Clusters Of Municipalities Research Articles

ДЕМОГРАФИЧЕСКАЯ ДИНАМИКА И ТРАНСФОРМАЦИЯ СИСТЕМЫ РАССЕЛЕНИЯ НА СЕВЕРЕ РОССИИ В КООРДИНАТАХ ПЕРЕПИСИ НАСЕЛЕНИЯ 2021 ГОДА

Over the past decades, the population of the North of Russia has decreased by more than a quarter. The leading cause is migration outflow, with natural population movement also contributing to the overall population decline. Comparing the data of the last two censuses will make it possible to understand the full depth of the transformational processes. The purpose of the study is to analyze the demographic trends and changes in population distribution in the North of Russia based on the results of the 2021 All-Russian Population Census. The originality of the article lies in the fact that the analysis was carried out at all levels: the macro level, regions, municipalities, and urban settlements. The article shows that with a general population decline, there is further urbanization of the northern regions and a reduction in the number and proportion of rural residents. Particularly high population decline is demonstrated by such regions of the European North as the republics of Komi and Karelia as well as the Arkhangelsk and Murmansk regions. The authors have identified trends in population distribution, such as an increase in the proportion of the population of large cities and a decrease in the average population of towns and urban-type settlements. Population distribution in the North of Russia is influenced by factors such as the size of the settlement, economic specialization, ethnic composition, and geographical location of the settlement. With the help of spatial analysis, the study identified the clusters of municipalities and settlements with the best and worst trends since 2010. Using Lorenz curves and the Gini coefficient, the authors studied the unevenness of population distribution and economic activity. The study also revealed that in the vast majority of municipalities in the North, the proportion of men has decreased, which may adversely affect local labor markets. The results of the study may find application in public administration and the development of strategies and programs for the socioeconomic development of the northern territories. Further research should consider such characteristics of the population as age, ethnic composition, place of birth, length of residence, number of children, and housing conditions.

Timely Access to Essential Surgery, Surgical Workforce, and Surgical Volume: Global Surgery Indicators in Mexico.

The Lancet Commission on Global Surgery seeks to improve surgical care outcomes and equity for the world population through 6 indicators outlined in its 2030 Global Surgery Report. Our study aimed to estimate the percentage of the Mexican population with access to surgical care within the 2-hour distance range (indicator 1), the surgical workforce density (indicator 2), and the number of surgical procedures performed per 100,000 inhabitants (indicator 3) during the year 2020. Knowing these indicators can help to design and implement policies to increase surgical care access coverage and equity in our country. Data related to population distribution, local referral hospitals, and surgical volume were obtained from the 2020 Mexican National Census. Information relating to hospital characteristics and surgical specialists was collected from the Secretariat of Health's public records. We calculated travel time between health care facilities and municipalities using the TrueWay Matrix API and R Studio. Taking into consideration the health care system affiliation, the proportion of the Mexican population with timely access to essential surgery was 81.7%, with 29.3 specialists per 100,000 inhabitants and 726.9 annual procedures performed per 100,000 inhabitants. We identified clusters of municipalities where a low proportion of the population has timely access to essential surgery. These findings illustrate that changes in Mexican policy are required to facilitate more equitable and timely access to essential surgical care among the population.

Contribuição financeira por grupo de produtos agropecuários dos municípios paulistas em relação à área produtiva ocupada.

Differences in agricultural productivity may not be clearly detected with the politicaladministrative division constituted by adjacent areas of municipalities. The objective of this work was to identify the productivity of rural areas, with productive activity, in groups of agricultural products in the municipalities of the state of São Paulo. It involved variables on the gross value of production and types of use in rural areas. Production in municipalities was concentrated in certain sets of products, with productive diversification not occurring in municipalities, but in clusters of municipalities and more sharply in regions with higher productivity.

Типология городских округов РФ по локальным факторам, объясняющим тяжесть прохождения пандемии COVID-19

The COVID-19 pandemic has demonstrated that the lack of consideration of the local specifics of territories, such as the specifics of socio-economic interactions, labor market characteristics, leads to serious social or economic consequences when developing response measures to epidemiological threats. The creation of a typology of territories (urban districts / okrugs) makes it possible to more accurately select measures to regulate socio-economic interactions in the event of future complications of the epidemiological situation. Clustering of municipalities according to a set of local factors that significantly explain the severity of the pandemic in the first year made it possible to identify three types of urban districts that differ in population size and intensity of socio-economic interactions (SEI): these are key service centers with a high intensity of SEI, local centers with medium SEI intensity, small towns with low SEI intensity

Spatial patterns of new leprosy cases in a northeastern state of Brazil, 2011-2021.

