Municipal Data Research Articles

Transferability and comparability of sewer deterioration models – a case study on Norwegian sewer data

ABSTRACT The adoption of emerging machine learning (ML) techniques in sewer deterioration modelling remains limited. This is primarily due to a lack of comparisons with popular models to evaluate if they perform superiorly and the extensive datasets required for ML training, which many small- to medium-sized municipalities do not possess. This study compares an emerging ML technique, random survival forest (RSF), with two established models: support vector machine (SVM) and GompitZ. Additionally, it examines the transferability of RSF models to different municipalities. The results indicate that both RSF and GompitZ have unique strengths and weaknesses, suggesting parallel use with statistical models. RSF’s performance was comparable to, or better than, SVM. Furthermore, globally trained RSF models, which use aggregated datasets of multiple municipalities, perform similarly to locally trained models, which are trained on representative municipal data. However, globally trained models are promising for municipalities lacking data or expertise to develop their own models.

Research on the Impact of Environmental Inequality on Labor Mobility—A Study Based on the China General Social Survey (CGSS)

In recent years, as the capital-driven economic growth model gradually encounters bottlenecks and regional economic competition intensifies, governments at all levels have turned to attracting high-quality talent to promote economic transformation and upgrading. Against this backdrop, the quality of the ecological environment has gradually become an important factor affecting labor mobility. This paper explores the impact of environmental inequality on labor mobility by matching provincial and municipal data with micro-data from the Chinese General Social Survey (CGSS), especially focusing on how environmental inequality affects labor decision-making through cognitive and non-cognitive abilities. The results from the benchmark regression confirm that environmental inequality significantly suppresses labor mobility, while robustness tests further validate these findings. Additionally, the mediation mechanism analysis reveals that environmental inequality influences labor mobility through impairments in both cognitive and non-cognitive abilities. The study also conducts an analysis of the heterogeneity of different social groups, finding that environmental inequality has a particularly significant impact on groups with higher levels of education, property owners, and residents living in economically disadvantaged areas. Based on this, the paper puts forward policy recommendations for different groups, emphasizing the importance of optimizing environmental quality, improving education levels, and enhancing the social support system. The research conclusions provide theoretical support for local governments in enhancing regional competitiveness and attracting high-quality labor.

Politics and city growth in Italy: Do connections matter?

AbstractIn this paper, I assess the existence of a political connection premium for Italian municipalities during the so-called “Second Republic” (1991–2011). The early 1990s witnessed a seminal event in Italian political history with the “Mani Pulite” (“Clean Hands”) investigation following the fall of the Berlin Wall. Subsequently, major political parties dissolved, culminating in a bipolar political landscape characterized by cyclical alternation between center-right and center-left factions (extending until at least 2011). Exploiting population data on small and medium-sized Italian municipalities between 1991 and 2011, I found that “politically connected” municipalities showed a population premium of about 2.5% compared to “non-politically connected” ones. The results are robust to NUTS-3 fixed effects, to a comprehensive set of controls, and to a full set of robustness checks. The relationship is confirmed using aggregate municipal income data as an additional proxy of local economic growth. Over and above this, social capital and the quality of local institutions seem to play a pivotal role in enabling the political connection. Election-related checks suggest the connection as two-way: using municipal data from the 2013 and 2018 general elections, I found that the treated municipalities depicted a higher turnout and fewer populist votes.

Threats and forced displacement in Colombia: an analysis based on municipal data

PurposeThis paper aims to examine the role of threats in the forced displacement of populations from their municipalities of origin in Colombia that is among the nations with the highest number of internally displaced people in the world with 6.7 million victims.Design/methodology/approachThe data cover the period 2011–2017 and were obtained from the Unit for Attention and Integral Reparation to Victims. Using general panel data models and a difference-in-differences identification strategy, this paper finds that threats within the context of the armed conflict show a positive and significant contribution to forced displacement in municipalities.FindingsDirect intimidations by illegal armed groups to the population seem to have an influence on the decision of the threatened person to leave the territory in general, although it is not a proportional reaction due to the fact that some victims of intimidations decide not to move to keep their assets and maintain territorial ties.Research limitations/implicationsThe study highlights that the implementation of the Peace Agreements signed in 2016 and the negotiation with illegal armed groups is an effort that the Colombian State must strengthen to prevent the occurrence of threats.Originality/valueThis paper’s contribution to the literature is twofold: the paper reveals that rising threats encourage forced displacement in Colombia, by embracing a municipality panel data perspective and a difference-in-differences design that were not applied in previous studies; and the results offer suggestive evidence of the importance of decreasing threats for positive peace outcomes, especially in the most affected territories.

