In this article, we analyze the rise of data visualization in social and political contexts. Against the background of the COVID-19 pandemic, we consider a case in Shenzhen, China, and demonstrate the impact of visualization as intermediation for data on policies and society. We propose an epistemology of visualization as infrastructure. In China, visualization has been developed as a technological system to support information dissemination and collective action in public crises. Three characteristics are proposed to describe China’s data visualization politics during the pandemic, in particular, to summarize the power relationships between visual brokers, policymakers, and public users in data visualization. Further, based on the conceptualization of the links between visualization, infrastructure, and data politics, visualization serves as an important component of information infrastructure, and is an easily overlooked technical process. The creation and use of data may seem like normalized actions, but data visualization in fact supports the perception, participation, proposal, and critique of data politics issues. Via a focus group discussion with members of the Shenzhen public health sector and in-depth interviews with Shenzhen residents, we developed a qualitative effectiveness measurement framework highlighting the potential for the involvement of visualization in politics and activism.
AllenWL (2018) Visual brokerage: communicating data and research through visualisation. Public Understanding of Science27(8): 906–922.
2.
AparicioMCostaCJ (2015) Data visualization. Communication Design Quarterly3(1): 7–11.
3.
BalibarE (2012) The 'impossible' community of the citizens: past and present problems. Environment and Planning D: Society and Space30(3): 437–449.
4.
Ball-RokeachSJKimY-CMateiS (2001) Storytelling Neighborhood. Communication Research28(4): 392–428.
5.
BatemanSMandrykRLGutwinC,et al (2010) Useful junk? The effects of visual embellishment on comprehension and memorability of charts. In: Proceedings of the SIGCHI conference on human factors in computing systems. New York, NY, USA, Association for Computing Machinery, 10 April 010. pp. 2573–2582.
6.
BeerD (2013) Popular Culture and New Media: The Politics of Circulation. Springer, pp. 118–119.
7.
BeraldoDMilanS (2019) From data politics to the contentious politics of data. Big Data & Society6(2): 2053951719885967.
8.
BourdieuP (1993) Language and Symbolic Power. Cambridge, MA: Harvard University Press.
9.
BoyJPandeyAVEmersonJ,et al (2017) Showing people behind data: does anthropomorphizing visualizations elicit more empathy for human rights data? In: Proceedings of the 2017 CHI conference on human factors in computing systems, New York, NY, USA, Association for Computing Machinery, 02May 2017. pp. 5462–5474.
10.
BoydDCrawfordK (2012) Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society15(5): 662–679.
11.
BorkinMAVoAABylinskiiZ, et al. (2013). What makes a visualization memorable?IEEE Transactions on Visualization and Computer Graphics19(12): 2306–2315.
12.
BreiterAHeppA (2018) The complexity of datafication: putting digital traces in context. In: Communicative figurations. Cham: Palgrave Macmillan, pp. 387–405.
13.
BrymanA (2004) Triangulation and Measurement. Loughborough, Leicestershire: Retrieved from Department of Social Sciences, Loughborough University. Available at: www.referenceworld.com/sage/socialscience/triangulation.pdf (accessed on 7 September 2021).
14.
BucchiMSaracinoB (2016) Visual science literacy images and public understanding of science in the digital age. Science Communication38(6): 812–819.
15.
CardM (1999) Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann.xiii
16.
CawthonNMoereAV (2007) The effect of aesthetic on the usability of data visualization. In: 11th International conference information visualization, Zurich, Switzerland, 4–6 July 2007. IEEE, IV07, pp. 637–648.
ChenMFloridiLBorgoR (2014) What is visualization really for? In: The Philosophy of Information Quality. Cham: Springer, pp. 75–93.
19.
China Weekly (2020) 2.2 billion visits to the "map" and the platform behind it: dxy. China Weekly. (in Chinese). Available at: http://www.chinaweekly.cn/46462.html (accessed on 31 August 2021).
20.
CNNIC (2021) 48th Statistical Survey Report on the Internet Development in China. (in Chinese). Available at: http://www.199it.com/archives/1302651.html (accessed on 7 September 2021).
21.
CorrellM (2019) Ethical dimensions of visualization research. In: Proceedings of the 2019 CHI conference on human factors in computing systems, New York, NY, USA, Association for Computing Machinery, 02May 2017. pp. 1–13.