To analyze the spatial patterns of leprosy in Pernambuco from 2011 to 2021. This is an ecological epidemiological study, carried out with data from the Notifiable Diseases Information System, based on new cases of leprosy among inhabitants of Pernambuco, between 2011-2021. An empirical Bayesian analysis of local and spatial dependence was performed with the global and local Moran indices. 25,008 new cases of leprosy were registered with an annual case detection rate in the general population of 16.51 cases/100,000 inhabitants - which is considered high. Among those younger than 15 years of age, there were 5.16 cases/100,000 inhabitants (high) and 0.89/100,000 inhabitants with degree II of physical disability (low); there were also many high-risk cases with an overall Moran index of 0.33 (p<0.001), active transmission (0.26; p<0.001), and subsequent diagnosis of the disease (0.12; p<0.006), as well as distribution in macro-region 1 and macro-region 4. There was a heterogeneous spatial distribution in the state, showing two overviews, the first being the presence of municipalities with high risk of disease transmission and the second with clusters of silent municipalities, reinforcing the character of leprosy neglect as a major public health problem. This study brings reflections for leprosy control actions, due to the identification of priority areas to combat this disease in Pernambuco.

Grapevine Yield Big-data for Soil and Climate Zoning. A case study in Languedoc-Roussillon, France

New winegrower and resource datasets appear to be a great opportunity to understand which are the environmental factors involved in grapevine yield spatially. Such analysis can help regional label managers and winegrowers for the conception of local adaptation strategies to climate change, reducing yield gaps. In the present study, we aggregated yield a big dataset obtained from Pays d’Oc winegrowers (n = 96677) between 2010 and 2018 at the municipality level (n = 606), located in the Languedoc-Roussillon region, in the South of France. We used a backward stepwise model selection process using linear mixed-effect models to discriminate and select significant indicators capable of estimating grapevine yield at the municipality level, these include: Soil Available Water Capacity (SAWC), soil pH, Huglin Index, the Climate Dryness Index, the number of Very Hot Days and Days of Frost. We then determined spatial zones by creating clusters of municipalities with similar soil and climate characteristics. The seven zones presented two marked yield levels. Yet, all zones had municipalities with both high yield and high yield gaps. On each zone, grapevine yield was found to be driven by a combination of climate and soil factors, rather than just by a single environmental factor. Environmental factors at this scale largely explained yield variability across the municipalities, but they were not performant in terms of annual yield prediction. Further research is required on the interactions between environmental factors, plant material and farming practices.

Análise têmporo-espacial da mortalidade por doenças cardiovasculares no Estado do Ceará, Brasil, entre 2009 e 2019

ABSTRACT Objective: To analyze the spatial distribution of mortality from cardiovascular diseases in the municipalities of the state of Ceará, Brazil, between 2009-2019. Methods: This is an ecological study with a spatial focus on the state of Ceará, considering the period from 2009 to 2019. Death data from the Brazilian Mortality Information System and population data from the Brazilian Institute of Geography and Statistics were used to calculate crude and standardized mortality rates from cardiovascular diseases. Temporal analysis was carried out using the Joinpoint Regression Program 4.9.0 software and spatial analysis of the municipalities’ average mortality. The values were smoothed by the local empirical Bayesian method using QGIS 3.16. For spatial clusters, the Global and Local Moran Index was used through Moran Map and LISA Map, with analyses carried out in TerraView 4.2.2. Results: A total of 132,145 deaths from cardiovascular diseases were recorded in the period, with an average increase of 3% per year. Higher mortality rates were observed in men, people aged ≥80 years, mixed-race ethnicity/skin color, married, and with lower level of education. There was the formation of clusters of municipalities with high mortality rates in the regions of Vale do Jaguaribe, Sertão Central, Centro Sul, Sertão dos Inhamuns and Serra da Ibiapaba. Conclusion: This study identified municipalities with high mortality and exposed the need for strategies aligned with the reality and particularities of these locations.

