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Data · dataset · 2022

Living Labs: A Creative and Collaborative Planning Approach

Listed in IISH Dataverse and ODISSEI Portal — shown once because both records carry DOI 10.34894/02wn3c

Communities around the world are facing an increasing number of ‘wicked problems’, many of which are spatial issues.

Description

Therefore, high expectations are placed on the role of planning in addressing them. However, such complex challenges cannot be easily solved with the traditional, sectoral planning approaches, or by planners and decision-makers alone; they require a cross-sectoral and societal-inclusive understanding and collaboration of various kinds of stakeholders.

Consequently, in the search for new approaches and tools that favour experimentation, flexibility, and collaboration, planners and decision-makers are turning their attention towards the private sector, where Living Labs have been extensively used in open and user-oriented innovation. Despite the growing number of studies that picture them as a ‘magic recipe’ for experimentation and development of creative solutions for ‘wicked problems’, Living Labs have only recently started gaining attention in the spatial domain.

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In this chapter, we position Living Labs as a creative and collaborative planning approach. Based on a literature review on Living Labs, and on empirical research carried out in three spatial planning and water infrastructure projects from the Netherlands, we propose a five-phased ‘living lab way of working’ and reflect on the role of Living Labs as a planning method. We end by outlining several recommendations that could be useful when considering the use of Living Labs as a planning method.

The conclusion of this chapter shows that despite the challenges they pose, Living Labs represent a relevant and growing practice in spatial planning, especially because they provide a conducive way of connecting local-scale and larger-scale planning issues and solutions.

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Inferred from text
Community planning 77%
Provenance · 2 source records, 17 field assertions
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IISH Dataversedoi:10.34894/02WN3C13 d agoJSON v1
ODISSEI Portaldoi:10.34894/02WN3C9 d agoJSON v1
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