key: cord-0753819-opiy6j1w
authors: Das, Arijit; Ghosh, Sasanka; Das, Kalikinkar; Basu, Tirthankar; Dutta, Ipsita; Das, Manob
title: Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India
date: 2020-10-31
journal: Sustain Cities Soc
DOI: 10.1016/j.scs.2020.102577
sha: cf2fdcbe741d25724cb59258d93365858a7f7f14
doc_id: 753819
cord_uid: opiy6j1w

The emergence of COVID-19 has brought a serious global public health threats especially for most of the cities across the world even in India more than 50 % of the total cases were reported from large ten cities. Kolkata Megacity became one of the major COVID-19 hotspot citiesin India. Living environment deprivation is one of the significant risk factor of infectious diseases transmissions like COVID-19. The paper aims to examine the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. COVID-19 hotspot maps were prepared using Getis-Ord-Gi* statistic and index of multiple deprivations (IMD) across the wards were assessed using Geographically Weighted Principal Component Analysis (GWPCA).Five count data regression models such as Poisson regression (PR), negative binomial regression (NBR), hurdle regression (HR), zero-inflated Poisson regression (ZIPR), and zero-inflated negative binomial regression (ZINBR) were used to understand the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. The findings of the study revealed that living environment deprivation was an important determinant of spatial clustering of COVID-19 hotspots in Kolkata megacity and zero-inflated negative binomial regression (ZINBR) better explains this relationship with highest variations (adj. R2: 71.3 %) and lowest BIC and AIC as compared to the others.

transmission to preventits spread to other neighbourhoods. As of May 4, 2020, there are 316 such containment zones have been identified(https://wb.gov.in). These containment zones are placed under geographic quarantine for more than 40 days (on May 05, 2020, it was 41 st Day lockdown) whereto and from movement of population (including movement for maintaining essential services which are being provided by the local government) is not be permitted except emergency services.

Recent studies have shown multiple environmental factors such as air temperature (Zhu and Xie, 2020; Yongjiana et al., 2020; Núñez-Delgado, 2020; Liu et al., 2020; , humidity (Auler et. al., 2020; Ma et. al., 2020; , air pollution , smoking (Taghizadeh-Hesary and Akbari, 2020) determine the severity as well as rapid spread of COVID-19. After a quick review of the previous studies, few notable gaps have been identified. Firstly, most of the previous studies focused to examine the impact of meteorological parameters (such as air temperature, humidity, rainfall) on COVID-19 outbreak (Zhu and Xie, 2020; Yongjiana et al., 2020; Núñez-Delgado, 2020; Liu et al., 2020; Auler et. al., 2020; Ma et. al., 2020; rather than socio-economic conditions of the people. Secondly,Living environment deprivation, especially in megacities,may increase the risk of COVID-19 spread by affecting the survival and transmission of the virus in a variety of ways, considerable evidence exists forhigher incidences of certain infectious diseases reported in an urban setting from deprived small neighbourhoods (Hughes and Gorton, 2014), overcrowded slums (Baker M, et al., 2000) , and segregated low-class residential areas (Acevado-Garcia D., 2000) . But still now no studies have been performed to assess the impact of overall living conditions of the households on COVID-19 cases. Thirdly, in few recent studies very few indicators have been considered to understand the urban vulnerability to COVID-19 (Misra et al., 2020; Das et al., 2020c) .

However it is very difficult to understand the relationship between living conditions and J o u r n a l P r e -p r o o f COVID-19 particularly in large megacities by considering these few indicators. Fourth, very few studies have been performed to examine the relationship between living conditions of the people and outbreak of COVID-19 (Wang and Su., 2020; .

