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Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks. The role of urban and regional planning for risk containment

Authors:
Beniamino Murgante at University of Basilicata
Beniamino Murgante
Ginevra Balletto at University of Cagliari
Ginevra Balletto
Giuseppe Bartolomeo Las Casas at University of Basilicata
Giuseppe Bartolomeo Las Casas
Giuseppe Borruso at University of Trieste
Giuseppe Borruso
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Abstract

This research develops from a set of basic geographical questions about the outbreak of Covid-19 out of China in Europe. The questions dealt with why and why with such strength Italy has been seriously hit, one of the most important cases in terms of death toll out of Hubei Province and mainland China, in the world, making the country a worldwide study case for epidemic concentration and diffusion. Questions were also related to geographical similarities among the areas hit, and particularly the Po Valley region and Wuhan metropolitan region in Hubei province, and also related to why such a divide of the virus spreading was identified in Italy between Northern and Central and Southern regions and provinces. In order to try to give an answer these questions, authors realized a vast and articulated database of indicators at provincial level in Italy, performing several geographical analyses - ecological approach - based on Spatial autocorrelation and Geographical Weighted Regression, coming to the conclusion that aspects such as land take, pollution can seriously influence the phenomenon and justify a pattern as that observable in Italy. The analyses and observation of the phenomenon also suggests that policies based on urban regeneration, sustainable mobility, green infrastructures, ecosystem services can create a more sustainable scenario able to support the quality of public health.
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TeMA
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TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
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Houshmand Ebrahimpour Masoumi, Technical University of Berlin, Germany
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Special Issue
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SCENARIOS, INSIGHTS, REASONING AND RESEARCH
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TeMA Special Issue Covid – 19 vs City – 20, 283-304
print ISSN 1970-9889, e-ISSN 1970-9870
DOI: 10.6092/1970-9870/6849
Received 12th May 2020, Available online 19th June 2020
Licensed under the Creative Commons Attribution – Non Commercial License 3.0
www.tema.unina.it
Geographical analyses of Covid-19's spreading
contagion in the challenge of global health risks
The role of urban and regional planning for risk containment
B. Murgante a*, G. Balletto b, G. Borruso c, G. Las Casas a P. Castiglia d, M. Dettori d
a School of Engineering
University of Basilicata, Potenza, Italy
e-mail: beniamino.murgante@unibas.it,
giuseppe.lascasas@unibas.it
* ORCID: https://orcid.org/0000-0003-2409-5959
* Corresponding author
b Department of Civil and Environmental Engineering and
Architecture, University of Cagliari, Italy
e-mail: balletto@unica.it,
c Department of Economics, Business, Mathematics and
Statistics “Bruno de Finetti”, University of Trieste, Italy
e-mail: giuseppe.borruso@deams.units.it
d Department of Medical, Surgical and Experimental
Sciences, University of Sassari, Sassari, Italy
e-mail: paolo.castiglia@uniss.it, madettori@uniss.it
Abstract
This research develops from a set of basic geographical questions about the outbreak of Covid-19 out of
China in Europe. The questions dealt with why and why with such strength Italy has been seriously hit, one
of the most important cases in terms of death toll out of Hubei Province and mainland China, in the world,
making the country a worldwide study case for epidemic concentration and diffusion. Questions were also
related to geographical similarities among the areas hit, and particularly the Po Valley region and Wuhan
metropolitan region in Hubei province, and also related to why such a divide of the virus spreading was
identified in Italy between Northern and Central and Southern regions and provinces. In order to try to give
an answer these questions, authors realized a vast and articulated database of indicators at provincial level
in Italy, performing several geographical analyses - ecological approach - based on Spatial autocorrelation
and Geographical Weighted Regression, coming to the conclusion that aspects such as land take, pollution
can seriously influence the phenomenon and justify a pattern as that observable in Italy. The analyses and
observation of the phenomenon also suggests that policies based on urban regeneration, sustainable
mobility, green infrastructures, ecosystem services can create a more sustainable scenario able to support
the quality of public health.
Keywords
Covid-19; Italy; Po-valley; Air quality, Climate changes; Land take; Spatial diffusion processes
How to cite item in APA format
Murgante, B., Borruso G., Las Casas G., Balletto, G., Castiglia, P., & Dettori, M. (2020). Geographical
analyses of Covid-19's spreading contagion in the challenge of global health risks.
Tema. Journal of Land
Use, Mobility and Environment
,
Special Issue Covid–19 vs City–20
. 283-304.
http://dx.doi.org/10.6092/1970-9870/6849
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
284 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
1. Introduction
With this research, we tried to find some answers to the questions raised by the Covid-19 outbreak in Italy,
first among European countries, after Southeastern Asia. In particular, an attempt was made to highlight some
elements connected to the causes of the diffusion of the virus in Northern Italy, in the Po Valley megalopolis,
which also includes the metropolitan city of Milan.
In this sense, we analyzed the data relating to Covid-19 - infected and deaths at the provincial level - as of 31
March 2020 and 30 April 2020, useful dates for observing the phenomenon after the country's lock down of
10 March, which placed severe restrictions on mobility and industrial production and services, in order to slow
down the spread of the epidemic, and the disease’s spatial behavior after such policies.
Furthermore, as a starting point it was possible to observe similarities between the Wuhan area in the province
of Hubei with those of the metropolis in the Po Valley, referring in particular to the geographical and climatic
conditions - presence of rivers and water bodies, flat land, limited atmospheric circulation and scarcity of wind
– socio-economic conditions - industrial production, transport and mobility infrastructures, population
distribution and density, population aging and life expectancy -, as well as similarities relating to concentrations
of pollutants in the atmosphere and soil consumption. We hypothesized the existence of a relationship between
pollutants and the spread of the virus in the outbreak of the epidemic and its lethality.
In particular, we took into consideration soil consumption and air pollution, referred to particulates - PM2.5 and
PM10 - and those deriving from human activities, as industry, traffic, domestic heating, agro-industry and
intensive farming, as CO2 and nitrogen-based components, such as NOx and NH3. - The basic idea is that the
presence of atmospheric pollutants can generate health pressures on the population and determine the pre-
conditions for the development of both stress on the diseases related to the respiratory system and of
complications related to them, including those that are health-threatening, which may explain the excess
lethality that occurred in the area under consideration.
Fig.1. Synthetic comparison - Wuhan urban agglomeration and Great Milan metropolis. Authors’ elaboration
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
285 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
In particular, relations between high concentration of atmospheric pollutants and the diffusion of pathogenic
microorganisms has already been demonstrated (Peng et al., 2020). Moreover, being exposed to higher
concentration of atmospheric pollutants can also explain the basa inflammation condition that can afflict the
population altering the physiological conditions and leading to a greater predisposition to infection and
symptomatic development of the disease (Chen & Schwartz, 2008; Conticini et al., 2020).
Furthermore, the particular weather conditions, including thermal inversion, typical of the winter period, may
have worsened the environmental situation in the areas - of Wuhan and the Po Valley - such as low rainfall
and a milder winter than the previous ones.
The two areas, in fact, have the same Köppen climatic classification Cfa subclass ‘humid subtropical’, typical
of temperate continental areas (
Global climate change, 2020
, 2020; Skarbit et al., 2018) and profound
analogies typical of the fluvial plain contexts, characterized by a fairly isotropic space. Both are located in an
alluvial plain, Wuhan urban agglomeration - Yangtze river and Great Milan metropolis - Po river (Fig.1).
Both mega urbanizations have industrial and post-industrial functions, with a heavy presence of manufacturing
companies, in machinery, automotive and ICT, as well as advanced and cultural services, particularly in the
major center. Both the areas share a strong promiscuity with agricultural activities and a wide progression of
the sprawl (Lu et al., 2020; Pezzagno et al., 2020; Romano et al., 2017; Senes et al., 2020).
