961 resultados para Integrated Land and Water Information System (ILWIS)


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Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

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Mode of access: Internet.

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A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

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Cover title.

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Achieving more sustainable land and water use depends on high-quality information and its improved use. In other words, better linkages are needed between science and management. Since many stakeholders with different relationships to the natural resources are inevitably involved, we suggest that collaborative learning environments and improved information management are prerequisites for integrating science and management. Case studies that deal with resource management issues are presented that illustrate the creation of collaborative learning environments through systems analyses with communities, and an integration of scientific and experiential knowledge of components of the system. This new knowledge needs to be captured and made accessible through innovative information management systems designed collaboratively with users, in forms which fit the users' 'mental models' of how their systems work. A model for linking science and resource management more effectively is suggested. This model entails systems thinking in a collaborative learning environment, and processes to help convergence of views and value systems, and make scientists and different kinds of managers aware of their interdependence. Adaptive management provides a mechanism for applying and refining scientists' and managers' knowledge. Copyright (C) 2003 John Wiley Sons, Ltd.

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Product Data Management (PDM) systems have been utilized within companies since the 1980s. Mainly the PDM systems have been used by large companies. This thesis presents the premise that small and medium-sized companies can also benefit from utilizing the Product Data Management systems. Furthermore, the starting point for the thesis is that the existing PDM systems are either too expensive or do not properly respond to the requirements SMEs have. The aim of this study is to investigate what kinds of requirements and special features SMEs, operating in Finnish manufacturing industry, have towards Product Data Management. Additionally, the target is to create a conceptual model that could fulfill the specified requirements. The research has been carried out as a qualitative case study, in which the research data was collected from ten Finnish companies operating in manufacturing industry. The research data is formed by interviewing key personnel from the case companies. After this, the data formed from the interviews has been processed to comprise a generic set of information system requirements and the information system concept supporting it. The commercialization of the concept is studied in the thesis from the perspective of system development. The aim was to create a conceptual model, which would be economically feasible for both, a company utilizing the system and for a company developing it. For this reason, the thesis has sought ways to scale the system development effort for multiple simultaneous cases. The main methods found were to utilize platform-based thinking and a way to generalize the system requirements, or in other words abstracting the requirements of an information system. The results of the research highlight the special features Finnish manufacturing SMEs have towards PDM. The most significant of the special features is the usage of project model to manage the order-to-delivery –process. This differs significantly from the traditional concepts of Product Data Management presented in the literature. Furthermore, as a research result, this thesis presents a conceptual model of a PDM system, which would be viable for the case companies interviewed during the research. As a by-product, this research presents a synthesized model, found from the literature, to abstract information system requirements. In addition to this, the strategic importance and categorization of information systems within companies has been discussed from the perspective of information system customizations.

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This paper presents a study on applying an integrated Global Position System (GPS) and Geographacial Information System (GIS) technology to the reduction of construction waste. During the study, a prototype study is developed from automatic data capture system such as the barcoding system for construction material and equipment (M&E) management onsite, whilst the integrated GPS and GIS technology is combined to the M&E system based on the Wide Area Network (WAN). Then, a case study is conducted to demonstrate the deployment of the system. Experimental results indicate that the proposed system can minimize the amount of onsite material wastage.

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The choice of an information systems is a critical factor of success in an organization's performance, since, by involving multiple decision-makers, with often conflicting objectives, several alternatives with aggressive marketing, makes it particularly complex by the scope of a consensus. The main objective of this work is to make the analysis and selection of a information system to support the school management, pedagogical and administrative components, using a multicriteria decision aid system – MMASSITI – Multicriteria Method- ology to Support the Selection of Information Systems/Information Technologies – integrates a multicriteria model that seeks to provide a systematic approach in the process of choice of Information Systems, able to produce sustained recommendations concerning the decision scope. Its application to a case study has identi- fied the relevant factors in the selection process of school educational and management information system and get a solution that allows the decision maker’ to compare the quality of the various alternatives.

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The relevance of endocrine-disrupting compounds as potential contaminants of drinking water is reviewed, particularly in the reuse of wastewater. Growing populations and increasing intensification of land and water use for industry and agriculture have increased the need to reclaim wastewater for reuse, including to supplement the drinking water supply. The variety of anthropogenic chemicals that have been identified as potential endocrine disruptors in the environment and the problems arising from their use as human and livestock pharmaceuticals, as agricultural chemicals and in industry are discussed. The potentially adverse impact of these chemicals on human health and the ecology of the natural environment are reviewed. Data for the removal of estrogenic compounds from wastewater treatment are presented, together with the comparative potencies of estrogenic compounds. The relative exposure to estrogens of women on oral contraceptives, hormone replacement therapy, and through food consumption is estimated. A brief overview of some methods available or under development for the assessment of estrogenic activity in environmental samples is provided. The review concludes with a discussion of the directions for further investigation, which include human epidemiology, methodology development, and wastewater monitoring. (C) 2006 Wiley Periodicals, Inc.