Optimizing for Equity: Sensor Coverage, Networks, and the Responsive City.
Decisions about sensor placement in cities are inherently complex, balancing social-technical, digital, and structural inequalities with the differential needs of populations, local stakeholder priorities, and the technical specificities of the sensors themselves. Rapid developments in urban data co...
| Publicado en: | Annals of the American Association of Geographers Vol. 112; no. 8; pp. 2152 - 2174 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=160199299&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160199299 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2022 vid: 112 iid: 8 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 160199299 10.1080/24694452.2022.2077169 ppf: 2152 ppct: 22 formats: tig: atl: Optimizing for Equity: Sensor Coverage, Networks, and the Responsive City. aug: au: Robinson, Caitlin Franklin, Rachel S. Roberts, Jack affil: Department of Geography and Planning, University of Liverpool, UK Centre for Urban and Regional Development Studies, Newcastle University, UK Research Engineering Group, The Alan Turing Institute, UK su: Decision making Sensor networks Smart cities Air quality Mathematical optimization sug: subj: Decision making Sensor networks Smart cities Air quality Mathematical optimization keyword: decision-making monitoring sensor networks smart urbanisms spatial optimization monitoreo optimización espacial redes de sensores toma de decisiones urbanismos inteligentes 传感器网络 决策 智慧城市 监测 空间优化 decision-making monitoring sensor networks smart urbanisms spatial optimization monitoreo optimización espacial redes de sensores toma de decisiones urbanismos inteligentes 传感器网络 决策 智慧城市 监测 空间优化 ab: Decisions about sensor placement in cities are inherently complex, balancing social-technical, digital, and structural inequalities with the differential needs of populations, local stakeholder priorities, and the technical specificities of the sensors themselves. Rapid developments in urban data collection and geographic data science have the potential to support these decision-making processes. Focusing on a case study of air-quality sensors in Newcastle-upon-Tyne, UK, we employ spatial optimization algorithms as a descriptive tool to illustrate the complex trade-offs that produce sensor networks that miss important groups—even when the explicit coverage goal is one of equity. We show that the problem is not technical; rather, it is demographic, structural, and financial. Despite the considerable constraints that emerge from our analysis, we argue the data collected via sensor networks are of continued importance when evidencing core urban injustices (e.g., air pollution or climate-related heat). We therefore make the case for a clearer distinction to be made between sensors for monitoring and sensors for surveillance, arguing that a wider presumption of bad intent for all sensors potentially limits the visibility of positive types of sensing. For the purpose of monitoring, we also propose that basic spatial optimization tools can help to elucidate and remediate spatial injustices in sensor networks. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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