censitários, sem correlação entre ambos. Não foram encontrados agrupamentos de casos em cães, possivelmente devido à subnotificação. As proporções casos humanos de leptospirose : população humana dentro e fora da área de desratização foram 7:199.600 e 9:257.980, respectivamente. Conclui-se que medidas de controle de roedores como a desratização foram responsáveis pela minimização dos efeitos dos fatores de risco para a transmissão de leptospirose para humanos.

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This study aimed to verify the impact of inhalable particulate matter (PM10) on cancer incidence and mortality in the city of São Paulo, Brazil. Statistical techniques were used to investigate the relationship between PM10 on cancer incidence and mortality in selected districts. For some types of cancer (skin, lung, thyroid, larynx, and bladder) and some periods, the correlation coefficients ranged from 0.60 to 0.80 for incidence. Lung cancer mortality showed more correlations during the overall period. Spatial analysis showed that districts distant from the city center showed higher than expected relative risk, depending on the type of cancer. According to the study, urban PM10 can contribute to increased incidence of some cancers and may also contribute to increased cancer mortality. The results highlight the need to adopt measures to reduce atmospheric PM10 levels and the importance of their continuous monitoring.

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OBJETIVO: Identificar aglomerados espaciais de microrregiões segundo taxas de óbito por acidentes de trânsito, no Estado de São Paulo, 1 ano antes e 1 ano após a Lei Seca. MÉTODOS: Estudo ecológico e exploratório, no período de 2007 e 2009, em 63 microrregiões do Estado de SP, Brasil. Utilizaram-se ferramentas de geoprocessamento com dados do DATASUS; analisando óbitos decorrentes de acidentes de trânsito em taxas por 100 mil habitantes, construindo coropletes. Nova estatística foi obtida subtraindo-se a taxa de 2009 de 2007, observando regiões de melhora ou piora. RESULTADOS: Em 2007, ocorreram 5.204 óbitos, com média de 83 óbitos/microrregião, variando entre 1 e 1.440. Já 2009 obteve 5.065 óbitos com a média de 80 óbitos/microrregião, variando entre 1 e 1.453. O coeficiente de Moran em 2007 foi I = 0,09 (p = 0,04) com correlação espacial positiva e 2009 obteve I = 0,04 (p = 0,16), sem correlação. A diferença entre as taxas obteve I = 0,23 (p = 0, 007), indicando associação espacial. Em 2007, Presidente Prudente, Rio Claro, Campinas, Bragança Paulista, Osasco e São Paulo tiveram altas taxas de óbito. Dessas regiões citadas, somente Osasco não se destacou em 2009. Ribeirão Preto, Ourinhos e Avaré obtiveram piora em 2009. A diferença entre as taxas de 2009 e 2007 mostrou que Amparo, Bragança Paulista e Campinas tiveram melhora, e Presidente Prudente e Ourinhos apresentaram piora. CONCLUSÃO: Foi possível identificar os locais com as maiores taxas de mortalidade, apontando locais onde as ações de fiscalização devem ser revisadas.

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The first part of my work consisted in samplings conduced in nine different localities of the salento peninsula and Apulia (Italy): Costa Merlata (BR), Punta Penne (BR), Santa Cesarea terme (LE), Santa Caterina (LE), Torre Inserraglio (LE), Torre Guaceto (BR), Porto Cesareo (LE), Otranto (LE), Isole Tremiti (FG). I collected data of species percentage covering from the infralittoral rocky zone, using squares of 50x50 cm. We considered 3 sites for location and 10 replicates for each site, which has been taken randomly. Then I took other data about the same places, collected in some years, and I combined them together, to do a spatial analysis. So I started from a data set of 1896 samples but I decided not to consider time as a factor because I have reason to think that in this period of time anthropogenic stressors and their effects (if present), didn’t change considerably. The response variable I’ve analysed is the covering percentage of an amount of 243 species (subsequently merged into 32 functional groups), including seaweeds, invertebrates, sediment and rock. 2 After the sampling, I have been spent a period of two months at the Hopkins Marine Station of Stanford University, in Monterey (California,USA), at Fiorenza Micheli's laboratory. I've been carried out statistical analysis on my data set, using the software PRIMER 6. My explorative analysis starts with a nMDS in PRIMER 6, considering the original data matrix without, for the moment, the effect of stressors. What comes out is a good separation between localities and it confirms the result of ANOSIM analysis conduced on the original data matrix. What is possible to ensure is that there is not a separation led by a geographic pattern, but there should be something else that leads the differences. Is clear the presence of at least three groups: one composed by Porto cesareo, Torre Guaceto and Isole tremiti (the only marine protected areas considered in this work); another one by Otranto, and the last one by the rest of little, impacted localities. Inside the localities that include MPA(Marine Protected Areas), is also possible to observe a sort of grouping between protected and controlled areas. What comes out from SIMPER analysis is that the most of the species involved in leading differences between populations are not rare species, like: Cystoseira spp., Mytilus sp. and ECR. Moreover I assigned discrete values (0,1,2) of each stressor to all the sites I considered, in relation to the intensity with which the anthropogenic factor affect the localities. 3 Then I tried to estabilish if there were some significant interactions between stressors: by using Spearman rank correlation and Spearman tables of significance, and taking into account 17 grades of freedom, the outcome shows some significant stressors interactions. Then I built a nMDS considering the stressors as response variable. The result was positive: localities are well separeted by stressors. Consequently I related the matrix with 'localities and species' with the 'localities and stressors' one. Stressors combination explains with a good significance level the variability inside my populations. I tried with all the possible data transformations (none, square root, fourth root, log (X+1), P/A), but the fourth root seemed to be the best one, with the highest level of significativity, meaning that also rare species can influence the result. The challenge will be to characterize better which kind of stressors (including also natural ones), act on the ecosystem; and give them a quantitative and more accurate values, trying to understand how they interact (in an additive or non-additive way).

