847 resultados para Geographic information systems -- Data processing
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Information systems (IS) outsourcing projects often fail to achieve initial goals. To avoid project failure, managers need to design formal controls that meet the specific contextual demands of the project. However, the dynamic and uncertain nature of IS outsourcing projects makes it difficult to design such specific formal controls at the outset of a project. It is hence crucial to translate high-level project goals into specific formal controls during the course of a project. This study seeks to understand the underlying patterns of such translation processes. Based on a comparative case study of four outsourced software development projects, we inductively develop a process model that consists of three unique patterns. The process model shows that the performance implications of emergent controls with higher specificity depend on differences in the translation process. Specific formal controls have positive implications for goal achievement if only the stakeholder context is adapted, while they are negative for goal achievement if in the translation process tasks are unintendedly adapted. In the latter case projects incrementally drift away from their initial direction. Our findings help to better understand control dynamics in IS outsourcing projects. We contribute to a process theoretic understanding of IS outsourcing governance and we derive implications for control theory and the IS project escalation literature.
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This book attempts to synthesize research that contributes to a better understanding of how to reach sustainable business value through information systems (IS) outsourcing. Important topics in this realm are how IS outsourcing can contribute to innovation, how it can be dynamically governed, how to cope with its increasing complexity through multi-vendor arrangements, how service quality standards can be met, how corporate social responsibility can be upheld and how to cope with increasing demands of internationalization and new sourcing models, such as crowdsourcing and platform-based cooperation. These issues are viewed from either the client or vendor perspective, or both. The book should be of interest to all academics and students in the fields of Information Systems, Management and Organization as well as corporate executives and professionals who seek a more profound analysis and understanding of the underlying factors and mechanisms of outsourcing.
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Most commercial project management software packages include planning methods to devise schedules for resource-constrained projects. As it is proprietary information of the software vendors which planning methods are implemented, the question arises how the software packages differ in quality with respect to their resource-allocation capabilities. We experimentally evaluate the resource-allocation capabilities of eight recent software packages by using 1,560 instances with 30, 60, and 120 activities of the well-known PSPLIB library. In some of the analyzed packages, the user may influence the resource allocation by means of multi-level priority rules, whereas in other packages, only few options can be chosen. We study the impact of various complexity parameters and priority rules on the project duration obtained by the software packages. The results indicate that the resource-allocation capabilities of these packages differ significantly. In general, the relative gap between the packages gets larger with increasing resource scarcity and with increasing number of activities. Moreover, the selection of the priority rule has a considerable impact on the project duration. Surprisingly, when selecting a priority rule in the packages where it is possible, both the mean and the variance of the project duration are in general worse than for the packages which do not offer the selection of a priority rule.
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Current research in the domain of geographic information science considers possibilities of including another dimension, time, which is generally missing to this point. Users interested in changes have few functions available to compare datasets of spatial configurations at different points in time. Such a comparison of spatial configurations requires large amounts of manual labor. An automatic derivation of changes would decrease amounts of manual labor. The thesis introduces a set of methods that allows for an automatic derivation of changes. These methods analyze identity and topological states of objects in snapshots and derive types of change for the specific configuration of data. The set of change types that can be computed by the methods presented includes continuous changes such as growing, shrinking, and moving of objects. For these continuous changes identity remains unchanged, while topological relations might be altered over time. Also discrete changes such as merging and splitting where both identity and topology are affected can be derived. Evaluation of the methods using a prototype application with simple examples suggests that the methods compute uniquely and correctly the type of change that applied in spatial scenarios captured in two snapshots.