An Axiology of Weak Areas: The Estimation of an Index of Abandonment for the Definition of a Cognitive Tool to Support the Enhancement of Inland Areas in Sicily

The marginalization of areas due to a progressive increase in social, material, economic, and infrastructural vulnerability is a phenomenon that afflicts many countries today, and it is growing rapidly. Agenda 2030, in highlighting the need to identify measures to counter this phenomenon, has promoted the development of a growing awareness of addressing this issue that cannot be postponed. With this in mind, in Italy, a map of inland areas was produced by the Interministerial Committee for Economic Planning and Sustainable Development (ICEPSD), a publication aimed at measuring the extent of the phenomenon and support the development of specific strategies that collectively define the National Strategy for Inland Areas (NSIA). In this study, starting from a critical analysis of the classification of areas in the National Strategy for Inner Areas, we propose a new cognitive tool of the phenomenon of abandonment developed from the perspective of an axiological approach of marginal areas. This tool is based on the mapping of an abandonment index Ia on QGIS with reference to the clusters of municipalities identified based on the quartiles of its values. This index was estimated as an aggregate weighted sum of the components identified because of the Principal Component Analysis (PCA) used to analyze the indicators of different forms of territorial capital of weak areas.

A k-means-based-approach to analyze the emissions of GHG in the municipalities of MATOPIBA region, Brazil

Brazil is the sixth largest emitter of GHG, with land use change and agriculture being the main source of these emissions. The recent expansion of agriculture in Brazil has been occurring mainly in the MATOPIBA region, a territorial division inserted on Cerrado. Clustering methods are useful for driving hyper-local GHG emission reduction strategies, but they have yet to be applied at the municipal level, nor from the emissions of various economic sectors. In order to contribute to the identification of the most critical areas in relation to GHG emissions for MATOPIBA, this study proposes an approach of municipal clustering according to the percentage contribution of Agriculture, Land Use Change, Energy and Waste sectors in total emissions. The clustering was performed with the k-means algorithm, using the elbow method and the silhouette score to define the number of clusters. In addition, statistical and geostatistical analyses were conducted to assess the consistency and spatial autocorrelation of the groups formed. The approach was able to generate six clusters with distinct characteristics, showing the heterogeneous profile of GHG emissions from MATOPIBA. At the same time, the clustering of similar municipalities can help in making decisions about the best pro-environmental measures to reduce/remove GHG to contain global warming.

Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid −19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Analysing the Recent Dynamics of Agricultural Sustainability in Portugal Using a Compromise Programming Approach

Society recognises the importance of agriculture to supply goods, which are essential for human survival and well-being. Sustainable agriculture is an important goal since resources need to be preserved for future generations. The recent agricultural policy orientations towards environmental concerns have also had consequences for Portuguese agriculture. The information provided by the 2019 Agricultural Census offers an opportunity to analyse the recent dynamics and establish rankings of municipalities related to agricultural sustainability. Sustainability in agriculture can be studied using different types of indicators, but its quantification and aggregation into an index is still difficult. This paper proposes an approach based on compromise programming to analyse sustainability considering the dynamics between the 2009 and 2019 Agricultural Census. This approach has three main steps: in the first one, the indicators are selected and a HJ-Biplot and Cluster analysis are carried out to identify groups of municipalities and general dynamics; in the second step, the weights of indicators are defined, and a novel compromise programming model is implemented to define the rankings of sustainability for each year; finally, in the third step, the spatial dynamics of the sustainability rankings are analysed and classified into the clusters of municipalities previously created. The analysis was implemented using data from the 308 Portuguese municipalities for 12 individual indicators encompassing the several dimensions of sustainability. The results were promising since the approach allowed for the identification of the main dynamics and tendencies regarding sustainability.

Understanding the Role of Nature in Urban-Rural Linkages: Identifying the Potential Role of Rural Nature-Based Attractive Clusters That Serve Human Well-Being

Rural areas provide unique amenities for recreational purposes which are highly appreciated by urban inhabitants. This generates an important but often hidden relationship between the urban and the rural. The aim of our study is first to provide empirical evidence for this linkage and then to identify for Italy, at the municipal level, those rural areas which actually function as nature-based attractive clusters. We used the data coming from a participatory webGIS survey that asked 1632 Italian respondents to mark attractive nature related places locally, regionally, nationally and world-wide to explain quantitatively and qualitatively the relationship between urban and rural. From the survey, indicators were developed to rank the nature-based attractive clusters. Our results pointed out a major (almost double) flow from urban to rural for natural amenities, which increased with the spatial level at which attractive nature areas were marked. This analysis allowed for the identification rural clusters of Italian municipalities that form nodal points for nature-based urban well-being; shedding light on an often neglected urban-rural relationship. The method is applicable in other countries and may stimulate better planning and management strategies for improving rural areas, taking an urban-rural perspective.

Clustering of disability pension and socioeconomic disadvantage in Sweden: a geospatial analysis.

