Measuring the effects of Compactness/Sprawl on COVID 19 spread patterns at the neighborhood level

Hejazi, Seyed Jafar; Arvin, Mahmoud; Sharifi, Ayyoob; Lak, Azadeh

doi:10.1016/j.cities.2022.104075

Cited by 13 publications

(2 citation statements)

References 112 publications

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“…Now, the sustainable development 50 of humanity has already been greatly challenged by the COVID-19 pandemic. As the virus prevails everywhere at the neighborhood level 51 , more environmental parameters 52 need to be considered and hence strict waste management 53 must be implemented.…”

Section: Discussionmentioning

confidence: 99%

Study on the spatial decomposition of the infection probability of COVID-19

Liu

2023

Sci Rep

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In the course of our observations of the transmission of COVID-19 around the world, we perceived substantial concern about imported cases versus cases of local transmission. This study, therefore, tries to isolate cases due to local transmission (also called community spread) from those due to externally introduced COVID-19 infection, which can be key to understanding the spread pattern of the pandemic. In particular, we offer a probabilistic perspective to estimate the scale of the outbreak at the epicenter of the COVID-19 epidemic with an environmental focus. First, this study proposes a novel explanation of the probability of COVID-19 cases in the local population of the target city, in which the chain of probability is based on the assumption of independent distribution. Then it conducts a spatial statistical analysis on the spread of COVID-19, using two model specifications to identify the spatial dependence, more commonly known as the spillover effect. The results are found to have strong spatial dependence. Finally, it confirms the significance of residential waste in the transmission of COVID-19, which indicates that the fight against COVID-19 requires us to pay close attention to environmental factors. The method shown in this study is critical and has high practical value, because it can be easily applied elsewhere and to other future pandemics.

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Section: Discussionmentioning

confidence: 99%

Study on the spatial decomposition of the infection probability of COVID-19

Liu

2023

Sci Rep

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“…It could be argued that if the interior of the city is already urbanized and partly compromised, land-use governance tools could lean toward further urbanization of the central areas (within the limits of saturation) justified by the nature of the urban economy [71]. This could contain the phenomenon of urban sprawl [72] and the impairment of natural ecosystems at the outer boundaries of the city.…”

Section: Methodsmentioning

confidence: 99%

Maps and SDG11: A Complex but Possible Relationship

Lami,

Abastante,

Mecca

et al. 2024

IJSDP

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Is it possible to visualize in an immediate and powerful way the indicators that measure the sustainability of cities? While the final answer is rather positive, the process of getting there is quite complex and dense with difficulties as illustrated in this paper. The 2030 Agenda and the Sustainable Development Goals (SDGs) constitute a fundamental apparatus for measuring and observing Countries based on specific analysis systems, but without an integration with spatial analysis tools as Geographic Information Systems (GIS). Although the potential of the latter in capturing efforts toward sustainable development is recognized by the United Nations (UN), their use in analyzing systems is limited considering data operability issues in terms of spatial and temporal accuracy and proprietary diffusion modes. This paper aims at bridging the gap between urban sustainability analysis and GIS tools, testing the integration of SDG11 indicators and GIS within the Italian context. The research here investigates the applicability and potential of such integration by considering the target 11.1 and 11.3 by focusing on a case study, the City of Turin (Piedmont, Italy). In operational terms, this paper implements an open access WebGIS database providing new spatialized sub-indicators based on the use of open data at the local urban scale.

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