Just Numbers

Jimmy Pan graduated from the Master in Design Studies program in 2020 and currently lives in Somerville, Massachusetts. He has recently co-established a design collective where he continues to develop the research he conducted during his time in the Risk and Resilience program, which focuses on the intersection of storytelling, design, and data.

This article was first published in The New School: Urban Systems Lab’s quarterly publication “Resilience”



There is a certain allure to numbers — clarity, coherence, control. They can be understood quickly, are used to assess aptitude, and assert confidence in decisions. In short, numbers calibrate uncertainty into momentary absolutes. Their abstracted sense of order helps explain complex issues and reduce esoteric crises into digestible approximations, but we must not forget, numbers are also interpretations tangled in complicated dynamics of power. As Sally Engle Merry notes, in her book The Seductions of Quantification, while “quantification is seductive” it is crucial to interrogate the origins of this form of knowledge and “keep open channels of contestation and resistance to their hegemony.”

The emergence of the novel coronavirus across the world and the proliferation of unpredictable crises stemming from this pandemic has brought the world view of quantification into sharp focus. COVID tracking dashboards, death counters, case number charts, virus timelines and graphs of the daily increase or decrease in new cases attempt to convey a story of comprehension, but instead reveal both the apathy of numbers and the desensitizing global appetite for infographic culture. We live in a world of itemized narratives governed by universalizing numbers.

Where are the stories of the dead and the accounts by the recovered in these ubiquitous maps? The digestible complexity of the Johns Hopkins Dashboard of global COVID cases clearly represents the significance of numbers to modern society. The at-a-glance difference between Figure 1 and Figure 2, taken 5.5 hours apart, are 119,725 new global cases and 2,269 new global deaths. But who exactly are these people? Death tolls amalgamate but dying is not identical. How does the seemingly endless numerical ascent of occurrences and fatalities demonstrate the suffering experienced at the human scale? What do these maps of consolidation and exactitude mean?

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Figure 1. “COVID-19 Dashboard, Global Map,” John Hopkins Center for Systems Science and Engineering (CSSE), (05:28 PM EDT).

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Figure 2. “COVID-19 Dashboard, Global Map,” John Hopkins Center for Systems Science and Engineering (CSSE), (11:01 PM EDT).

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Figure 3. “COVID-19 Status Report, Kings County, New York,” John Hopkins Center for Systems Science and Engineering (CSSE)



My hometown, Kings County, ranks third on the dashboard’s chart of counties with the most numbers of deaths (as of October 16th, 2020). Excavating deeper into the trove of data I search for additional context to this ranking and eventually discover an infographic status report for the ‘county’ scale, as seen in Figure 3. My immediate unsettling anxieties about my mother, who is living in this top fatality area, are fleeting, quickly replaced with a disorientation by numbers. The sense of urgency about the real dangers of the pandemic is immediately challenged by the report’s visualization of the county case data — 5,719 deaths to 70,569 cases. An 8% death rate is alarming yet the visual cues, a small red bar compared to a large blue bar, makes it seem acceptable.

Graphics derived from tallies can impart insight about a situation quickly and reinforce understanding, but it is also important to recognize the limitations to representation. Information absent of empathic methods in knowledge creation will inevitably be contentious, fuel ideological conflict, and exacerbate disparity. Data, by itself, does not create inequality but it can be co-opted to contribute to bias. While the COVID pandemic, racial injustice, and climate change are vastly different in material terms, they are all nonetheless afflicted by lethargic data — numbers without narratives.

In our age of data richness, 33 zettabytes to be exact (measured by the IDC in 2018), didactic information is par for the course, but quantifiable knowledge cannot be the prevailing authority of the future. A 2017 Dresner Advisory Services study of Big Data, shows that the operational strategies of global, regional, and even local organizations are increasingly becoming more entangled with digital data — 17% usage of big data in 2015 to 53% adoption in 2017.

Deep caches of data are complex and grasping their effects at an individual scale is challenging. Take for example the following statistics: (1) A 2020 IHME study claims that if 95% of the U.S. population wear masks during the COVID-19 pandemic, 33,000 lives could be saved. (2) The 2019 Green New Deal claims that a rise of 2° Celsius, or more, beyond pre-industrialized levels could cause $1 trillion in damage to property and infrastructure in the future. (3) A 2017 NRDC report claims that a 3 feet of sea level rise might inundate 4.2 million homes.

How are these threats measured and evaluated? By what means, at the human scale, can we detect or perceive effective change towards escaping these threats? What information can be used to gain the confidence of those who are skeptics? When facing such monumental problems, it is expected for trust to fall upon statistics; But it is equally important to accept that projections concerning intricate problems are often abridged and numbers are not the only arbiters of the future. I do not want my America to be a mono-reality, defined only by sterile numbers on a page.

