The Big Data revolution: How data-driven design is transforming project planning
There are literally hundreds of applications for
deep analytics in planning and design projects. We profile some early
successful applications.
David Barista, Editor-in-Chief
Sasaki
Associates used detailed network visualizations like this traffic flow
pattern of the College Hill neighborhood in Providence, R.I., to advise
Brown University officials on strengthening the school’s presence in the
nearby Jewelry District.
This article is part of BD+C's special five-part Technology Report 2014: Top tech tools and trends for AEC professionals.
Gregory Janks feels a bit like an impostor at his firm. A
self-proclaimed “numbers geek” with a PhD in mathematics and degrees in
science and economic science, Janks relies on a much different process
for developing and vetting planning and design strategies than the
tried-and-true approaches utilized by many of his peers at Sasaki
Associates (
www.sasaki.com).
His rigorous, data-driven methods have opened the eyes of many at the
design table, and have helped the firm’s clients—primarily higher
education and healthcare institutions—tackle some excruciatingly
difficult capital planning questions, most notably: What should we build
next? And where?
“There’s a facilities arms race happening in higher education and
other sectors, where the feeling is, ‘More is better,’” says Janks,
Principal and Director of Sasaki Strategies, a group formed in 2005 to
bring a strong analytical function to the firm’s planning and design
work. “What we’re trying to promote is more isn’t necessarily better;
better is better. We’re using a more objective, somewhat scientific
approach to help our clients indentify the projects that will add the
most value. Instead of asking, ‘What do you think you need?,’ we’re able
to help them prioritize their needs.”
Data analytics, for example, helped the Sasaki team make a strong
case to Brown University leaders that the school’s plan to move its
engineering department off campus to an innovation district nearby would
be detrimental, both financially and strategically, to the Ivy League
institution.
4 ways Building Teams benefit from data-driven design
1. Enhance iterative design. Designers are able to
capture and analyze key building performance metrics, such as energy use
intensity, during conceptual design. That information can then be used
to tweak and optimize early prototypes.
2. Use project data on future work. By collecting data on every project, design firms can eliminate rework and apply best practices on future projects.
3. Understand how people interect with spaces. Firms can test and evaluate design concepts against the real world, using feedback data from building occupants.
4. Automate the planning process. Some firms are applying algorithm-based approaches to improve the traditional project planning process.
The firm came to this conclusion after analyzing numerous
datasets—from course enrollment numbers to faculty collaboration
patterns to university financial data—and then creating a series of
network visualizations to help “tell the story” to school officials,
says Janks. Data collection methods included custom mobile surveys to
faculty and real-time, crowdsourced feedback on how students and faculty
use the campus via Sasaki’s Web-based interactive mapping program,
MyCampus.
“The results were dramatic,” he says. “For example, it showed that
there’s a wonderful intermixing of how students behave in course
enrollment, including engineering. It’s inseparable. You can’t pull this
core network out without greatly affecting the students, faculty
collaboration, and ultimately research dollars. The data helped us
convince them to go in another direction.”
Make way for the rise of data-driven design
Janks may be in the minority at his firm, but he’s among a growing
number of data analysis and software programming experts to make their
way into the AEC field in recent years. A number of BIM and technology
consultancies have popped up, as well, to meet the growing demand for
data expertise.
Firms like CASE Design Inc. (
http://case-inc.com) and Terabuild (
www.terabuild.com) are making their living at the intersection where data meets design. Others, like San Bruno, Calif.-based Aditazz (
www.aditazz.com), were built from the ground up as data-centric design and planning firms. It’s Silicon Valley meets the AEC field.
“Using data in the AEC industry is not new. The built environment has
long been an abundant source of data,” says Randy Deutsch, AIA, LEED
AP, Associate Professor in the School of Architecture at the University
of Illinois at Urbana-Champaign. “What is new is the amount of data that
is available to us, our capacity to measure and ability to capture,
process, and act on that data, and, frankly, our industry’s urgent need
to do so.”
Arup used a variety of data collection methods, including mobile
surveys, security camera footage, and traffic flow reports, to better
unify two neighborhoods in Pittsburgh: North Side and Oakland. The
resulting scheme incorporates improved wayfinding, public artwork, open
spaces, and interactive components like real-time public transportation
and weather information and pay-by-phone bike sharing.
Deutsch, who is currently writing a book on Big Data applications for
the AEC field, says the data boom represents an opportunity to
completely transform how firms design, construct, and operate buildings.
But getting there means they must overcome some significant barriers,
namely interoperability, reliability of the data, privacy, and security.
There’s also the trust factor. Data-driven design, for instance, may
require the client to open its books or allow greater access to its
employees or end users for feedback.
“The use of Big Data in decision making in design involves securing a
commitment within teams and the organization, reinventing internal and
external processes, and modifying organizational behavior,” says
Deutsch. “The AEC industry is among the last to address these
challenges. We need to catch up, and quickly.”
Lessons from early data-driven application
There are literally hundreds of applications for deep analytics in
planning and design projects, not to mention the many benefits for
construction teams, building owners, and facility managers. For the
purposes of this report, we’re focusing on data-driven design and
planning applications only. Here are some early successful applications,
according to the experts interviewed for this report:
Enhance iterative design. By condensing the feedback
loop on conceptual design schemes from days (or even weeks) to just
hours, design teams can tweak and optimize early prototypes based on the
simulated performance and characteristics of the design. Software
providers like Sefaira and Autodesk offer off-the-self solutions for
on-the-fly energy modeling (see page 34), but some firms are taking the
iterative design process to new levels with customized solutions.
Sasaki Associates is among a number of design firms to develop
custom survey and interactive mapping tools to better understand how
building occupants and end users interact with the built environment.