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The aim of this study was to determine cancer mortality rates for the United Arab Emirates (UAE) and to create an atlas of cancer mortality for the UAE. This atlas is the first of its kind in the Gulf country and the Middle East. Death certificates were reviewed for a period from January 1, 1990 to December 31, 1999 and cancer deaths were identified. Cancer mortality cases were verified by comparing with medical records. Age-adjusted cancer mortality rates were calculated by gender, emirate/medical district and nationality (UAE nationals and overall UAE population). Individual rates for each emirate were compared to the overall rate of the corresponding population for the same cancer site and gender. Age-adjusted rates were mapped using MapInfo software. High rates for liver, lung and stomach cancer were observed in Abu Dhabi, Dubai and the northern emirates, respectively. Rates for UAE nationals were greater compared to the overall UAE population. Several factors were suggested that may account for high rates of specific cancers observed in certain emirates. It is hoped that this atlas will provide leads that will guide further epidemiologic and public health activities aimed at preventing cancer. ^
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This paper describes the creation of a GIS database index to the collection of historical aerial photographs of Connecticut housed in the Map and Geographic Information Center in the Homer Babbidge Library at the University of Connecticut. The index allows patrons to search for scanned aerial photograph images for a specific location across multiple years and to retrieve digital scans from the Library server. Procedures for scanning and georeferencing the images, preparing metadata for the images, and creating the GIS database index are described.
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In the United States, approximately 4,000 pregnancies each year are affected by the two most common birth defects, spina bifida and anencephaly. Studies have shown that exposure to environmental chemicals before and after conception may adversely affect reproduction by inducing cell death or dysfunction, which leads to infertility, fetal loss, lowered weight at birth, or birth anomalies in the offspring. The objective of the study was to evaluate the relationship between Neural Tube Defect births and residence at conception in proximity to hazardous waste sites in the Texas-Mexico border region between 1993 and 2000. ^ The study design was a nested matched case-control and utilized secondary data from a project, “The role of chemical and biological factors in the etiology of neural tube birth defects births along the Texas-Mexico Border” (Irina Cech, Principal Investigator). Geographic Information Systems (GIS) database methods were used to compare Neural Tube Defects cases to controls on status of conception residence occurring within a one-mile radius from hazardous waste sites, as compared to conception residence further away. Information on the exposures was obtained from the OnTarget Database and Environment Protection Agency website. Conditional logistic regression was used for the matched case-control study to investigate the relationship between an outcome of being a case or a control and proximity to hazardous waste sites. ^ The result of the study showed a 36 percent non-significant increased risk of having an NTD birth associated with maternal proximity to abandoned hazardous waste sites (95% CI = 0.62–3.02). In addition, there was a 24% non-significant elevated risk of having an NTD birth when living in proximity to air pollutant sites than when living further away (95% CI = 0.67–2.32). Although this study did not find statistically significant associations, it will expand on the existing knowledge of the relationship between NTD and proximity to hazardous waste sites. ^
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Southeast Texas, including Houston, has a large presence of industrial facilities and has been documented to have poorer air quality and significantly higher cancer rates than the remainder of Texas. Given citizens’ concerns in this 4th largest city in the U.S., Mayor Bill White recently partnered with the UT School of Public Health to determine methods to evaluate the health risks of hazardous air pollutants (HAPs). Sexton et al. (2007) published a report that strongly encouraged analytic studies linking these pollutants with health outcomes. In response, we set out to complete the following aims: 1. determine the optimal exposure assessment strategy to assess the association between childhood cancer rates and increased ambient levels of benzene and 1,3-butadiene (in an ecologic setting) and 2. evaluate whether census tracts with the highest levels of benzene or 1,3-butadiene have higher incidence of childhood lymphohematopoietic cancer compared with census tracts with the lowest levels of benzene or 1,3-butadiene, using Poisson regression. The first aim was achieved by evaluating the usefulness of four data sources: geographic information systems (GIS) to identify proximity to point sources of industrial air pollution, industrial emission data from the U.S. EPA’s Toxic Release Inventory (TRI), routine monitoring data from the U.S. EPA Air Quality System (AQS) from 1999-2000 and modeled ambient air levels from the U.S. EPA’s 1999 National Air Toxic Assessment Project (NATA) ASPEN model. Further, once these four data sources