Comparative analysis of COVID-19 diagnoses and mortality among hospitalized indigenous and non-indigenous populations in Chile: 2020–2021

BackgroundCurrent literature presents mixed effects of the COVID-19 pandemic on Indigenous communities. We aim to highlight potential disparities and temporal shifts in both the impact of COVID-19 and vaccine uptake among hospitalized Indigenous populations in Chile.MethodsWe conducted an observational analysis utilizing 1,598,492 hospitalization records from 2020 to 2021 based on publicly accessible hospital discharge data spanning 65 healthcare facilities of medium and high complexity funded through the Diagnosis-Related Groups (DRG) mechanism in Chile, representing roughly 70% of the country’s total hospitalizations. This was supplemented with publicly available municipal data on COVID-19 vaccinations and socio-demographic variables. We performed logistic regression analysis at 0.05 level of significance to assess the bivariate and multivariable association of Indigenous status with COVID-19 diagnosis and COVID-19 deaths among hospitalized populations. We also performed univariate and multiple linear regression to assess the association of COVID-19 vaccination rate and Indigenous status at the municipality level. In addition, we report the distribution of top 10 secondary diagnoses among hospitalized COVID-19 cases and deaths separately for Indigenous and non-Indigenous populations.ResultsIndigenous populations displayed lower adjusted odds for both COVID-19 diagnosis (OR: 0.76, 95% CI: 0.74, 0.77) and death (OR: 0.91, 95% CI: 0.85, 0.97) when compared to non-Indigenous groups. Notably, the adjusted odds ratio for COVID-19 diagnosis in Indigenous populations rose from 0.59 (95% CI: 0.57, 0.61) in 2020 to 1.17 (95% CI: 1.13, 1.21) in 2021. Factors such as the significantly higher median age and greater number of comorbidities in the non-Indigenous hospitalized groups could account for their increased odds of COVID-19 diagnosis and mortality. Additionally, our data indicates a significantly negative adjusted association between COVID-19 vaccination rates and the proportion of Indigenous individuals.ConclusionAlthough Indigenous populations initially showed lower odds of COVID-19 diagnosis and mortality, a marked rise in diagnosis odds among these groups in 2021 underscores the urgency of targeted interventions. The observed negative association between the proportion of Indigenous populations and vaccination rates further underscores the necessity to tackle vaccine access barriers and work towards equitable distribution.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Support services for informal carers: a register-based case–control study of Norwegian care recipients

This article aims to describe support services for informal carers and identify the characteristics of the care recipient and municipality that are associated with the allocation of these services. Care recipients were selected from the Norwegian Registry for Primary Health Care on 31 December 2019. Cases received care benefits (N = 9,816), respite care in institutions (N = 2,594) or respite care at home (N = 7,346); controls received home-based services only (N = 193,402). Municipal data were retrieved from the Municipality-State-Reporting register. Child and adolescent care recipients most often received support services, but allocation to this age group differed extensively between municipalities. Greater cognitive disability and cohabiting increased the odds of support services.

Forecasting the waste production hierarchical time series with correlation structure

AbstractContinuous increase in society’s prosperity causes overwhelming growth of the produced municipal solid waste. Circular economy initiatives help to solve this problem by creating closed production cycles, where the produced waste is recycled, or its energy is recovered. An embedment of such principles requires implementation of new waste management strategies. However, these novel strategies must be based on the accurate forecasts of future waste flows. Municipal solid waste production data demonstrate behavior of hierarchical time series. Among all possible approaches to hierarchical times series forecasting, this article is focused on the reconciliation of the base waste generation forecasts. The novel method, that is based on the game-theoretically optimal reconciliation of hierarchical time series, is presented. The modified approach enables to incorporate interdependencies between time series using correlation matrix and to obtain the forecasts corresponding to the unique solution of the optimization problem. The potential of the proposed abstract approach is demonstrated on the waste production data of paper, plastics (both primarily sorted by households), and mixed municipal solid waste from the Czech Republic.