Using the firm’s MyCampus and MyBuilding (pictured) mapping tools,
Sasaki designers are able to collect real-time information on everything
from where people hang out on campus to what they like most and least
about a building.
The Chicago office of RTKL recently commissioned CASE Design to
develop a software program that captures dozens of key project
metrics—including total building area, floor area ratio, area based on
project program requirements, percent of green space on the site,
percent of green roof area, and façade area—straight from the 3D
conceptual design programs RTKL’s designers regularly use (SketchUp and
Rhino). The program will even run a solar analysis of early conceptual
designs, providing feedback on shading requirements and daylighting
performance. The early stage design data is fed automatically into an
Excel document and shared with the client and design team in real time
using charts and dashboards.
“Instead of using building information as a capture of the final
design, it’s fed back into the early design process to help the team
make decisions that will ideally lead to a better building downstream,”
says Nathan Miller, Associate Partner and Director of Architecture and
Engineering Solutions with CASE. “The program automates what typically
is a manual process—capturing building information from conceptual
models—which allows the team to adjust and tweak the design on the fly.”
Capture project data for future use. The data
feedback loop extends beyond the project, as well. Firms like Skidmore,
Owings & Merrill are creating databases of past project information
and geometries for use on future work. If a particular design component
worked well on a project, the firm will be able to share that
information for use on other designs. This approach also minimizes
rework, allowing designers to focus their time on solving new
challenges.
Understand how people interact with spaces. Design
teams are tapping into a host of data sources—from Twitter feeds to
mobile surveys to security camera footage—to observe how people use and
move through spaces. Often, they discover that design intent does not
match reality.
“In theory, using the formulas and benchmarks that most standard
building programming exercises depend on, you’ll be able to predict
exactly what people need,” says Sasaki’s Janks. “But when we test
designs against the real world, we find that often there is zero
relationship between the two.”
Silicon Valley startup Aditazz used a largely algorithm-based
process to develop its winning design scheme for Kaiser Permanente’s
Small Hospital, Big Idea competition (the firm tied for first place). By
supplementing tried-and-true design and planning methods with
computer-driven processes, the firm was able to quickly develop and test
thousands of design schemes to find the best solution.
Janks offers a dramatic example: During the initial planning stages
for a renovation project at Harvard University’s John F. Kennedy School
of Government, the Sasaki team used its MyCampus interactive mapping
program to poll the students, faculty, and staff on what they liked and
didn’t like about the campus. They also tracked their movement
throughout the spaces. Among the findings: hardly anyone entered the
building through the front door.
How Big Data will improve urban planning
Last November, Arup and the Royal Institute of British Architects released a joint report, Designing with Data: Shaping our Future Cities,
which explores the many potential uses and benefits of Big Data
analytics in an urban environment. The report highlights four key
applications:
1. Designing for people. By collecting real-time
data (via social media, mobile surveys, video cameras, sensors, etc.) on
how people use public spaces and infrastructure, design teams and
cities will have a better understanding of user needs and can create
spaces that better meet those needs.
2. Experimentation. Data and modeling tools could
allow designers and planners to save time and potentially money by
testing designs before they enter the construction process. This could
also help identify likely objections and model solutions, saving time in
the planning process.
3. Improve policy implementation. Cities have the
potential to use the vast amounts of data they hold to improve the
planning and delivery of services to citizens, by using the data to
identify and address urban problems.
4. Transparency. By making more data publicly
available, cities and governments can make it easier for designers and
planners to get critical information on development sites faster.
“The map showed this torrent of people coming in through the back
door, and absolutely nobody going through the front door,” says Janks.
“It’s the kind of thing that previously we’d argue back and forth with
the client about. But when we showed them the map, the entire
conversation changed instantly. As a result, the campus plan was
completely reorganized to what was the back side of the building.”
Automate the planning process. Could a computer one
day replace a human planning team? Not likely. But it could take on a
large portion of the traditional design planning tasks on projects,
according to Deepak Aatresh, Founder of Silicon Valley planning and
design startup Aditazz.
An entrepreneur with a background in silicon chip design and
manufacturing, Aatresh is bringing his analytical approach for
designing, testing, and building microchips to the healthcare design
world. The firm, which launched in 2009, applies a series of
algorithm-based processes, virtual simulations, and other digital
planning tools to design healthcare facilities based on the hundreds of
programmatic and operations inputs from the client, as well as outside
factors such as building code requirements and green standards.
The goal is to develop the optimal design solution as quickly as
possible by relying on computers to do what humans can’t: perform
millions of complex calculations in fractions of a second. For instance,
by applying automatic space-planning tools on projects, Aditazz
designers can test thousands of iterations of building configurations
and layouts in seconds.
The firm’s process kicks out thousands of design options based on
client input, and then quickly narrows the list down to a few ideal
solutions. Aatresh argues that as much as 80% of the typical decision
making that goes into creating a healthcare facility—from building
orientation and space layout to patient wait times and traffic flow—can
be handled through computation.
Aditazz surprised many in the architectural community in March 2012,
when its largely algorithm-driven design scheme tied for first place in
Kaiser Permanente’s Small Hospital, Big Idea competition, beating out
more than 100 entrants. The firm has gone on to win numerous commissions
for healthcare work, most recently a cancer hospital project at Shantou
University Medical College in Guangdong, China.
The data-driven future
Our report highlights just a few potential applications for
data-driven design. Many more uses will be discovered, our experts
argue, when the industry reaches a tipping point where the majority of
project stakeholders—from AEC firms to building owners to government
agencies—get serious about analytics for the design and planning of
building projects.