The precariousness of statistics is clearly illustrated by Rebecca C. Hetey and Jennifer L. Eberhardt’s 2018 study of inequality in the criminal justice system. Their observations revealed that when the public was presented with statistical evidence of extreme racial disparities without context, they became more supportive of policies that develop disparities, not less.

“If learning that Black men are 6 times more likely than White men to be imprisoned triggers the stereotype that Blacks are criminals and criminals are Black, then such information is no longer concerning on its face. Rather, it becomes expected or even justifiable. Motivated to maintain the status quo, people take evidence of what is (such as the overrepresentation of Blacks in prison) and justify it as how things should be.”

In the above example, the presentation of somber statistics did not provoke a reflection on racial bias or inspire inclinations to change the criminal justice system. Instead, the numbers validated bias as a ‘reality’ that should exist. In other words, numbers created facts. Numerical data and their corresponding portrayals — like indicators, as Sally Engle Merry points out — “aspire to measure the world but, in practice, create the world they are measuring.”

From a behavioral science perspective, it would seem as if society — in this current moment — is stuck in cycles of entrapment by consonant data, where the process of “action, justification, further action” will only continue to intensify partisan postulations. In addition to this definition of entrapment, Carol Tavirs and Elliot Aronson, in Mistakes Were Made (But Not by Me), cautions us about the powerful authority that confirmation bias has over knowledge creation with a reminder about self-justification: “everyone can see a hypocrite in action except the hypocrite.”

If we can accept this anthropological assertion, then we must also admit that the question — “what is a resilient and equitable vision for cities post-COVID?” — while not wrong, should not be the first question asked in the process of reimagination. The more pertinent question is ‘what kind of data should be consumed before reimagination?’.

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Figure 4. “PlaNYC: A Stronger, More Resilient New York,” Special Initiative for Rebuilding and Resiliency.


Resilience is fashionable, and the promise of being resilient is captivating. It is often used to inspire tenacity in the aftermath of disasters. In the wake of Hurricane Sandy, New York City released a report titled A Stronger, More Resilient New York, and prominently defined resilience on page two of its document as presented in Figure 4 above. In addition to the common definitions, the report appends a more spirited definition: “resilient, synonym: TOUGH, see also: New York City.” Because of its affiliations with perseverance, it is not surprising to see why the idea of resilience “has emerged as an attractive perspective with respect to cities” and their planning. It is generally understood as a term with ecological origins (C.S. Holling), and while it has only recently evolved into its socio-ecological and socio-technical contemporaries, resilience has never really escaped its interpretation as the capacity to persist when encountering disturbance.

Furthermore, the term resilience is much older than commonly understood. As shown in Alexander’s etymological research, early interpretations of resilience in fact had negative undertones. Meerow, Newell, and Stult’s meta-analysis of urban resilience further develops the complex history of resilience by cataloging its existence across many fields and revealing how it has been inconsistently defined, concluding insightfully that the most crucial facet to assess is not ‘what kind of’, but ‘how’ resilience thinking is applied.

For ‘resilience’ to be justly invoked when visioning the future, we must first scrutinize what Holling’s original definition claims must persist: “the persistence of systems and their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables.” From this we can conclude that the roles of the system’s parts are rarely reconfigured. A resilience for whom, what, when, where and why is an approach that questions both positions of power and their redistribution. It is a strategy that is slow, imprecise, messy, but necessary if we, both as a society and as individuals, want to arrive at a more equitable spatial and social future.

In Sherry R. Arnstein’s 1969 article about citizen participation, she states, “it is the redistribution of power that enables the have-not citizens… to be deliberately included in the future.” COVID-19 and other complicated urban dilemmas often fall prey to solutions of ‘tokenism’ instead of providing for real ‘citizen power.’ Resilience can therefore be understood as a mask, ‘informing’ disguised as ‘citizen control’. In order to arrive at a state of restorative justice, we must first disband universalized accepted truths, ‘fast data’, and reincorporate ‘slow data.’

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Figure 5. “Social Distancing Scoreboard,” Unacast COVID-19 toolkit.


‘Fast’ data is the ‘at-a-glance’ data. It is the numerical knowledge prevalent in crises which manifest as complex toolkits and abstract grading systems (Figure 5) that introduce aspects of competition and gamification. They claim to exist in ‘real-time’, but in practice are always trailing the present. This attitude of data analysis has become consumed by a game of comparative binaries, ‘more’ or ‘less’, ‘better’ or worst’, ‘truths’ or ‘lies’, ‘left’ or ‘right’, ‘facts’ or ‘fake news’, and we, the individuals living through it, have become trapped in a spectrum between fear and negligence.

