Clusters Of Municipalities Research Articles

Clustering Portuguese municipalities based on population health indicators

Abstract Background The large number of publicly available health-related indicators poses challenges in health planning to policymakers. Identifying patterns among municipalities may assist these processes, by grouping similar contexts and pointing out relevant differences among neighbouring locations. The aim of this work was to define clusters of municipalities based on health-related indicators. Methods All health-related indicators available at municipality level for the year 2021 were obtained from the Portuguese National Statistics Office website. Missing values were imputed with the closest available year or excluded. Indicators were standardized and K-means clustering was applied. Elbow criterion was used to select the optimal number of clusters. Sensitivity analysis was performed with a priori exclusion of indicators less directly associated with health outcomes. Results The clustering analysis revealed five distinct clusters of municipalities. Clusters were associated with specific socio-demographic patterns, providing insights into common health challenges and strengths within each group. Interestingly, geographic profiles emerged (e.g. coastal vs inland), despite no geographic information being specifically provided. One outlier municipality was identified (Lisbon), highlighting a need for special consideration. Conclusions Clustering techniques provide a data-driven approach to inform public health planning. Our findings show previously unrecognized patterns and relationships between municipalities. Using these clusters, policymakers may implement interventions based on what has been proven to work in similar contexts, enhance collaboration opportunities and promote a tailored policy development, contributing to an overall improvement of public health outcomes. Key messages • Clustering machine learning techniques provide a data-driven approach to inform public health planning. • Identifying comparable municipalities based on health-related indicators allows targeted public health interventions.

Analysis of the national structure of care provided to older people in Slovenia

Ageing in place has for many years been at the forefront of political goals for care provision for older people in Europe. The organization of care in terms of the national governing structure must therefore be continually observed to better understand the care actually offered and then used by older people. In this study, we examined the organization of social home care and institutional care across municipalities and administrative units in terms of what is offered to older people as formal care. Variation across municipalities and administrative units is observed with explorative cluster analysis and analysis of means of key dependent variables across clusters to better understand the variability in the care offered to older people in those municipalities and administrative units. The results show that while social home care is temporally available across several clusters of municipalities, and the affordability of social home care is also stable across clusters of municipalities, there are still municipalities where social home care has a limited time availability and municipalities where the out-of-pocket contribution to social home care delivery is nearly the twice the average of such contributions. In terms of institutional care, one can find administrative units with either a deficit or a surplus of placements of such care available.

Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques

Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques. Coordination among actors in the supply chain is essential for ensuring a well-organized, efficient, and effective response to humanitarian crises, ultimately leading to better outcomes for those affected by disasters. Effective coordination ensures that resources, information, and aid are distributed efficiently and promptly to those in need during humanitarian crises. It also helps optimize resource allocation, prevent duplication of efforts, and ensure that aid reaches the right places at the right time. It supports identifying and mitigating risks in the supply chain, such as delays, bottlenecks, or disruptions, which can impact aid delivery. Coordination fosters better communication among stakeholders, enabling them to share information, collaborate on solutions, and make informed decisions. Precise coordination mechanisms help establish accountability among actors, ensuring that responsibilities are defined, monitored, and fulfilled. This document proposes an efficient logistics system capable of providing aid in a rapid and coordinated manner through unsupervised learning and a medoid partitioning algorithm called PAM (Partitioning Around Medoids) that achieves a global optimum for grouping the vulnerable municipalities of Mexico using INECC data. In addition, the PAM algorithm was compared with the P-Median to check the efficiency of the global optimum.

Spatial clusters and social inequities in COVID-19 vaccine coverage among children in Brazil.

This study examined the spatial distribution and social inequalities in COVID-19 vaccine coverage among children aged 5-11 in Brazil. First and second dose vaccine coverage was calculated for all Brazilian municipalities and analyzed by geographic region and deciles based on human development index (HDI-M) and expected years of schooling at 18 years of age. Multilevel models were used to determine the variance partition coefficient, and bivariate local Moran's I statistic was used to assess spatial association. Results showed significant differences in vaccine coverage rates among Brazilian municipalities, with lower coverage in the North and Midwest regions. Municipalities with lower HDI and expected years of schooling had consistently lower vaccine coverage rates. Bivariate clustering analysis identified extensive concentrations of municipalities in the Northern and Northeastern regions with low vaccine coverage and low human development, while some clusters of municipalities in the Southeast and South regions with low coverage were located in areas with high HDI-M. These findings highlight the persistent municipal-level inequalities in vaccine coverage among children in Brazil and the need for targeted interventions to improve vaccine access and coverage in underserved areas.

