Household Data Research Articles

Spatial-Temporal Distribution of 12-Year Periodontal Disease Prevalence in a Large Population Using Geographical Information Systems: A Longitudinal Study.

Periodontal diseases, commonly linked to dental biofilm and affecting adults, were studied using Geographic Information Systems (GIS) and Kernel Analyses with epidemiological data. This paper presents a hybrid method for use in epidemiological studies by evaluating the spatiotemporal distribution of disease prevalence. This study analy ed 47,757 patients from the Department of Periodontology out of 662,351 visitors to University Faculty of Dentistry (2012 to July 2023). The central districts of Kayseri in Turkey were selected as the study areas. Periodontitis prevalence was assessed through radiographic evidence and clinical examination. Point-based location data, including gender, age, and disease type, matched household data, creating building-based spatial data. Kernel Density (KD) and Average Nearest Neighbor (ANN) analyses examined patient concentration and disease types in specific regions. Accordingly, standard deviation ellipses were prepared by year to assess the spatial changes in the regions where patients resided. The study found higher periodontitis prevalence in males, increasing with age, while gingivitis decreased. After 2017, periodontitis prevalence notably declined. Location-based data exhibited clustering in patient distribution. KD maps showed similar patient distributions over the years, with more applications from areas closer to the Faculty of Dentistry. The spatial distribution of the patients applying has remained consistent over the last 5 years. Through GIS, KD maps reveal the spatial-temporal distribution of periodontitis patients. This aids in identifying high-prevalence regions and guiding strategic healthcare facility placement. Implementing preventive programs in high-demand areas, particularly in family health centers (local health facilities), can reduce community-wide periodontal disease prevalence.

Spatial suitability of urban land use models for poverty alleviation in the cities of Nigeria

Many models depicting the spatial dimensions of poverty are based on cities in developed countries, raising questions about their appropriateness for urban spaces in developing countries. This study evaluates the spatial suitability of these models in understanding poverty distribution and informing alleviation efforts related to Sustainable Development Goal (SDG). One in cities of developing nations. A grid sampling technique in ArcGIS 10.8 was used to select 1098 households across 56 wards in three mid-sized Nigerian cities. Household data were collected through structured questionnaires using GeoODK, and Maxar high-resolution imagery was used to assess urban building density. The Foster-Greer-Thorbecke (FGT) poverty indices and Pearson correlation were applied for analysis. Results showed that over 29 wards had a poverty incidence and gap index greater than 0.20, while poverty severity remained below 0.20 in most wards. High building density was more prevalent in the city’s interior. Significant correlations were found between the income poverty gap (p = 0.005, r = 0.367), poverty severity (p = 0.009, r = 0.346), and building density. The study concludes that these models are suitable for informing poverty alleviation policies in mid-sized cities of developing countries, especially Nigeria.

Intersecting social and environmental determinants of multidrug-resistant urinary tract infections in East Africa beyond antibiotic use

The global health crisis of antibacterial resistance (ABR) poses a particular threat in low-resource settings like East Africa. Interventions for ABR typically target antibiotic use, overlooking the wider set of factors which drive vulnerability and behaviours. In this cross-sectional study, we investigated the joint contribution of behavioural, environmental, socioeconomic, and demographic factors associated with higher risk of multi-drug resistant urinary tract infections (MDR UTIs) in Kenya, Tanzania, and Uganda. We sampled outpatients with UTI symptoms in healthcare facilities and linked their microbiology data with patient, household and community level data. Using bivariate statistics and Bayesian profile regression on a sample of 1610 individuals, we show that individuals with higher risk of MDR UTIs were more likely to have compound and interrelated social and environmental disadvantages: they were on average older, with lower education, had more chronic illness, lived in resource-deprived households, more likely to have contact with animals, and human or animal waste. This suggests that interventions to tackle ABR need to take account of intersectional socio-environmental disadvantage as a priority.