22.
CukierKMayer-SchoenbergerV (2014) The Rise of Big Data: How It’s Changing the Way We Think about the World. NJ, USA: Princeton University Press, pp. 20–32.
23.
DawesSS and HelbigN (2010) Information strategies for open government: Challenges and prospects for deriving public value from government transparency. In: International Conference on Electronic Government: 50-60, Berlin, Heidelberg, 29August 2010. Springer.
24.
D'IgnazioC and KleinLF (2016) Feminist data visualization. In: Workshop on visualization for the digital humanities (VIS4DH), Baltimore, 24October 2016. IEEE.
25.
DruckerJ (2010) Graphesis: visual knowledge production and representation. Poetess Archive Journal2(1): 1–50.
26.
EdwardsPNJacksonSJBowkerGC (2007) Understanding infrastructure: dynamics, tensions, and design. Working paper, January. Available at: http://deepblue.lib.umich.edu/handle/2027.42/49353 (accessed on 31 August 2021).
27.
EmersonJ (2008) Visualizing Information for Advocacy: An Introduction to Information Design. Tactical Technology Collective.
Foucault WellesBMeirellesI (2015) Visualizing computational social science: The multiple lives of a complex image. Science Communication37(1): 34–58.
30.
FrancisJJJohnstonMRobertsonC, et al. (2010) What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychology and Health25(10): 1229–1245.
31.
GabrysJPritchardHBarrattB (2016) Just good enough data: figuring data citizenships through air pollution sensing and data stories. Big Data & Society3(2): 2053951716679677.
32.
GibbsA (1997) Focus groups. Social Research Update19(8): 1–8.
33.
GoughPHoXDunnK, et al (2014) Art and chartjunk: a guide for NEUVis. In: Proceedings of the 7th international symposium on visual information communication and interaction,New York, NY, USA, Association for Computing Machinery, August 2014. pp. 171–177.
34.
GravesA and HendlerJ (2013) Visualization tools for open government data. In: Proceedings of the 14th annual international conference on digital government research, New York, NY, USA, Association for Computing Machinery, 17June 2013.
35.
GrayEJenningsWFarrallS, et al. (2015) Small big data: using multiple data-sets to explore unfolding social and economic change. Big Data and Society2(1): 2053951715589418.
36.
GrayJGerlitzCBounegruL (2018) Data infrastructure literacy. Big Data & Society5(2): 2053951718786316.
37.
GreggM (2015) Inside the data spectacle. Television & New Media16(1): 37–51.
38.
GrinsteinUMFGGWierseA (2002) Information Visualization in Data Mining and Knowledge Discovery. Massachusetts, USA: Morgan Kaufmann.
39.
GutiérrezM (2018) Data Activism and Social Change. Heidelberg, Germany: Springer, pp. 49–105.
40.
HagenLKellerTEYerdenX, et al. (2019) Open data visualizations and analytics as tools for policy-making. Government Information Quarterly36(4): 101387.
41.
HarozSWhitneyD (2012) How capacity limits of attention influence information visualization effectiveness. IEEE Transactions on Visualization and Computer Graphics18(12): 2402–2410.
42.
HilbertMLópezP (2011) The world’s technological capacity to store, communicate, and compute information. Science332(6025): 60–65.
43.
HillRLKennedyHGerrardY (2016) Visualizing junk: big data visualizations and the need for feminist data studies. Journal of Communication Inquiry40(4): 331–350.
44.
HuangWEadesPHongSH (2009) Measuring effectiveness of graph visualizations: a cognitive load perspective. Information Visualization8(3): 139–152.
45.
HullmanJDiakopoulosN (2011) Visualization rhetoric: framing effects in narrative visualization. IEEE Transactions on Visualization and Computer Graphics17(12): 2231–2240.
46.
JoffeH (2008) The power of visual material: persuasion, emotion and identification. Diogenes55(1): 84–93.
47.
KelleherCWagenerT (2011) Ten guidelines for effective data visualization in scientific publications. Environmental Modelling & Software26(6): 822–827.
48.
KennedyH (2015) Seeing Data: Visualisation Design Should Consider How We Respond to Statistics Emotionally as Well as Rationally. Impact of Social Sciences Blog.
