Urban Data and What to Do With It

Business Opening Hours of Lower Manhattan from Google Maps, KPF Urban Interface

Data, Spatial Data, & GIS

Data is organized, structured information. It allows us to compare objects and information of the same or similar phenomena/category/object, study trends, and predict future outcomes.

Data Science is the practice of using scientific methods, processes, and algorithms to process, study, and extract actionable insights from data.

Spatial Data is data that contains location information, typically in coordinates relative to the Earth. Spatial data provides a new level of insight as one can begin to extract spatial relationships between abstract data points.

Spatial Data = what’s happening+ where it’s happening

Relationships between information + location are immensely valuable to create a deeper understanding of the physical world. Using the often cited John Snow’s map of Cholera, it is clear that we gain deeper insights when we contextualize information in physical space.

John Snow, Map of cholera cases in Soho, London, 1854

The practice of organizing and analyzing spatial data is referred to as “Geospatial Information Systems” (GIS). Today, People use spatial data daily, often without consciously recognizing it as spatial data. With the advancement of technology, we now have access to tons of spatial data at our fingertips through web maps — from something as simple as looking for a restaurant on Google maps, to tracking COVID cases across the world.

(Left): Business Location Data from Google Maps | (Right): Web map of the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU).

Urban Data

Urban data belongs to a subset of the many different types of datasets in the world. Urban data can encompass both spatial and non-spatial (tabular) datasets.

• Institutional: data that is maintained by government agencies, scientific organizations, and research institutions. (City Planning, Transportation, Public Infrastructure, NASA, FEMA)

(Left): Population Change by County from 2010–2020, Census 2020 | (Right): New York City Facilities Database, Department of City Planning

• Private Entities: data that is owned and generated by private companies, typically related to their products and users. (Google Maps, Yelp, Uber, Airbnb, etc.)

(Left): Uber Trip Data Visualization | (Right): Location Data Platform, Radar.io

• Communities: crowd-sourced datasets managed by communities with common interests and goals. (Open Street Map, StreetCred, Citizen)

(Left): Map View & Interface, Open Street Map | (Right): Query Feature Mode, Open Street Map

Data + Design

So how does all this relate to design? It’s clear that urban analytics are huge drivers of planning, economic, and social decisions, it should be without doubt that urban data should also inform the placemaking and city-building decisions.

Data-Driven Design → Evidence-Based Design

The term data-driven design has been used often and has grown to encompass many things. What I wish to focus on in this class is placing a larger emphasis on the reason why we should learn and use data-driven design. The term Evidence-Based Medicine describes the practice of using the best scientific evidence available from research to help make decisions of care on individual patients. I think it is interesting to draw a parallel between medicine and architecture — no patient is the same, just as no design is. However, I believe that designers lack the rigor in incorporating design research. That’s why in this course I am going to suggest the term Evidence-Based Design as the mission as we create analysis and simulations throughout the course.

Data science has boiled this learning process into the Data-Information-Knowledge-Wisdom (or DIKW) pyramid

DIKW Pyramid, Matt Triano

• Data: observations or measurements of phenomena in the natural world.
• Information: data that has been structured to show a pattern or framed in a context relevant to people.
• Knowledge: information that has been organized and interpreted so that people can act on the information.
• Wisdom: knowing when and how to act on that knowledge to achieve desired goals.

Translating this methodology into the materials covered in this course, it’ll look like something like this:

Modified DIKW pyramid for Data Mining the City

Data Action

In Professor Sarah Williams’s book Data Action, she illustrated three calls to action: Build it! Hack it! Share it!. She also urged practitioners and designers to use data ethically and responsibly — that data should be for the public good. We should think critically about how we use data to design. Today, there are many tools and technology that help us interpret data.

It is also important to recognize that data is never “raw”, it is collected and often biased. As we work with various city datasets, it is especially important to recognize where information comes from and the implicit/explicit bias that comes with them. The goal of this course is to ensure that everyone is familiar with accessing the resources and concepts around urban datasets and having the skills to leverage these processes to formulate one’s insight and action through data.