Particularly it remained unexplored in Indian context. Urban living environment deprivation is a multidimensional phenomenon that results from the complex interaction of socio-demographic, socio-economic, and eco-environmental factors.The urbaninduced adverse eco-environmental impacts such as decreasing vegetation cover (Du et al., 2019; Yao et al., 2019; Gui et al., 2019; Sussman et al., 2019) , increasing impervious surfaces and the concomitant rise in land surface temperature (Sultana and Satyanarayana, 2020; Li et al., 2018; Portela et al., 2020; Sejati et al., 2019; Zhang et al., 2017; Fu and Wang., 2016; Yang et al., 2017; Jiang et al., 2015; Fonseka et al., 2019; Bian et al., 2017; Guo et al., 2015; Zhang and Sun., 2019; Arulbalaji et al., 2020) ; socio-demographic factors such as the high density of population and households (HHs) negatively influences urban living environment deprivation Musse et. al, 2018) . The urban living environment deprivation leads to deterioration of health and human comfort in cities that increases the susceptibility of infectious diseases (EPA, 2008) . Therefore, it is logical to assess whether and how urban living environment deprivation affects the spread of COVID-19. But till now to the best of our knowledge, no study has addressed this issues on severely COVID-19 affected megacities in India. To fill-up the existing research gap, the relationship between spatial clustering of COVID-19 containment zones and living environment deprivation in Kolkata megacity has been assessed in this study. The goal of this study is to provide scientific evidence about the influence of living environment deprivation on spatial clustering of COVID-19 hotspots in Kolkata megacity. Since, the socio-economic deprivations of HHs itself are determined by multiple aspects that negatively influence the quality of living of the HH (Mishra, 2018; . Index of Multiple Deprivation J o u r n a l P r e -p r o o f (IMD) has been developed to examine the spatial pattern of deprivations.IMD developed by Mishra (2018) on the basis of its applicability to the context of COVID-19 using Geographically Weighted Principal Component Analysis (GWPCA) has been improved in this study. This improved variant of IMD includes non-availability of WaSH (water, sanitation, and hygiene) services within household (HH) premises which may increase the transmission rate of COVID-19 in a variety of ways (Das et al., 2020) . For example, the households (HH) having no availability of drinking water source and sanitation facilities within premises are more vulnerable to COVID-19 transmissions as they are dependent on community tape well or community toilets. Thus this study has an immense potentiality to understand the relationship between COVID-19 hotspots and living environment deprivation in Kolkata megacity on a robust and scientific basis.

Kolkata megacity (22°34′N, 88°22′E) is the third-largest metropolis in India (after Mumbai and NewDelhi) with a population of 4.5 million (https://censusindia.gov.in). It is the most importanturban centre in Eastern India, which has a typical subtropical, warm humid, monsoon climate classified as Aw(tropical wet and dry) in the Köppen climate classification (Kottek et al., 2006) . With mild and moderate winters and very hot summers, the average annual temperature and rainfall are 26.8°C and 1582 mmrespectively (Banerjee et. al, 2020) .

During summer months (April to June) the air temperature (Ta) often cross 40°C with relative humidity (RH) of more than 70%. The winter (November to February) exhibits mild Ta of 25-30°C and RH of 60%. Kolkata megacity belongs to the red zone with high COVID-19 incidences and 316 numbers of containment zones distributed heterogeneously in its 141 subcities (i.e. electoral wards which are the lowest administrative units). The provision of WaSH in Kolkata megacity is not satisfactory compared to other megacities of India. The scenario of J o u r n a l P r e -p r o o f WaSH is particularly poor in deprived areas reflected from low per capita availability of community latrines, stand posts, and tube well (Mukherjee et al., 2020) . The lower availability of WaSH services and other factors of living environment enhances the chances of local community transmission of COVID-19.

To execute this study, three sources of information were used: 1) The numbers of COVID-19 containment zones were collected from the official websites of the Department of Health & Family Welfare, Govt. of West Bengal (www.wbhealth.gov.in); 2) variables required for devising IMD obtained from Census of India (https://censusindia.gov.in); Landsat OLI/TIRS satellite images of April 6, 2020, identified by path 138 and row 44, collected from by the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov) J o u r n a l P r e -p r o o f

The steps followed in this study were outlined in Fig. 2 . The processing of Landsat TM image involved enhanced band combinations, a geometric correction, conversion of digital numbers to the spectral radiance of spectral bands, and finally derivation of land surface temperature (Musse et al., 2018) . The biophysical indicators from the processed image were obtained by using the equations shown in Table 2 using the 'raster' package of the Rprogramming language. Table 1 ).

