Decision making within the Civic Data Science framework

TLDR: My observations on why decision making needs the same “big” treatment as data or devices. I refer to past decisions that cost NYC dearly that can be used as learning opportunities. I also include some pedantic descriptions of decision making from academia and bring it back to relevant examples of how technology and decision making come together well. I added pictures to correct for eye-glazing effect.

At ARGO we are building towards a sustainable framework to better scope, understand and eventually deliver solutions for urban problems. So far we have “not disagreed” on the Data Discovery, Data Analysis and Data Integration buckets to define distinct data tasks that fit into a larger normative structure of Device, Data & Decisions framework to encompass the civic data science framework. Together along with rapid prototyping we offer a comprehensive and flexible lens towards a digitally native service delivery model.

Device, Data & Decisions Image credits: from the Noun Project Router by Yorlmar Campos ; Export Database by Arthur Shlain ; strategy by Gregor Črešnar ; cube by Ates Evren Aydinel;
Device, Data & Decisions Image credits: from the Noun Project Router by Yorlmar Campos ; Export Database by Arthur Shlain ; strategy by Gregor Črešnar ; cube by Ates Evren Aydinel

Our process is a result of many discussions amongst ourselves and others ranging from the epistemological to the semantic. The overarching mission at ARGO is to partner with city agencies and local governments to help them make qualitatively better decisions about delivering services better. “Better”, however, is a loaded term.

In 2009, “Better” meant spending $549,000,000 to develop a citywide wifi network that turned out to be obsolete in 5 years.

In 2013, when Hurricane Sandy hit, “Better” meant spending billions in disaster response that was sometimes dysfunctional.

These were well-intentioned and understandably debatable decisions that were not the best use of public $$$ but as we are moving head first into the digital age where policy making today is more than ever reliant on data, these errors of the past are also immense learning opportunities. While tools to “grab the damn data” are evolving at breakneck speed, we need to consider whether our abilities to make actionable decisions are also evolving instep. This is often not the case and also not amongst the typical data science skill set.

The decision maker (often not data savvy) ends up swimming/drowning in data and left with inadequate tools to convert the <<<insert awesome predictive analysis using ridiculous amounts of data but woefully difficult to replicate or communicate >>> into decisions to move the proverbial needle on said policy intervention.

Created using
Created using

Whenever I sit in a room with “data scientists” or “data-{dashes}”, I often wonder how they define terms such as “Algorithm”, “Big data” & “Urban Science”. I can argue that if asked, their definitions of the term would form the basis of inherent biases that could very well lead us down the path of the afore-mentioned billion $$$ errors. I often question my own definitions of these terms as they are heavily contextual. ( Disclosure: I spent some time supporting Algorithmic Trading systems at a big bank )

As a Master’s student at Penn State’s IST program, I researched decision making within crisis management. This included the study of Computer Supported Collaborative Work (CSCW), Human Computer Interaction (HCI) and Human Factors (ergonomic design). I ended up writing my thesis on a theory of team cognition called Transactive memory that seeks to better understand group behavior based on the processes by which individual members of a group makes sense of incoming information. Most of the work dealt with developing a theoretical model to better situate crisis responders to organize incoming information so that they can make effective decisions on the field.

The transactive memory command center is the application of Daniel Wegner's Transactive memory theory to an information environment where decisions are facilitated by individuals who have specific information roles to organize incoming data. This was presented along with a research colleague at a 2008  Department of Homeland Security University network summit focused on catastrophes and complex systems
The transactive memory command center is the application of Daniel Wegner’s Transactive memory theory to an information environment where decisions are facilitated by individuals who have specific information roles to organize incoming data. This was presented along with a research colleague at a 2008 Department of Homeland Security University network summit focused on catastrophes and complex systems

A big takeaway from this study was my affinity to the Common Operational Picture — a concept that is heavily used in the military for command & control in a distributed command structure that I find to be immensely useful out of the military context, underutilized in data intensive environments and could be useful in the civic space. As I self plagiarize from my thesis:

