A spatial decision support framework for equitable sensor network distribution in the smart city

Abstract This paper introduces a proof‐of‐concept spatial decision support system (SDSS) that assists decision‐makers to generate equitably distributed sensor networks and evaluate their placement with reference to specific population‐based coverage criteria. Our approach centres equity in infrastructure distribution; we focus on the decision‐making process required to achieve the best possible sensor coverage of the geographical area for selected vulnerable populations and visualise trade‐offs in coverage inherent in infrastructure distribution. The development of the tool brings together expertise from quantitative geography, urban planning, data science and software engineering, and its technical development is underpinned and shaped by interviews with decision‐makers and their iterative feedback. Through this, we ask: how can decision support tools help with the work of building equitable infrastructure? As well as a technical application, our approach develops a conceptual framework for evaluating sensor network purpose and distribution before actual placement. The unique combination of distribution algorithms, user interface and decision‐maker input, developed by an interdisciplinary team, offers a novel approach to sensor network conceptualisation and generation. Our research contributes to the understanding of the distribution of essential infrastructure and can be repurposed for any sensor type and geographical location to promote equity in infrastructure distribution.

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Publication Details

Journal
Geographical Journal
Published
2024-08-05
DOI
https://doi.org/10.1111/geoj.12605
Citations
4
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
2.73

Funders

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article

A spatial decision support framework for equitable sensor network distribution in the smart city

Rachel S. Franklin, Caitlin Robinson, Eman Zied Abozied, Jack Roberts et al.
4 citations
Geographical Journal
Human Mobility and Location-Based Analysis
2.73
article

A spatial decision support framework for equitable sensor network distribution in the smart city

Rachel S. Franklin, Caitlin Robinson, Eman Zied Abozied, Jack Roberts, Kate Court
article en
4 citations

Abstract

Abstract This paper introduces a proof‐of‐concept spatial decision support system (SDSS) that assists decision‐makers to generate equitably distributed sensor networks and evaluate their placement with reference to specific population‐based coverage criteria. Our approach centres equity in infrastructure distribution; we focus on the decision‐making process required to achieve the best possible sensor coverage of the geographical area for selected vulnerable populations and visualise trade‐offs in coverage inherent in infrastructure distribution. The development of the tool brings together expertise from quantitative geography, urban planning, data science and software engineering, and its technical development is underpinned and shaped by interviews with decision‐makers and their iterative feedback. Through this, we ask: how can decision support tools help with the work of building equitable infrastructure? As well as a technical application, our approach develops a conceptual framework for evaluating sensor network purpose and distribution before actual placement. The unique combination of distribution algorithms, user interface and decision‐maker input, developed by an interdisciplinary team, offers a novel approach to sensor network conceptualisation and generation. Our research contributes to the understanding of the distribution of essential infrastructure and can be repurposed for any sensor type and geographical location to promote equity in infrastructure distribution.

Geographical JournalVol. 191(1)
Turing Institute (GB), University of Bristol (GB), The Alan Turing Institute (GB), Newcastle University (GB)
Alan Turing Institute, UK Research and Innovation
Openalex Percentile: Top 11%
Human Mobility and Location-Based Analysis
2.73
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