Fig.2 The climatic classification of Köppen. Authors’ elaboration
In this complex international framework, we developed the present research, which does not pretend to be
exhaustive, but to show the first results of an interdisciplinary ecological approach. In this regard, the basic
conditions refer - for both cases examined as Wuhan agglomeration and Greater Milan metropolis - to an
intense and prolonged exposure to air pollution, as peaks of concentration of fine dust and other pollutants,
constitute a pejorative factor in cases of epidemics Covid-19 (Setti et al., 2020). We also paid particular
attention to the relationship between climate and air quality (Du et al., 2019). Climate changes on the one
hand affect the atmospheric processes and on the other cause changes in the functioning of terrestrial and
marine ecosystems which can, in turn, affect the atmospheric processes (Jacob & Winner, 2009). However,
these two environmental emergencies are still considered separately both at the level of the scientific
community and those responsible for environmental policies, as in the case of the Covid-19 emergency (Setti
et al., 2020).
According to the EEA - European Environmental Agency - although air pollution (European Environment
Agency, 2018) affects the whole population, as collective health costs, only a part is more exposed (European
Environment Agency, 2020) to individual risks (Chalvatzaki et al., 2019; Mitsakou et al., 2019; Reames &
Bravo, 2019). In particular, Greater Milan metropolis and most of the Po Valley represent the outcome of
industrial, agricultural and intensive farming globalization in Italy, which presenting an increasingly critical
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
286 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
quality of air (Pezzagno et al., 2020). Although in the last decade in Italy there have introduced important
taxation and incentive measures for the purchase or improvement of the ecological performance of home
heating (Magnani et al., 2020) ) and public and private road vehicles however, the levels of air pollution for
150 days (2018) have exceeded EU regulatory limits - much lower than WHO ones. Furthermore, this situation
is prolonged in time as high level of air pollution and concentration of pollutants in the air have been constantly
reported in the previous years (Legambiente, 2020). In addition, the climatic and geographical ‘handicap effect’
of Greater Milan metropolis, is not secondary in the air quality (Zullo et al., 2019). This geographical framework
- mainly isotropic - is also characterized by a community with a high life expectancy and a strong national
hospital migration that can put health services under stress (Volpato et al., 2020).
The rest of the paper is organized as follows. Paragraph 2 is dedicated to Materials and Methods. In paragraph
2.1 we present the study area, Italy with its intermediate administrative units; paragraph 2.2 presents the
data used for the analysis. In paragraph 2.3 the methodology adopted is presented, consisting in the ecological
approach and a set of fitted for purposes spatial analyses, including the SMR (Standardized Mortality Ratio)
and areal analysis for autocorrelation and estimates, as the GWR (Geographical Weighted Regression) and
LISA (Local Indicator for Spatial Autocorrelation).
Paragraph 3 hosts the results, with paragraph 3.1 dedicated to the results obtained from GWR, while paragraph
3.2 dedicated to the results obtained from LISA; paragraph 4 Discussion and 5 Conclusions the paper and
propose further developments.
2. Materials and methods
2.1 The study area (Italy)
The analysis regards Italy as the area where the outbreak of Covid-19 is analyzed. Italy spans over a surface
of 302,072.84 sq km, with a population of 60,359,546 inhabitants (ISTAT, 2019) for an average population
density of 200 inhabitants per square kilometer. Italy is organized in 20 Regions - one of them,Trentino Alto
Adige, organized in 2 Autonomous Provinces with regional competences. In the analysis, we considered the
intermediate administrative subdivision in Provinces, Metropolitan Cities (Ref. L. No. 56 of April 7, 2014) and
a set of former provinces now used only for statistical purposes.
We considered the overall country for the analysis, although our initial attention was concentrated on the Po
Valley geographical region. Such an area covers approximately 55,000 km2, with nearly 22 million inhabitants,
with a population density of 400 inhabitants per km2 - double than that of the rest of the peninsula, reaching
different peaks in the main urban areas of the Greater Milan metropolis - the neighboring Milan and Monza
Provinces exceed 2000 inhabitants per square kilometer. It is the economic engine of the country, and is also
the mostly affected area by Covid-19 outbreak.
2.2 Data
The research has been performed using different datasets mainly referred to Italy and related to the Covid-
19 outbreak, as well as socio-economic and environmental data, considered useful for examining the territorial
aspects of the virus outbreak in Italy. Covid-19 data considered the number of total infected people at 31
March and 30 April 2019 at province level, as reported by Italian Ministry of Health, as collected by the Civil
Protection. An important novel dataset, originally built from scratch by the research group, is the number of
deaths at province level. In many cases data were provided by regional administration, while in other cases
the research required counting and referring data to provinces from the local health agencies and other official
sources (Istituto nazionale di statistica (ISTAT), 2020; Istituto superiore di sanità (ISS), 2020).
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
287 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
Among the others, the major difficulties were found in locating at province level data for important regions in
terms of the Covid-19 outbreak as Lombardy and Piedmont; also, big regions as Liguria, Lazio, Campania and
Sicily required an extra-effort for locating deaths at provincial level.
Date
Provincial level
deaths
Regional level deaths
Provincial level
infected
Regional level infected
31 March 2020
12,105
12,428
102,440
105,792
30 April 2020
27,249
27,967
215,084
205,463
Source
authors
data - set
Italian civil
protection
authors
data-set
Italian civil
protection
Tab.1 Covid-19 deaths and infected localized at Provincial and Regional level
The complete study data set consists of over 100 indicators/indices. However, to simplify the discussion, we
indicate only the specific ones mentioned in the paper, divided into four categories representative of the
ecological approach taken. (Tab.1).
Fig. 3 Data set - Ecological approach
In particular, the retrieval of data - open data -, their cataloging, representation has always been consistent
with the ecological approach (Fig.3), precisely in order to evaluate the phenomena in their complexity and
entirety, to confirmed within the geospatial correlation - GWR and LISA, as presented in the next paragraph.
2.3 Methodology
The research carried on is based on an ecological approach, where the physiological traits of the virus are
examined together with a set of selected relevant variables, covering different environmental and socio-
economic aspects. Virus-related data as infected cases and (standardized) deaths were examined and referred
to several variables. We concentrate on particular elements related to aspects that, in an integrated manner,
can be considered important in understanding the human-environment relations between human activities,
geographic and climatic conditions and virus outbreaks.
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
288 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
The quantitative analysis supporting our approach has been performed on area-based methods, particularly
aimed at analyzing spatial autocorrelation among the area units considered – data at Italian province level.
Autocorrelation is analyzed at local and global level by means of Geographically Weighted Regression (GWR)
and Spatial Autocorrelation (LISA).
Standardized Mortality Ratio (SMR)
Mortality has been standardized using Standardized Mortality Ratio, that is keeping tracks of the different age
structures that can be found in different regions considered for an analysis. It in fact takes into account the
fact that different regions can have different population structures, and / or different mortalities. Standardized
Mortality Ratio is therefore a method to analyze the patterns of deaths considering age composition. It
calculates the expected number of deaths over the distribution of population by age group, and considering
the age-specific rates of deaths for each areal unit considered.
Values around unity portrays a situation where mortality is behaving ‘as expected’, or in line with the trend of
the area. Values higher than 1 are characteristic of a situation of a higher than expected mortality, while values
lower than 1 suggest a mortality lower than that expected (Gatrell & Elliott, 2002). Mortality was standardized
for the Italian provinces and for age groups - 10 groups; first group 0-9 years; last group 90-∞ -, with reference
to the national population figures in the year 2019 (Istituto Nazionale di Statistica, 2019).
An indirect standardization was performed for computing specific mortality values by age group, obtained by
dividing the number of Covid-19 deaths confirmed by the Italian Higher Institute of Health Care (ISS - Istituto
Superiore della Sanità, sorveglianza integrata Covid -19) with the 10 defined age groups. The number of
expected deaths for Italian provinces for the age groups previously identified and based on the Italian
provincial population at provincial level (Istituto Nazionale di Statistica, 2019), was calculated as in the
formula:
! " #$!%!&'
"
!#$
(1)
with ni being the specific age group population in each observed province; Ri the national mortality rate for
the specific age group.
The Standardized Mortality Ratio (SMR) was than calculated comparing the number of events observed in each
province with the respective number of expected events:
()% "*++,
!&
(2)
with d the number of observed deaths and e the number of expected deaths.