were evaluated, we narrowed them down to two: the routine monitoring data from the AQS for the years 1998-2000 and the 1999 U.S. EPA NATA ASPEN modeled data. We applied kriging (spatial interpolation) methodology to the monitoring data and compared the kriged values to the ASPEN modeled data. Our results indicated poor agreement between the two methods. Relative to the U.S. EPA ASPEN modeled estimates, relying on kriging to classify census tracts into exposure groups would have caused a great deal of misclassification. To address the second aim, we additionally obtained childhood lymphohematopoietic cancer data for 1995-2004 from the Texas Cancer Registry. The U.S. EPA ASPEN modeled data were used to estimate ambient levels of benzene and 1,3-butadiene in separate Poisson regression analyses. All data were analyzed at the census tract level. We found that census tracts with the highest benzene levels had elevated rates of all leukemia (rate ratio (RR) = 1.37; 95% confidence interval (CI), 1.05-1.78). Among census tracts with the highest 1,3-butadiene levels, we observed RRs of 1.40 (95% CI, 1.07-1.81) for all leukemia. We detected no associations between benzene or 1,3-butadiene levels and childhood lymphoma incidence. This study is the first to examine this association in Harris and surrounding counties in Texas and is among the first to correlate monitored levels of HAPs with childhood lymphohematopoietic cancer incidence, evaluating several analytic methods in an effort to determine the most appropriate approach to test this association. Despite recognized weakness of ecologic analyses, our analysis suggests an association between childhood leukemia and hazardous air pollution.^
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La disminución del agua destinada al uso agrícola, la salinización de los acuíferos subterráneos y el advenimiento de la tecnología de Sistemas de Información Geográfica (SIG) han posibilitado conocer la calidad de los sitios, fundamentalmente los riesgos de salinización de los suelos del oasis del río Mendoza-Argentina. El presente trabajo se fundamenta en dos estudios anteriores: uno de relevantamiento de suelos y el otro de análisis de calidad de aguas subterráneas. En el primero se efectúo la actualización del relevantamiento de suelos del río Mendoza usando SIG. El muestreo de suelos y los análisis físicos (textura) y químicos (salinidad, conductividad eléctrica) se realizaron en 1974. Los lugares de muestreo y sus atributos, graficados como cobertura de puntos, se extrapolaron a sus zonas de influencia convirtiéndolos en polígonos y posteriormente se rasterizaron. El segundo trabajo fue la digitalización y georreferenciación, también al sistema de coordenadas Universal Transverse Mercator (UTM), de los mapas de las curvas de isosalinidad. La salinidad está medida por la conductividad eléctrica específica del agua subterránea de los tres niveles de explotación que existen en la cuenca norte de Mendoza. El monitoreo se realizó en el período 1990/1991. Las isolíneas, posteriormente, fueron rasterizadas. Con los procesos de superposición y tabulación cruzada de los SIG se integraron las diversas "capas" de datos de suelos y calidades de aguas subterráneas y se generaron mapas temáticos que expresan la clasificación y localización regional de calidades del sitio, basado fundamentalmente en los riesgos de salinización de los suelos.
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Los indicadores ambientales son una herramienta para efectuar el monitoreo de la biodiversidad a través de la recolección sistemática de datos obtenidos mediante mediciones u observaciones en series de tiempo y espacio. Se entiende por indicador ambiental a una variable o suma de variables que proporciona una información sintética sobre un fenómeno ambiental complejo que permite conocer y evaluar el estado y variación de la calidad ambiental. Para la caracterización y detección de indicadores ambientales del litoral de Río Negro se identifican y jerarquizan los conflictos o problemáticas del ambiente, a partir de lo cual se seleccionan las principales variables que componen el sistema de indicadores y por último, se recopilan los niveles de información existentes y los que requieren ser relevados e incorporados a bases de datos relacionales. Los requisitos que deben tener los indicadores seleccio nados son: ser medibles (cuali y cuantitativamente), compresibles, fáciles de usar e interrelacionar, tener dimensión espacial y temporal, ser objetivos sensibles a los cambios y permitir el diagnóstico y pronóstico en función de la detección de situaciones de alerta ambiental. Se realiza una aproximación a la selección de variables e indicadores con el fin de definir el modelo de datos y categorías de agrupamiento. El sistema de indicadores generados se agrupa en función de la disponibilidad de datos existentes y la posibilidad de recopilación para un correcto funcionamiento del prototipo del Observatorio. El modelo adoptado incorpora 3 subsistemas (ambiental, social y económico) interrelacionando con 3 nodos institucionales (que proveen y/o precisan estos datos para la toma de decisiones). Cada indicador se describe en una ficha metodológica, cuyo diseño es normalizado para un correcto funcionamiento del Observatorio. La implementación del modelo de indicadores exige contar con una infraestructura que permita la aplicación de mediciones, observaciones y registros y contar además, con personal idóneo para una correcta manipulación y análisis.
