کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6964603 | 1452311 | 2013 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spatial model steering, an exploratory approach to uncertainty awareness in land use allocation
ترجمه فارسی عنوان
فرمان مدل فضایی، یک رویکرد اکتشافی برای آگاهی نااطمینی در تخصیص استفاده از زمین
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کلمات کلیدی
فرمان مدل فضایی، تخصیص استفاده از زمین، سیستم پشتیبانی تصمیم تصادفی معماری سرویس گرا، آگاهی نااطمینی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
One evidenced based approach for exploring future agricultural land use change scenarios is Land Use Allocation (LUA). This approach can be used to support medium to long term strategic planning. Specifically, land managers can consider a number of diverse environmental social, economic and physical factors, and explore land use allocation scenarios before choosing to produce one or more commodities in a given region. One of the most successful ways to implement a LUA approach is through the integration of geoprocessing with Multi-Criteria Decision Making methods (MCDM). Leveraging this spatial MCDM modeling approach with the Service Oriented Architecture (SOA) paradigm, we have developed a Spatial Model Steering (SMS) framework that enables users to explore the decision space and thus increase their awareness of the influence of key variables. In this framework a user can visually steer the LUA model key factors, explore and compare “what if” future land use scenarios by changing these factors and visualizing a range of potential LUA outcomes. In doing so, we believe that users can develop increased confidence in their understanding of the key factors governing the underlying models and ultimately obtain greater awareness of the uncertainty in the outcomes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 39, January 2013, Pages 70-80
Journal: Environmental Modelling & Software - Volume 39, January 2013, Pages 70-80
نویسندگان
Marcos Nino-Ruiz, Ian Bishop, Christopher Pettit,