Finally, the 95% confidence intervals (95% CI) were calculated, following the rule as in Vandenbroucke
(Vandenbroucke, 1982).
Geographically Weighted Regression (GWR)
Geographically Weighted Regression (GWR) (Brunsdon et al., 1996; Casetti & Jones, 1992; Casetti, 2010;
Fotheringham et al., 2002; Fotheringham et al., 1997; Stewart Fotheringham et al., 1996) is a method which
allows to analyse how a phenomenon spatially changes within a particularly place. Starting from Tobler (Tobler,
1970) first law of geography "Everything is related to everything else, but near things are more related than
distant things", GWR can be considered as a spatial extension of multiple linear regression. GWR is not limited
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
289 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
to global parameters, but it considers also local parameters. Also, the mathematical formulation is very similar
to the typical regression analysis (equations 3, 4).
-!" .%/.$0$! / .&0&! /111/.'0'! /2!&&&3456&&4 " *1111 $
(3)
Where:
−
yi
= Dependent variable
−
xi
= Independent (also the term Explanatory is adopted) variables
−
β0
= Coefficients (sometimes the term Parameters is used) expressing the relationship between
dependent and independent variables.
−
εi
= Residuals, i.e. the part of dependent variable not explained in the model
−
-!" .%78!'9!:/ .$&78!'9!:&0$! /.&&78!'9!:&0&! /111/.'&78!'9!:&0'! /2!
(4)
In Geographically Weighted Regression the term (
ui,vi
) is also considered, which represents coordinates of
point i in the space.
It is possible to have positive or negative relationships between dependent and independent variables:
according to the kind of relationship, a sign (+/-) is associated to the coefficients.
In order to model in the best way, the phenomenon to be investigated it is fundamental to define all factors
which may influence the analyses. The central point is to find the main variables in phenomenon modelling,
defining the dependent variable and identifying the possible independent variables. It is also important, before
analysing data with GWR, to test with Ordinary Least Squares the possible independent variables to adopt.
Two main measures of Ordinary Least Squares are useful in understanding if the variables adopted in the
analysis are meaningful: R2 or adjusted R2 and Akaike. R2 results are generally included between 0 and 1. A
better predictive performance has been highlighted by values close to 1. Akaike Information Criterion (AIC)
(Akaike, 1973; Hurvich et al., 1998) has not an absolute scale of measure, but it is useful in comparing two
models, with the same dependent variable, in order to assess which of them fits better the phenomenon.
Smaller values of the AIC indicate a better simulation, if the difference is not big, less than 3, two models can
be considered equivalent.
Another important check in model performance concerns Residuals. It is fundamental to analyse that spatial
dependence does not occur in residuals, verifying a random spatial distribution. Residuals have to be analyzed
by Moran Index I. Moran Index I (Moran, 1948) is a global measure of spatial autocorrelation and its values
can be included between -1 and 1. If Moran Index I is close to zero data are randomly distributed, if the term
is higher than zero, autocorrelation is positive, otherwise it is negative.
Regression coefficients are estimated using nearby feature values. Consequently, main parameters are kernel
and bandwidth which provide a definition of nearby.
There are two kinds of kernel, fixed and adaptive: the first one defines nearby according to determined fixed
distance band; while adaptive kind defines nearby according to determined number of neighbours.
Fixed kernel is adopted if observation points are regularly located, otherwise, if observation points are
clustered, adaptive kernel is more suitable.
Bandwidth controls the size of kernel and can be defined in three ways: directly by the analyst (it is possible
to directly define distance or neighbours number), by means of AIC method, which minimises Akaike
Information Criterion (AIC), or by using CV, which minimises the CrossValidation score.
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
290 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
Spatial Autocorrelation
Geographical objects are generally described by means of two different information categories: spatial location
and related properties. In data analysis there is a huge literature concerning methods which separately
compute attributes from spatial components.
The most interesting property of spatial autocorrelation is the capability to analyze at the same time locational
and attribute information (Goodchild, 1986). Consequently, spatial autocorrelation can be considered as a very
effective technique in analyzing spatial distribution of objects assessing at the same time the degree of
influence of neighbour objects. This concept is well synthesized in the first law of geography defined by Waldo
Tobler (Tobler, 1970) "All Things Are Related, But Nearby Things Are More Related Than Distant Things".
Adopting Goodchild (Goodchild, 1986) approach, (Lee & Wong, 2000) defined spatial autocorrelation as
follows:
(;< "( ( )!"*!"
#
$%&
#
!%&
( ( *!"
#
$%&
#
!%&
,
(5)
Where:
− N is the number of objects;
− i and j are two objects;
− cij is a degree of similarity of attributes i and j;
− wij is a degree of similarity of location i and j.
defining xi as the value of object i attribute; if
cij
= (
xi
−
xj
)
2
, Geary C Ratio (Geary, 1954) can be defined as
follows:
< " +,-$./+( ( *!"'"! +0!-0".(.
&/+( ( *!""! .(+0!-01.(
!
,
(6)
If
!)* = ($)− $
&
)($*− $
&
)
, Moran Index I (Moran, 1948) can be defined as follows:
= " ,/ ( ( *!"'+0!-01.+0"-01.
"!
/+( ( *!""! .(+0!-01.(
!
,
(7)
These two indices are very similar, mainly differing in the cross-product term in the numerator, which in Moran
is calculated using deviations from the mean, while in Geary is directly computed.
These two indices are global indicators of spatial autocorrelation. They provide an indication about the
presence of autocorrelation. The precise location of elevated values of autocorrelation is provided by Local
Indicators of Spatial Association. One of the most adopted indices of local autocorrelation is LISA-Local
Indicator of Spatial Association developed by Anselin (Anselin, 1988, 1995), considered as a local Moran index.
The sum of all local indices is proportional to the value of Moran one:
>=!! " ?&=,
(8)
The index is calculated as follows:
=!"+2!-2
3.
/4+
(>7@!5/705A0B::
,
5#$ ,
(9)
It allows, for each location, to assess the similarity of each observation with its surrounding elements. Five
scenarios emerge:
− locations with high values of the phenomenon and high level of similarity with its surroundings (high-
high H-H), defined as
hot spots
;
− locations with low values of the phenomenon and high level of similarity with its surroundings (low-low
L-L), defined as
cold spots
;
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291 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
− locations with high values of the phenomenon and low level of similarity with its surroundings (high-low
H-L), defined as potentially
spatial outliers
;
− locations with low values of the phenomenon and low level of similarity with its surroundings (low-high
L-H), defined as potentially
spatial outliers
;
− locations completely lacking significant autocorrelations.
LISA (Local Indicator of Spatial Association) provides an effective measure of the degree of relative spatial
association between each territorial unit and its surrounding elements, allowing highlighting type of spatial
concentration for the detection of spatial clusters.
In equations 5, 6, 7, 9 the only term not well formalized is wij related to neighbourhood property. The most
adopted approach in formalizing this property is spatial weights matrix, wij are elements of a matrix considered
as spatial weights, equal to 1 if i and j are neighbours equal to 0 in the case of self-neighbour or if i and j are
not neighbours. This approach is based on the concept of contiguity, where elements share a common border
of non-zero length. It is important to give a more detailed definition of contiguity and more particularly what
does a border of non-zero length exactly mean.
Adopting chess game metaphor (O’Sullivan & Unwin, 2010), contiguity can be considered as allowed by paths
of
rook
,
bishop
and
queen.
3. Results
3.1 Ordinary Least Squares and Geographically Weighted Regression (GWR)
All variables, previously described, have been tested using Ordinary Least Squares in order to understand in
which measure they are reliable. First results and elaborations of statistical tests suggested to exclude some
variables from the analyses for low correlation or redundancies. More particularly, the number of deaths have
been considered as a dependent variable and annual average of PM2,5 and PM10, Ozone (O3 - number of days
to exceed the 8 hour moving average of 120 μg/mc), Wind gusts (annual days with gusts> 25 knots), Fog,
Surface waterproofed to year 2016, Wind (Km/h, Jan - Feb - Mar 2020), Hospital emigration, Commuting, CO2
in not urbanized areas have been adopted as explanatory variable. The results are quite interesting, R2 is
0.705979, Adjusted R2 0.671935 and Akaike Information Criterion (AIC) 1392.44. Variance Inflation Factor is
less than 7.5, all values are lower than 3.5, this means that the explanatory variables are not redundant.
