24 resultados para Geographic Information System (GIS).
em Scielo Saúde Pública - SP
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ABSTRACT Geographic Information System (GIS) is an indispensable software tool in forest planning. In forestry transportation, GIS can manage the data on the road network and solve some problems in transportation, such as route planning. Therefore, the aim of this study was to determine the pattern of the road network and define transport routes using GIS technology. The present research was conducted in a forestry company in the state of Minas Gerais, Brazil. The criteria used to classify the pattern of forest roads were horizontal and vertical geometry, and pavement type. In order to determine transport routes, a data Analysis Model Network was created in ArcGIS using an Extension Network Analyst, allowing finding a route shorter in distance and faster. The results showed a predominance of horizontal geometry classes average (3) and bad (4), indicating presence of winding roads. In the case of vertical geometry criterion, the class of highly mountainous relief (4) possessed the greatest extent of roads. Regarding the type of pavement, the occurrence of secondary coating was higher (75%), followed by primary coating (20%) and asphalt pavement (5%). The best route was the one that allowed the transport vehicle travel in a higher specific speed as a function of road pattern found in the study.
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ABSTRACT Permanent Preservation Areas (PPAs) along watercourses have been the focus of numerous studies, not only because of the fragility and ecological relevance of riverine vegetation, but also because of the inefficiency demonstrated in conforming to the legislation protecting it. One of the major difficulties encountered in terms of guaranteeing the effective conservation of these riverside areas is the absence of methodologies that can be used to define them rapidly and accurately without manually determining the widths of the rivers or assigning only uniform linear values for the entire watercourse. The present work sought to develop a spatial analysis methodology capable of automatically defining permanent preservation areas along watercourses using geographic information system (GIS) software. The present study was undertaken in the Sergipe River basin, "considering the river itself and its principal affluents. We used the database of the Digital Atlas of Hydrological Resources (SEMARH/SE), and the delimitations of the PPAs were performed using ArcGIS 10.1 and the XToolPro 9.0 extension. A total of 5,003.82 hectares of Permanent Preservation Areas were delimited along the margins of the rivers analyzed, with a margin of error of <1% in delimiting the widths of the rivers within the entire area considered. The methodology described here can be used to define PPAs efficiently, relatively rapidly, and with very small margins of error, thus representing a technological advance in terms of using GIS for land management.
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This study used chemometric tools and a Geographic Information System (GIS) to determine the influence of organic matter and anthropogenic activity on the distribution of metal species between two major communities of the Middle Negro River Basin-AM. Higher concentrations of metal species were determined in flooded regions. The chemometric analysis showed the affinity of organic matter for potentially toxic metals, indicating its selectivity. GIS spatial analysis has shown that proximity to urban areas is a variable that is likely to influence the pattern of concentration of organic matter, and consequently the distribution of metal species between flooded and non-flooded areas.
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Parameters such as tolerance, scale and agility utilized in data sampling for using in Precision Agriculture required an expressive number of researches and development of techniques and instruments for automation. It is highlighted the employment of methodologies in remote sensing used in coupled to a Geographic Information System (GIS), adapted or developed for agricultural use. Aiming this, the application of Agricultural Mobile Robots is a strong tendency, mainly in the European Union, the USA and Japan. In Brazil, researches are necessary for the development of robotics platforms, serving as a basis for semi-autonomous and autonomous navigation systems. The aim of this work is to describe the project of an experimental platform for data acquisition in field for the study of the spatial variability and development of agricultural robotics technologies to operate in agricultural environments. The proposal is based on a systematization of scientific work to choose the design parameters utilized for the construction of the model. The kinematic study of the mechanical structure was made by the virtual prototyping process, based on modeling and simulating of the tension applied in frame, using the.
