This article discusses the spatial intelligence of cities, the use of information communication technologies (ICTs) and institutional frameworks that support innovation ecosystems of cities and increase the problem-solving capability of communities and cities. It is based on three case studies on Bletchley Park, Cyberport Hong Kong and Smart Amsterdam, which highlight different architectures of spatial intelligence: (1) orchestration intelligence that stems from collaboration within a community and integration of people's skills, know-how, and collective and machine intelligence, (2) amplification intelligence based on learning, up-skilling and talent cultivation using open technology platforms and ICT infrastructure offered by the city, and (3) instrumentation intelligence based on streams of information generated from the functioning of cities, which enable more informed decisions to be taken by citizens and organizations. The article contributes to understanding different processes that make communities more intelligent, and how collective intelligence, people-driven innovation and use of smart devices advance the efficiency, operation and governance of cities.

There is an enormous amount of intricate research available on specific aspects of building performance in relation to users. However, there is currently no framework for designers to bring together these diverse findings. As a result, a significant opportunity is being missed to support radically more positive outcomes for the occupants of buildings. To help address this gap, this article proposes a framework that captures the essence of an individual's holistic experience of spaces, created by the combined impacts received through their senses and mediated by their brain. The early sections illustrate the complex and dynamic ways in which humans receive information about their surroundings through their senses, with profound, but fragile and interactive effects on human health, mood and performance. This is strategically framed using neuroscience theories about the way the human brain makes calculations to link perceptions to actions. This leads to the proposition that design should take into account issues of naturalness, individualization and level of stimulation. It is further suggested that a multidisciplinary, multi-level research approach is needed to support the creation of spaces that provide people with sensory environments that help them reach, or sustain, their fullest possible potential.

This is a case study on the development of the Gardens by the Bay, Bay South and Conservatory Complex to provide intelligent and integrated building responses. This article demonstrates the effectiveness of combining a number of environmental design and building services optimization steps to achieve a sustainable design. Key initiatives undertaken include high-level daylighting design of the building skin to achieve exacting natural light requirements inside the spaces, while at the same time limiting thermal loads. Desiccant dehumidification systems are applied to minimize system cooling loads, so that waste heat from biomass combined heat and power (CHP) can drive substantive portions of the conditioning requirements. A waste stream made up of horticultural biomass is also identified and proposed to be utilized to provide the primary fuel for the biomass CHP system. In addition, this article provides a case study of how external structures are integrated to the building to provide ventilation and renewable energy harvesting.

(2010). Intelligent buildings for advancing sustainability: Contextualisation of management frameworks in social settings. Intelligent Buildings International: Vol. 2, No. 2, pp. 158-159.

The objective of this study is to facilitate the design of intelligent space based on our assessment of user needs. The acceptance of smart living technologies is a challenging issue, yet to be proven. In this study, a hypothetical model is proposed on the basis of the hedonic technology acceptance model, and is exemplified by the Living 3.0 Demo Center. Safety, health, sustainability, convenience are the four factors modelled. The findings from a questionnaire and data analysed by structural equation modelling demonstrate that technologies for safety are significantly perceived as being ‘useful’, whereas the applications for convenience are significantly perceived as being ‘enjoyable’.

The article explores how feedback is provided in the design and delivery of two green Canadian office buildings and how this shapes the knowledge and adaptive behaviour of occupants. A central theme is whether occupants are treated as passive or active in establishing the comfort conditions of their immediate environment. In this exploration, the authors define feedback acting on a number of timescales/processes as a key element of occupant engagement. The available metrics of feedback and engagement are tested through a comparative post-occupancy evaluation of the two buildings, showing that occupants draw on a range of sources to form opinions about how well they know a building, how ‘green’ it is and whether or not it is comfortable. Results suggest that occupants can only truly be active participants if they receive effective feedback on their adaptive behaviour (engaging with building controls and complaints). Lack of effective feedback can render occupants passive participants, whose adaptive behaviour rather than improving their understanding of how the building works and overall comfort levels can have the opposite effect. Findings underscore the many challenges in interpreting occupants' subjective statements about knowledge, comfort and engagement with a building.

The paper presents an evaluation of an installation of the Beastie wireless sensor network to monitor a creative workplace—the University of the Arts Innovation Centre in London. The sensor network was tasked with passively monitoring the environment and space usage taking into account environmental conditions and activity. The paper focuses on one of the many platforms used in the trial—the Beastie, using an architectural description language implementation called Tesserae—to measure meeting room and social space usage. These data were correlated using feature mapping at node level on tiny 8-bit devices, and state changes were propagated up the network to the database. Some monitoring results are provided and the performance of the algorithms on the small devices discussed. The lessons learnt are reviewed and observations are presented on the practicalities of installing and maintaining the sensor application in a commercial office environment.

Within the context of this study, a walk-through energy audit was performed for a typical office building in Hong Kong. The focus of energy saving opportunities was mainly on mechanical ventilation and air conditioning (MVAC) and lighting. The emphasis was mainly on more energy-efficient equipment, a better control of the equipment, indoor air quality and human behaviour. Among others, the measures considered to reduce MVAC energy were replacing the air-cooled reciprocation chillers with water-cooled centrifugal chillers; installing variable speed drives (VSDs) for the secondary chilled water pump motors; maintaining the indoor air quality within reasonable limits while monitoring the indoor CO2 concentration to adjust the fan speed of primary air-handling units (PAUs) by VSDs and re-adjusting the temperature setpoint to use less energy for air conditioning; and encouraging environmentally friendly human behaviour within the built environment, especially in hot and humid countries such as Hong Kong. The measures to reduce lighting energy were mainly replacing the lamps with energy-efficient lamps and preventing over-illumination. The results led to the conclusion that an annual saving of around 48% of MVAC and 62% of lighting energy is possible for this building. The measures discussed are also applicable to other similar office buildings in Hong Kong or similar areas, especially to old buildings. Considering that a tremendous amount of electrical energy is consumed by office and other commercial buildings, any measures to reduce the energy use in these sectors will contribute significantly to the reduction of CO2 emissions and preservation of the environment.

Chlorination has made water supply safe from bacteria and viruses, and has almost completely eliminated risks of waterborne diseases such as typhoid fever, cholera and dysentery. However, the health benefit of chlorination has introduced some possible risks from the by-products of the disinfection process. In this work, we propose the use of a learning algorithm ‘support vector machine (SVM)’ for the prediction of the water pollutant total exposure index (TEI) and cumulative risk (CR). The SVM is an artificial intelligent approach that could capture the input/output mapping from the given data. Support vector machines were developed based on the structural risk minimization principle from statistical learning theory in which the empirical risk and the machine complexity are optimized. The proposed algorithm was validated by using statistical measurements on experimental data different from those used for the training (out-of-sample testing) to evaluate the algorithm's ‘generalizability’. The experimental results demonstrate a good correlation between the SVM predicted values and those conventionally estimated using probabilistic (Monte Carlo) simulations. In both cases, the TEI and CR values were estimated using a set of data extracted from the national database IRIS. The SVM, once trained with representative data, could be used easily and effectively in comparison with other conventional estimation methods of TEI and CR.