Before executing GWPCA to device the IMD for Kolkata megacity, the overall significance of the indicators (the factorability test) was performed by using Kaiser-Meyer-Olkin (KMO) test and Bartlett's Test of Sphericity (Bartlett, 1950; Antony & Visweswara Rao, 2007) . In this study, KMO value was more than 0.800 and the chi-square value is 0.00 which indicates the indicators were very much suitable to devise IMD for Kolkata megacity. Initially, 25 indicators were selected, but 3 indictors were dropped due to multi-collinearity (1 indicator with |r|<0.2 dropped which was practically uncorrelated and 2 indicators dropped because they were very tightly correlated (|r|>0.8). The IMD is devised by employing GWPCA.

GWPCA is now recognized as a very effective tool for the detection of the local nonstationary effects of variance in a data structure (Lloyd, 2010; Harris et al., 2011; Kumar et al., 2012) . The local principal components and local variance derived from GWPCA are suitable in devising IMD (Mishra, 2018) .

Mathematically, the local eigen decomposition of GWPCA transformation can be written in its algebraic expression as:

is a diagonal matrix obtained fromoptimal bandwidths (here adaptive) based on the 'Bi-square' kernel weighting scheme.The details description on GWPCA is given in the appendix section. To reduce noise and locate important factors of IMD, the first 3 PCs with eigen-values greater than 1 (i.e., λi ≥ 1) were retained (Hair et al., 2006) . The initial deprivationindex (Si) at the sub-city level for each megacity is a weighted aggregation of components scores (C). Where, Si= Initial deprivation index, Cik=Value of a component score for k th PC of ward i, and Wk=Combined weight of IMD components for k th PCs for Ward i, m=3.

Applying the min-max normalization method, the initial deprivation index score for 141

wardswere standardized IMD (0 to 100). The IMD is obtained for sub-cities of Kolkata megacity using the following formula: Si, Smin, and Smax are respectively the initial deprivation score for sub-city 'i', the lowest and highest values of the initial deprivation score are considered. The construction of the final J o u r n a l P r e -p r o o f Index of Multiple Deprivation (IMD) assigns a multiple deprivation score to each urban ward for Kolkata megacity. The IMD value '0' stands for the 'bottom ranking' sub-city, 100 for the 'top-ranking' electoral ward, and varies between '0' and '100' for other wards.

Essentially, it tells us where a particular sub-city stands, between the 'top' and 'bottom' ranking sub-city on a linear scale. For instance, an IMD value of 50 means that the ward is situated in the "halfway" between the top and bottom ranking wards in terms of multiple urban deprivations. The higher value of IMD correspondence to higher the level of multiple deprivations and vice versa. The IMD devised in this study was validated by comparing with the results of Mishra (2018) , along with information obtained from Google earth images of randomly selected 100 neighbourhoods and local knowledge. J o u r n a l P r e -p r o o f

Hotspot spatial analyses are widely used in the ecological study Jia et al. 2018 ) to determine spatial clusters of high values of a particular phenomenon. In this paper, the hotspot analysis tool of ArcGIS 10.2 software (Getis-Ord Gi*) was used to explore the spatial clustering of high containment Zones of COVID-19 and high IMD values.

A descriptive analysis was performed for all the data. The distribution of COVID-19 containment zones was discrete and positively skewed with many wards did not have any containment zones. The distribution of COVID-19 containment zones in Kolkata megacity was negative binomial because its variance was higher than the means.5 count data regression models (Appendix2), namely, Poisson regression (PR), negative binomial regression (NBR), hurdle regression (HR), zero-inflated Poisson regression (ZIPR), and zero-inflated negative binomial regression (ZINBR) are considered to analyse the impact of living environment deprivation on the spatial distribution of COVID-19 containment zones in Kolkata megacity.

The explanatory factors considered to perform the regression analysis are listed in table 2.

The best count data regression model is obtained by comparing the values of the likelihood ratio (LR) test, Akaike's information criterion (AIC), the Bayesian information criterion (BIC), andthe adjusted coefficient of determination (R 2 adj). The values of AIC, BIC, and R 2 adj are acquired using the following formula (Pinheiro and Bates, 2000; Zeng, 2015) .

Where, yi , yi , yi sequentially are observed value, the estimated value, and the mean value of the biomass; n is the number of samples; p is the number of parameters; tais the t value at confidence level a with n-p degree of freedom; and logLik is the log-likelihood values of the non-linear regression model.The two-sided statistical analyses were carried out at a 5% level of significance. All analyses were conducted using R software (version 3.5.3) with the "glm"

and "pscl" package.