BackgroundTo characterize geospatial patterning of disadvantage in Sweden, we examined whether municipal-level indicators of socioeconomic disadvantage and disability pension (DP) rate were clustered, whether the different geospatial clusters were overlapping and whether the findings were similar among women and men.MethodsAdministrative national data from all 290 Swedish municipalities were used to determine the prevalence of DP and socioeconomic disadvantage [poverty, long-term unemployment, income inequality (GINI Index) and income inequality between women and men]. Geospatial cold spots (clusters of municipalities with a DP/socioeconomic disadvantage prevalence lower than the nationwide prevalence) and hot spots (clusters of municipalities with a DP/socioeconomic disadvantage higher than the nationwide prevalence) were identified, and whether a hot spot was overlapping with another hot spot and a cold spot overlapping with another cold spot were analysed using the Getis-Ord Gi statistics.ResultsAmong women and men, cold spots of DP were most consistently located in the Stockholm area. Hot spots of DP were found in the mid-south Sweden, characterized by mid-sized urban centres in rural territories. High DP rate and socioeconomic disadvantage were overlapping, except for income inequality. Clusters of gender income inequality and women’s high DP rate were observed in mid-south Sweden.ConclusionDP and socioeconomic disadvantage are not randomly distributed in Sweden. Geospatial analyses revealed clusters of municipalities with high risk of both DP and socioeconomic disadvantage in certain areas and low risk in other areas. Further research is needed to identify preventive actions to decrease regional inequalities in work capacity.

Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.

National policies encounter municipal realities: A critical analysis of the outcomes of the List of Priority Municipalities in curbing deforestation in the Brazilian Amazon

At the peak of Amazonian deforestation in the mid-2000s, a suite of initiatives to curb deforestation was implemented, narrowing their scopes to particular agents, critical municipalities, and economic activities and supply chains. The List of Priority Municipalities (LPM) launched in 2008 became a central tenet of these efforts. It requires local agents in listed municipalities to individually and collectively reduce deforestation and implement a comprehensive farm-geocoding registry across the municipality. We combine region-wide spatial–temporal land-cover analysis and census data with in-depth fieldwork to examine the LPM policy at regional and municipal levels, discussing the policy's limitations in inducing effective responses across diverse municipalities. At the regional level, our study presents a new historical-geographic categorization and map of 530 Amazonian municipalities. We propose four regional clusters of municipalities according to patterns of deforestation, agricultural activities, demographic and agrarian structures, emancipation history, and socioenvironmental protection. We draw on this analysis to contextualize the trajectories of the 62 listed municipalities within the region’s moving deforestation frontier and discuss why many of the achievements observed locally have not been replicated regionally. At the municipal level, we investigate four case studies in-depth to unveil the factors underlying the LPM policy mixed outcomes. We discuss how local agents’ responses to the LPM policy are context-specific, reflecting their perception of trade-offs between the economic impacts of sanctions, incentives to collaborate, and potential benefits arising from environmental compliance. We detail and discuss how dynamic macro-political and institutional settings altering law enforcement mechanisms and market incentives interact with subnational environmental governance, either facilitating or inhibiting individual and collective actions locally. Finally, we discuss the role of inter-institutional collaboration and coordination among command-and-control policies, market-oriented initiatives, and incentives for local collective actions in triggering incentives for land-use and governance innovations against deforestation, both locally and regionally.

Winners and Losers of the CAP’s Rural Development Policy in Poland

The purpose of this study was to identify territorial clusters of Polish municipalities whose rural residents demonstrated higher or lower interest in the four programs of the CAP’s rural development policy compared to the country’s average interest. The clusters were identified on an aggregate basis and described with synthetic indexes of agriculture and rural development. The Moran’s global spatial autocorrelation coefficient was used to check for spatial autocorrelation. The results demonstrated that the support offered under the rural development programs covered by this analysis was higher in the regions with well-developed agricultural structures, while the areas with structural defects and the areas in need of development had lower-than-average levels of applications for all programs (regardless of their objectives). These findings call for a clear strategy to change the unfavorable structures in Polish agriculture. Otherwise, the regional development gaps in Polish agriculture will only deepen under these programs.

Primary health care performance according to clusters of convergent municipalities in the state of São Paulo.