It is also important to analyze residuals. The residuals of a good model should be normally distributed with a
mean of zero. I our case the residuals histogram matches the normal curve indicated in blue in Fig.4.
Fig. 4 Histogram of Distribution of Standardized Residuals
The other important aspect is the spatial distribution of residuals. More particularly, standardized values of
residuals, calculated by means of Ordinary Least Squares, have been used as input data in calculating spatial
autocorrelation, in order to understand if residuals were autocorrelated or not.
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Spatial autocorrelation has been calculated adopting Moran scatter plot and considering standardized variables
of residuals as abscissa and spatial weighted standardised variable of residuals as ordinate. In the graph,
Moran Index corresponds to direction coefficient of linear regression, which represents the scatter plot. Positive
autocorrelation corresponds to spatial clusters in upper right and lower left quadrants. Lower right and upper
left quadrants can be classified as spatial outliers.
Fig.5 Moran scatter plot of residuals standardized variable
Fig.6 Moran scatter plot of residuals standardized variable
Fig.5 shows that the slope of Moran Index is close to zero coinciding with abscissas axis, this means that
residuals are not spatially autocorrelated.
Ordinary Least Squares allows to analyse the relationship between dependent variable and explanatory
variables, deleting also redundant and not significant variables.
After the good results obtained with Ordinary Least Squares it is important to analyse how these relationships
vary over space. This is possible using Geographical Weighted Regression. As expected GWR results are better
than those achieved with OLS: namely, R2 is 0.741573518, Adjusted R2 0.689031597 and Akaike Information
Criterion (AIC) 1389.801618.
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Also, in this case residuals are not spatially autocorrelated. Fig.6 shows that Moran Index is 0.05 this means
that the spatial distribution is random.
Local R2 is a parameter included between 0.0 and 1.0, it is an indicator on how the local regression model fits
the observed values. If the indicator is close to zero the local model is far from the observed values. In this
case all values are close to 0.7 (figure 7) and there are not great differences. Despite the local values are quite
equivalent the map at national scale describes the Italy divided in four zones with the highest level of R2 in
the northern part of Italy.
Fig.7 Local R2 map
(a)
(b)
Fig.8 Observed (a) and predicted (b) values
Analyzing Fig.8 It is possible to observe that there are small differences between the observed and predicted
values also at local level, this means that the local regression model fits very well the analysed phenomenon.
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3.2 Local Indicators of Spatial Autocorrelation (LISA)
In the Po Valley megalopolis the local climate changes (Fig. 9) and the significant change in relative humidity
and air quality - non-disjointed phenomena - (Blum, 2017; Maione et al., 2016; Manes et al., 2016; Reames
& Bravo, 2019) ) affect the quality of life, which is exposed to several actions combined with poor air quality
(Reames & Bravo, 2019).
Fig.9 Local Climate Change - Great Milan Metropolis
These are in fact apparently unrelated phenomena, which in reality, besides being profoundly dependent on
each other, do not act as a mere sum on environmental ecosystems and communities, but in the form of
combinations that are in turn related to urban geography - land use: efficient land use, sprawl and ecosystem
services. It should be noted that the law on air quality has substantial differences from country and country
(Fig 10). Furthermore, the reference targets for making comparisons are still those of the 2005 WHO
guidelines. It is worth mentioning WHO Guidelines (
WHO Air quality guidelines for particulate matter, ozone,
nitrogen dioxide and sulfur dioxide
, 2006) that for PM2.5, they set a daily limit of 25 µg/m3; year limit 10 µg/m3
and that in Italy with Legislative Decree of 13 August 2010, n. 255: 25 µg/m3 year limit was lowered to 20
µg/m3 only from 1st January 2020. However, this new national target of PM2.5 does not take into account the
direct and indirect effects deriving from the geography, urban and climatic weather conditions of the contexts,
which in the Po Valley are certainly an important element for the purposes of air quality (Ferrero et al., 2019).
In particular, local climate changes such as temperature and humidity, poor air quality and the persistent
absence of wind, make the Po Valley a one of a kind area, both at national and international level. Furthermore,
frequent and persistent thermal inversion phenomena in the winter months, especially in periods of high
atmospheric pressure, traps the cold air near the ground, together with the pollutants (Caserini et al., 2017).
In the Po valley, urban phenomena of industrialization and intensive farming (Romano & Zullo, 2016),
intertwine and more than 50% of national GDP is produced, as well as almost 50% of national energy is
consumed (Arpa Emilia-Romagna ARPAE, 2018). The transition to compatible solutions related to well-being
seems not exactly close, despite the several regional and national plans to monitor and improve air quality
(Marongiu et al., 2019). Furthermore, life expectancy for both genders is substantially stable, confirming for
longer in Northern Italy (Istituto Nazionale di Statistica, 2019), also a consequence of social inequalities,
disabilities and access to health services, contributing to the North - South social divide. Finally, also in the
North there is also a greater and specific health care over 65. In this complex framework, land use takes on a
significant dimension.
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In this research aimed at evaluating why Northern Italy was marked by Covid-19, the data set - selected from
different sources and open data) used for the development of the Lisa Maps played an important role,
supporting the interdisciplinary ecological approach: Land use; Air quality; Climate and weather; Population,
health and life expectancy. In particular, the LISA maps on indicators relating to these phenomena: Cov_14,
Cov_15, Cov_19 and Cov_72 - confirm these first evaluations of the air pollution in Po Valley megalopolis (Fig.
11, Fig. 12 and Fig.13) and at the same time highlight the other side of the metropolis: the increase in life
expectancy and the provision of related health care (no. of geriatricians / 1000 ab over 65).
Fig.10 The WHO PM 2.5 target in 2019
(a)
(b)
Fig.11 Lisa Maps: Cov_14 PM2.5 (a); Cov_15 PM10 (b)
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(a)
(b)
Fig.12 Lisa Maps: Cov_19 Ozone (a); Cov_72 PM10 and Ozone (b)
(a)
(b)
Fig. 13 Lisa Maps: Cov_102 Increase in life expectancy (2002-2017) (a); Cov_103 No. of geriatricians / 1000 ab over 65
(2019) (b)
In other words, in this complex case study of the Po Valley megalopolis we can observe the persistency of:
poor air quality - climatic handicap (Ferrero et al., 2019), inefficiency of land use (Romano et al., 2017) and
increased life expectancy (Poli et al., 2019; Sarra & Nissi, 2020) of the population with a presumably
consequent impact of Covid-19 in terms of both infections and deaths (ISTAT & ISS, 2020).
As previously explained, Po Valley has spatial configuration as basin completely closed by Alpine Chain and
Apennines and it is characterized by an a homogeneous and isotropic space. While the former feature
represents a strong impediment to air circulation and distribution the later increases the probability that urban
sprawl phenomenon occurs. The two aspects are strongly related because the soil is an important element in
the carbon cycle allowing CO2 sequestration and storage. Consequently a lack of attention to the spatial
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planning can generate negative effects producing a loss of these properties (Zomer et al., 2017). Soil and
related ecosystem services are important elements in the improvement of air quality reducing PM10 and O3
(Fusaro et al., 2017; Manes et al., 2016). Po Valley is the most attractive area of the country because great
part of productive activities are concentrated, consequently it is fundamental to have a lot of not urbanized
areas capable of allowing CO2 storage. Unfortunately annual reports of Italian Institute for Environmental
Protection and Research (ISPRA) (Munafò, 2019), other important researches (Martellozzo et al., 2018; Pileri
& Maggi, 2010; Romano & Zullo, 2016) highlight that Land take phenomenon in Italy is mainly concentrated
in the northern part of the country.
