Assessing cities: Applying GIS-based methods for mapping cross-scale spatial indicators
In recent years, several systems and tools to assess energy consumption and carbon emissions at scales beyond that of merely buildings, such as LEED, CASBEE and BREEAM communities have been development. However, reviews reveal a lack of robustness in these methods both in terms of an unstructured mi...
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description | In recent years, several systems and tools to assess energy consumption and carbon emissions at scales beyond that of merely buildings, such as LEED, CASBEE and BREEAM communities have been development. However, reviews reveal a lack of robustness in these methods both in terms of an unstructured mix of qualitative and quantitative criteria and lack of focus on urban form parameters found to influence energy consumption and carbon emissions. A promising quantitative assessment system including various urban form indicators is developed by the Urban Morphology Institute (UMI) in Paris. Within the research centre on zero emission neighbourhoods in smart cities (ZEN), a GIS-based method is applied to analyze conditions of urban form known to contribute to carbon emissions. In this paper we demonstrate how a selection of the UMI indicators describing proximity can be further specified applying GIS-based methods. The potential of applicability of urban assessment system in planning as well as design processes will increase when linked to tools that are already implemented, and map visualizations as well as data provided by these methods are highly applicable in planning and urban design. As further research, methods described in recent research within ZEN and specified measures for calculating UMI indicators, will be tested in analyses of urban development areas in Norway. |
doi_str_mv | 10.1088/1755-1315/352/1/012055 |
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subjects | Carbon Emissions Energy consumption Green buildings Indicators Morphology Research facilities Urban areas Urban development Urban planning |
title | Assessing cities: Applying GIS-based methods for mapping cross-scale spatial indicators |
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