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Medium-resolution satellite images have been widely used for the identification and quantification of irrigated areas by center pivot. These areas, which present predominantly circular forms, can be easily identified by visual analyses of these images. In addition to identifying and quantifying areas irrigated by center pivot, other information that is associated to these areas is fundamental for producing cadastral maps. The goal of this work was to generate cadastral mapping of areas irrigated by center pivots in the State of Minas Gerais, Brazil, with the purpose of supplying information on irrigated agriculture. Using the satellite CBERS2B/CCD, images were used to identify and quantify irrigated areas and then associate these areas with a database containing information about: irrigated area, perimeter, municipality, path row, basin in which the pivot is located, and the date of image acquisition.3,781 center pivots systems were identified. The smallest area irrigated was 4.6 hectares and the largest one was 192.6 hectares. The total estimated value of irrigated area was 254,875 hectares. The largest number of center pivots appeared in the municipalities of Unaí and Paracatu, with 495 and 459 systems, respectively. Cadastral mapping is a very useful tool to assist and enhance information on irrigated agriculture in the State of Minas Gerais.
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This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.
USE AND CONSEQUENCES OF PARTICIPATORY GIS IN A MEXICAN MUNICIPALITY: APPLYING A MULTILEVEL FRAMEWORK
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This paper seeks to understand the use and the consequences of Participatory Geographic Information System (PGIS) in a Mexican local community. A multilevel framework was applied, mainly influenced by two theoretical lenses – structurationist view and social shaping of technology – structured in three dimensions – context, process and content – according to contextualist logic. The results of our study have brought two main contributions. The first is the refinement of the theoretical framework in order to better investigate the implementation and use of Information and Communication Technology (ICT) artifacts by local communities for social and environmental purposes. The second contribution is the extension of existing IS (Information Systems) literature on participatory practices through identification of important conditions for helping the mobilization of ICT as a tool for empowering local communities.
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AbstractINTRODUCTION:We present a review of injuries in humans caused by aquatic animals in Brazil using the Information System for Notifiable Diseases [ Sistema de Informação de Agravos de Notificação (SINAN)] database.METHODS:A descriptive and retrospective epidemiological study was conducted from 2007 to 2013.RESULTS:A total of 4,118 accidents were recorded. Of these accidents, 88.7% (3,651) were caused by venomous species, and 11.3% (467) were caused by poisonous, traumatic or unidentified aquatic animals. Most of the events were injuries by stingrays (69%) and jellyfish (13.1%). The North region was responsible for the majority of reports (66.2%), with a significant emphasis on accidents caused by freshwater stingrays (92.2% or 2,317 cases). In the South region, the region with the second highest number of records (15.7%), jellyfish caused the majority of accidents (83.7% or 452 cases). The Northeastern region, with 12.5% of the records, was notable because almost all accidents were caused by toadfish (95.6% or 174 cases).CONCLUSIONS:Although a comparison of different databases has not been performed, the data presented in this study, compared to local and regional surveys, raises the hypothesis of underreporting of accidents. As the SINAN is the official system for the notification of accidents by venomous animals in Brazil, it is imperative that its operation be reviewed and improved, given that effective measures to prevent accidents by venomous animals depend on a reliable database and the ability to accurately report the true conditions.
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Abstract: INTRODUCTION: Geographic information systems (GIS) enable public health data to be analyzed in terms of geographical variability and the relationship between risk factors and diseases. This study discusses the application of the geographic weighted regression (GWR) model to health data to improve the understanding of spatially varying social and clinical factors that potentially impact leprosy prevalence. METHODS: This ecological study used data from leprosy case records from 1998-2006, aggregated by neighborhood in the Duque de Caxias municipality in the State of Rio de Janeiro, Brazil. In the GWR model, the associations between the log of the leprosy detection rate and social and clinical factors were analyzed. RESULTS: Maps of the estimated coefficients by neighborhood confirmed the heterogeneous spatial relationships between the leprosy detection rates and the predictors. The proportion of households with piped water was associated with higher detection rates, mainly in the northeast of the municipality. Indeterminate forms were strongly associated with higher detections rates in the south, where access to health services was more established. CONCLUSIONS: GWR proved a useful tool for epidemiological analysis of leprosy in a local area, such as Duque de Caxias. Epidemiological analysis using the maps of the GWR model offered the advantage of visualizing the problem in sub-regions and identifying any spatial dependence in the local study area.
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Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R² = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.