Questions like ‘what data can we trust?’ entrench us further into dichotomous habits of validation or doubt. In a Harvard Business Review article at the height of the pandemic with this exact question — Which Covid-19 Data Can You Trust? — two statements stand out: “models are produced and presented without appropriate expertise” and “we are facing a deluge of data and interpretation from the wrong kinds of experts, resulting in a high noise-to-signal ratio.”

Models, I would contend, are also absent of appropriate levels of compassion. In order to construct better models, we need a higher level of anecdotal ‘noise’ from the afflicted to support the ‘signals’ of interpretation by the so-called experts. Where science struggles to make meaning, design thinking must lead, by deploying artistic and ethnographic methodologies to create images of the world that emphasize the importance of intimate storytelling.

The first step must be the choice to compose humanized data, the ‘slow’ data of autobiographical nature — thorough records about the experiences of suffering and survivor stories, not merely obituaries of names and their professions. What an individual has accomplished is who they are, but how they suffered is not always revealed as a part of their existence — a world filled with sanitized identity. The media is far too often obsessed with eulogy.[1]

What the pandemic stricken public needs are alternative methods of data organization, led by resident leaders of local communities. What should we make of informal forum threads such as Reddit’s “COVID-19 positive” subredditOr other stories of survival? Where are the datasets about the intimacy of death? Are these non-scientific stories the so-called noise that is vexing data aggregation and expert assessments? Do not be mistaken, science is paramount in our present moment. We must find a vaccine for society to move past the pandemic, but the media and other forms of expression must take caution to learn from visceral stories beginning in trauma and avoid predominantly publishing sensationalized stories of loss or designing bountifully hopeful futures.

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Figure 6. “Hot Spots Map,” The New York Times.


Solutions for the future must be pre-design and employ tools of design thinking, like deep observation, to find unconventional realities. The above map, from the New York Times, represents the current hot spots and development of the pandemic, yet the warning of danger feels camouflaged by the beauty of graphics. Sometimes data does not need to be beautified to be beautiful. Authenticity can be achromatic and hauntingly meaningful. On Sunday, May 24, 2020 the New York Times printed an atypical representation of death titled “U.S Deaths Near 100,000, An Incalculable Loss” (Figure 7) which utilized the space of the page to convey, arguably better, the gravity of the situation.

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Figure 7. “U.S. Deaths Near 100,000, An Incalculable Loss,” The New York Times.


‘Predictions’ about the future, especially about complex situations like COVID-19, racial justice, or climate change will inevitably influence large swaths of society. Real lives are at stake, but ‘realness’ is subjective and multifarious. If an individual has not been personally affected by the pandemic, racism, or climate stress, their ‘truth’ may in fact be that these threats do not exist, at least not yet. If we — designers, planners, and policy makers — can be critical about how cities are designed and how policies governing society are defined, then we must be able to examine the questions we ask when trying to envision esoteric and entangled futures as well as the statistics those questions construct.

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Figure 8. “Covid-19-data,” The New York Times (disaggregated) 

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Figure 9. “Tested Positive (iteration 2),” Aggregated data from select posts on Covid-19Positive subreddit.


My quasi call-to-action for the designers, fact-checkers, and artists in the digital domain: find ways to subvert vapid data and ask more intimate questions, learning from such individuals like Giorgia Lupi and her reimagination of U.S. Census questions. My quasi-proposal to the current power-holders of society: consider the meaning of “A Ladder of Citizen Participation”, find the “have-not citizens”, and shadow them to experience their struggles. My quasi-petition to the ‘public’: find people with dissenting opinions from yours, acknowledge their predilections, and exchange vulnerable stories, not obstinate facts. Facts can be discredited by accusations of ‘fake news’ just as numbers can be rejected as fraudulent. As we in the US viscerally understand, numbers (in this case popularity) can lose elections.

We can vote and elect all the officials we want, but empty promises of ‘reaching across the aisle’ are exhausting and will persist if both the ‘whys’ and the ‘hows’ of dissenting opinions are taken for granted. Specifically, we must challenge ourselves to ask why others experience the world differently than ourselves, and how they came to those conclusions. Allowing the possibility for divergent views to be legitimate alleviates the need to accept dualistic opinions of ‘for’ or ‘against’ as terminal, and allows appreciation for the potentials of difference to flourish. Chasing ideals of non-partisanship only further divides people into factions. Moreover, as we learned from Hetey and Eberhardt, requests for ‘objective’ data might inadvertently exacerbate antagonism.