49.
KennedyH (2018) Living with data: aligning data studies and data activism through a focus on everyday experiences of datafication. Krisis: Journal for Contemporary Philosophy1: 18–30.
50.
KennedyHAllenW (2016) Data visualisation as an emerging tool for online research. The Sage Handbook of Online Research Methods: 307–326.
51.
KennedyHHillRL (2018) The feeling of numbers: emotions in everyday engagements with data and their visualisation. Sociology52(4): 830–848.
52.
KennedyHHillRLAielloG, et al. (2016a) The work that visualisation conventions do. Information, Communication and Society19(6): 715–735.
53.
KennedyHHillRLAllenW, et al. (2016b) Engaging with (big) data visualizations: Factors that affect engagement and resulting new definitions of effectiveness. First Monday21 11.
54.
KloseA (2015) The Container Principle: How a Box Changes the Way We Think. Cambridge: The MIT Press
55.
KnightM (2015) Data journalism in the UK: a preliminary analysis of form and content. Journal of Media Practice16(1): 55–72.
56.
LakeR (2017) Big Data, urban governance, and the ontological politics of hyper-individualism. Big Data & Society4(1): 1–10.
57.
MasonSAzzamT (2019) In need of an attitude adjustment? the role of data visualization in attitude change and evaluation influence. American Journal of Evaluation40(2): 249–267.
58.
MateiSBall-RokeachSJQiuJL (2001) Fear and misperception of Los Angeles urban space: a spatial-statistical study of communication-shaped mental maps. Communication Research28(4): 429–463.
59.
McGuireWJ (1968) Personality and Attitude Change: An Information-Processing Theory. Psychological foundations of attitudes, pp. 171–196.
60.
MichiS, WestR and HarveyN (2020) The concept of “fatigue” in tackling covid-19. Bmj: British Medical Journal371: m4171.
61.
MilanSVan der VeldenL (2016) The alternative epistemologies of data activism. Digital Culture & Society2(2): 57–74.
62.
NærlandTU (2020) The political significance of data visualization: four key perspectives 4. The political significance of data visualization: Four key perspectives. In: Data Visualization in Society. Amsterdam, The Netherlands: Amsterdam University Press, pp. 463–474.
63.
NanDu Institute of Big Data Research (2020) More than 70% of cities disclose the spatial trajectory of COVID-19 infected patients. Southern Metropolis Daily. (in Chinese). Available at: https://xw.qq.com/cmsid/20200215A0E52200 (accessed on 31 August 2021).
64.
National Health Commission of the PRC (2020) From 00 to 24:00 on January 20, 77 new confirmed cases of novel coronavirus infection. People's Daily. (in Chinese). Available at: http://health.people.com.cn/n1/2020/0121/c14739-31559040.html (accessed on 31 August 2021).
65.
NightingaleF (1858) Diagram of the causes of mortality in the army in the east. Public Domain. Retrieved February 21: 2015.
66.
OttenJJChengKDrewnowskiA (2015) Infographics and public policy: using data visualization to convey complex information. Health Affairs34(11): 1901–1907.
67.
PandeyAVManivannanANovO, et al. (2014) The persuasive power of data visualization. IEEE Transactions on Visualization and Computer Graphics20(12): 2211–2220.
68.
PandeyAV, RallK, SatterthwaiteML,et al (2015) How deceptive are deceptive visualizations? An empirical analysis of common distortion techniques. Proceedings of the 33rd Annual ACM conference on human factors in computing systems,New York, NY, USA, Association for Computing Machinery, 18 April2015. pp. 1469–1478.
69.
ParkS and Gil-GarciaJR (2017) Understanding transparency and accountability in open government ecosystems: the case of health data visualizations in a state government. In: Proceedings of the 18th annual international conference on digital government research,New York, NY, USA, Association for Computing Machinery, 07 June 2017. pp. 39–47.
70.
PeckEM, AyusoSE, and El-Etr O (2019) Data is personal: attitudes and perceptions of data visualization in rural pennsylvania. In: Proceedings of the 2019 CHI conference on human factors in computing systems, New York, NY, USA, Association for Computing Machinery, 02May 2019. pp. 1–12.