To describe the performance of Primary Health Care, according to conglomerates of São Paulo cities that present homogeneous indicators. This is a descriptive study, based on secondary data extracted from official sources of the Unified Health System, for the year 2018. An analysis matrix was created, with the proposition of performance (access, effectiveness and adequacy) and context indicators (population, health determinants and financing) selected and organized in dimensions and sub-dimensions. Cluster Analysis was used to identify the groups of homogeneous municipalities. 645 municipalities were divided in 6 conglomerates. Clusters 2 and 3 were formed predominantly by small municipalities with greater access to health; cluster 3 has less social vulnerability and greater investment in health. Clusters 1, 4 and 5 were formed by the largest municipalities with less access to health; cluster 4 presents greater social vulnerability, less coverage of private health plans and a greater percentage of health resources; cluster 5 was characterized by greater Gross Domestic Product per capita and greater coverage of private health plans. Cluster 6, formed by the city of São Paulo, was a particular case. Cluster 2 drew attention, as it was shown to have increased coverage, but signaled lower efficacy and adequacy levels. Cluster 3 had the best performance among all clusters. These findings can support regional and municipal management, given the complexity of the territory of São Paulo, pointing to scenarios that demand broader public management initiatives.

Geography of two COVID-19 waves in Latvia

The COVID-19 pandemic and its waves of incidence have had a significant impact on our personal and professional lives. This has led to scholarly interest in various fields, including geography. A growing body of scholarly literature is trying to understand the reasons why some places were and still are more affected than others. The aim of this study was to shed light on the geography of the Covid-19 pandemic in Latvia and explain the geographical patterns of the pandemic during its first two waves. This was done, firstly, by mapping the number of cases per 10,000 in the municipalities of Latvia; secondly, the analysis also combined that same data with a total of eleven variables, by using bivariate correlation. The results show that during the first wave, Covid-19 incidence was relatively low. However, during the second wave, there were several large clusters of municipalities with a rather high number (over 500) confirmed Covid-19 cases per 10,000 inhabitants. Results of the correlation analysis suggest that geographical patterns of COVID-19 incidence were a result of a complex set of factors, varying throughout the country. Also, the presence of large clusters of municipalities with a rather high number of cases per capita (mostly) during the second wave points to the possible impact of proximity.

Evaluation of the Financial Performance of the Municipalities in Slovakia in the Context of Multidimensional Statistics

In some studies, only financial aspects are emphasized, but we also see cases of assessing the financial health of municipalities through socio-economic indicators. Public organizations worldwide have had to increase their financial performance by adopting management practices. Nonetheless, financial performance might be mostly predicted by contingencies that are not within direct managerial control. The purpose of this paper is to identify clusters of municipalities on the basis of agglomerate cluster analysis, the results of which will point to the financial situation of the municipalities in the selected region. The main aim of this contribution is to identify the location of the municipalities of the chosen self-governing region of Slovakia using the clustering method by selected financial indicators. Individual clusters have similar properties and they differ from the characteristics of businesses in other clusters. The results show that organizational and environmental contingencies affect financial performance, but a significant amount of variation in financial performance is unexplained—indicating that management creates better financial health in the municipality and creates a clearer budget for the management, employees, and residents of the municipality.

Applying a multiple exposures framework to explore complex spatial inequalities of Covid-19-related excess mortality in Lombardy region of Northern Italy

BACKGROUND AND AIM: Spatial inequalities of mortality rates caused by Covid-19 have been observed in many countries, including Italy. While such spatial inequalities may be influenced by a combination of multiple place-based factors, studies thus far have used only conventional multivariable regression approaches to explore spatial determinants of Covid-19-related mortality. METHODS: We use mortality data from all of 2020 and model excess mortality using the previous 5-year mortality average for each municipality in Italy’s Lombardy region. Using Bayesian profile regression (BPR), a non-parametric clustering algorithm, we fit 19 spatial covariates to identify clusters of municipalities with similar ‘spatial exposure profiles’ and explore which exposure profiles are associated with either a higher or lower adjusted risk of excess mortality during the pandemic year (2020). These 19 spatial covariates include six air pollutants and a variety of socio-demographic factors, land use indicators, and health facility contextual information. RESULTS:BPR resulted in 23 clusters of exposure profiles. Controlling for spatial autocorrelation and health protective agency, we find that the highest risk cluster, located in the sparsely populated southern sub-region of Pavia province, has the highest proportion of their population 65 years or older and is characterized with elevated ozone and SO2 air pollutant levels, and has relatively low access to health facilities. Clusters with elevated levels of three or more air pollutants exhibited significantly elevated excess mortality risk only if they were in densely populated urban areas, if the air pollutants appeared to be traffic-related, and if they were located further away from major capital cities of Lombardy provinces. CONCLUSIONS:Our results suggest a complex web of spatial determinants that interact to influence spatial inequalities of Covid-19-related excess mortality in Italy’s Lombardy region. Studies must apply a multiple exposures framework to help unravel the complex and multi-dimensional nature of spatial inequalities of Covid-19 impacts on public health. KEYWORDS: Covid-19, air pollution, inequalities, exposome