Abstract

The main objective of the paper at hand is to investigate material flows and stock changes of subway infrastructure associated with its refurbishment. Specific attention is given to the relation between recycling, reuse and virgin material flows. Furthermore, the extent to which policy targets are achieved in a specific refurbishment process is investigated. Thereto the refurbishment of a subsection of Vienna’s subway network was chosen as a case study. To fulfil the objective, a bottom up material flow analysis (MFA) of the refurbishment process on the subsection was performed. Results show that the overall material stock is 360,000 t, of which three-fourths remained unchanged within the refurbishment. Within the refurbishment process, in total material with a mass of around 155,000 t was built into the system. The share of recycling material was significantly higher than the use of virgin material. In detail 39% virgin construction material, 15% recycling construction material, 41% on site-recycling construction material and 5% reuse construction components were built into the section.

Abstract

large number of applications have been launched to gather geo-located information from the public. This article introduces an approach toward generating land-use patterns from volunteered geographic information (VGI) without applying remote-sensing techniques and/or engaging official data. Hence, collaboratively collected OpenStreetMap (OSM) data sets are employed to map land-use patterns in Vienna, Austria. Initially the spatial pattern of the landscape was delineated and thereafter the most relevant land type was assigned to each land parcel through a hierarchical GIS-based decision tree approach. To evaluate the proposed approach, the results are compared with the Global Monitoring for Environment and Security Urban Atlas (GMESUA) data. The results are compared in two ways: first, the texture of the resulting land-use patterns is analyzed using texture-variability analysis. Second, the attributes assigned to each land segment are evaluated. The achieved land-use map shows kappa indices of 91, 79, and 76% agreement for location in comparison with the GMESUA data set at three levels of classification. Furthermore, the attributes of the two data sets match at 81, 67, and 65%. The results demonstrate that this approach opens a promising avenue to integrate freely available VGI to map land-use patterns for environmental planning purposes.