The Data Shake

Concilio, Grazia; Pucci, Paola; Raes, Lieven; G, Mareels

doi:10.1007/978-3-030-63693-7

Cited by 5 publications

(1 citation statement)

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“…Many scientists recently called for an improvement in data visualization and graphical representations as a fundamental step towards a stronger public appropriation of societal, environmental, and health phenomena (Concilio et al, 2021;Dixon et al, 2021;Harold et al, 2016;Murray et al, 2020). The present results lead to the following suggestions to improve the perception of exponentials in scatterplots.…”

Section: Evidence-based Suggestions To Improve Graph Perceptionmentioning

confidence: 64%

Analyzing the misperception of exponential growth in graphs.

Ciccione¹,

Sablé-Meyer²,

Dehaene³

2021

Preprint

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Exponential growth is frequently underestimated, an error that can have a heavy social cost in the context of epidemics. To clarify its origins, we measured the human capacity to extrapolate linear and exponential trends in scatterplots. Four factors were manipulated: the function underlying the data (linear or exponential), the response modality (pointing or venturing a number), the scale on the y axis (linear or logarithmic), and the amount of noise in the data. While linear extrapolation was precise and largely unbiased, we observed a consistent underestimation of noisy exponential growth, present for both pointing and numerical responses. A biased ideal-observer model could explain these data as an occasional misperception of noisy exponential graphs as quadratic curves. Importantly, this underestimation bias was mitigated by participants’ math knowledge, by using a logarithmic scale, and by presenting a noiseless exponential curve rather than a noisy data plot, thus suggesting concrete avenues for interventions.

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Section: Evidence-based Suggestions To Improve Graph Perceptionmentioning

confidence: 64%

Analyzing the misperception of exponential growth in graphs.

Ciccione¹,

Sablé-Meyer²,

Dehaene³

2021

Preprint

View full text Add to dashboard Cite

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Turning Data into Actionable Policy Insights

Verstraete¹,

Acar²,

Concilio

et al. 2021

The Data Shake

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It is becoming clearer that data-supported input is essential in the policy making process. But at which point of the process, and in which format, can data aid policy making? And what does an organisation need to turn data into relevant insights? This paper explores the role of data from two perspectives. In the first part, data and data analysis are situated in the policy making process by mapping them onto the data supported policy making model and highlighting the different roles they can assume in each stage and step of the process. The second part discusses a practical framework for policy-oriented data activities, zooming in on the data-specific actions and the actors performing them in each data-supported step of the policy making process. We observe that a close collaboration between the policy maker and data scientist in the framework of an iterative approach permits to transform the policy question into a suited data analysis question and deliver relevant insights with the flexibility desired by decision makers. In conclusion, for data to be turned into actionable policy insights it is vital to set up structures that ensure the presence and the collaboration of policy-oriented and data-oriented competences.

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Making Policies with Data: The Legacy of the PoliVisu Project

Acar¹,

Raes²,

Rosseau³

et al. 2021

The Data Shake

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The PoliVisu project has the goal to investigate the potential of data use and visualisation in urban policy making. The project has explored how data supported policy making is adopted by public administrations and what we can learn from their experience. This is done by enrolling pilot cases with different and specific policy problems. From the experience of the PoliVisu pilots the influence and added value of data in the policy making process is assessed. Considering the recent “shake” in data production and use, PoliVisu has adopted four driving questions, as follow: what are the new roles data can play in the policy making process?, What is the added value of data for policy making? How can innovative visualisations contribute to improve the use of data in policy making processes? To what extent can an increased adoption of data affect the policy making process? How is the data shake affecting the involvement of non-institutional actors in the policy making process? This paper explores these questions, by presenting the experiences and the lessons learnt, also focussing on specific pilots’ initiatives and results.

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The Data Shake

Cited by 5 publications

References 0 publications

Analyzing the misperception of exponential growth in graphs.

Analyzing the misperception of exponential growth in graphs.

Turning Data into Actionable Policy Insights

Making Policies with Data: The Legacy of the PoliVisu Project

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