Regional development policy in Galilee periphery in Israel

AbstractIn Israel, regional clusters serve as a mechanism for regional development, achieved through the integration of interests, pooling of resources, knowledge transfer, and the facilitation of agglomerations. This study gains insights from two EU regional development programs, LEADER and URBACT, to potentially benefit the regional clusters in Galilee, Israel's northern periphery. The research examined five municipal clusters in Galilee, aiming to identify their distinct characteristics and place them within a wider theoretical and comparative framework. A qualitative research approach was adopted, involving semi‐structured interviews with the clusters' CEOs and an analysis of pertinent policy documents. The findings suggest that Galilee's municipal clusters encounter challenges similar to those faced by the EU's LEADER and URBACT programs. These include establishing regional networks, achieving recognition for their capabilities, and cultivating trust and cooperation gradually. The CEOs shared their experiences in navigating the challenges posed by wary mayors and government ministries. The recognized success of these clusters, as evidenced in policy documents, could signify a shift from Israel's traditional centralist political culture to a more decentralized power structure.

A spatial decision support system for multi-dimensional sustainability assessment of river basin districts: the case study of Sarno river, Italy

Effective management of the complex Socio-ecological systems (SES) of the river basin, influenced by a range of social, economic, and environmental variables, requires multi-purpose Spatial Decision Support Systems (SDSSs) to identify and select balanced and sustainable regeneration scenarios. In many cases, the lack of cooperation between administrative institutions and the low level of participation of the stakeholders involved are the main obstacles to effective planning and regeneration practices and call for new forms of governance. However, to address these critical issues, few studies use quantitative approaches to assess the multidimensional resources of river basins in an integrated perspective in terms of their capacity to activate cooperation networks between river-crossed cities. This study aims to suggest a methodological approach combining Multi-Criteria Decision Analysis and Geographic Information Systems to achieve integration in pursuing sustainable regeneration pathways for a river basin district, analysing the case study of the Sarno River, composed of 20 administrative units, in Southern Italy. The specific purpose is to provide decision-makers with an SDSS to evaluate sustainability scenarios based on the multi-dimensional performances of River Basin District Units (RBDU). The results delivered four partial rankings of RBDU related to social, cultural, ecological, and economic scenarios and one overall ranking for an integrated sustainability scenario. Each ranking includes a clustering of municipalities according to three classes: Leader, Link and Follower. The outcome is twofold: to provide decision-makers with recommendations for supporting governance models based on sustainability pathways and to suggest valuable methods and tools for dealing with the spatial misfits, considered as the mismatch between the physical boundaries of a natural system and the management area of an organization.

National or Regional Recruitment: “Market Area” of University Cities

The depopulation of eastern Portugal and concentration on the coast has been a longstanding process. Since 1973 new HEIs has been disseminated around the country, and nowadays, every Portuguese region holds a set of HEIs. However, those closer to the coast tend to have a higher demand than those more distant. It is not clear if each one has a kind of restricted market area, such as a spatial monopoly in its neighbouring area or a national recruitment for some of them. This paper is the first exercise of determination of these areas for the Portuguese public network of HEIs. We use a database from the Ministry of Education (DGEEC-Ministry of Education) with information of the pair, place of residence (municipality level) and respective HEI, for enrolled students. Through techniques of Exploratory Spatial Data Analysis (ESDA), we will look for clusters of municipalities that could be market areas for each “university city” considered. We also use fractional regression models that confirm the positive correlation between the chosen university city and the distance to the family home and belonging to the market area. Despite some overlaps, the market areas of the university cities look to be well defined.