Table2 COVID-19 containment zones and its determining factors

Ward wise Number of Containment Zones NA

Urban Patch Density (UPD) Number of urban Patch/ Hectare Area

Household Density (HHD) No. of Household/ Area

In some previous research studies, deprivation of the households were assessed across cities using GWPCA (Basu and Das., 2020; Charlton et al., 2010) . But recent studies reported that there were a close association of COVID-19 transmissions and living condition of the households (Wang and Su., 2020; . For example, urban slums are more vulnerable to infectious diseases due to lack of availability, accessibility of households to the basic services and amenities (Arifeen et al., 2001; Checkley et al., 2016; Corburn et al., 2020) .Thus it is clear that living environment of the households largely influence transmissions of infectious diseases. In this study, an attempt has been made to examine the impact of living environment on COVID-19 transmissions using Index of Multiple Deprivation (IMD) for the first time in India. Most of the recent studies tried to interlink COVID-19 transmissions with WASH (water, sanitation and hygiene) provisions but ignored overall living conditions of the households. In addition to this, the COVID-19 hotspot maps were prepared (i) to understand the high-high and low-low concentrations of COVID-19 and (ii) to examine the relationship between COVID-19 hotspots and deprivation within the city.

Thus the findings of this method will surely assist tooverall living environment of the households.

The regression models (Poisson regression (PR), negative binomial regression (NBR), hurdle regression (HR), zero-inflated Poisson regression (ZIPR), and zero-inflated negative binomial regression (ZINBR)) were also used to assess the impact of living environment on COVID-19 hotspot in Kolkata megacity.

J o u r n a l P r e -p r o o f 3. Results

To analyze the distribution of deprived areas, this study categorizes IMD into five different classes of multiple deprivations based on equal interval methods, with IMD ≤20.00 as least deprived and IMD >80.00 as a most deprived category. Table3 As per the result of the study, it was observed that maximum number of wards (59) 

The spatial extent and distribution of IMD are unable to explore the geography of multiple deprivations in Kolkata megacity. The spatial heterogeneity of multiple deprivations in Kolkata megacity was examined using spatial hotspot analyses using the following formula: 

Where xj is the feature attribute value for j, wi,j represents spatial weight value between feature i and j, n indicating total number of features. For a better presentation of the relationship between COVID-19 containment clustering (hotspots) and its various covariates of living environment deprivation, we have selected four clusters (2 from COVID-19 hotspots and 2 from Cold Spot) namely Window-A, Window-B, Window-C, and Window-D. Table 5 shows the cluster-specific distribution of COVID-19 containment zones and their association with living environment characteristics. It is clear from Table 5 that there are striking differences in the living environment deprivation between COVID-19 hotspots and cold spots. This provides strong initial evidence that the living environment deprivation has a strong influence on spatial clustering of hotspots in Kolkata megacity. 

socio-economic, socio-demographic and bio-physical covariates.

The descriptive analysis was performed for all the data. Table 6 

The comparisons of test statistics presented in table7and the values ofLR, AIC, and BIC indicate that the ZINBR model was the best fit for this study. The value of LR, AIC, and BIC is lowest for the ZINBR, which suggests that the model is better. ZINBR with two-sided tests, and p<0.05 was considered statistically significant. 

In this research, we compiled 35 variables that could potentially explain the spatial pattern of COVID-19 hotspots in Kolkata megacity. These variables were grouped into two different aspects that determine the living environment deprivation, namely socio-economic and ecoenvironmental. A synthetic IMD was developed by employing GWPCA and using local variance as the weight for the dimensions. The widely used PCA cannot account for the local variance (Lloyd, 2010; Harris, Brunsdon, & Charlton, 2011; Kumar, Lal, & Lloyd, 2012) . In Sun et al.,2020) . Secondly, access to basic services and amenities (housing conditions, water availability, sanitations, limited outdoor spaces) can also affect respiratory disease as well as deadly COVID-19

transmissions (Das et al., 2020; Mishra et al., 2020; Naddeo & Liu, 2020) . Thirdly, the areas with socio-economically deprived are highly vulnerable because the people living in these areas are often employed in such an occupations that are not provide opportunities to work at their home. Thus from the overall analysis it was clear that the living conditions (or living environment) are closely linked with the transmissions of COVID-19. In this study also it was well documented that northern part of Kolkata megacity are relatively high vulnerable due to high population density as well as relatively limited availability as well as accessibility to the basic services and amenities of the people. The result also clearly suggests that there was an impact of living environment on COVID-19 transmissions. The findings suggested that socio-economic dynamics must be incorporated for formulating mitigation strategies to combat COVID-19 pandemic situation.

