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The authors take as their focus the advice on interior design and decoration that Singapore's Housing Development Board (HDB) distributed to residents as part of its programme of universal housing provision. Through a series of regular articles appearing in the HDB publication Our Home (1972–1989), readers were presented with stories that showed how selected HDB residents decorated their newly acquired highrise flats. The authors detail the relationship between this design advice and the commitment to modernist design principles, the self-conscious pragmatism of the HDB, and the reliance on a limited market logic ('homeownership'). The HDB's vision of the benefit of its highrise housing programme was, from the outset, complexly entangled with cultivating individual investments in the home by way of interior design and decoration practices. The paper contributes not only to the specific story of Singaporean housing, but also to wider scholarship on modernism, the everyday practices of interior design, and housing consumption.

The advent of state-sponsored mass high-rise housing in post-war Britain brought into view a range of issues about the role of technology in everyday life. This paper draws on approaches in the study of science and technology in order to deepen our understanding of the socio-technical aspects of such high-rise housing, past and present. This thinking is elaborated empirically by examining a 1960s high-rise development, Red Road, Glasgow. The paper examines the inaugural phase of development and the most recent phase of 'redevelopment', the first stage of which is demolition. The paper extends existing accounts of residential high-rises generally and Red Road specifically, as well as elaborating an alternate analytical framework for understanding high-rise and supertall dwellings.

Urban planning relies on the definition, modelling and evaluation of multidimensional phenomena for informed decision-making. Urban building energy modelling, for instance, usually requires knowledge about the energy use profile and surface area of each use that takes place within a building. We do not have a detailed understanding of such information for mixed-use developments, which are gaining prominence in urban planning. In this paper, we developed a methodology to quantitatively define the characteristics of mixed-use developments using archetypes of programme profiles (ratios of each programme type) of a city's mixed-use plots. We applied our methodology in Singapore, resulting in 163 mixed-use zoning archetypes using Singapore's master plan data and Google Maps API data. In a case study, we demonstrated how these archetypes can be used to provide more detailed data for urban building energy modelling, including energy demand forecasts and energy supply system design. To enable future automation of the workflow, the archetype definitions were represented and stored as a machine-readable ontology. This ontology can later be extended with for example, the mobility properties of archetypes; thus, enabling the archetypes' use in other urban planning applications beyond building energy modelling.

In Singapore, decision-makers from multiple ministries and government agencies participate in urban planning and management. This often results in siloed datasets, different data formats and domain specific software: a lack of interoperability that hinders the integration of (big) data in planning. Semantic Web Technology (SWT) can help to solve data interoperability issues. With SWT, computers can infer semantic relationships between heterogeneous data that are linked using ontologies (i.e. 'common languages'). Knowledge Graph (KG) data structures allow such linking, and thus support SWT applications. The Cities Knowledge Graph (CKG) research project uses a KG to facilitate the use of multi-domain data in city planning. We present a use case that demonstrates how KGs enable the creation of planning indicators, building on various openly available datasets in Singapore. The first step was to transform datasets containing geospatial and regulatory data on zoning, parcels and buildings to CityGML. Then it was loaded into a KG structured using a CityGML-based ontology. Retrieving raw data values or composite metrics (essentially manifold combinations of queries on different datasets) was done using SPARQL. However, our goal was to develop indicators that could benefit urban planners – examples include 'GPR potential' (unused Gross Plot Ratio per zone) or 'allowable programmes per plot' (which uses could exist on a plot, given its zone). These and other indicators have many potential applications, including in urban energy modelling. For instance, we developed an indicator for district cooling potential based on geometric parcel data as well as zoning and density data. We demonstrated how these indicators can be used to analyse a part of downtown Singapore, and visualised the results. We show that KG technology allows planners to analyse cities through multi-domain indicators, which would be difficult to develop based on individual datasets. Another benefit of KGs is highly malleable data architecture, allowing data to be updated, expanded or created. Future work includes integrating new datasets into our CKG and developing new analyses. Ultimately, these could be carried out autonomously by a multi-agent system.