Working groups solving problems together often need to achieve a common consensus on the important elements of the problem. This common understanding is necessary so that decision-making for an evolving and complex situation can be effectively enabled if knowledge about the situation is aggregated onto a common space for all the decision-makers to make use of collectively. The centralization of information that facilitates such convergent processes is referred to as the common operational picture (COP).
A COP is first and foremost a visual representation; it is a structurally emergent artifact that visually illustrates the relevant information characterizing the situation. (USJFCOM, 2008). A COP is most useful when multiple groups operating under a multi-level organizational structure require quickly accessible and actionable knowledge to rapidly make decisions.
Design and Development of a Transactive memory prototype for geo-collaborative crisis management, Adibhatla, V, Master’s Thesis, 2008, Penn State University

Anthony Townsend, in his book, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia refers to a similar concept ; Topsight as described by David Gelernter in his prophetic & seminal book: Mirror Worlds: or the Day Software Puts the Universe in a Shoebox.

Some gems from Mirror World’s on topsight:

Topsight is what comes from a far-overhead vantage point, from a bird’s eye view that reveals the whole — the big picture; how the parts fit together.
It’s easy to organize a data-gathering project, and you can count on a rush of neo-Victorian curatorial satisfaction as your collection grows. But analyzing data requires at least a measure of topsight, and topsight is a rare commodity
The desire for the ultimate topsight. Rio Operations Center, 2012. [IBM]
Mission Control Center, Houston, 1965. [NASA]. These images were taken from Mission Control: A History of the Urban Dashboard (Mattern, Shannon. “History of the Urban Dashboard.” Places Journal (2015)).

Townsend makes the argument that this need to gain ultimate “topsight” in a city is what drives Rio de janeiro & IBM to build massive top-down surveillance systems using Billions of $$$. These systems eventually yield something similar to an “Informatics of domination” situation originally articulated in Donna Haraway’s’ Cyborg Manifesto and referred to in “Critiquing Big Data: Politics, Ethics, Epistemology”. This is an unfortunate outcome that is reminiscent of a Robert Moses approach to constructing digital infrastructures for civic applications.

The Common Operational Picture although originating from the military where the domination narrative is not only implied but required, I argue, can be effectively repurposed for a less grandiose, localized and practical approach to making day-to-day operational decisions in city agencies.

The Department of Sanitation’s Bladerunner platform is a superb example of how a Common Operational Picture looks like in a city operations setting. The platform uses data from the GPS devices used on DSNY vehicles that transmit data using a “cellular network” (curios to know if NYCWin is used here) and then feeds into a flexible UI (the Common Operational Picture) that can be manipulated to allow DSNY managers locate and group DSNY vehicles in real-time by distinct functions (Plowing, Salting, Collection, Supervision) and attain an innocuous yet extremely usable topsight. Bladerunner too costed several million $$ to implement but I’d bet that without it DSNY managers would find themselves operationally crippled (feel free to call me out on this)

DSNY’s Bladerunner platform, a Common Operation Picture for DSNY managers

Finally, we designed SQUID to follow the same principles of decision making. Providing a common operational picture on street quality, we hope, would optimize the $1,400,000,000 ($1.4 Billion) budgeted for NYC street resurfacing over the next 10 years. (Ten-Year Capital Strategy, Fiscal Years 2016–2025, The City of New York, Pg 22) . A 1% savings as a result of better decision making around street resurfacing projects would more than pay for the SQUID program not only in NYC but more so in small to medium sized cities where their street paving dollars are limited.

I leave you with some maps that we made for NYU’s GIS DAY 2015 that aggregate the sensor data from SQUID to the Neighborhood Tabulation Areas (NTAs) as provided by the Department of City Planning. We intend to follow similar design principles used in Bladerunner to develop an effective Common Operational Picture using SQUID data.