In most of the recent studies, the factors affecting COVID-19 transmissions were assessed either from different perspectives considering population density Sun et al., 2020) , meteorological parameters such as temperature, humidity, wind speed, pressure, rainfall (Xie and Yongjian et al., 2020; Liu et al., 2020; Wang et al., 2020a; Auler et al., 2020; Ma et al., 2020; Gupta et al., 2020; Wu et al., 2020; Jiang and Xu., 2020) . However, to the best of our knowledge, no studies were performed previously to examine the impact of living environment (living conditions) in relation to COVID-19 transmissions.

A deprived household can be defined as the lack of accessibility as well as availability to the basic services and amenities. Thus limited access to the basic services and amenities J o u r n a l P r e -p r o o f influence overall living conditions of the households (Das et al., 2020; Saroj et al., 2020) .

Particularly the people living in slum like conditions are relatively more vulnerable to infectious disease due to limited access to basic services and amenities (Corburn et al., 2020; Mishra et al., 2020) . More than 30% of the total urban population in Kolkata lives in slum areas. Most of the slums are located in eastern and western (dock area) and northern (Cossipore) part of the city. Ray (2016), performed a study over some selected slums in Kolkata and findings of the study showed that there were only one community tap for entire slum areas (about 600 people collect water from this community tape). As per as findings of Bag et al., (2016) , more than 70% slum dwellers are dependent on public sanitation facilities in Delhi, Kolkata and Mumbai. Being COVID-19 an infectious disease, is it not vulnerable for entire slum population? If there is single COVID-19 positive slum dweller, will it not increase the risk of COVID-19 transmissions? In previous studies it was also well documented that deprive people had very limited access to the basic services and amenities (Sajjad, 2014; Phukan, 2014; Goswami, 2014 ). Recent studies also reported that provision of basic services had significant impact on COVID-19 transmissions (Das et al., 2020; Corburn et al., 2020; Mishra et al., 2020) . Thus from the overall analysis, it was clear that deprivations of households may have significant impact on the formations of COVID-19 hotspots in Kolkata megacity also.

In Northern part of Kolkata, the population density is relatively high as compared to south

Kolkata. In recent studies it was well recognized that the transmissions of COVID-19 is largely determined by population density Carozzi, 2020; Kodera et al., 2020) . Thus from overall analysis, it was clear that there were a strong positive correlation between population density and COVID-19 transmissions. Thus in North Kolkata, high population density may have a significant factor for COVID-19

transmission as compared to other parts of the megacity.

In developing countries, environmental issues received very less attentions in policy making framework and most of the time environmental degradation moves parallel with economic development (Das and Das, 2019b) . Environmental factors (such as vegetation cover, land surface temperature, water bodies etc.) are largely influenced by socio-demographic and economic factors (such as population density, living environment of the households, household density). In recent studies it was documented that socio-demographic factors have crucial impact on COVID-19 (Sannigrahi et al., 2020; Kumar et al., 2020) . In this study also, it was recorded that socio-economic status (living environment of the households) has an impact on COVID-19 transmissions. In developing countries like India, unplanned and haphazard expansion of cities not only affect quality of urban people but also degradation of environment (such as loss of forest cover, water etc.) (Shahbaz et al., 2014; Capps et al., 2016; Azam and Khan; Das and Das., 2019a; 2019b; Chun et al., 2020) . In Kolkata megacity also, rapid urban expansion causes deteriorations of ecosystem health (Ghosh et al., 2019; Das et al., 2020) . Thus from the previous studies, it was well recognized that there were a strong nexus between socio-economic and environmental factors.

Based on our study, three remotely sensed eco-environmental factors (LST, NDVI, and PDU) have influential role spatial clustering of COVID-19 incidence in Kolkata megacity. The findings are similar to previous studies (Ma et al.,2020) , but unlike these studies which have used meteorological data, we have obtained the eco-environmental data using remote sensing for the first time to explore the impact of bio-physical indices on COVID-19 incidences.

While we did not find NDWI and MNDW to be significantly influential in COVID-19

incidences. 