Contextual factors influencing voluntary organizations’ inclusiveness and individual participation in leisure activities: The Case of Norway

ABSTRACT While research on participatory inequalities in organized leisure has mainly focused on individual resources and contextual factors, studies linking the individual, organizational and contextual levels are lacking. This study remedies this by investigating the degree to which contextual, organizational and individual factors enhance or inhibit participation in membership-based voluntary leisure organizations among children and adolescents in Norway. We studied the individual, organizational and contextual factors by examining how these factors affect participation across municipalities. Our analyses based on individual, organizational, and municipal data investigate, using a mixed-effects approach, the extent to which organizational resources, organizational inclusiveness, and local public policy are associated with increased participation in leisure activities. Our findings show that contextual factors have little direct explanatory power but moderate individual and organizational characteristics while public policies addressing the supply of leisure activities are at best ineffective and should be aimed to a greater extent at reducing socio-economic inequalities at the individual level.

Using shanghai municipal data to examine whether student sacrifice exercise in response to academic pressure

The competitive education environment in Mainland China pressure many students to take up after-school lessons, cutting into their leisure time and exercise less, setting a path towards a more sedentary lifestyle. We use a survey on sports consumption for Shanghainese adolescent ages 6 to 17 to examine whether they sacrifice sports activity as they advance through the grade. We found that students a year older exercise 16.2 fewer minutes per week, 1.38 percent less likely to spend on sports training, 1.42 percent more likely to watch sports events, 1.90 percent more likely to buy more sports related goods. Our result shows that students substitute sports activities with more expenditure on sports products and events. When older adolescent exercise less, we are concerned that it may contribute to rising obesity and a more sedentary lifestyle.

Impact of the circular economy on waste management performance: The case of the environmental goods and services sector in South Korea

In South Korea, the Framework Act on Resource Circulation (FARC) was enacted in 2016, making the circular economy (CE) related data limited. This study examines waste management performance in South Korea from the perspective of waste reduction and recycling by using environmental goods and services sector (EGSS) data as a proxy for CE data to assess how adopting EGSS data in policies affects waste management. The current study assumes that the use of EGSS data for environmental policy decisions will result in less waste and increased recycling. To test this assumption, this study conducted a descriptive analysis and segmented linear regression using municipal solid waste (MSW) and EGSS data from 1986 to 2021. The results indicated that the adoption of EGSS data only positively affected the recycled amount and recycling rate of MSW, while its effects on the amount of generated and landfilled MSW was not significant.

The impact of land expropriation on changing livelihoods: The case of displaced peri-urban farmers in Kon and Gashena towns, Ethiopia

The research examines how urban growth and land expropriation have affected farming communities in the towns of Gashena and Kon over the past twenty years. The study used a case study approach, utilizing methods such as open-ended interview checklists, structured questionnaires, and focus group discussions. It reveals a significant reduction in available farmland due to horizontal urban growth, leading to the displacement of numerous farm holdings and disruption of agricultural practices. By analyzing municipal data and employing simple regression modeling, the research anticipates further expansion, underscoring persistent urban growth trends. Economic displacement emerges as a pressing issue, as farmers grapple with finding alternative sources of income, exacerbating food insecurity within affected communities. Moreover, the study unveils diverse impacts on displaced farm holdings, including shifts away from agriculture in livelihoods and substantial income losses. The loss of land assets poses a threat to agricultural productivity and community sustainability. Despite assurances from municipal authorities, the lack of support, guidance, and training contributes to community dissatisfaction. The research underscores the interconnectedness of land, food, culture, and identity, emphasizing the necessity for comprehensive strategies to address the multifaceted impacts of land expropriation. Recommendations include policy interventions, support for transitioning farmers, promotion of sustainable land use practices, and fulfillment of promises regarding support and infrastructure development. Providing training and guidance services for displaced individuals is also crucial for addressing the challenges of land expropriation effectively.