(a)
(b)
Fig.14 Lisa Maps: Cov_3: land take between 2014-2018 (a); Cov_52: land take up to 2000 (b)
(a)
(b)
Fig.15 Lisa Maps: Cov_49: sealed soils (a); Cov_82: CO2/non urbanized areas (b)
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Also spatial autocorrelation analysis confirms this trend with the more elevated values concentrated in
Lombardy region. More particularly figures 14 and 15 analyze LISA index of land take between 2014-2018 and
up to 2000, sealed soils at 2016 and CO2/non urbanized areas. The concurrence of these factors led to a
dangerous combination with a high concentration of elements dangerous to health with a strong decrease of
areas which, in some way, represent the only possibility to air cleaning.
4. Discussion
In the geographical context of Po Valley, elements as land use, life expectancy, commuting, climate handicap
and poor air quality certainly played a role and contributed to increasing the effects of the epidemic.
Furthermore, local climate changes such as temperature and humidity, poor air quality and the absence of
wind make the Po Valley an area unique of its kind, both nationally and internationally. It represents an
isotropic territorial context suitable for anthropic activities, but at the same time with latent health risks. In
particular, in the Po Valley, urban industrialization phenomena are characterized by a high entropy and by an
increasing consumption of resources, given its contribution to over 50% of national GDP and, as a side effect,
the consumption of almost 50% of national energy. Despite the existence of several regional and national
plans to monitor and improve air quality, climate, soil consumption, etc. the transition to compatible solutions
related to well-being does not seem close. Furthermore, on the occasion of the Covid-19 epidemic, PM10
emissions in the Po Valley were high and sometimes exceeded the limits and must be related to the combined
climate - wind, winter thermal inversion - and human actions - remained active in the lockdown period -
domestic heating, urban logistics, food production and retail. In this context, certainly not simple for both
human, environmental and anthropic geography, the ecological approach has allowed us to obtain the first
results and the first policy proposals with supported by GWR and LISA analysis, referring to the combined
action between urban design, monitoring and health risk plan (Fig.16).
Fig.16 Ecological approach and Policies - Po Valley Area.
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The health emergency has highlighted even more how in many cases the urban and territorial plans are old,
very far from the current reality or based on old laws, which do not allow the production of tools to respond
rapidly to current and immediate problems. The outcome is a scenario that leads to a consumption of resources
greater than the planet's capacity. The result is that the main planning goals are very far from providing a
serious response to the transformation demands that daily arise. This old system, based on vintage planning
(Romano et al., 2018) or ghost planning (Scorza et al., 2020), can lead to a situation of potential peak and
overshooting of the environmental carrying capacity (represented on left part of Fig.17).
Fig.17 Ecological approach and Policies - Po Valley Area
As in the most dramatic moments of the Covid-19 outbreak a quest for flattening the curve was requested to
avoid a peak and an extra stress over the national health systems, planning seem facing, now more than ever,
the same risks and challenges. If, as said, ghost and vintage planning can barely allow tackling short run,
ordinary solutions, for long run and extraordinary cases a change of pace is needed. An alternative to be
pursued is an ecological approach based on simulations in assessing transformations impacts, that allows
planners to take into account several land use scenarios, choosing the more suitable solutions for
transformations. Such an approach to planning can also consider the possible losses of ecosystem services in
simulations (Geneletti D., 2016; Gobattoni et al., 2016). The vast amount of data to date available, together
with the vast array of instruments for modelling scenarios, as Multiagent Systems, Space Syntax, Geodesign
(Cocco et al., 2020; Steinitz, 2012), etc. can take into account a lot of components in detailed simulations. A
Performance Based Planning represents the summary and the container for all of these models and simulation
tools. Such capacity of gathering and elaborating data to produce scenarios can help in meeting objectives of
protecting natural areas and consequently of human health be more easily.
Furthermore, adopting urban policies based on urban regeneration, sustainable mobility (Battarra et al., 2018;
Bonotti et al., 2015; Papa et al., 2018; Tira et al., 2018) and the creation of green infrastructures (Balletto et
al., 2020; Lai et al., 2018; Ronchi et al., 2020) can create a more sustainable scenario able to flatten the curve
under the Earth carrying capacity (Gargiulo & Russo, 2018; Maragno et al., 2020; Pietrapertosa et al., 2019).
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5. Conclusions
In this paper we focused our attention on the Covid-19 outbreak in Italy and on the effect of the interaction
among geographical, environmental and socio-economic characteristics. The occasion of the massive outbreak
in the Po Valley area, which has been analyzed and compared in its main character with the Wuhan – Hubei
Province in China in terms of some similarities, led us to consider a wide set of variables and analyze them by
using spatial analytical methods. This was done to evaluate some relations and to provide with some
suggestion in terms of integrated planning and policy actions. From a wide selection of variables we highlighted
four big families, grouped for ‘land use’, ‘air quality’, ‘climate and weather’ and ‘population, health and life
expectancy’. These were related to mortality, expressed in terms of SMR – Standardized Mortality Ratio -
examined in terms of spatial autocorrelation, and considered both from the spatial and attribute point of view.
Geographically Weighted Regression - GWR - and Local Indicators of Spatial Autocorrelation LISA – Spatial
analytical techniques as were performed and were useful in confirming a relation between a set of conditions
and the spreading of the Covid-19. The analysis helped to understand more the relation between
environmental conditions and health aspect, and on the need to introduce and systematize analytical tools to
support spatial decisions, to plan in ordinary and extraordinary conditions.
Future developments will concern the systematization of medium and long-term policies in relation to health
risk.
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Authors’ profile
Beniamino Murgante
He is professor of spatial panning at the School of Engineering of the University of Basilicata. Member of the Editorial Board
of many international journals, scientific committees of a lot of national and international conferences and the scientific
council of some national and international organizations. Co-General Chair of the International Conference on Computational
Science and Its Applications (ICCSA). More information can be found on the personal web page:
http://service.unibas.it/utenti/murgante/Benny.html
Ginevra Balletto
She is an Associate Professor of urban and territorial planning at the DICAAR, University of Cagliari. She is the scientific
coordinator of the Urban and territorial planning sector of the Strategic Plan of the metropolitan city of Cagliari (Sardinia,
Italy), also for post Covid-19 actions. Her actual research interests are related to urban planning and environmental
sustainability in vulnerable areas (https://people.unica.it/ginevraballetto/prodotti-della-ricerca/).
B. Murgante et al. - Geographical analyses of Covid-19's spreading contagion in the challenge of global health risks
304 - TeMA Journal of Land Use Mobility and Environment. Special Issue | Covid-19 vs City-20
Giuseppe Borruso
He is an Associate Professor of Economic and Political Geography at the DEAMS -Department of Economics, Business,
Mathematics and Statistics "Bruno De Finetti, University of Trieste. His actual research interests are related to economic
geography, with particular reference to urban geography, transport and population.
Giuseppe Las Casas
Full Professor Urban and Regional Planning at the School of Engineering of University of Basilicata, former director of
Department of Architecture, Transport and Planning of University of Basilicata and Vice-rector for the higher education of
University of Basilicata.
Paolo Castiglia
Full Professor of Hygiene at the Department of Medical, Surgical and Experimental Sciences, University of Sassari. Head of
the Unit for Hygiene and Control of Hospital Infections for the University Hospital of Sassari.
Marco Dettori
Biologist specialist in Microbiology and Virology, assistant professor of General and Applied Hygiene at the Department of
Medical, Surgical and Experimental Sciences, University of Sassari.
... While some research has validated the inverse effect of income levels (Cordes and Castro, 2020;Mollalo et al., 2020) and/or educational levels (Cordes & Castro, 2020;Hamidi et al., 2020;Ramirez-Aldana et al., 2020) on COVID-19 prevalence, a study conducted in Russia by Zemtsov and Baburin (2020) has shown a positive association between these variables. Furthermore, studies conducted in various countries, such as the United States (Hamidi et al., 2020), Italy (Kuchler et al., 2022;Murgante et al., 2020), China (Kang et al., 2020;Xiong et al., 2020), and Canada (Wu et al., 2020a), have examined the impact of commuting patterns and social contact on the prevalence rates of the virus. ...
