بررسی عملکرد چشم انداز پایدار در سراسر جمهوری مولداوی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|154977||2018||15 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Habitat International, Volume 72, February 2018, Pages 77-91
Sustainability remains an undeniable, yet obscure, destination for humanity to reach. Although progress has been made, there remains no agreed upon method for spatial scientists, nor landscape and regional planners to use during sustainable development assessments. Furthermore, limited examples exist that investigate relationships between-landscape form (e.g. urban configuration) and population dynamics (e.g. number of settlements)- and a local measure of sustainable development. Using a recently published local sustainable development index (LSDI) for Moldova, a regional spatial analysis was created to further elucidate strengths and weaknesses of index-based assessments of sustainable landscape function. Using a one-to-many relationship, sixty-six landscapes were joined to 399 mean LSDI sample locations for the quantitative spatial assessment (nÂ =Â 399). A rarity of this study was that it employed the Eastern School of Geography's âlandscape unitsâ for Moldova during geospatial data aggregation and spatially enabled regression. Moran's I scatterplot and spatial correlogram were used to visualize spatial autocorrelation dynamics of LSDI. Three local conditional autoregressive (CAR) models were made, with all explaining over 70% of LSDI variation. The two strongest positive predictors of LSDI were city population density and road intersection density, while the two most consistent negative were settlement density and distance between urban land cover patches (ENN_AM). Findings suggest index-based landscape valuations could suffer from spurious inferential correlations when landscape-calculated sub-metrics (i.e., proportion agricultural land) are included within evaluation indices. This phenomenon complicates the interpretation of results during regional analyses, thus potentially hindering sustainable development planning and policy responses across spatial scales.