Situational analysis of hypertension management at primary health care level in São Paulo, Brazil: population, healthcare professional and health system perspectives

BackgroundGovernment-led, population-wide initiatives are crucial for advancing the management of hypertension − a leading cause of cardiovascular disease (CVD) morbidity and mortality. An urban population health initiative was conducted against this backdrop, focussing on hypertension in the primary health system in São Paulo, Brazil. Within the frame of the initiative and under the supervision and leadership of the municipal health authorities, a situational analysis was conducted on the needs in hypertension management, marking the first phase of a Design Thinking process. This article describes the situational analysis process and presents the identified elements to be strengthened considering hypertension diagnosis, treatment and control.MethodsFirst, a mixed-methods approach was used, starting with a literature review of municipal hypertension data followed by meetings (N = 20) with the local public health administration to assess health system level components. To investigate activities on hypertension diagnosis, treatment and control, nine primary healthcare units were selected from two districts of São Paulo city– Itaquera and Penha– which received an online form addressed to managers, participated in conversation circles of staff and patients, and underwent shadowing of community health agents.ResultsData gave rise to two main outputs: (i) a patient care journey map; and (ii) a matrix summarizing the identified needs at patient, healthcare professional and health system level for diagnosis, treatment and control of hypertension. Patient awareness and knowledge of hypertension was found to be insufficient and its management needs to be improved. For health professionals, disease awareness, technical training, more time dedicated to patients, and simplified guidelines and clinical decision-making tools for hypertension management were identified as principal needs. The situational analysis found that the healthcare systems efficiency might be improved by establishing defined treatment and care delivery goals with a focus on outcomes and implemented through action plans.ConclusionsThis situational analysis identified several needs related to hypertension control in São Paulo that are in line with global challenges to improve the control of CVD risk factors. Findings were also confirmed locally in an expansion phase of this situational analysis to additional primary care facilities. As a consequence, solutions were designed, promptly taken up and implemented by the municipal health secretariat.

Towards Municipal Data Utilities: Experiences Regarding the Development of a Municipal Data Utility for Intra- and Intermunicipal Actors within the German City of Mainz

This paper describes the requirements analysis phase towards the establishment and implementation of a municipal data utility (KDW = Kommunales Datenwerk, German) to facilitate data sharing between intra- and intermunicipal stakeholders. Against the backdrop of increasing digitisation and the growing importance of data-driven decision making in municipal governance, this paper aims to address the pressing need for efficient data management solutions within and across municipalities. Based on a structured self-developed methodology, the authors use a qualitative research approach: the paper examines the experiences and challenges encountered during the requirements phase, the design phase, and the development phase of the KDW. The findings indicate that the establishment of a robust KDW requires (1) extensive stakeholder engagement, (2) clear governance structures, and (3) a robust technical infrastructure. In addition, the study highlights the critical importance of establishing a sound legal framework that addresses data ownership, privacy, security and regulatory compliance. Addressing legal and regulatory barriers to data sharing is paramount to the successful implementation and operation of the KDW. The paper concludes by highlighting the potential benefits of KDWs and outlining future work. The overall methodology, approach, and outcome are validated within the city of Mainz, and the lessons learned are accommodated in the insights presented in the rest of the paper.

Study on Driving Factors and Spatiotemporal Differentiation of Eco-Environmental Quality in Jianghuai River Basin of China