J o u r n a l P r e -p r o o f One of the limitations of this study was the availability of the finest spatial granularity COVID-19 positive cases at the electoral ward level. However, the identification, containment areas and adaptation of strict geographic quarantine measures in these containment zones indicate the large outbreaks of COVID-19 in these areas. Therefore, making inferences on COVID-19 based on the spatial distribution of COVID-19 is not problematic until or unless an appropriate and best fit statistical analysis (count regression models in general and ZIBR in particular) is used to model the association between COVID-19 hotspots and living environment deprivation.

There is no conflict between the authors.

GWPCA analysis helps to access the local level statistics, which utilizes the geographically weighted variancecovariance matrix to acquire the geographically weighted mean (Eq. A01) (Lloyd, 2010) : Here, d ij = inter-distance between the locations i and j.  = bandwidth that signifies the kernel size.

Later on, by standardizing the geographic weights to one, then, geographic mean will be as:

Geographically weighted standard deviation is acquired using Eq. A04 (Lloyd, 2010) . The obtained correlation matrix is used to derive the PC for each location.

Appendix -2:% count regression model. 

Where X 1 ≡ 1; β 1 = intercept; The regression coefficient β 1 ,β 2 , ….. β k represents the unknown parameters which are estimated from a data set.Following this notation, The Poisson regression model can be expressed for the observation 'i' as equation 12 

Where X 1 ≡ 1; β 1 = intercept; The regression coefficient β 1 ,β 2 , ….. β k represents the unknown parameters which are estimated from a data set and the estimates are epitomized as b 1, b 2, …., b k .Following this notation, the negative binomial regression model can be expressed for the observation 'i' as Eq. A10 Where y i = dependent variable value for the i th observation 'i' = 1, …, N ), z i = vector of length 'J' denoting the predictor variables number in the zero part, χ i = vector of length 'K' denoting predictor variables numbers in the hurdle part, γ = vector of coefficients which belongs to 'z', and β = vector of coefficients which is related to 'x' [Zeileis et al., 2008] . f zero = probability density function at least on {0, 1} (binary) or {0, 1, 2, …} (count), and f count = probability density function on {0, 1, 2, …}.

The zero-inflated poisson model deals with the two zero generating processes. The first one deals with the generation of the zero and the second one is associated with the Poisson distribution which generates counts.

Within these counts, some of may be zero. The following fixates can be described as Eq. A12 P r ( 0 ) 

Where y j = the outcome variable with the value of any non-negative integer; λ = expected poisson count at the 'i' th observation; π represents the probability of the extra zeros.

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The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the Semarang Metropolitan Region

Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates

City noise-air: an environmental quality index for cities

Quality of life in metropolitan Athens, using satellite and census data: comparison between 1991 and

Assessing the state of environmental quality in cities e a multi-component urban performance (EMCUP) index

Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000-2018 over a sub-tropical Indian City

COVID-19: epidemiology, evolution, and cross-disciplinary perspectives

Impacts of increased urbanization on surface temperature, vegetation, and aerosols over Bengaluru

The powerful immune system against powerful COVID-19: a hypothesis

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

The Social Impact of COVID-19

United States Geological Survey (USGS) website

Real-time estimation and prediction of mortality caused by COVID-19 with patient information-based algorithm

A preliminary assessment of the impact of COVID-19 on environment-A case study of China

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

District Wise Containment Zones

World Health Organization (WHO), 2020a. Rolling Updates on Coronavirus Disease (COVID-19

Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)

COVID-19) Situation Report-103

Exposure to Air Pollution and COVID-19 Mortality in the United States

Assessing the impacts of urbanization-associated green space on urban land surface temperature: A case study of Dalian

Urbanization effects on vegetation cover in major African cities during 2001-2017

Association between shortterm exposure to air pollution and COVID-19 infection: evidence from China

Regression models for count data in R

Using nonlinear mixed model and dummy variable model approaches to construct origin-based single tree biomass equations

An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables

Spatial-temporal impacts of urban land use land cover on land surface temperature: Case studies of two Canadian urban areas

Mapping of Fe mineral potential by spatially weighted principal component analysis in the eastern Tianshan mineral district

Association between ambient temperature and COVID-19 infection in122 cities from China

Table: Summary of the previous studies on living environment deprivation