Hit the Road: Spatial Characteristics of Labor Absorption in South Africa

Unequal spatial concentration is at the heart of economic imbalance in South Africa. This research aimed at spatially characterizing labor absorption patterns and exploring the link to spatial development. Labor absorption is often used as an indicator of labor market well-being and for many countries, the indicator is often more insightful than the unemployment rate. The research used secondary data to create a longitudinal data set of labor absorption, functional literacy, the ratio of urban population and gross domestic product growth rate, among others, at a municipal level. Exploratory spatial data analysis and thematic mapping were undertaken to illustrate the characteristics of the labor market, while spatial grouping created clusters of municipalities based on labor absorption. A positive upward trend was observed between labor absorption and the share of people in an urban area; that is, the more urban an area, the higher the labor absorption rate. Furthermore, the spatial clustering of municipalities highlighted linear patterns and when this was overlayed with roads and development corridors, clearly indicated the impact of regional spillovers. The findings suggest a more integrated focus on regional innovation policy to reduce spatial disparities in the economic landscape.

Municipal heat provision experiences and expectations in Germany

BackgroundCompared to other sectors, the building sector is seriously lagging in efforts to mitigate climate change. In particular, heat provision needs to move to low-carbon options at greater speed. Municipalities are essential players in the transition to low-carbon heating. However, little is known about their experiences in developing heat provision infrastructure and their expectations of low-carbon options, such as district heating based on renewable energy. To explore how the experiences and expectations of municipalities concerning low-carbon heating are related, we conducted a survey of officers responsible for heating technology in municipal authorities across Germany. The questionnaires were analysed using multiple quantitative data analysis techniques.ResultsOur findings suggest that officers in larger municipalities have more positive expectations of low-carbon heating technologies than those in smaller and medium-sized municipalities. They also have more experience with these technologies. We identified four different clusters of municipalities based on their attitudes towards heating systems. The findings suggest that attitudes and experiences have a significant influence on the desirability and expected feasibility of the expansion of district heating. Furthermore, there are differences between south, north, and east Germany.ConclusionsExchange of experiences and expectations between larger and smaller municipalities could facilitate the transition to low-carbon heating. Public policy could strategically provide spaces for the required exchange, but it also needs to engage with more complex questions of finance and regulation.

Temporal trends and spatial analysis of leprosy surveillance indicators in the municipalities of the state of Mato Grosso, 2008-2022.

In 2022, Mato Grosso (MT, Brazil) reported the highest detection rate of new leprosy cases (66.20 per 100,000 inhabitants) among all Brazilian states. Monitoring of leprosy indicators is an important control strategy in hyperendemic areas. We aimed to describe the temporal trends and identify clusters of municipalities according to leprosy surveillance indicators in MT between 2008 and 2022. Data from the Notifiable Diseases Information System were used to analyze new case detection rate of leprosy (NCDR), new case detection rate of leprosy among children aged <15 years (NCD15), and rate of new cases with grade 2 physical disability (G2DR). Spatial scan statistics with pure spatial analysis and spatial autocorrelation maps were used to analyze the spatial patterns. Joinpoint regression was used to estimate the annual percentage change (APC) in these indicators. The NCDR decreased (APC: -20.2%, 95% confidence interval (CI): -38.7% to -7.4%) between 2019 and 2021. The NCD15 also decreased (APC: -19.2%, 95% CI: -36.4% to -10.3%) between 2018 and 2022. Conversely, the G2DR remained stable throughout the study (APC: 3.2%, 95% CI: -0.1% to 6.7%). Global Moran's index (Moran's I) confirmed the existence of spatial dependence among the municipalities for NCDR (Moran's I=0.348), NCD15 (Moran's I=0.269), and G2DR (Moran's I=0.275). Clusters with high NCDR levels included 13 municipalities in the northern and eastern macroregions, while clusters with high G2DR levels included 12 municipalities in the northwestern, northern, and eastern macroregions. The NCDR and NCD15 decreased, but the G2DR remained stable between 2018 and 2022. The coronavirus disease 2019 (COVID-19) pandemic had a potential negative impact on leprosy case detection, highlighting the need to strengthen leprosy surveillance efforts. The identified clusters of MT municipalities can significantly assist in this task.

Spatiotemporal analysis of diarrhea-related hospitalizations of children in Brazil's Midwest region from 2011 to 2020.