For an in-depth analysis of the ecosystems of the Jianghuai Valley, this study utilized municipal data from 2017 to 2021. In addition, this study established an index scale evaluation system for the quality of the ecological environment in the Jianghuai Valley. This system encompasses five critical dimensions: drivers, pressures, states, impacts, and responses, in accordance with the DPSIR model. The entropy-weighted TOPSIS method combined with the gray correlation method was used to assess the ecological status of each region of the Jianghuai Valley at different time periods and the driving factors affecting the ecological quality of the Jianghuai Valley. Our study yields several key conclusions. First, it was observed that the ecological environment within the Jianghuai Valley showed a continuous upward bias in inter-annual variability. Second, there exists variation in ecological environment quality among the eleven urban areas within the Jianghuai Valley, highlighting regional disparities. Third, among the eleven urban areas in the Jianghuai Valley, Anqing has the best ecological quality, and Huainan has the worst ecological performance. Fourth, the ecological environment quality within the Jianghuai Valley demonstrates an aggregated pattern. From west to east, this pattern is delineated by distinct areas: one marked by excellent ecological environment quality, another exhibiting average ecological environment quality, followed by a zone characterized by good ecological environment quality, and finally, an area with poor ecological environment. Fifth, our analysis reveals that Q9 (indicating the percentage of excellent air days) and Q13 (denoting the annual average temperature) have a pronounced correlation with the Jianghuai Valley’s ecological quality. Conversely, Q3, which pertains to the rate of natural population growth, had the lowest relevance to the ecological quality of the Jianghuai Valley.

An Efficient Multi-Label Classification-Based Municipal Waste Image Identification

Sustainable and green waste management has become increasingly crucial due to the rising volume of waste driven by urbanization and population growth. Deep learning models based on image recognition offer potential for advanced waste classification and recycling methods. However, traditional image recognition approaches usually rely on single-label images, neglecting the complexity of real-world waste occurrences. Moreover, there is a scarcity of recognition efforts directed at actual municipal waste data, with most studies confined to laboratory settings. Therefore, we introduce an efficient Query2Label (Q2L) framework, powered by the Vision Transformer (ViT-B/16) as its backbone and complemented by an innovative asymmetric loss function, designed to effectively handle the complexity of multi-label waste image classification. Our experiments on the newly developed municipal waste dataset “Garbage In, Garbage Out”, which includes 25,000 street-level images, each potentially containing up to four types of waste, showcase the Q2L framework’s exceptional ability to identify waste types with an accuracy exceeding 92.36%. Comprehensive ablation experiments, comparing different backbones, loss functions, and models substantiate the efficacy of our approach. Our model achieves superior performance compared to traditional models, with a mean average precision increase of up to 2.39% when utilizing the asymmetric loss function, and switching to ViT-B/16 backbone improves accuracy by 4.75% over ResNet-101.

Study on the Impact of Carbon Emission Trading Pilot on Green Land Use Efficiency in Cities

Under the overarching principle of sustainability, the reliance solely on expanding the landmass to meet the demand for high-quality economic growth is unsustainable. To address the need for harmonious ecological–economic development, this paper examines the influence of carbon emissions trading (CET) policies on the urban land green utilization efficiency (LGUE) from an environmental regulation perspective. Harnessing municipal panel data from 278 cities across China between 2011 and 2020, the study initially employs a super-efficient SBM model to estimate the urban LGUE. Further, a progressive difference-in-differences methodology is utilized to delve into CET’s impact on the LGUE. The main results are as follows. (1) Through the visual analysis of the time–space evolution trend, the LGUE displays pronounced spatial agglomeration, with the LGUE values being higher in the central and western regions compared to the east, and in the south versus the north. Over time, it follows a “U-shaped” change pattern. (2) The CET policy exerts a statistically significant positive influence on the LGUE, although this effect is accompanied by a temporal lag. Following a number of approaches to validate the results, the impact remains significant. (3) Regarding the heterogenous effects, the CET policy appears to have a greater impact on resource-based cities and those in the eastern part of China relative to non-resource-based and central–western cities. This research offers empirical evidence and countermeasure recommendations for the further refinement of the CET policy to enhance the urban LGUE.