... The majority of studies, with the exception of those conducted by Gargiulo et al. (2020) and Ramirez-Aldana et al. (2020), have provided evidence supporting an inverse correlation between temperature and incidence rates. Additionally, the concentration of suspended particles under 2.5 microns emerged as a significant environmental factor that influences the transmission or severity of diseases (Coker et al., 2020;Cole et al., 2020;Murgante et al., 2020;Wu et al., 2020b). Table 1 provides a summary of 28 spatial factors identified in prior research as influential in the spread of COVID-19. ...
... Confirming studies Rejecting studies Activity density exhibits the most effectiveness and susceptibility on the resilience index, while the potential effect of traffic density has been a more significant factor in the severity of disease outbreaks. Kang et al. (2020), Murgante et al. (2020), Niu et al. (2021), Xiong et al. (2020) - ...
Spatiotemporal analysis of urban development patterns and COVID-19 in Mashhad, Iran
Article
Full-text available
  • Mar 2024
This study evaluates the impact of city spatial development patterns on the spread of airborne pandemics, focusing on Mashhad, Iran. Using ArcGIS spatial statistics, 91 spatial indices were examined between March 5, 2020, and October 2, 2021, during five distinct waves of the virus, revealing that private vehicle ownership and limited use of public transportation did not significantly reduce COVID-19 infection rates in Mashhad. Instead, factors such as proximity to transportation hubs, healthcare facility polarization, and retail shop distribution had a significant impact. Surprisingly, demographic vulnerability and conventional population-related factors had relatively minor effects. The study also highlights the influence of building concentration with high floor area ratios (FAR) and the impact of local authorities' optimism in reducing social distancing measures, especially in travel and traffic control, on the increased spread of the virus during recent waves.
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... As a global indicator, 'Moran's I neglects the instability of local spatial processes, which led to the development of the local version of Moran's I which identifies both the spatial clustering of entities with similar values and the occurrence of divergent values. This latter version is also known as a Local Indicator of Spatial Association (LISA) (14,37,38,65,59) and (41) in China (50), and (58) in the United States, and (66) in Mexico (50), in Italy (11), in Malaysia and (15) in Bangladesh 'produced LISA cluster maps to analyze the characteristics of COVID-19 at various spatial levels of aggregation'. ...
... It can consider 'non-stationary variables (such as climate, demographic factors, and environmental factors) and models the local relationships between those predictors and the patterns under study' (50). It facilitates 'the analysis of spatial variation in a phenomenon in a given place, following Tobler's first law of geography that everything is related to everything else, but near things are more related than distant things' (Tobler, 1970). ...
... Regarding COVID-19, GWR has been used to study the relationships between environment, disease, and a variety of socioeconomic activities. For example (45), used GWR and other models to 'assess the evolution of air pollution during 2020 in urban contexts in China' (50). studied 'the geographic parallels between affected areas in the Po Valley, Italy, and Wuhan, China, where they found that pollution and land use play an important role in the distribution of COVID-19 in both regions' (1). ...
Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19
Preprint
Full-text available
  • Jun 2023
According to the World Health Organization (WHO), COVID‑19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID‑19. The present review offers an assessment of research that used regional data systems to study the COVID‑19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID‑19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time‑based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.
View
... As a global indicator, 'Moran's I neglects the instability of local spatial processes, which led to the development of the local version of Moran's I which identifies both the spatial clustering of entities with similar values and the occurrence of divergent values. This latter version is also known as a Local Indicator of Spatial Association (LISA) (14,37,38,65,59) and (41) in China (50), and (58) in the United States, and (66) in Mexico (50), in Italy (11), in Malaysia and (15) in Bangladesh 'produced LISA cluster maps to analyze the characteristics of COVID-19 at various spatial levels of aggregation'. ...
... Another common method is Geographically Weighted Regression (GWR), using the variables previously included in OLS regression' (42,43,(45)(46)(47)70). 'GWR creates a local model and calculates the parameters for all points of the sample considering the spatial variation in the relationships' (49,71). It can consider 'non-stationary variables (such as climate, demographic factors, and environmental factors) and models the local relationships between those predictors and the patterns under study' (50). It facilitates 'the analysis of spatial variation in a phenomenon in a given place, following Tobler's first law of geography that everything is related to everything else, but near things are more related than distant things' (Tobler, 1970). ...
... Regarding COVID-19, GWR has been used to study the relationships between environment, disease, and a variety of socioeconomic activities. For example (45), used GWR and other models to 'assess the evolution of air pollution during 2020 in urban contexts in China' (50). studied 'the geographic parallels between affected areas in the Po Valley, Italy, and Wuhan, China, where they found that pollution and land use play an important role in the distribution of COVID-19 in both regions' (1). ...
Mapping the pandemic: a review of Geographical Information Systems‑based spatial modeling of Covid‑19
Article
Full-text available
  • Nov 2023
According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.
View
... This latter version is also known as a local indicator of spatial as-sociation (LISA) [65]. [37], [38], [59], [14] and [41] in China, [50] and [58] in the United States, and [66] in Mexico, [50] in Italy, [11] in Malaysia and [15] in Bangladesh produced LISA cluster maps to analyze the characteristics of COVID-19 at various spatial levels of aggregation. ...
... This latter version is also known as a local indicator of spatial as-sociation (LISA) [65]. [37], [38], [59], [14] and [41] in China, [50] and [58] in the United States, and [66] in Mexico, [50] in Italy, [11] in Malaysia and [15] in Bangladesh produced LISA cluster maps to analyze the characteristics of COVID-19 at various spatial levels of aggregation. ...
... GWR creates a local model and calculates the parameters for all points of the sample considering the spatial variation in the relationships ( [49]; [71]). It can consider non-stationary variables (such as climate, demographic factors, and environmental factors) and models the local relationships between those predictors and the patterns under study [50]. It facilitates 'the analysis of spatial variation in a phenomenon in a given place, following Tobler's first law of geography that everything is related to everything else, but near things are more related than distant things (Tobler, 1970). ...
Mapping the Pandemic: A Review of GIS-based Spatial Modeling of COVID-19
Preprint
Full-text available
  • Jun 2023
According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.
View
... Green-blue space is the most important natural element in urban areas, and is found to have a strong relationship with public health, including infectious diseases, chronic diseases and mental diseases Yu et al. 2024;Zhao et al. 2024). Murgante et al. (2020) found that landscape pattern played a key role in the spread of COVID-19 in the Po River Basin in Italy and Wuhan in China. The cities with more built-up areas and less green-blue space were reported to have more cases and faster transmission of COVID-19 (Ye & Qiu 2021). ...
Scale-specific effects of urban landscape pattern on the COVID-19 epidemic in Hangzhou, China
Article
Full-text available
  • Nov 2024
  • LANDSCAPE ECOL
Context Understanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas. Objective Based on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China. Methods We collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic. Results Eight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effects of landscape contagion, the number of water bodies, LSI of cropland and built-up areas, and the density of commercial houses were detected to vary between urban and suburban areas. The effects of LSI of water bodies and the density of shopping malls were found to vary among different districts. Conclusions In this study, we firstly discriminated the scale-specific effects of different landscape variables on the COVID-19 epidemic in the urban region. These findings can help to optimize the differentiated zoning prevention and control strategies for COVID-19 in cities and guide policy-making and urban planning at a multi-scale hierarchical perspective to improve public health and urban sustainability.
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... Particularly during the Covid-19 pandemic, scholars have connected the concept of "health risks" with the spread and impact of Covid-19, examining how health risks manifest geographically during the pandemic and how urban and regional planning can address these risks. [36] Thus, we adopt these perspectives, considering "health risks" as various risk factors related to health, such as disease transmission, environmental pollution, unsafe food and water sources, especially in the context of sudden public health events such as infectious disease outbreaks or environmental disasters, posing threats to public health. ...
Dynamic analysis of major public health emergency transmission considering the dual-layer coupling of community–resident complex networks
Article
Full-text available
  • Jun 2024
We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.
