Development of a visual decision support system for sustainable redevelopment of social housing : a case study for Linstone Housing Association, Scotland
Arshad, Maryam (2022)
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2022111222546
https://urn.fi/URN:NBN:fi:amk-2022111222546
Tiivistelmä
Social housing in Scotland has been reported to have high levels of disrepairs, with over 52% of homes having inadequate quality of essential components in 2019. Furthermore, over 19% of all homes were in a much worse state described as “urgent disrepair”. However, these issues are difficult to address because decision-makers face immense complexities in making decisions with regards to improving social housing in Scotland due to inundation of unorganized data, new policies, and modified guidelines.
However, an effective multi-criteria decision support system (MCDSS) can cater to this issue by encouraging smooth decision-making through use of decision support systems (DSS) to support the implementation of multi-criteria decision-making (MCDM). This MCDSS was developed for Linstone Housing Association and encapsulated the needs and requirements of tenants, housing providers and governing bodies. The method adopted consisted of a case study approach that brought together findings from previous research, stakeholder engagement through questionnaires and interviews and statistical analysis. A series of qualitative and quantitative methods were adopted to identify sustainability attributes in the redevelopment/retrofit of social housing within Scotland, create a hierarchal system using the combined approach of Simple Additive Weighting and Saaty’s 1-9 scale, and prioritize housing units in need of urgent attention.
The results were represented on ArcGIS Dashboard, thereby creating a DSS. This was important as this type of aid can provide an effective visual display for critical information required to achieve ones’ goals and objectives, especially in urban areas. They are also interactive tools which can be adopted to multiple types of developments. In this case, the DSS helped capture data and highlighted critical indicators and hotspots for properties in a simple and interactive way. It also helped simplify complex data for decision-makers and stakeholders. This process also helped identify the data gaps in housing associations, thereby, providing a body of work to collect meaningful data to aid their decision-making processes in the future.
However, an effective multi-criteria decision support system (MCDSS) can cater to this issue by encouraging smooth decision-making through use of decision support systems (DSS) to support the implementation of multi-criteria decision-making (MCDM). This MCDSS was developed for Linstone Housing Association and encapsulated the needs and requirements of tenants, housing providers and governing bodies. The method adopted consisted of a case study approach that brought together findings from previous research, stakeholder engagement through questionnaires and interviews and statistical analysis. A series of qualitative and quantitative methods were adopted to identify sustainability attributes in the redevelopment/retrofit of social housing within Scotland, create a hierarchal system using the combined approach of Simple Additive Weighting and Saaty’s 1-9 scale, and prioritize housing units in need of urgent attention.
The results were represented on ArcGIS Dashboard, thereby creating a DSS. This was important as this type of aid can provide an effective visual display for critical information required to achieve ones’ goals and objectives, especially in urban areas. They are also interactive tools which can be adopted to multiple types of developments. In this case, the DSS helped capture data and highlighted critical indicators and hotspots for properties in a simple and interactive way. It also helped simplify complex data for decision-makers and stakeholders. This process also helped identify the data gaps in housing associations, thereby, providing a body of work to collect meaningful data to aid their decision-making processes in the future.