How do you measure the 'soul' of a city? As an architect and
urban designer, I've often struggled with conveying the subtle, intangible
benefits of urban design. Elements like "quality of space" or
"sense of place" are central to creating vibrant urban environments,
yet they resist easy quantification. In our increasingly data-driven world,
this presents a challenge. Numbers are the language of policymakers and
stakeholders, but how do we translate the human experience of a city into data?
After sharing on LinkedIn that I completed the
"Econometrics: Methods and Applications" course from Erasmus
University Rotterdam, several friends reached out, curious about how
econometrics fits into my work. This blog is a response to them and anyone
interested in the intersection of design and data.
Aspects like improved pedestrian connectivity, enhanced
social interactions, etc are central to urban placemaking but don't easily fit
into spreadsheets or reports. This led me to wonder: how do economists manage
to quantify complex, intangible concepts and gain widespread acceptance? The
answer I found was econometrics.
Determined to bridge the gap between the qualitative aspects
of urban design and quantitative data, I enrolled in that econometrics course.
Initially, I wasn't sure how mathematical models would fit into my work. The
world of equations, regressions, and time series felt daunting—far more
math-heavy than anything I'd tackled before. But as I progressed, I discovered
how incredibly rewarding and applicable these tools could be.
The course covered everything from simple and multiple
regression analysis to hypothesis testing, model diagnostics, time series
analysis, and panel data techniques. The hands-on approach of the course meant
I got to use statistical software to estimate models, interpret regression
results, and tackle common issues like multicollinearity and
heteroskedasticity. This journey not only sharpened my analytical skills but
also empowered me to critically evaluate empirical studies, carry out my own
econometric analyses, and apply quantitative methods to assess real-world problems.
Ordinary Least Squares method: This is one of the most
fundamental tools in econometrics. It’s used for estimating the relationships
between variables. Say, you want to understand how the size of a public square
impacts the number of people using it. Starting with Simple Regression models,
one can work up to Multiple Regressions and that helps you quantify that
relationship by looking at the data you’ve collected and drawing a “best fit”
line through it. It can also look into how things like increased pedestrian
connectivity can improve the vitality of a space.
For instance, one project involved analysing a dataset on
housing prices to determine what factors most significantly affect property
values. Using multiple regression analysis, I examined variables like location,
square footage, number of bedrooms, and proximity to amenities. This exercise
required cleaning the data, selecting the appropriate model, and interpreting
the results to draw meaningful conclusions.
Binary Outcome Evaluation: Binary outcome models fascinated
me, especially their potential to answer yes/no questions. While
commonly used in finance to predict consumer behaviour, I see potential
applications in urban design, like determining whether introducing informal
street vendors increases foot traffic and economic activity.
In the context of urban design, binary outcomes can extend
to various factors like determining whether better street lighting influences
people’s feeling of safety, or if incorporating more green space encourages
more public usage. It turns subjective feelings into tangible outcomes that you
can work with, and this changes how you approach the design itself. By generating
Odds-ratio and Maximum Likelihood, one can better understand outcome probabilities.
Time Series: Time series analysis was another monster
altogether. Slightly steeper learning curve too. It allows to account for
change over time. The training was for forecasting Industrial Production based
on a Composite Leading Index. Running tests to determine Stationarity,
transforming the data to achieve Stationarity, trying out AR and ADL models (that’s
Auto Regressive and Augmented Dickey Fuller for those who are curious enough!),
checking for Granger Causality, to finally predicting the forecasts, and
further evaluating both models to select the better one.
Cities are dynamic, and people's behaviours shift over time.
This tool allows one to track changes. This could include pedestrian movement
patterns, traffic congestion, or changes in land use. Further, with remote sensing
data of a place spread over time can be an interesting potential application.
One application using time series to analyse how different
design interventions affect urban flow over the long term. It could also provide
clues on user charges to be levied for financial feasibility assessment, or may
be use data from sensors on movement patterns and create predicted models. With time series, it is possible to analyse
patterns that emerge slowly, providing solid evidence for or against certain
design interventions.
The Role of R software: Learning R, an open-source statistical
software, was like learning a new language. It wasn't plug-and-play, but the
power it offers is unmatched. I remember spending hours troubleshooting code,
but the satisfaction of seeing my data visualizations come to life was worth
every minute. R allows you to run sophisticated econometric models, visualize
data, and make predictions all in one place. I used the R Studio version.
What surprised me was how quickly I started to see
connections between the mathematical models and the real-world urban issues. Whether
you're an architect, engineer, or even a social scientist, the ability to
handle data in R is becoming essential. Trust me, if I can learn it, so can
you.
I've realized that quantitative analysis doesn't replace the
creativity or intuition inherent in design—it enhances it. By justifying
decisions with data, we can better communicate the value of our work to
stakeholders who rely on numbers, like city planners or policymakers. It
strengthens our position, ensuring that design-driven decisions aren't easily
swayed by subjective opinions.
So basically, econometrics gives one the tools to talk about
urban design in a language that everyone understands—numbers. It doesn’t
diminish the beauty or the human aspect of the work we do as architects or
urban designers. If anything, it strengthens it. We’re now able to back up our
claims with data, making it harder for subjective opinions to sway what should
be objective, design-driven decisions.
Learning Econometrics has taught me that the gap between
subjective design quality and quantitative analysis isn't as wide as I once
thought. With the right tools, it's possible to bridge that gap, giving urban
design the rigor and respect it deserves in data-driven discussions.
Now, I'm venturing into learning GIS (Geographic
Information Systems) software, and I find that my newfound data skills are
easing this journey. I'm excited to combine econometrics and GIS to further
explore urban design from a data-driven perspective. GIS allows for spatial
analysis, which, when combined with econometric models, can provide deeper
insights into urban patterns and relationships.