To examine spatiotemporal variability and identify clustering patterns of hospitalization rates for diarrhea in children younger than five years in Mato Grosso, Brazil, from 2011 to 2020. An ecological study was conducted using hospitalization records associated with diarrhea from the Brazilian Hospital Information System/Unified Health System. The relative risk of hospitalization for diarrhea in each municipality was calculated using SaTScan software considering a statistical significance level of 5% and 999 Monte Carlo replications. A total of 13,315 diarrhea-associated hospitalizations for 5-year-old children were recorded. From 2011 to 2020, the annual rates for hospitalizations related to diarrhea decreased from 8.50 to 3.45/1,000 live births among children younger than one year and from 4.99 to 1.57 for children aged 1-4 years. Clusters of municipalities with high relative risk for hospitalizations due to diarrhea, statistically significant, predominated in the North, Northeast, and Southwest health administrative macro-regions of Mato Grosso for both age groups until 2016. From 2016 to 2020, clusters of the lowest relative risk were identified in the North and Center South health administrative macro-regions for children younger than five years. Results showed that hospitalization rates for diarrhea in children younger than five years reduced with the presence of low-risk clusters in Mato Grosso in the final years of the study. Public health surveillance should incorporate spatial analysis to investigate the diarrhea-related morbidity.

Prevalence of Root Caries Among 65-75 Year Olds of Khordha District, Odisha: A Cross Sectional Study

This study assessed the prevalence of root caries and its risk factors in the elderly population of Khordha district, Odisha, India. Further, microbiological profiling was done for the same patients. The Khordha district was divided into four zones, and municipal clusters were selected randomly from each zone. Residents aged 65-75 years and who provided consent were included in the study, except those with physical disabilities and terminal illnesses. A pilot study on 25 subjects who met the inclusion criteria and the sample size was calculated to be 373 based on the formula N = Za2 {p (1-p)}/L2 x D.E. and rounded off to 380. The majority (70%) of the study population comprised males residing in urban areas having a minimum of middle school education. Most of them cleaned their teeth at least once daily. Tobacco use was prevalent in the population, with 37.3% smokers and 87.4% chewing tobacco, significantly more in males. The overall prevalence of root caries was 20.8%, with 16.3% in males and 4.5% in females, respectively. The mean RCI score for males in the study population was 8.09 ± 14.8, while that for females was significantly lower at 5.89 ± 8.23 (p

Оценка количества малых и средних предприятий в границах городов и агломераций: применение методов машинного обучения к данным России

Introduction: the development of agglomerations, which, as research shows, are becoming productivity growth poles, is one of the priorities in the spatial development policy in Russia. Despite numerous discussions, the term “urban agglomeration” is currently absent in federal legislation, but it is widely used in the regulatory framework at other levels of government. This leads to a lack of coordinated, accurate, and up-to-date information on the size and structure of agglomerations, thus bringing forward the need to formulate a well-based approach to identify their economic and geographical boundaries. Objectives: developing an approach to identify the boundaries of agglomerations in Russia, considering the spatial distribution of economic activity. Methods: machine learning, statistical analysis. The use of these methods makes it possible to identify the boundaries of agglomeration with high accuracy using open data on the height of the urban area. Results: the article presents the results of spatial clustering algorithm (DBSCAN) testing on residential development data in Russia; a list of “sustainable municipalities clusters” defining the agglomerations boundaries has been identified and estimates have been made for the number and revenue of firms and SMEs within the largest cities and agglomerations boundaries defined both analytically and formally in regulatory documents. Using the example of individual regions and average data for Russia, it has been shown that the number of firms (total and those registered as SME) within agglomerations can be underestimated by more than one-third and total revenues – by almost 60 %. Conclusions: considering sustainable municipalities clusters can improve the accuracy of agglomeration effects and business activity estimates and contribute to the formation of more accurate government support measures.