Land Use Modeling Scenarios using Spatial Dynamic Model in Badung Regency

Management of land use is necessary to maintain the balance between human needs and environmental aspects. Known as the highest-income region in Indonesia, Badung Regency has substantial demand for land utilization for tourism activities. The conversion of productive agricultural land to tourism areas would threaten the sustainability of natural ecosystems in Badung. This study aims to predict the future land use in Badung Regency in 2033 through two development scenarios, Bussiness As Usual (BAU) and Rapid Economic Growth (REG). The simulation was carried out using spatial dynamic models of cellular automata. Several satellite imageries, municipal data, field surveys, and influencing factors of land transition have been utilized as input for the model. The result indicates that the residential area dominated the development in the BAU scenario with the direction of residential development spreading to the south, and new tourism development portrayed in the north close to the agricultural area. While in the REG scenario, tourism land use had significant growth that does not only approach agricultural land but also protected forest areas in the north. The model accuracy showed moderate agreement according to the Kappa index both in BAU and REG scenarios. The finding of this study could support the decision maker to create a better policy considering the potential impact of land use growth direction.

Digital inclusive finance, industrial structure, and economic growth: An empirical analysis of Beijing-Tianjin-Hebei region in China.

As a product of combining digital technology and traditional finance, digital inclusive finance plays a vital role in economic growth. This paper deeply analyzes the impact of digital inclusive finance on economic growth and the specific transmission path. This research selects the municipal panel data of Beijing-Tianjin-Hebei from 2011 to 2020 and empirically studies the impact of digital inclusive finance on economic growth. From the perspectives of industrial structure transformation speed, industrial structure upgrading, and industrial structure rationalization, this study analyzes the role of industrial structure in the impact of digital inclusive finance on economic growth and tests the heterogeneity of the impact of digital inclusive finance on economic growth. The results show that digital inclusive finance has a significant role in promoting economic growth. The depth of use of digital inclusive finance has the most significant impact, followed by the breadth of coverage, and the degree of digitization is the smallest. The industrial structure transformation speed and the industrial structure rationalization play a significant intermediary role in the economic growth effect of digital inclusive finance, and the industrial structure upgrading has no significant impact on the economic growth effect of digital inclusive finance; the promotion effect of digital inclusive finance on economic growth is bigger in the economically developed group, the higher digital inclusive finance group and the technologically developed group, and the promotion effect is smaller in the economically underdeveloped group, the lower digital inclusive finance group and the technologically underdeveloped group. The results provide a strong reference for policy formulation to promote the development of digital inclusive finance and economic growth.

Transitioning towards circular economy through municipal solid waste analysis and characterisation using SowaCLINK software

Municipal solid waste constitutes environmental challenges globally, especially in developing countries, due to increasing waste generation, population growth, inadequate infrastructure, lack of data and poor planning. This study aims to conduct a comprehensive waste audit on the municipal solid waste generated in Aba, a metropolis in southeastern Nigeria. Aba is a commercial city considered the messiest because of the massive municipal solid waste generation and poor management. The study investigated the energy potential and waste regeneration. Municipal solid waste data was sought to provide insight into the quantity and composition of municipal solid waste. The methodology was site-based, in line with the standard test method for determining unprocessed municipal solid waste (ASTM-D5231-92) and SowaCLINK software, a computer-based environmental application, was used for characterization. Linear extrapolation was adopted to quantify the rate of municipal solid waste generated. The geometric mean was applied to forecast the area’s population for a 10-year design period. The chemical elements of the characterized municipal solid waste were utilized based on the ASTM-D5291 standard for municipal solid waste thermochemical conversion, and the high and low heating values were analyzed. The outcomes provided energy recovery potential, the electrical power potential, and the power to the grid of electrical power of the municipal solid waste. The results obtained were 0.7813 kg/p/d and 490,268 t/y for a population of 1,719,185 persons. The percentage of the municipal solid waste components with energy potential was 71%, comprising 48% combustible and 23% organic components on average. The high heating value computed was 176.5 MJ/kg, and the low heating value was 14 MJ/kg. The energy recovery potential was 3,709,463 MWh, the electrical power potential was 38,680 MW, and the power to the grid was 26.1 MW daily. The research reveals a promising direction in transitioning from the linear economy of municipal solid waste management toward implementing an integrated sustainable municipal solid waste management based on the circular economy model. The study recommends adopting detailed steps to proffer solutions to the environmental challenges associated with municipal solid waste in most low-middle-income countries to achieve sustainable municipal solid waste management while generating electricity and bio-fertilizers through incineration and anaerobic digestion.