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Precision horticulture and spatial analysis applied to orchards are a growing and evolving part of precision agriculture technology. The aim of this discipline is to reduce production costs by monitoring and analysing orchard-derived information to improve crop performance in an environmentally sound manner. Georeferencing and geostatistical analysis coupled to point-specific data mining allow to devise and implement management decisions tailored within the single orchard. Potential applications range from the opportunity to verify in real time along the season the effectiveness of cultural practices to achieve the production targets in terms of fruit size, number, yield and, in a near future, fruit quality traits. These data will impact not only the pre-harvest but their effect will extend to the post-harvest sector of the fruit chain. Chapter 1 provides an updated overview on precision horticulture , while in Chapter 2 a preliminary spatial statistic analysis of the variability in apple orchards is provided before and after manual thinning; an interpretation of this variability and how it can be managed to maximize orchard performance is offered. Then in Chapter 3 a stratification of spatial data into management classes to interpret and manage spatial variation on the orchard is undertaken. An inverse model approach is also applied to verify whether the crop production explains environmental variation. In Chapter 4 an integration of the techniques adopted before is presented. A new key for reading the information gathered within the field is offered. The overall goal of this Dissertation was to probe into the feasibility, the desirability and the effectiveness of a precision approach to fruit growing, following the lines of other areas of agriculture that already adopt this management tool. As existing applications of precision horticulture already had shown, crop specificity is an important factor to be accounted for. This work focused on apple because of its importance in the area where the work was carried out, and worldwide.

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La ricerca proposta si pone l’obiettivo di definire e sperimentare un metodo per un’articolata e sistematica lettura del territorio rurale, che, oltre ad ampliare la conoscenza del territorio, sia di supporto ai processi di pianificazione paesaggistici ed urbanistici e all’attuazione delle politiche agricole e di sviluppo rurale. Un’approfondita disamina dello stato dell’arte riguardante l’evoluzione del processo di urbanizzazione e le conseguenze dello stesso in Italia e in Europa, oltre che del quadro delle politiche territoriali locali nell’ambito del tema specifico dello spazio rurale e periurbano, hanno reso possibile, insieme a una dettagliata analisi delle principali metodologie di analisi territoriale presenti in letteratura, la determinazione del concept alla base della ricerca condotta. E’ stata sviluppata e testata una metodologia multicriteriale e multilivello per la lettura del territorio rurale sviluppata in ambiente GIS, che si avvale di algoritmi di clustering (quale l’algoritmo IsoCluster) e classificazione a massima verosimiglianza, focalizzando l’attenzione sugli spazi agricoli periurbani. Tale metodo si incentra sulla descrizione del territorio attraverso la lettura di diverse componenti dello stesso, quali quelle agro-ambientali e socio-economiche, ed opera una sintesi avvalendosi di una chiave interpretativa messa a punto allo scopo, l’Impronta Agroambientale (Agro-environmental Footprint - AEF), che si propone di quantificare il potenziale impatto degli spazi rurali sul sistema urbano. In particolare obiettivo di tale strumento è l’identificazione nel territorio extra-urbano di ambiti omogenei per caratteristiche attraverso una lettura del territorio a differenti scale (da quella territoriale a quella aziendale) al fine di giungere ad una sua classificazione e quindi alla definizione delle aree classificabili come “agricole periurbane”. La tesi propone la presentazione dell’architettura complessiva della metodologia e la descrizione dei livelli di analisi che la compongono oltre che la successiva sperimentazione e validazione della stessa attraverso un caso studio rappresentativo posto nella Pianura Padana (Italia).