Evaluating Autoencoders as a Dimensionality Reduction Mechanism to Support Clustering Brazilian Agricultural Diversity

Brazilian agricultural production presents high spatial diversity, challenging the conception of public policies. This article proposes an approach for grouping Brazilian municipalities according to their agricultural production. We combine a feature extraction using autoencoders and clustering based on k-means and Self-Organizing Maps. We used panel data from IBGE’s annual estimates of the production value of permanent and temporary crops, animal products, aquaculture, plant extractivism, forestry, planted areas, and herd population between 1999 and 2018. We analyzed different structures of simple stacked and incomplete autoencoders, varying the number of layers and neurons in each, and evaluated the asymmetric exponential linear loss function to handle the sparse data. We applied the Isomap, Kernel PCA, Truncated SVD, and MDS dimensionality reduction methods for comparative purposes. Results showed that the autoencoders could extract characteristics from the transformed raw data to allow the clustering of municipalities to reveal regional and evenintra-regional patterns. The autoencoders improved comparative performance as the intrinsic dimensionality increased.

Under-spending, over-spending or substitution among services? Spatial patterns of unexplained shares of health care expenditures

Using individual-level administrative data, we investigate the spatial patterns of unexplained shares of health care expenditures (HCE) at the municipality level. The focus is on the elderly population in the Italian Region Friuli-Venezia Giulia observed over the period 2017-2019. The empirical analysis comprises two steps. First, random-effects two-part models are estimated to analyze the effect of age, morbidity, and death on the probability and amount of positive individual total HCE and its components. Second, the unexplained shares of HCE at the municipality level are examined to identify areas with under- or over-spending and substitution among services. Results confirm the existing findings on the determinants of HCE and reveal geographic patterns in the unexplained shares of expenditures. We identify clusters of municipalities with observed HCE higher than predicted for each type of service and clusters with substitution between home care and all other services. These findings are associated with the degree of urbanization of these areas and, consequently, with the ease of access to health care. This is crucial from a policy perspective, as it indicates specific policy targets for public health intervention.

Fuzzy clustering of spatial interval-valued data

In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, i.e. the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster.In both methods, the units are endowed with a relation of contiguity, represented by a symmetric binary matrix. This can be intended both as contiguity in a physical space and as a more abstract notion of contiguity. The performances of the methods are proved by simulation, testing the methods with different contiguity matrices associated to natural clusters of units. In order to show the effectiveness of the methods in empirical studies, three applications are presented: the clustering of municipalities based on interval-valued pollutants levels, the clustering of European fact-checkers based on interval-valued data on the average number of impressions received by their tweets and the clustering of the residential zones of the city of Rome based on the interval of price values.

A Comparative Study of Polarization Management Around Energy Transition-Related Land-Use Conflicts in The Netherlands

The Dutch national government has decided to push the implementation of the “energy transition” it aspires to by inviting clusters of municipalities (so-called RES regions) to develop a regional energy strategy (RES). However, since the new renewable energy land-use claims compete with a growing number of land-use demands, RES implementation confronts land-use conflicts, resulting in complex trade-offs for conflict resolution and planning around polarization. In the Dutch context of land scarcity and a rich planning tradition that arose specifically to deal with this and ensuing conflicts, the need for integrated landscape management seems obvious. This article offers a comparative case study of two RES-related land-use conflicts and their management in South Limburg, focusing on the question of how far these cases display elements of an integrated landscape approach (ILA). The ILA is applied as an analytical framework to evaluate the land-use conflict management processes of the case studies by assessing which elements of ILA are present and whether their relative presence and quality help to resolve the conflicts. Based on document and media content analysis and 15 interviews, this article analyzes the different land-use claims, objectives, and landscape values identified in two RES areas and how they overlap or compete, resulting in conflicts or synergies. Our findings show that the ILA provides useful guidelines for tackling RES-related land-use conflicts, but does not pay sufficient attention to the political dimension.