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La información básica sobre el relieve de una cuenca hidrográfica, mediante metodologías analítico-descriptivas, permite a quienes evalúan proyectos relacionados con el uso de los recursos naturales, tales como el manejo integrado de cuencas, estudios sobre impacto ambiental, degradación de suelos, deforestación, conservación de los recursos hídricos, entre otros, contar para su análisis con los parámetros físicos necesarios. Estos procesos mencionados tienen un fuerte componente espacial y el empleo de Sistemas de Información Geográfica (SIG) son de suma utilidad, siendo los Modelos Digitales de Elevación (DEM) y sus derivados un componente relevante de esta base de datos. Los productos derivados de estos modelos, como pendiente, orientación o curvatura, resultarán tan precisos como el DEM usado para derivarlos. Por otra parte, es fundamental maximizar la habilidad del modelo para representar las variaciones del terreno; para ello se debe seleccionar una adecuada resolución (grilla) de acuerdo con los datos disponibles para su generación. En este trabajo se evalúa la calidad altimétrica de seis DEMs generados a partir de dos sistemas diferentes de captura de datos fuente y de distintas resoluciones de grilla. Para determinar la exactitud de los DEMs habitualmente se utiliza un grupo de puntos de control considerados como "verdad de campo" que se comparan con los generados por el modelo en la misma posición geográfica. El área seleccionada para realizar el estudio está ubicada en la localidad de Arrecifes, provincia de Buenos Aires (Argentina) y tiene una superficie de aproximadamente 120 ha. Los resultados obtenidos para los dos algoritmos y para los tres tamaños de grilla analizados presentaron los siguientes resultados: el algoritmo DEM from contourn, un RMSE (Root Mean Squared Error) de ± 0,11 m (para grilla de 1 m), ± 0,11 m (para grilla de 5 m) y de ± 0,15 m (para grilla de 10 m). Para el algoritmo DEM from vector/points, un RMSE de ± 0,09 m (para grilla de 1 m), ± 0,11 m (para grilla de 5 m) y de ± 0,11 m (para grilla de 10 m). Los resultados permiten concluir que el DEM generado a partir de puntos acotados del terreno como datos fuente y con el menor tamaño de grilla es el único que satisface los valores enumerados en la bibliografía, tanto nacional como internacional, lo que lo hace apto para proyectos relacionados con recursos naturales a nivel de ecotopo (predial). El resto de los DEMs generados presentan un RMSE que permite asegurar su aptitud para la evaluación de proyectos relacionados con el uso de los recursos naturales a nivel de unidad de paisaje (conjunto de ecotopos).
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Este trabajo propone una metodología basada en Sistemas de Información Geográfica para estimar la demanda de viajes en estaciones de redes de transporte público, tomando como ejemplo la red de metro de Madrid. Primero se emplea una serie de datos descriptivos para caracterizar la red, clasificar las estaciones y obtener una tipología de las mismas. Luego, con el objetivo de explicar y predecir los viajes (entradas a la red) se generan dos modelos: uno sencillo a partir de las tasas de penetración de uso del metro en función de la distancia (distance decay), y otro más complejo basado en un modelo de regresión lineal múltiple (MRLM) que incorpora variables relativas a la estación y su entorno (densidad, mezcla de usos, diseño urbano, presencia de modos competidores). Su aplicación muestra resultados alentadores, y se plantea como una alternativa a los clásicos modelos de cuatro etapas, más complejos y con un mayor coste económico.