The Data Shake : : Opportunities and Obstacles for Urban Policy Making.

• Superior document: SpringerBriefs in Applied Sciences and Technology Series
• Author: Concilio, Grazia.
• TeilnehmendeR: Pucci, Paola.; Raes, Lieven.; Mareels, Geert.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: SpringerBriefs in Applied Sciences and Technology Series
• Physical Description: 1 online resource (134 pages)

Table of Contents

• Intro
• Preface
• Contents
• Editors and Contributors
• About the Editors
• Contributors
• Part IThe Data Shake: Open Questions and Challenges for Policy Making
• 1 The Data Shake: An Opportunity for Experiment-Driven Policy Making
• 1.1 Introduction
• 1.2 Evidence-Based Policy Making: New Chances Coming from the Data Shake
• 1.2.1 About Evidence-Based Policy Making
• 1.2.2 Evidence-Based Policy Making and the Data Shake: The Chance for Learning
• 1.3 The Smart Revolution of Data-Driven Policy Making: The Experimental Perspective
• 1.3.1 About Policy Experiments and Learning Cycles
• 1.3.2 Policy Cycle Model Under Experimental Dimension
• 1.3.3 The Time Perspective in the Experimental Dimension of Policy Making
• 1.4 Conclusions: Beyond the Evidence-Based Model
• References
• 2 Data Ownership and Open Data: The Potential for Data-Driven Policy Making
• 2.1 Introduction and Context
• 2.1.1 From Smart City to Data City
• 2.1.2 Exploring the Cities' Points of View
• 2.2 Challenges and Questions Related to (Open) Data Policies
• 2.2.1 Data Hygiene in the Organization
• 2.2.2 IoT and Open Data
• 2.2.3 Centralization vs Decentralization
• 2.2.4 Government and the Market
• 2.2.5 Open Data Checklist
• 2.3 Data and Procurement
• 2.3.1 Examples of Model Clauses
• 2.4 Discussion and Conclusion
• References
• 3 Towards a Public Sector Data Culture: Data as an Individual and Communal Resource in Progressing Democracy
• 3.1 The Balance of a Data-Driven Democracy
• 3.2 The Conflicting Logics of Emerging Public Sector Data Cultures
• 3.3 The Project Democracy Data-Lessons on Cultivating Local Data Culture from the Swedish Social Services
• 3.3.1 Proposals 1: Promote Holistic Data-Literacy
• 3.3.2 Proposals 2: Design Your Data-Driven Services as if Democracy Depended on It (Because It Does).
• 3.3.3 Proposals 3: Conceptualize Data as Democratic Artifact
• References
• 4 Innovation in Data Visualisation for Public Policy Making
• 4.1 Introduction: Data Visualisation Between Decision Support and Social Influence
• 4.2 Scoping the Experiences of Data Scientists
• 4.2.1 Multiple Data Source Management
• 4.2.2 Rigorous Data Integration
• 4.2.3 Actionable Information Delivery
• 4.2.4 Personalised User Experience
• 4.3 A Critical Eye on Technology Innovation Trends
• 4.4 Conclusions and Way Forward
• References
• Part IIThe PoliVisu Project
• 5 Policy-Related Decision Making in a Smart City Context: The PoliVisu Approach
• 5.1 Evidence-Based Policy Making and the Rise of ICT
• 5.2 ICT-Enabled Policy Making in a Smart City Context
• 5.3 The Unique Characteristics of the PoliVisu Approach
• 5.4 Barriers and Limitations to the Full Exploitation of Data Potential in Policy Making
• 5.5 Conclusion
• References
• 6 Turning Data into Actionable Policy Insights
• 6.1 Introduction
• 6.2 Policy Making Supported by Data
• 6.2.1 Policy Design
• 6.2.2 Policy Implementation
• 6.2.3 Policy Evaluation
• 6.3 Policy-Oriented Data Activities
• 6.3.1 Differentiating Roles and Competences
• 6.3.2 Balancing Flexibility and Usability
• 6.3.3 Transforming Iterations into Experimental Drivers
• 6.4 Conclusions
• References
• 7 Data-Related Ecosystems in Policy Making: The PoliVisu Contexts
• 7.1 Introduction
• 7.2 The PoliVisu Project as a Testbed for Digital Innovation
• 7.3 Actors and Roles in Data-Related Policy Making Ecosystems
• 7.4 Data-Related Relations
• 7.5 Conclusion: Dealing with Complexity in the Era of the Data Shake
• References
• 8 Making Policies with Data: The Legacy of the PoliVisu Project
• 8.1 Data Supported Policy Making Through the Eyes of the PoliVisu Pilots
• 8.1.1 Data for Dialogue.
• 8.1.2 Between Precision and Usability
• 8.1.3 Proneness to Iterative Process
• 8.1.4 Actors Involved in Data Supported Policy Making
• 8.2 Bottlenecks and New Practices Detected in Policy Making
• 8.2.1 Bottlenecks
• 8.2.2 New Practices and Knowledge
• 8.3 Conclusions
• 8.3.1 Lessons Learnt from the PoliVisu Project
• 8.3.2 Some Recommendations
• References
• Acknowledgments.