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The structural stability and restructuring ability of a soil are related to the methods of crop management and soil preparation. A recommended strategy to reduce the effects of soil preparation is to use crop rotation and cover crops that help conserve and restore the soil structure. The aim of this study was to evaluate and quantify the homogeneous morphological units in soil under conventional mechanized tillage and animal traction, as well as to assess the effect on the soil structure of intercropping with jack bean (Canavalia ensiformis L.). Profiles were analyzed in April of 2006, in five counties in the Southern-Central region of Paraná State (Brazil), on family farms producing maize (Zea mays L.), sometimes intercropped with jack bean. The current structures in the crop profile were analyzed using Geographic Information Systems (GIS) and subsequently principal component analysis (PCA) to generate statistics. Morphostructural soil analysis showed a predominance of compact units in areas of high-intensity cultivation under mechanized traction. The cover crop did not improve the structure of the soil with low porosity and compact units that hamper the root system growth. In areas exposed to animal traction, a predominance of cracked units was observed, where roots grew around the clods and along the gaps between them.
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The graphical representation of spatial soil properties in a digital environment is complex because it requires a conversion of data collected in a discrete form onto a continuous surface. The objective of this study was to apply three-dimension techniques of interpolation and visualization on soil texture and fertility properties and establish relationships with pedogenetic factors and processes in a slope area. The GRASS Geographic Information System was used to generate three-dimensional models and ParaView software to visualize soil volumes. Samples of the A, AB, BA, and B horizons were collected in a regular 122-point grid in an area of 13 ha, in Pinhais, PR, in southern Brazil. Geoprocessing and graphic computing techniques were effective in identifying and delimiting soil volumes of distinct ranges of fertility properties confined within the soil matrix. Both three-dimensional interpolation and the visualization tool facilitated interpretation in a continuous space (volumes) of the cause-effect relationships between soil texture and fertility properties and pedological factors and processes, such as higher clay contents following the drainage lines of the area. The flattest part with more weathered soils (Oxisols) had the highest pH values and lower Al3+ concentrations. These techniques of data interpolation and visualization have great potential for use in diverse areas of soil science, such as identification of soil volumes occurring side-by-side but that exhibit different physical, chemical, and mineralogical conditions for plant root growth, and monitoring of plumes of organic and inorganic pollutants in soils and sediments, among other applications. The methodological details for interpolation and a three-dimensional view of soil data are presented here.
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Irrigation schemes and dams have posed a great concern on public health systems of several countries, mainly in the tropics. The focus of the present review is to elucidate the different ways how these human interventions may have an effect on population dynamics of anopheline mosquitoes and hence, how local malaria transmission patterns may be changed. We discuss different studies within the three main tropical and sub-tropical regions (namely Africa, Asia and the Pacific and the Americas). Factors such as pre-human impact malaria epidemiological patterns, control measures, demographic movements, human behaviour and local Anopheles bionomics would determine if the implementation of an irrigation scheme or a dam will have negative effects on human health. Some examples of successful implementation of control measures in such settings are presented. The use of Geographic Information System as a powerful tool to assist on the study and control of malaria in these scenarios is also highlighted.
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INTRODUCTION: The northeast region of Brazil is endemic for zoonotic visceral leishmaniasis (ZVL). The aim of this study was to determine the prevalence of infection in dogs in Petrolina.METHODS: Blood samples were collected from dogs (n = 600), and bone-marrow biopsy was performed in animals with positive serological test results that presented clinical signs of ZVL. The serological analyses were performed using an enzyme-linked immunosorbent assay (ELISA) (S7(r)Biogene).RESULTS: Of the 600 dogs tested, 19% (115/600) presented anti-L. infantum chagasi antibodies.CONCLUSIONS: Our data are important because canine infection is an important risk factor for the human disease.
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A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomosis, animal production, agriculture and land use has recently been developed in Togo. This paper addresses the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. A discriminant analysis model is tested using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration and Meteosat platforms. A split sample technique is adopted where a randomly selected part of the field measured data (training set) serves to predict the other part (predicted set). The obtained results are then compared with field measured data per corresponding grid-square. Depending on the size of the training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analysis for the prediction, not only of the tsetse distribution, but more importantly of their abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen or substitute one another and thus reduce costs of field surveys.