Infrastructure inequality is a characteristic of urbanization

Pandey, Bhartendu; Brelsford, Christa; Seto, Karen C.

doi:10.1073/pnas.2119890119

Cited by 85 publications

(36 citation statements)

References 43 publications

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“…Urban settings undergo rapid gentrification, and poorer neighborhoods face boosted investment and an influx of new residents of higher socioeconomic status, potentially deepening urban inequality and health disparities [21]. Inequalities in urban infrastructure may vary between countries [22]; however, this challenge is global, and in this regard, the Database on Urban Inequality and Amenities has been developed [23].…”

Section: Hypothesismentioning

confidence: 99%

Urban inequality: a hypothetic risk factor for myopia

Nouraeinejad¹

2022

Med Hypothesis Discov Innov Optom

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Background: Myopia is a common refractive error with an expected increase in prevalence. Inequalities in access to ocular health care for sight-threatening or life-threatening ocular diseases have been described. Urbanization is a risk factor for both the incidence and progression of myopia; however, socioeconomic inequalities in urban settings are a new concept in myopia incidence.Hypothesis: Considering the proven impact of inequalities existing in general and ocular health, urban inequality could be a new risk factor for both the incidence and progression of myopia. Inequalities in urban infrastructure vary between countries; however, there are currently various manifestations of urban inequality, and scholars are concerned about intra-urban inequality as a public health risk. Considering the significant influence of urban inequality on health of children, and because myopia develops and progresses faster during childhood, the effect of urban inequality on myopia must be examined. Although urban inequality could be a putative risk factor for myopia, a causal relationship should be investigated. However, myopia is multifactorial in etiology, originating from the interaction of environmental and genetic factors. Thus, causality between urban inequality and myopia should be investigated through a randomized, controlled trial with strict matching of genetic backgrounds and environmental factors. Conclusions: Several risk factors for myopia have been proposed, and studies have confirmed causal relationships with most of these factors. Considering the proven impact of urban inequality on both general and ocular health, experimental studies are necessary to confirm the possible causal relationship between urban inequality and myopia. Certainly, there will be substantial challenges in the implementation phase.

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Section: Hypothesismentioning

confidence: 99%

Urban inequality: a hypothetic risk factor for myopia

Nouraeinejad¹

2022

Med Hypothesis Discov Innov Optom

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“…To evaluate energy access, NTL data may be combined with on-the-ground information from utilities, GIS data, and local knowledge of energy access (Zhao et al, 2019). Fusing satellite data products with data from mobile phones can also support assessments of energy use, energy poverty, and disaster response (Steele et al, 2017), while NTL data combined with census data, national household surveys, or meter data can help users better understand and address inequities in energy infrastructure and access at scale (Mann et al, 2016;Pandey et al, 2022). Satellites can also identify changes in energy demand, such as those associated with COVID-19 or holidays from different cultures (Román and Stokes, 2015;Elvidge et al, 2021;Stokes and Román, 2022).…”

Section: Energy Use and Infrastructurementioning

confidence: 99%

Satellite Data Applications for Sustainable Energy Transitions

Edwards¹,

Holloway²,

Pierce³

et al. 2022

Front. Sustain.

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Transitioning to a sustainable energy system poses a massive challenge to communities, nations, and the global economy in the next decade and beyond. A growing portfolio of satellite data products is available to support this transition. Satellite data complement other information sources to provide a more complete picture of the global energy system, often with continuous spatial coverage over targeted areas or even the entire Earth. We find that satellite data are already being applied to a wide range of energy issues with varying information needs, from planning and operation of renewable energy projects, to tracking changing patterns in energy access and use, to monitoring environmental impacts and verifying the effectiveness of emissions reduction efforts. While satellite data could play a larger role throughout the policy and planning lifecycle, there are technical, social, and structural barriers to their increased use. We conclude with a discussion of opportunities for satellite data applications to energy and recommendations for research to maximize the value of satellite data for sustainable energy transitions.

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“…Inequality: The inequality metric was proposed by Pandey et al [11] to measure the infrastructure inequality across multiple geographic regions (Equation 4). 𝜎 is the standard deviation and 𝜇 is the mean.…”

Section: 𝐼𝑛𝑡𝑟𝑎/𝐼𝑛𝑡𝑒𝑟 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑖𝑜mentioning

confidence: 99%

Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions

Liang,

Zhu,

et al. 2022

Preprint

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Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components. As a special type of networks, spatial networks are usually generated by the connections among geographic regions. Identifying the spatial network communities can help reveal the spatial interaction patterns, understand the hidden regional structures and support regional development decision-making. Given the recent development of Graph Convolutional Networks (GCN) and its powerful performance in identifying multi-scale spatial interactions, we proposed an unsupervised GCN-based community detection method region2vec on spatial networks. Our method first generates node embeddings for regions that share common attributes and have intense spatial interactions, and then applies clustering algorithms to detect communities based on their embedding similarity and spatial adjacency. Experimental results show that while existing methods trade off either attribute similarities or spatial interactions for one another, region2vec maintains a great balance between both and performs the best when one wants to maximize both attribute similarities and spatial interactions within communities. CCS CONCEPTS• Computing methodologies → Artificial intelligence.

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Infrastructure inequality is a characteristic of urbanization

Cited by 85 publications

References 43 publications

Urban inequality: a hypothetic risk factor for myopia

Urban inequality: a hypothetic risk factor for myopia

Satellite Data Applications for Sustainable Energy Transitions

Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions

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