71.
PlantinJCLagozeCEdwardsPN, et al. (2018) Infrastructure studies meet platform studies in the age of Google and Facebook. New Media & Society20(1): 293–310.
72.
RallKSatterthwaiteMLPandeyAV, et al. (2016) Data visualization for human rights advocacy. Journal of Human Rights Practice8(2): 171–197.
73.
RobinsonI (2016) Data Visualisation: Contributions to Evidence-Based Decision-Making. Wallingford: SciDevNet/Centre for Agriculture and Biosciences International.
74.
RuppertEIsinE (2020) Being Digital Citizens. London: Rowman & Littlefield International, pp. 173–189.
75.
RuppertEIsinEBigoD (2017) Data politics. Big Data & Society4(2): 2053951717717749.
76.
RuppertTDambruchJKrämerM, et al. (2015) Visual decision support for policy making: advancing policy analysis with visualization. In: Policy Practice and Digital Science. Cham: Springer, Vol. 321-353.
77.
ShaxsonL (2005) Is your evidence robust enough? Questions for policy makers and practitioners. Evidence & Policy: A Journal of Research, Debate and Practice1(1): 101–112.
78.
SnowJ (1854) The cholera near golden-square, and at deptford. Medical Times and Gazette9(321–322): 566.
79.
Southern Metropolis Daily (2020) The official account of "Shenzhen Public Health Sector" has gained millions of new followers in half a year. Southern Metropolis Daily. (in Chinese). Available at: http://www.infzm.com/contents/188957 (accessed on 31 August 2021).
80.
StarSL (1999) The ethnography of infrastructure. American Behavioral Scientist43(3): 377–391.
81.
StarSLBowkerGC (2006) How to infrastructure. In: Handbook of New Media: Social Shaping and Social Consequences of ICTs, pp. 230–245.
82.
State Market Regulatory Administration of China (2020) Personal Health QR Code" Series of National Standards. State Market Regulatory Administration of China. (in Chinese). Available at: http://www.samr.gov.cn/xw/zj/202005/t20200501_314959.html (accessed on 31 August 2021).
83.
Tactical Tech Collective (2013) Visualising information for advocacy. Bangalore: Tactical Tech Collective, Vol. 3.
84.
TufteERGraves-MorrisPR (1983) The Visual Display of Quantitative Information. Cheshire, CT: Graphics press, Vol. 2.9.
85.
Van DijckJ (2021) Seeing the forest for the trees: visualizing platformization and its governance. New Media & Society23(9): 2801–2819.
86.
Van KleefGAVan den BergHHeerdinkMW (2015) The persuasive power of emotions: effects of emotional expressions on attitude formation and change. Journal of Applied Psychology100(4): 1124.
87.
WilliamsonB (2016) Digital education governance: data visualization, predictive analytics, and ‘real-time’policy instruments. Journal of Education Policy31(2): 123–141.
88.
XuBKraemerMUGutierrezB, et al. (2020) Open access epidemiological data from the COVID-19 outbreak. The Lancet Infectious Diseases20(5): 534.
89.
ZhaoLM (2020) What are those Chinese doing when stayed at home during the COVID-19 pandemic?The Paper. (in Chinese). Available at: https://www.thepaper.cn/newsDetail_forward_5662782 (accessed on 31 August 2021).
90.
ZhaoTWuZ (2020) Citizen–state collaboration in combating COVID-19 in China: experiences and lessons from the perspective of co-production. The American Review of Public Administration50(6–7): 777–783.
91.
ZhengJYWenC (2020) Residents Asked to Announce Confirmed Cases Precisely to the Floor. Shenzhen Health Commission responded. Shenzhen Economic Daily. (in Chinese). Available at:http://shenzhen.news.163.com/20/0212/10/F567H44M04178D6R.html (accessed on 31 August 2021).
92.
ZhuY (2007) Measuring effective data visualization. In: International symposium on visual computing, Berlin, Heidelberg, 26November 2007, Springer, pp. 652–661.
93.
ZouHShuYFengT (2020) How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infectious Diseases of Poverty9(1): 1–4.
94.
ZurkowskiPG (1984) Integrating America's infostructure. Journal of the American Society for Information Science35(3): 170–178.
