Ce que l’ open data fait à l’administration municipale

Courmont, Antoine

doi:10.3917/res.218.0077

Cited by 14 publications

(3 citation statements)

References 14 publications

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“…Local authorities have been leading in developing open data in France (Courmont, 2019). Still, when it comes to data sharing, the role of local public authorities is sometimes overlooked in work being done at the legislative level.…”

Section: Introductionmentioning

confidence: 99%

Data collaborations at a local scale: Lessons learnt in Rennes (2010–2021)

Chignard¹,

Glatron²

2023

Data & Policy

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Data sharing is a requisite for developing data-driven innovation and collaboration at the local scale. This paper aims to identify key lessons and recommendations for building trustworthy data governance at the local scale, including the public and private sectors. Our research is based on the experience gained in Rennes Metropole since 2010 and focuses on two thematic use cases: culture and energy. For each one, we analyzed how the power relations between actors and the local public authority shape the modalities of data sharing and exploitation. The paper will elaborate on challenges and opportunities at the local level, in perspective with the national and European frameworks.

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Section: Introductionmentioning

confidence: 99%

Data collaborations at a local scale: Lessons learnt in Rennes (2010–2021)

Chignard¹,

Glatron²

2023

Data & Policy

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“…Increasingly, city governments rely on digital technologies to carry out their mandates (Bernardin and Jeannot 2019;Courmont 2019), including interactions amongst and with their constituents (Levenda et al 2020). In the process, city governments have transformed their organisations from within, and more generally, the ways in which they govern (Jeannot and Maghin 2019).…”

Section: Introductionmentioning

confidence: 99%

Governing cities as more-than-human entities

Mouton¹,

Rock²

2021

City

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The field of urban studies has scrutinised digital technologies and their proliferation, but rather little attention has been paid to databases. Furthermore, contributions to date have focused almost exclusively on how digital technologies interface with human populations in cities. By contrast, we draw attention to databases maintained by city governments that contain identifying information about pet dogs and their legal owners in cities. Methodologically, our study merges database ethnography with multi-species ethnography. Conceptually, we contend that "dog data" contribute to orderly conduct in urban space. This orientation to urban governance illustrates "trans-biopolitics," in the sense of socially-situated and technologically-mediated power relations that operate through multi-species entanglements. As such, this article extends the literature on (neoliberal) urban policing by providing a fine-grained analysis of how emergent forms of social control become palpable. In general terms, the adoption and use of digital technologies by city governments has increased their capacity to enforce rules and regulations. Overall, we find that the more legible dogs and their legal owners become in databases, the more governable both dogs and people become in urban life.

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“…Open data has a capital importance to SCs all around the world, as it boosts innovation, eases the knowledge extraction and use in many sectors, and provides a positive change to their complex ecosystems (Neves et al [2020]). Paris is an outstanding example of its effect, as their "Welcome City Lab" has developed several applications for tourism which work within the SC infrastructure based on its available open data (Courmont [2016]).…”

Section: Open Datamentioning

confidence: 99%

Deep Learning for Computer Vision in Smart Cities

Retuerta¹

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The Digital Age has caused a rapid shift from traditional industry to an economy mainly based upon information technology. According to recent studies, 74 zettabytes (ZB) of data have been generated, captured and replicated in the world in 2021, with video accounting for 82% of internet traffic. This figure has been amplified due to the coronavirus pandemic, and it is expected to keep increasing, reaching 149 ZB by 2024. Processing this impressive amount of information is one of the main scientific challenges of our time. Against this backdrop, Machine Learning (ML) and two related paradigms have emerged: big data and deep learning. These disciplines take advantage of mathematical optimization methods, bioinspiration and modern Graphics Processing Units (GPUs) to manage large datasets efficiently and effectively.Cities from around the world have adapted the previous methods to make use of the newly available data, promoting themselves as "smart". Apart from aiming to integrate I would like to express my gratitude to all those who, with their support, encouragement and kindness; have made this great milestone of my career possible -this dissertation is dedicated to you.First of all, I must thank my supervisors, Sara Rodríguez and Pablo Chamoso, for their dedication and guidance. This thesis wouldn't have been possible without their valuable support. Secondly, I want to express my profound gratitude to John A. Lee and the rest of the MIRO Machine Learning Group from the Université catholique de Louvain for their overwhelming hospitality during my research stay in their lab. The months I spent working with them greatly enriched my scientific capabilities and the research which lead to this dissertation. I also want to thank Besik Dundua for his earnest hospitality, valuable pieces of advice and for going out of his way to welcome me. His interest in quality research, not quantity, taught me to deal with the state of the art as an old friend with whom to have an enjoyable conversation, instead of as a intimidating professor I must learn from. The months I spent under your supervision truly touched me. Moreover, I also want to thank my colleagues at BISITE research group for their company and friendship over the years. May the future take us apart, may the destiny bring us together.Lastly, I am grateful to my encouraging relatives, my fellow professors of "Programación II" and my hard-working students.

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Ce que l’ open data fait à l’administration municipale

Cited by 14 publications

References 14 publications

Data collaborations at a local scale: Lessons learnt in Rennes (2010–2021)

Data collaborations at a local scale: Lessons learnt in Rennes (2010–2021)

Governing cities as more-than-human entities

Deep Learning for Computer Vision in Smart Cities

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