Empathic data appeals to curiosity, elicits more critical questions, and does not accept a cursory reading of our multiple realities as a single, monolithic reality. The fragile impermanence of our individual realities reveals the personal adaptive capacity that emerges in each of us when we face adversity. The coupling of this humanized, unrefined, and dissimilar qualitative data with comprehensive refined statistics is crucial for any attempts at activating restorative justice. What form does empathy take in tables of data and how can numerical data elicit emotion? What constitutes valuable data and who is it valuable for? How can data say more?[2]

Dear data, you are powerful, so please, be more nuanced.


  1. Refer to bibliographic citations for CBS, NBC, and ABC news. It is important to take note of how each person is described by their profession and who they were with no mention of their experience with the virus and complications in their individual fight with COVID-19
  2. Refer to Figure 8 and Figure 9 for comparative representation between a typical quantitative dataset typically and iteration 2 of a qualitative dataset focused on collecting stories.




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Daveypageviews (u/daveypageviews). “Why didn’t anyone say anything about this back pain?” Reddit. October 2, 2020, 03:06 P.M. EDT. https://www.reddit.com/r/COVID19positive/comments/j40sub/why_didnt_anyone_say_anything_about_this_back_pain.

Diobscuri (u/diobscuri). “I’m pissed.” Reddit. October 4, 2020, 08:55 P.M. EDT. https://www.reddit.com/r/COVID19positive/comments/j5a6qr/im_pissed.

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Hetey, Rebecca C., and Jennifer L. Eberhardt. “The Numbers Don’t Speak for Themselves: Racial Disparities and the Persistence of Inequality in the Criminal Justice System,” Current Directions in Psychological Science 27, no. 3 (June 2018): 183–187. https://doi.org/10.1177/0963721418763931.

Holling, Crawford Stanley. “Resilience and Stability of Ecological Systems,” Annual Review of Ecology and Systematics 4 (November 1973): 1–23, https://doi.org/10.1146/annurev.es.04.110173.000245.

“image, n., 5.a.” OED Online. September 2020. Oxford University Press. Accessed October 16, 2020. https://www-oed-com.azp1.lib.harvard.edu/view/Entry/91618.

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Johns Hopkins Center for Systems Science and Engineering. “COVID-19 Dashboard.” Accessed October 16, 2020. https://coronavirus.jhu.edu/map.html.

Johns Hopkins Center for Systems Science and Engineering. “COVID-19 Status Report, Kings, New York.” Accessed October 16, 2020, 03:25 PM EDT. https://bao.arcgis.com/covid-19/jhu/county/36047.html.

Jorgitox (u/Jorgitox). “Very Mild Case.” Reddit. September 30, 2020, 04:30 P.M. EDT. https://www.reddit.com/r/COVID19positive/comments/j2u29a/very_mild_case.

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List of Figures

Figure 1 “COVID-19 Dashboard, Global Map.” John Hopkins Center for Systems Science and Engineering (CSSE). Accessed October 16, 2020, 05:28 PM EDT. https://coronavirus.jhu.edu/map.html.

Figure 2 “COVID-19 Dashboard, Global Map.” John Hopkins Center for Systems Science and Engineering (CSSE). Accessed October 16, 2020, 11:01 PM EDT. https://coronavirus.jhu.edu/map.html.

Figure 3 “COVID-19 Status Report, Kings County, New York.” John Hopkins Center for Systems Science and Engineering (CSSE). Accessed October 16, 2020, https://bao.arcgis.com/covid-19/jhu/county/06037.html.

Figure 4 “PlaNYC: A Stronger, More Resilient New York.” Special Initiative for Rebuilding and Resiliency. New York: City of New York, June 2013, 2.

Figure 5 “Social Distancing Scoreboard.” Unacast COVID-19 toolkit. Accessed October 17, 2020. https://www.unacast.com/covid19/social-distancing-scoreboard.

Figure 6 “Hot Spots Map.” The New York Times. Accessed October 17, 2020. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

Figure 7 “U.S. Deaths Near 100,000, An Incalculable Loss.” The New York Times. May 23, 2020. https://www.nytimes.com/2020/05/23/reader-center/coronavirus-new-york-times-front-page.html.

Figure 8 “Covid-19-data.” The New York Times. Accessed October 17, 2020. https://github.com/nytimes/covid-19-data/blob/master/live/us-counties.csv.

Figure 9 “Tested Positive.” Covid-19Positive subreddit. Accessed October 18, 2020. https://www.reddit.com/r/COVID19positive/top/?t=all&f=flair_name%3A%22Tested%20Positive%22. Aggregated data from select posts, see bibliography for individual citations from Reddit.