By Varis Niwatsakul - 15th April, 2018
By Carolyn A. Schultz - 15th April, 2018
There are so many ways to collect and analyze data--especially when architects and urban designers are creating the next generation of smart, digitally enabled buildings and cities. An IoT (Internet of Things) is, quite literally, on our doorstep.
But is there a right way to utilize this? How do we bridge the different methods and decide what’s best? Recommendations from experts in a range of fields were shared at this sold out discussion organized by the AIANY Social Science and Architecture Committee.
Committee co-chair and moderator, Melissa Marsh, Founder and Executive Director of PLASTARC, introduced why mixed methods and a variety of perspectives are so important: because understanding the relationship between social science data and architecture offers an enormous potential for influencing our future environment. With vast technological tools and analytical capabilities available to help interpret interactions of people, buildings, and spaces, the topic is particularly timely.
An important theme discussed is that there’s no single "right answer" or unilateral approach--but we get the most accurate and complete understanding when there is diversity of data collectors, along with diversity of methods used to research, analyze, and make recommendations based on that data.
The panelists demonstrated the importance of bridging the WHAT (big data/seeing patterns) with the WHY (little data/individual stories) to build a holistic understanding of what a situation entails.
Regarding team member diversity, Daniel Davis, Director of Research at WeWork, emphasized that mixed methods require diversity of talent, as people who are good at one method may not be as good at another. Social research is not a homogeneous set of skills. His team worked to analyz the WeLive residential project with anthropological, mathematical, and other methods. Working within the diversity of social science or across the discipline of architecture and design requires building trust. This includes trusting in yourself to try different methods as well as recognizing and respecting different skills of others. The process of seeking out a variety of perspectives not only helps us better capture a comprehensive picture, but it also helps reduce individual or unintended research biases.
As an example of diverse methods implemented to find those "what" and “why” answers, the MIT Civic Data Design Lab and Director Sarah Williams’s team’s “ghost cities” project involved collecting big data from social media, and building analytical models to understand the relationships between them, and then validating findings with individual stories in the field in China. In this, Williams continued a theme from the 2015 AIANY event on data: “Leverage this available data to actively listen, understand, and effectively design for your audience.”
By starting with the little data of stories and individual observations, Gehl Institute and Executive Director Shin-pei Tsay’s team took an opposite--but complementary--research trajectory. Their work explores a search for "commonalities" of what makes great public spaces work, accumulating them through open data protocols with public participation, and building evaluation frameworks to inform and accelerate systemic changes. Combining protocols (particularly public ones) are essential to a shared future, where data can shape policy decisions benefitting the common good. “People play a role in the spaces around them,” as Tsay emphasized, and “we have found that even when there are really big differences in culture and climate, which we really want to acknowledge, there are also commonalities we can rely on about how people experience spaces.”
Williams also advised that after collecting data, "make the model and ask people" (who the model represents) what they think about it. This enables you to build a better model, and vet out any of your own biases that may have been built into that model. Also, Williams urges us to, think about voids: things you can’t measure, where data is not readily available for collection, or in some cases has been erased or removed.
Finally, Nitzan Hermon, Principal of Studio VV6, emphasized that any form of human knowledge production is a mediation between the human intellect and the tool that he/she uses to understand the world. He talked about examples from history of the past few hundred years when social data has been used to understand human behavior. Although we’ve come to rely on machines and technology as key tools for data collection, we also must realize that our own human brain is the most persistent and important tool for understanding.
During Q&A, one audience member questioned: aren’t some things obvious, so do we really need to spend time and money on complex studies and analysis? To put it another way, isn’t it obvious that people are going to form relationships most easily when they live and work near each other? As "Bubbe wisdom" would suggest and as discussed at a previous AIANY event, we can just ask our grandmothers to give us intuitive answers. However, as several panelists pointed out, in order to make the case for why data should shape designs and policies, you need the data to backup your assumptions and give tangible proof building the case to make meaningful changes.
Overall, this comprehensive discussion achieved the goal of showing why multiple perspectives are important, and will surely lead to future conversations about the importance of data for architects, social scientists, and everyone who seeks improvements to and benefits from high performance buildings and spaces. Marsh’s closing remarks, reminded the audience that this evidence, which can be assembled through the work of social and data science, presents a powerful tool for change and which is an essential compliment to the annual theme of AIANY President Guy Geier of Architect|Activist. This theme emphasizes architects who "use their design thinking to help solve the most challenging issues of our time."