916 resultados para Geographic information science|Information science


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This study examines the impact of utilising a Decision Support System (DSS) in a practical health planning study. Specifically, it presents a real-world case of a community-based initiative aiming to improve overall public health outcomes. Previous studies have emphasised that because of a lack of effective information, systems and an absence of frameworks for making informed decisions in health planning, it has become imperative to develop innovative approaches and methods in health planning practice. Online Geographical Information Systems (GIS) has been suggested as one of the innovative methods that will inform decision-makers and improve the overall health planning process. However, a number of gaps in knowledge have been identified within health planning practice: lack of methods to develop these tools in a collaborative manner; lack of capacity to use the GIS application among health decision-makers perspectives, and lack of understanding about the potential impact of such systems on users. This study addresses the abovementioned gaps and introduces an online GIS-based Health Decision Support System (HDSS), which has been developed to improve collaborative health planning in the Logan-Beaudesert region of Queensland, Australia. The study demonstrates a participatory and iterative approach undertaken to design and develop the HDSS. It then explores the perceived user satisfaction and impact of the tool on a selected group of health decision makers. Finally, it illustrates how decision-making processes have changed since its implementation. The overall findings suggest that the online GIS-based HDSS is an effective tool, which has the potential to play an important role in the future in terms of improving local community health planning practice. However, the findings also indicate that decision-making processes are not merely informed by using the HDSS tool. Instead, they seem to enhance the overall sense of collaboration in health planning practice. Thus, to support the Healthy Cities approach, communities will need to encourage decision-making based on the use of evidence, participation and consensus, which subsequently transfers into informed actions.

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The Cardiac Access-Remoteness Index of Australia (Cardiac ARIA) used geographic information systems (GIS) to model population level, road network accessibility to cardiac services before and after a cardiac event for all (20,387) population localities in Australia., The index ranged from 1A (access to all cardiac services within 1 h driving time) to 8E (limited or no access). The methodology derived an objective geographic measure of accessibility to required cardiac services across Australia. Approximately 71% of the 2006 Australian population had very good access to acute hospital services and services after hospital discharge. This GIS model could be applied to other regions or health conditions where spatially enabled data were available.

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Seismic hazard and microzonation of cities enable to characterize the potential seismic areas that need to be taken into account when designing new structures or retrofitting the existing ones. Study of seismic hazard and preparation of geotechnical microzonation maps has been attempted using Geographical Information System (GIS). GIS will provide an effective solution for integrating different layers of information thus providing a useful input for city planning and in particular input to earthquake resistant design of structures in an area. Seismic hazard is the study of expected earthquake ground motions at any point on the earth. Microzonation is the process of sub division of region in to number of zones based on the earthquake effects in the local scale. Seismic microzonation is the process of estimating response of soil layers under earthquake excitation and thus the variation of ground motion characteristic on the ground surface. For the seismic microzonation, geotechnical site characterization need to be assessed at local scale (micro level), which is further used to assess of the site response and liquefaction susceptibility of the sites. Seismotectonic atlas of the area having a radius of 350km around Bangalore has been prepared with all the seismogenic sources and historic earthquake events (a catalogue of about 1400 events since 1906). We have attempted to carryout the site characterization of Bangalore by collating conventional geotechnical boreholes data (about 900 borehole data with depth) and integrated in GIS. 3-D subsurface model of Bangalore prepared using GIS is shown in Figure 1.Further, Shear wave velocity survey based on geophysical method at about 60 locations in the city has been carried out in 220 square Kms area. Site response and local site effects have been evaluated using 1-dimensional ground response analysis. Spatial variability of soil overburden depths, ground surface Peak Ground Acceleration’s(PGA), spectral acceleration for different frequencies, liquefaction susceptibility have been mapped in the 220 sq km area using GIS.ArcInfo software has been used for this purpose. These maps can be used for the city planning and risk & vulnerability studies. Figure 2 shows a map of peak ground acceleration at rock level for Bangalore city. Microtremor experiments were jointly carried out with NGRI scientists at about 55 locations in the city and the predominant frequency of the overburden soil columns were evaluated.

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A Low-Level Geographic Information System (LL-GIS) was developed to provide a simple low-cost mapping program which can be executed in any personal computer, by individuals with different levels of knowledge in computing. MAPPER is an add-on module of FishBase - a global database with key information on the biology of fish - where it creates on-screen maps with information on biodiversity and the occurrence of species. In another application, MAPPER is used to display and analyzed geographical information on the Philippines.

