Table 3 Selected literature on associations between urban characteristics and COVID-19 confirmed case rate
References | Study area | Spatial scale | Study period | Method | Relationship with variables | |||||
|---|---|---|---|---|---|---|---|---|---|---|
Elderly rate | College-education rate | Economic condition | Population density | Poor housing conditions | Land use/Urban geometry/ Activities | |||||
Yang et al. [4] | Massachusetts, US | City/ town | As of Apr 29, 2020 | Spatial lag model | -* | - (P = 0.05) |  + * | N/A | N/A | N/A |
Sun et al. [5] | US | County | As of Jun 28, 2020 | Ordinary least square (OLS), spatial lag, spatial error, and spatial autoregressive combined (SAC) model | -* (spatial lag, SAC) | N/A | Nonsig |  + * ~  + *** (all models) | Nonsig | N/A |
Zhang & Gary [6] | US | County | As of May 1, 2020 | Multiple linear regression |  + * | N/A | N/A |  + *** | N/A | N/A |
Hamidi et al. [7] | 913 metropolitan counties in the US | County | As of May 25, 2020 | Pearson's r, structural equation modeling (SEM) | -** (r), -*** (SEM) | -* (r), -*** (SEM) | N/A | Population + employment density:  + ** (r), nonsig (SEM) | N/A | Metropolitan area population, indicating connectivity: + ** (r), + *** (SEM) |
Karmakar et al. [8] | US | County | Mar 25– Jul 29, 2020 | Cross-sectional study | -*** | -*** | -*** |  + *** |  + *** | Rate of public transport commuters: + *** |
Ahmad et al. [9] | 3135 US counties | County | As of Apr 21, 2020 | Cross-sectional study | N/A | N/A | N/A | N/A |  + * | N/A |
Kan et al. [10] | Hong Kong | Intra-city | As of Apr 14, 2020 | Quartile analysis | N/A | N/A |  +  | - | N/A | Building density, building height, green spaces, public residential, open and recreation land: -; Private residential: +  |
Kwok et al. [11] | Hong Kong | Intra-city | As of Apr 30, 2020 | Logistic regression, case–control, lasso regression |  + ** | Nonsig | Male: -**, female: Nonsig |  + ** | N/A | Summed building height: + ** (logistic); average street length: -** (logistic, case–control) |
Karaye and Horney [12] | US | County | As of May 12, 2020 | OLS, geographically weighted regression (GWR) | N/A | Overall SES: global result by OLS: + (P = 0.05) | N/A | Housing and transportation vulnerability: global result by OLS: -***, local variation by GWR: -1.10 – 1.53 | ||
Ulimwengu and Kibonge [13] | US | County | May 1 – Dec 15, 2020 | Spatial Durbin model | N/A | Overall SES: direct effect: -*** in May 16–Sept 2020, total effect: + *** in May 16–Sept 2020 | Direct effect: usually + ***, total effect:-*** in Aug 16–Dec 2020 | Housing and transportation vulnerability: direct effect: + ** ~  + ***, total effect: -*** in Oct – Dec 2020; Environment with high epidemic risk (prison, healthcare, and high-risk industries), total effect: + * ~  + *** in Aug–Dec 2020 | ||