What are the design variables that affect the sampling of information? Sampling is a term borrowed from electronic music and refers to the practice of using small loops of music to generate a composition.8 Considering this stage of the process in terms of a range of possibilities makes it explicit that input specifi cation is a design variable. Specifi cation determines what information is to be sampled and, as such, excludes or includes opportunities. The scan of contemporary practice revealed the sampling of either environmental control data or socio-cultural sources. Is there any advantage to be gained from mixing data types? In the context of kinetic facades, most approaches to data sampling are tuned to particular uses, predominantly that of environmental control. It would seem self-evident that data sampling will depend on the objec- tives of the design. Typically, in an environmental context a wind gauge would not be used for temperature control, while motion sensors, for example, are regularly used for media facades but seldom considered for environmental facades. However, multiple data sources can be combined to give a more accurate representation of either environmental or socio-cultural context. For example, combining information on wind and temperature allows incorporation of windchill factor. While for some climates, the percentage humidity is as important as temperature. The range of potential samples could also include motion sensors to determine occupancy, add- ing to the range of variables to be processed by the control system. In terms of socio-cultural sampling, the majority of examples occur in media facades. There is an extensive range of precedent for devices developed for interactive media, which sample data from both local and remote sources. Architectural facades have a long tradition as a form of communication and there exists an opportunity to utilize the
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precedent of such installations to revitalize the agenda of facades in a world of digital information.9
How would sampling infl uence kinetic pattern? From the above discus- sion, arguably one key variable would be the range of data sources sampled. Take, for example, a kinetic screening system that is designed to moderate direct sunlight on a facade. Light sensors that monitor the presence of direct sunlight could be installed. If this was the only data sampled and the shades were repositioned to allow particu- lar views out when not needed, the resulting pattern of movement would be a map of daily cloud activity and sun position. There would be a regular daily pattern, based on position of sun and the screening of adjacent urban or natural form, interspersed with periods of cloud cover. This daily pattern would gradually shift over the course of the year. However, if other data were sampled, such as allowing users to override the system for an individual location, this pattern would be interspersed with isolated and generally unpredictable events. Another scenario might be to utilize the screen- ing system in its ‘down time’ – cloudy days or at night – as a low-resolution tangible interface. For example, graphic forms could be embedded that communicated an aspect of local interest, or a programme of artworks could be commissioned.10 The added value of using such multiple data sources is outside the scope of this research, but clearly multiple data sources potentially infl uence the complexity of kinetic pat- tern formation. It is argued that data source is a key variable. However, rather than use a distinction between environmental and socio-cultural source, it is proposed to use the generic distinction between quantitative and qualitative data. This more robust terminology allows the incorporation of qualitative input into environmental systems and vice versa.
A second aspect that could be considered in terms of articulating data type could be the distinction between discrete and continuous data.11 We could anticipate that, if the type of data were highly discrete, the pattern formation would be less ‘smooth’ than continuous data. This would be highly signifi cant, if the sam- pled data was directly interfaced with the tectonic output, by-passing the control plane. However, as evidenced by the survey of contemporary practice, this is not typical. For the purposes of this study of morphology, a more generally infl uential aspect of data sampling would be more appropriate. Beyond type of data, it would be reasonable to consider density of data sources. Arguably, the quantity and spatial distribution of samples would have a more consistent and direct impact on pattern formation. Referring to the previous hypothetical example of a sunscreening system, it can be posited that the number of sensors across a facade will affect the resolution of the outcome. The greater the number of sensors, the more fi ne-grained the sam- pled information and, potentially, the more complex pattern formation would result. In summary, it is proposed that the two broad continuums that have a consistent infl uence of pattern formation are data source, with a range between quantitative and qualitative sources, and density of data sources, with a range between a sparse and a dense number of sampling devices.