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Article Comparative Analysis of Three Innovative Housing Models in Copenhagen for Social Mix(2020) Bican, Nezih BurakCopenhagen has been attracting residents both from abroad and other regions of Denmark, embracing a comprehensive development plan following an economic boom since the 1990s. Local decision-makers have been striving to transform the housing stock of the city in line with the evolving demographics of the city and consequent new demands of the urban society. At the same time, people are seeking cheaper and flexible alternatives of living; thus, social housing (almen bolig) emerges as an affordable option with reasonable qualities for Copenhagen residents. This study uses a comparative analysis to evaluate spatial approaches of three innovative social housing models developed by partnerships of some non-profit housing associations with Copenhagen municipality in 2015. Each model has a distinct motto; Generationernes Byhus (GBYH) builds up neighbourhood across generations; Boliger for Alle (BOFA) provides opportunity of transition across ownership types; and Almene Storbyboliger (ASBB) creates flexible/plastic system addressing demographical structure under change. Methodologically, the research is based on interviews with key stakeholders and in-depth analysis of visual and written documents. It provides a comparative analysis of the models, concentrating particularly on dwelling design approaches which address social mix and diversity. The paper concludes that although the social housing market is strictly controlled for socio-economic reasons, it still has the potential to support the evolution of the urban demography of Danish society thanks to embracement of innovative perspectives both by governmental authorities and forerunning housing associations.Article Data Driven Approach for Weight Restricted Data Envelopment Analysis Models With Single Output(2023) Kurt, Şenol; Yüksel, Mustafa Kerem; Dinçergök, BurcuThis study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model. In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs). Then, an ML algorithm is trained that aims to predict the single output using inputs. Ranking of input features based on relative feature importance values obtained from the trained ML model is fed to the DEA model as weight restrictions. As a result, the two DEA models are compared with each other. ML-based insights (feature importance ranking) improve the DEA model in the direction of fewer zero weights. The additional weight restrictions are data depdendent, and hence realistic. As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.