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... The results obtained from MGWR revealed that the two socio-economic factors had more significant impacts than meteorological factors (20). Using MGWR, it was determined that land use and pollution have a significant impact on the distribution of COVID-19 in both the impacted areas of the Po Valley in Italy and Wuhan in China (21). The spatially non-stationary relationships between sociodemographic determinants and COVID-19 incidence rates were also investigated in Oman using MGWR, and it was discovered that the population density, the proportion of elderly people aged 65 and over, hospital beds, and the prevalence of diabetes were statistically significant determinants of COVID-19 incidence rates (22). ...
Spatial statistical methodologies in COVID-19 Studies: A systematic review
Article
Full-text available
  • Sep 2023
Objective: As of August 2023, COVID-19 had claimed 7 million lives, making it the pandemic with the highest mortality rate. Therefore, The use of cutting-edge technologies and methods is essential when battling the COVID-19 epidemic. This paper aims to systematically review and synthetize applications of spatial statistical methodologies in the analysis of COVID-19. Material and Methods: 55 articles in total were screened from four main digital databases including Web of Science, SCOPUS, PubMed/MEDLINE, and Google schoolar. Three distinct concerns with the use of spatial statistical techniques in the analysis of COVID-19 are discussed, namely (i) applications of spatial regressions in the evaluation of COVID-19's effects, (ii) COVID-19 mapping using of hotspots and spatial clustering analyses, and (iii) applications of interpolation and geostatistics on COVID-19 studies, respectively. Results: Spatial regressions can support the assessment of the COVID-19 impacts on social-economy and environment. Whereas, hotspots and spatial clustering analysis can help effectively on COVID-19 mapping. Last but not least, geostatistics and interpolation are crucial for predicting COVID-19. Conclusion: This review not only emphasises the significance of spatial statistical techniques in COVID-19 studies, but it also sheds light on the practical applications of spatial statistics in COVID-19 research.
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... Still in the United States, the percentage of non-English-speaking households, uninsured individuals under the age of 65, and that of individuals living at or below the poverty line also proved important variables (Fielding-Miller et al., 2020); finally, Rollston and Galea (2020) identified spending on health care and health outcomes. Murgante et al. (2020) identified Covid-19 and geographical correlations in Italy: Their study analyzed spatial autocorrelations among area units (province level) and the effect of the interaction among (a) geographical, (b) environmental, and (c) socio-economic characteristics. Ataguba and Ataguba (2020) and Shammi et al. (2020) identified demographic risk groups in a Covid-19-ridden Bangladesh based on the public perception of a socioeconomic crisis and human stress levels in a resource-limited setting. ...
Social Determinants, Urban Planning, and Covid-19 Response: Evidence From Quito, Ecuador
Article
Full-text available
  • Jun 2023
p>Covid-19 has put all urban planning systems around the world to the test. Cities’ design and how these are managed are being observed, analyzed, and even questioned from the perspective of the pandemic. Density and poverty have been two fundamental aspects to manage in the pandemic scenario in cities of the Global South, which face this challenge along with other pre-pandemic planning problems. In the city of Quito, Ecuador, the response to the pandemic has been coordinated through regulations issued by the emergency operations center at the national level, and the information (number of cases) has been recorded per parish. The objective of this research is to determine if there is a relationship between Covid-19, poverty, and population density at the parish level for the canton of Quito. The results have shown that there is no correlation. What they did show is that due both to the difficulties of responding to the pandemic and the city’s planning structure, another type of characterization, or characterizations, of the territory (for example, by scenarios or by situations) is needed, which can respond to the needs of the most vulnerable groups. Another observable result was that the gap between urban planning and management instruments and the complexity of territorial needs contributes to the polarization of local government approaches, which compromises urban planning with minimum continuity and coherence.</p
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... La regresión geográficamente ponderada permite analizar como un fenómeno observado cambia dentro de un lugar, a partir del supuesto de Tobler donde todo está relacionado con todo, pero las cosas más cercanas tienen más relación entre sí (Murgante, et al. 2020). Esta técnica de análisis es particularmente útil para comprender mercados de la vivienda en busca de revisar componentes que configuran áreas homogéneas de la ciudad, lo que permite determinar con mayor eficacia la rentabilidad a obtener de cada sector urbano (Massimo, et al. 2018). ...
Déficit de la vivienda en el Gran Santiago: evidencia empírica sobre conflictos y argumentos para repensar la planificación metropolitana
Article
Full-text available
  • May 2023
Resumen En Chile existe un déficit de vivienda estructural que ha consolidado su aumento a pesar de la importante cantidad y montos otorgados como parte de la política habitacional, la cual se basa en la entrega de subsidios a la demanda. El presente artículo evalúa espacialmente la relación contradictoria entre esta política habitacional y variables socioeconómicas que permiten concluir que los subsidios no permiten romper con el proceso reproductivo del déficit habitacional. Para esto se aplica un estudio estadístico espacial en base a una regresión lineal múltiple para una evaluación preliminar y una regresión geográficamente ponderada para precisar los alcances territoriales de los resultados. El estudio se centra en la ciudad de Santiago de Chile, donde se concentra el 40% de la población nacional. Los hallazgos dan cuenta de la profunda contradicción entre la lógica subsidiaria, el fuerte desarrollo inmobiliario y el aumento del déficit habitacional, en un momento preciso dado los cambios a la constitución que se están desarrollando en Chile, para lo cual el derecho a la vivienda es uno de los aspectos que se deben incorporar la nueva carta magna de esa nación.
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Stochastic modeling of cascade dynamics: A unified approach for simple and complex contagions across homogeneous and heterogeneous threshold distributions on networks
Article
  • Apr 2024
  • PRE
The COVID-19 pandemic has underscored the importance of understanding, forecasting, and avoiding infectious processes, as well as the necessity for understanding the diffusion and acceptance of preventative measures. Simple contagions, like virus transmission, can spread with a single encounter, while complex contagions, such as preventive social measures (e.g., wearing masks, social distancing), may require multiple interactions to propagate. This disparity in transmission mechanisms results in differing contagion rates and contagion patterns between viruses and preventive measures. Furthermore, the dynamics of complex contagions are significantly less understood than those of simple contagions. Stochastic models, integrating inherent variability and randomness, offer a way to elucidate complex contagion dynamics. This paper introduces a stochastic model for both simple and complex contagions and assesses its efficacy against ensemble simulations for homogeneous and heterogeneous threshold configurations. The model provides a unified framework for analyzing both types of contagions, demonstrating promising outcomes across various threshold setups on Erds-Rényi graphs.
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The effects of air pollution and meteorological factors on measles cases in Lanzhou, China
Article
Full-text available
  • Apr 2020
  • ENVIRON SCI POLLUT R
By collecting daily data on measles cases, air pollutants, and meteorological data from 2005 to 2009 in Chengguan District of Lanzhou City, semi-parametric generalized additive model (GAM) was used to quantitatively study the impact of air pollutants and meteorological factors on daily measles cases. The results showed that air pollutants and meteorological factors had effect on the number of daily measles cases, and there was a certain lag effect. Except for SO2 and relative humidity, other factors showed statistically significant associations with daily measles cases: NO2 lag 6 days, PM10 and maximum temperature lag 5 days, minimum temperature and average temperature and average air pressure lag 4 days, visibility, and wind speed lag 3 days had the greatest impact on the number of daily measles cases. Under the optimum lag conditions, the number of daily measles cases increased by 15.1%, 17.6%, 7.0%, 116.6%, 98.6%, 85.7%, and 14.4% with the increase of 1 IQR in SO2, NO2, PM10, maximum temperature, minimum temperature, average temperature, and wind speed; with the increase of 1 IQR in average pressure, relative humidity, visibility, and daily measles cases decreased by 12.8%, 9.7%, and 13.1%, respectively. And different factors showed different seasonal effects. The effects of SO2 and temperature factors on daily measles cases were greater in spring and winter, but PM10 in summer.