Mortality by road transport injury in Brazilian municipalities between 2000 and 2018

ObjectivesThis study aimed to investigate patterns of mortality by road transport injury (RTI) in Brazilian municipalities, focused on deaths of motorcyclists, between 2000 and 2018, and their relation with population size and economic status. Study designThis was an ecological epidemiological study with a descriptive and analytical nature. MethodsThe age-standardized RTI mortality rates were calculated for the Brazilian municipalities, referring to the 3-year periods of 2000/2002 (T1), 2009/2011 (T2), and 2016/2018 (T3). The rates were stratified according to macroregion and population size and were compared in terms of percentage variation from one 3-year period to another. The Moran Global and Local indices were used in the spatial point-pattern analysis of the rates. To verify the association with the gross domestic product (GDP) per capita, the Spearman correlation coefficient was applied. ResultsA decline in RTI mortality rates was found between 2000 and 2018, with the most significant declines observed in municipalities from the South and Southeast regions of Brazil. However, increases were observed among motorcyclists. Clusters of municipalities were detected, which presented high mortality rates among the motorcyclists in the Northeast region and in some states of the North and Midwest regions. The mortality rates showed a negative correlation with the GDP per capita of the Brazilian municipalities. ConclusionsAlthough there were decreases in RTI mortality rates between 1990 and 2018, there was a significant increase in deaths among motorcyclists, especially in the Northeast, North, and Midwest regions of the country. Such differences can be explained by unequal growth in the size of the motorcycle fleet in those regions, by less law enforcement capability, and by the implementation of educational actions.

Grapevine yield gap: identification of environmental limitations by soil and climate zoning in the region of Languedoc-Roussillon (South of France)

Often unable to fulfill theoretical production potentials and to obtain the maximum yields set by wine quality labels, many vineyards and cellars need to solve the issue of so-called grapevine yield gaps in order to assure their durability. These yield gaps particularly occur in Mediterranean wine regions, where extreme events have intensified because of climate change. Yield gaps at the regional level have been widely studied in arable crops using big datasets, but much less so in perennial crops, such as grapevine. Understanding the environmental factors involved in yield gaps, such as those linked to climate and soil, is the first step in grapevine yield gap analysis. At a regional scale, there are numerous studies on ‘terroir’ linked to wine typicity and quality; however, none have classified spatial zones based on environmental factors identified as being involved in grapevine yield. In the present study, we aggregated into one big dataset information obtained from producers at the municipality level in the wine region Languedoc-Roussillon (South of France) between 2010 and 2018. We used a backward stepwise model selection process using linear mixed-effect models to discriminate and select the statistically significant indicators capable of estimating grapevine yield at the municipality level. We then determined spatial zones by using the selected indicators to create clusters of municipalities with similar soil and climate characteristics. Finally, we analysed the indicators of each zone related to the grapevine yield gap, as well as the variations among the grapevine varieties in the zones. Our selection process evidenced 6 factors that could explain annual grapevine yield annually (R2 = 0.112) and average yield for the whole period (R2 = 0.546): Soil Available Water Capacity (SAWC), soil pH, Huglin Index, the Climate Dryness Index, the number of Very Hot Days and Days of Frost. The clustering results show seven different zones with two marked yield gap levels, although all the zones had municipalities with no or high yield gaps. On each zone, grapevine yield was found to be driven by a combination of climate and soil factors, rather than by a single environmental factor. The white wine varieties showed larger yield gaps than the red and rosé wine varieties. 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 variables and plant material and farming practices, as well as on vineyard strategies, which also play an important role in grapevine yield gaps at vineyard and regional scale.

Environmental and Social Factors Associated with High Chronic Kidney Disease Mortality Rates in Municipalities of Guatemala: An Ecological Study of Municipal-Level Mortality Data

The purpose of this study was to determine the association between social and environmental indicators and high mortality rates from chronic kidney disease (CKD) in municipalities of Guatemala. An ecological study of municipal-level factors associated with CKD mortality in Guatemala was conducted. Crude mortality rates were calculated for the 2009-2019 period for each of the country's 340 municipalities, by gender and age groups. Municipal-level social and environmental indicators were used as independent variables. Linear regression was used for bivariate and multivariate analysis. A total of 28,723 deaths from CKD were documented for the 2009-2019 period. Average crude mortality rate for all ages for the country's 340 municipalities was 70.66 per 100,000 [0-502.99]. Very highly positive associations with high mortality rates were found in two agrarian territories where land use is mainly for permanent crops (e.g., sugar cane, coffee, rubber, banana, plantain, African palm) and pastures for cattle, with very low percentages of land covered by forests or protected areas. Social factors related to poverty and environmental factors related to agricultural use of land may play a role in the high CKD mortality rates documented in a cluster of municipalities of Guatemala.