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Economic analysis of the trawl fishery of Brunei Darussalam was conducted using cost and returns analysis and based on an economic survey of trawlers and B:RUN, a low-level geographic information system. Profitability indicators were generated for the trawl fleet under various economic and operational scenarios. The results show that financial profits are earned by trawlers which operate off Muara, particularly those with high vessel capacity, and that these profits could be further enhanced. On the other hand, a similar fleet operating off Tutong would generate profits due mainly to high fish biomass. Trawling operations offshore are deemed financially unfeasible. Incorporating realistic opportunity costs and externalities for existing trawl operations off Muara results in economic losses.

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B:RUN is a low-level GIS software designed to help formulate options for the management of the coastal zone of Brunei Darussalam. This contribution presents the oil spill simulation module of B:RUN. This simple module, based largely on wind and sea surface current vector parameters, may be helpful in formulating relevant oil spill contingency plans. It can be easily adapted to other areas, as can the B:RUN software itself.

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This contribution is the first part of a four-part series documenting the development of B:RUN, a software program which reads data for common spreadsheets and presents them as low-resolution maps of slates and processes. The program emerged from a need which arose during a project in Brunei Darussalam for a 'low level' approach for researchers to communicate findings as efficiently and expeditiously as possible. Part I provides a overview of the concept and design elements of B:RUN. Part II will highlight results of the economics components of the program evaluating different fishing regimes, sailing distances from ports and fleet operating costs. Environmental aspects will be presented in Part III in the form of overlay maps. Part IV will summarize the implications of B:RUN results to coastal and fishery resources management in Brunei Darussalam and show how this approach can be adapted to other coastlines and used as a teaching and training tool. The following three parts will be published in future editions of Naga, the ICLARM Quarterly. The program is available through ICLARM.

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This study concerns the spatial allocation of material flows, with emphasis on construction material in the Irish housing sector. It addresses some of the key issues concerning anthropogenic impact on the environment through spatial temporal visualisation of the flow of materials, wastes and emissions at different spatial levels. This is presented in the form of a spatial model, Spatial Allocation of Material Flow Analysis (SAMFA), which enables the simulation of construction material flows and associated energy use. SAMFA parallels the Island Limits project (EPA funded under 2004-SD-MS-22-M2), which aimed to create a material flow analysis of the Irish economy classified by industrial sector. SAMFA further develops this by attempting to establish the material flows at the subnational geographical scale that could be used in the development of local authority (LA) sustainability strategies and spatial planning frameworks by highlighting the cumulative environmental impacts of the development of the built environment. By drawing on the idea of planning support systems, SAMFA also aims to provide a cross-disciplinary, integrative medium for involving stakeholders in strategies for a sustainable built environment and, as such, would help illustrate the sustainability consequences of alternative The pilot run of the model in Kildare has shown that the model can be successfully calibrated and applied to develop alternative material flows and energy-use scenarios at the ED level. This has been demonstrated through the development of an integrated and a business-as-usual scenario, with the former integrating a range of potential material efficiency and energysaving policy options and the latter replicating conditions that best describe the current trend. Their comparison shows that the former is better than the latter in terms of both material and energy use. This report also identifies a number of potential areas of future research and areas of broader application. This includes improving the accuracy of the SAMFA model (e.g. by establishing actual life expectancy of buildings in the Irish context through field surveys) and the extension of the model to other Irish counties. This would establish SAMFA as a valuable predicting and monitoring tool that is capable of integrating national and local spatial planning objectives with actual environmental impacts. Furthermore, should the model prove successful at this level, it then has the potential to transfer the modelling approach to other areas of the built environment, such as commercial development and other key contributors of greenhouse emissions. The ultimate aim is to develop a meta-model for predicting the consequences of consumption patterns at the local scale. This therefore offers the possibility of creating critical links between socio technical systems with the most important challenge of all the limitations of the biophysical environment.

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Perfect information is seldom available to man or machines due to uncertainties inherent in real world problems. Uncertainties in geographic information systems (GIS) stem from either vague/ambiguous or imprecise/inaccurate/incomplete information and it is necessary for GIS to develop tools and techniques to manage these uncertainties. There is a widespread agreement in the GIS community that although GIS has the potential to support a wide range of spatial data analysis problems, this potential is often hindered by the lack of consistency and uniformity. Uncertainties come in many shapes and forms, and processing uncertain spatial data requires a practical taxonomy to aid decision makers in choosing the most suitable data modeling and analysis method. In this paper, we: (1) review important developments in handling uncertainties when working with spatial data and GIS applications; (2) propose a taxonomy of models for dealing with uncertainties in GIS; and (3) identify current challenges and future research directions in spatial data analysis and GIS for managing uncertainties.