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Definition of a Land Quality Index to preserve the best territories from future land take. An application to a study area in Lombardy (Italy)
Article
Full-text available
  • Feb 2020
“Land take” is a process of land-use change in which the agricultural and natural land is taken by residential, industrial, infrastructure and other developments. This change causes the loss of a non-renewable resource, such as the agricultural/natural soil, and the relative natural, cultural and landscape resources. The growing awareness about the loss of ecosystem services related to land take led developed countries to try to reduce the quantity of land taken with new laws and regulations. The European Union has set the goal of zero land take by 2050. It is not only a problem of limiting and slowing down the phenomenon, but it is always clearer that the quality of the land taken has to be assessed and adequately considered during the land-use planning process. In fact, in some cases like in the Lombardy Region, the law focuses not only on reducing the amount of land take, but also on limiting the loss of land with “high qualities”, requiring municipalities to assess the productive, naturalistic and landscape qualities of the territory. In this paper, the authors develop, using the GIS technology, a methodology to define and calculate a composite “Land Quality Index” (LQI). The methodology has been applied to a case study in the Lombardy region and has allowed to assess the quality of the territory in a rigorous and transparent way using available official data. In order to take into account the relative importance that stakeholders and land-use planners can give to the different components of LQI, AHP (Analytic Hierarchy Process) has been performed ad 4 different scenarios have been developed. LQI can support the land-use planning process in an ex-ante evaluation of different transformations hypotheses and in the definition of “quality-based” quantitative thresholds and monitoring of their trend over the time.
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Mapping Heat Stress Vulnerability and Risk Assessment at the Neighborhood Scale to Drive Urban Adaptation Planning
Article
Full-text available
  • Feb 2020
Climate change is one of the most complex issues of the 21st century, and even though there is general consensus about the urgency of taking action at the city level, the planning and implementation of adaptation measures is advancing slowly. The lack of data and information to support the planning process is often mentioned as a factor hampering the adaptation processes in cities. In this paper, we developed and tested a methodology for heat stress vulnerability and risk assessment at the neighborhood scale to support designers, planners, and decision makers in developing and implementing adaptation strategies and measures at the local level. The methodology combines high-resolution spatial information and crowdsourcing geospatial data to develop sensitivity, adaptive capacity, vulnerability, exposure, and risk indicators. The methodology is then tested on the urban fabric of the city of Padova, Italy. Our results show that different vulnerability and risk values correspond to different typologies of urban areas. Furthermore, the possibility of combining high-resolution information provided by the indicators and land use categories is of great importance to support the adaptation planning process. We also argue that the methodology is flexible enough to be applied in different contexts.
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Spatial Planning Policy for Sustainability: Analysis Connecting Land Use and GHG Emission in Rural Areas
Article
Full-text available
  • Jan 2020
Anthropogenic greenhouse gas (GHG) emissions are the highest they have ever been and the climate change they have triggered is having consequences on both human and natural systems. The aim of the paper is to demonstrate that an integrated reading of urban and rural land uses in relation to GHG emissions is feasible and useful at the regional level in order to reach emissions reduction. The Po Valley in Italy is an emblematic case study because its features are unique in Europe for high population density, urban sprawl, intensive agriculture, livestock management and consequently high emission levels. The methodology examines the total GHG emissions in relation to urban and rural areas. Between 2000 and 2010, the trend of CO2-equivalent emissions for the macro-regions of Italy shows a national decrease in contrast to the area of our case study which has seen a steady increase and growth trend over time. The paper analyzes some possible reasons linked to this anomaly, and it presents an estimation of the CO2-equivalent emissions related to the use of agricultural land. The main output of the paper is a new overview for research that aims to propose integrated solutions and policies at the local level with a wider vision focused on GHG emission knowledge, supported by Strategic Environmental Assessment.
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Is Urban Sprawl Decoupled from the Quality of Economic Growth? Evidence from Chinese Cities
Article
Full-text available
  • Dec 2019
This paper investigates how urban sprawl and the quality of economic growth interact and further studies the spatial-temporal decoupling characteristics of both. To achieve this, a framework was developed to better explain both the different dimensional effects urban sprawl exerts on the quality of economic growth and their reverse feedback relation. A sample of 285 Chinese cities (2003 to 2016) were analyzed, employing both a decoupling model and spatial correlation analysis. The findings indicated that urban sprawl and the quality of economic growth are related via scale, structure, technological efficiency, and technological progress effects. In practice, with increasing quality of economic growth, the urban sprawl index decreases at the national level. At prefecture-city level, the types of decoupling between urban sprawl and the quality of economic growth showed clear periodical and unbalanced characteristics. Furthermore, decoupling showed a significant agglomeration effect in Chinese cities, which is mainly mediated by the types High-High and Low-Low. This study provides a significant contribution to the relevant acknowledge system by providing a comprehensive theoretical framework toward an understanding of how urban expansion interacts with the quality of economic growth. Furthermore, their decoupling types and spatial differences that are critical for the urban sustainable development have been identified, thus providing several important insights for both academics and urban policy makers.
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Geodesign Process Analytics: Focus on Design as a Process and Its Outcomes
Article
Full-text available
  • Dec 2019
This paper argues that the opportunities offered by currently available collaborative Planning Support Systems (PSS) are useful not only for applying a systems approach and coordinating actors in the planning process, but also for tracking the evolution of design alternatives toward a final plan. The availability of process log-data in the latest PSS opens new paths for understanding (geo)design dynamics. With the aim of taking full advantage of this new data source, a novel Geodesign Process Analytics is described in detail from log-data extraction and pre-processing methods and tools to the development of the set of spatial, performance, temporal and design evolution indicators. The study also demonstrates how the proposed measures are appropriate for display in a dynamic dashboard, making available a real-time process analysis tool to the team coordinators, thus supporting their leading role in facilitating the geodesign process. The research assumptions were tested using the Geodesignhub PSS and data from a geodesign study developed within the International Geodesign Collaboration.
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Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?
Article
  • Apr 2020
  • ENVIRON POLLUT
This paper investigates the correlation between the high level of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) lethality and the atmospheric pollution in Northern Italy. Indeed, Lombardy and Emilia Romagna are Italian regions with both the highest level of virus lethality in the world and one of Europe's most polluted area. Based on this correlation, this paper analyzes the possible link between pollution and the development of acute respiratory distress syndrome and eventually death. We provide evidence that people living in an area with high levels of pollutant are more prone to develop chronic respiratory conditions and suitable to any infective agent. Moreover, a prolonged exposure to air pollution leads to a chronic inflammatory stimulus, even in young and healthy subjects. We conclude that the high level of pollution in Northern Italy should be considered an additional co-factor of the high level of lethality recorded in that area.
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Energy retrofitting of urban buildings: A socio-spatial analysis of three mid-sized Italian cities
Article
  • Apr 2020
  • ENERG POLICY
The current paper analyses the issue of energy retrofitting of buildings in Italian cities. In particular a mixed-method approach is used combining the socio-spatial analysis of data on the most relevant policy tool, namely tax deduction, together with qualitative analysis of three case studies of middle-sized cities. The results show that on the one hand tax deduction has not been very effective in promoting a deep renovation of buildings and it may exacerbate already existing inequalities. On the other hand, it emerges that progress in eco-retrofit of buildings depends mainly on creation of new intermediators and intermediation incentives. They are increasingly necessary in an urban panorama that has become inevitably polycentric.
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Integrating green infrastructure into spatial planning regulations to improve the performance of urban ecosystems. Insights from an Italian case study
Article
  • Oct 2019
Traditional land-use planning models have been unable to completely address environmental protection concerns without taking into account the suitability of the land to host a specific function or its consistency within a wider territorial context. Nowadays, new Planning approaches tend to a new paradigm considering a qualitative dimension of the public city based on performance standards considering the possible provision of multiple benefits in terms of regulating, supporting, provisioning and cultural services. The paper provides the results of an experience conducted in the municipality of Rescaldina, located in the northern part of the Milan Metropolitan area, where traditional quantitative standards were overpass towards a performance standards approach based on Ecosystem Services (ES) provision. ES provision via spatial planning has been advocated through Green infrastructure (GI) while NBS are considered as the greening design actions that can contribute to developing GI. The areas (public and private) included in the GI have a specific planning regulation for its implementation and its operability. The GI strategy was defined in the Strategic Environmental Assessment (SEA) as a support tool for setting Planning strategies.
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