Household structure and dietary diversity among older adults in rural and urban China: a cross-sectional study

BackgroundHousehold structure can significantly affect older adults’ eating behaviours and diet quality. However, the difference in dietary diversity in various household structures in urban and rural has rarely been investigated. This study aimed to investigate the association between household structure and dietary diversity among older Chinese adults and examine whether the association differed by urban and rural.MethodsThe study used data from the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The participants were individuals aged 60 and over with available dietary and household structure data. Dietary diversity was defined as ‘high’ if respondents had a dietary diversity score (DDS) above the mean value. Household structures were classified into four mutually exclusive categories: (1) living alone; (2) spouse only; (3) at least with a great/grandchild(ren); (4) non-empty-nested. Binary logistic regression was applied to investigate the association between household structures and dietary diversity.ResultsNon-empty-nested older adults had the highest probability (OR = 1.64, 95% CI = 1.44–1.87) of having high dietary diversity. People who only lived with their spouse and at least lived with a great/grandchild(ren) were 1.45 (OR = 1.45, 95% CI = 1.23–1.71) and 1.23 (OR = 1.23, 95% CI = 1.08–1.41) times as likely to have diverse diets than older adults who lived alone. The difference in dietary diversity among various household structures is more evident in urban than rural areas. Solo-living older adults were the most disadvantaged regarding dietary diversity in urban and rural areas.ConclusionsHousehold structures are significantly associated with dietary diversity among older Chinese adults aged 60 years and over. The findings emphasise the need to reduce nutritional inequality, encourage dietary diversity, and consider both the household structures and residences when providing health intervention programs to the older population.

Environmental Sustainability: Ecological Footprint Analysis of Ibadan North, Nigeria

The ecological implications of global development have been a primary focus in discussions surrounding sustainability. As urban development expands globally, addressing its many ecological concerns becomes imperative. However, the ecological footprint (EF) analysis of urban sustainability within the sub-Saharan region, particularly Nigeria, remains largely unexplored. Here, we used the EF indices to explore the environmental sustainability of Ibadan City, one of Nigeria's fastest-growing urban centres. We implemented a bottom-up EF approach in the study, employing a cross-sectional design. We used an EF questionnaire to gather monthly household consumption data on food, energy, and water. We analysed these data descriptively and methodologically using EF formulae. Findings indicate environmental sustainability, as reflected in the low EF of 0.43gha/capita, with the energy footprint accounting for the majority (93%) at 0.4gha/capita. In comparison, the food footprint had the lowest share (below 3%) at 0.01gha/capita. Our findings demonstrate the significant impact of energy consumption on the overall EF, reinforcing the need for more sustainable energy solutions in urban planning, thus contributing to urban sustainability by informing urban planning and energy policy in alignment with the global sustainability drive.

Using Spatial Literacy for Disaster Management in Coastal Communities of Small Island Developing States (SIDS): A Case Study from Lavongai, Papua New Guinea

This study investigates the use of participatory geographic information systems (PGIS) for hazard assessment in small island developing states (SIDS), with a focus on spatial literacy and community-based disaster management. By partnering with the Lavongai community on Papua New Guinea, this research aimed to empower community members through skill development in geodata processing. The program leveraged local knowledge and the global positioning system to create participatory maps, enhancing both community capacity and researcher data quality. Workshops and focus group discussions (FGDs) were conducted to assess the community’s understanding of spatial concepts related to disaster risks. The core objective was a preliminary assessment of the community’s social and economic vulnerability to coastal disasters, using household data and GIS analysis. The results showed varied vulnerability levels within the community, highlighting the need for targeted disaster mitigation training and nature-based solutions. High-resolution satellite imagery and a simple bathtub model simulated sea level rise, identifying land-uses at risk. The program concluded with a community presentation of thematic maps, fostering collaboration and transparency. Future projects will address environmental challenges identified by local leaders and prioritize skill development, social data collection, and water resource mapping.

Smallholder RSPO certification, economic benefits and agrochemical use

Abstract Sustainability standards, such as the Roundtable for Sustainable Palm Oil (RSPO), can potentially mitigate the trade-offs between economic benefits and environmental harm of oil palm expansion. Using unique primary household and farm level panel data collected from Jambi Province, Indonesia, we first document the differences in agricultural inputs and outputs between certified and non-certified smallholder farmers. Second, we illustrate the potential implications and mechanisms of certification. In this study, therefore, we aim to first document agricultural input and output differences between certified and non-certified smallholder farmers, second to illustrate potential effects of certification with observational data and third to provide guidance for future rigorous causal analyses. We find generally positive correlations between RSPO certification, land productivity, profits, and fertilizer use, and a negative correlation with toxic herbicide use. However, we do not find price premiums for RSPO-certified farmers. Overall, the results of this study are indicative of potentially modest effects of RSPO-certification that may operate through higher productivity due to improved management practices that remain agrochemical intensive but avoid some particularly harmful chemicals. Our findings are relevant to better understand the strong sustainability claims of RSPO marketing and to provide guidance for rigorous causal experimental and quasi-experimental studies.

Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo

Abstract The high prevalence of food insecurity and malnutrition in Kasai Oriental, Democratic Republic of the Congo, prompted an investigation into the complex relationship between household livelihood activities and food security. Despite many rural households relying on subsistence farming, this alone may not ensure improved food security in Kasai Oriental. Consequently, non-farm sectors like artisanal mining offer a potential solution to address food insecurity among rural households. The aim of this study is to explore the association between engagement in artisanal diamond mining, food insecurity, and children’s nutritional status in Kasai Oriental. The research utilizes household cross-sectional data collected between November and December 2022. Fixed effects and Instrumental variable models were employed to address household heterogeneity and potential endogeneity related to participation in artisanal diamond activities. The regression results reveal a significant relationship between participation in artisanal diamond mining and food security. This implies that participation increases households’ cash, enabling them to access sufficient food and potentially mitigating the risk of falling into food insecurity. However, involvement in artisanal mining has not shown a significant association with children’s malnutrition. These findings call for further research on “hidden hunger.” Policies aiming to encourage and formalize artisanal diamond mining should integrate specific extension services and inform rural households about hidden hunger.

Estimating the risk and spatial spread of measles in populations with high MMR uptake: Using school-household networks to understand the 2013 to 2014 outbreak in the Netherlands.

Measles outbreaks are still routine, even in countries where vaccination coverage exceeds the guideline of 95%. Therefore, achieving ambitions for measles eradication will require understanding of how unvaccinated children interact with others who are unvaccinated. It is well established that schools and homes are key settings for both clustering of unvaccinated children and for transmission of infection. In this study, we evaluate the potential for contacts between unvaccinated children in these contexts to facilitate measles outbreaks with a focus on the Netherlands, where large outbreaks have been observed periodically since the introduction of mumps, measles and rubella (MMR). We created a network of all primary and secondary schools in the Netherlands based on the total number of household pairs between each school. A household pair are siblings from the same household who attend a different school. We parameterised the network with individual level administrative school and household data provided by the Dutch Ministry for Education and estimates of school level uptake of the MMR vaccine. We analysed the network to establish the relative strength of contact between schools and found that schools associated with low vaccine uptake are highly connected, aided by a differentiated school system in the Netherlands (Coleman homophily index (CHI) = 0.63). We simulated measles outbreaks on the network and evaluated the model against empirical measles data per postcode area from a large outbreak in 2013 (2,766 cases). We found that the network-based model could reproduce the observed size and spatial distribution of the historic outbreak much more clearly than the alternative models, with a case weighted receiver operating characteristic (ROC) sensitivity of 0.94, compared to 0.17 and 0.26 for models that do not account for specific network structure or school-level vaccine uptake, respectively. The key limitation of our framework is that it neglects transmission routes outside of school and household contexts. Our framework indicates that clustering of unvaccinated children in primary schools connected by unvaccinated children in related secondary schools lead to large, connected clusters of unvaccinated children. Using our approach, we could explain historical outbreaks on a spatial level. Our framework could be further developed to aid future outbreak response.

The Impact of Cultivated Land Fragmentation on Farmers’ Ecological Efficiency of Cultivated Land Use Based on the Moderating and Mediating Effects of the Cultivated Land Management Scale

To date, scholars have increasingly focused on the reduction in crop yields caused by cultivated land fragmentation, yet its effects on the ecological efficiency of cultivated land use are often overlooked. This oversight leads to land resource waste and environmental pollution. It is essential to explore this problem to achieve moderate-scale farming operations and promote the green transformation of agricultural land. This study theoretically analyzed the mechanisms by which cultivated land fragmentation and management scales influence the ecological efficiency of cultivated land use. Based on 2023 household data from Changde and Shaoyang, China, empirical tests were conducted using the stochastic frontier analysis method, Tobit model, and structural equation model. The research results indicate that: (1) The mean ecological efficiency of cultivated land use among the total sample households was 0.822, and the eco-efficiency in the plains was slightly lower than that in the hilly areas. (2) The scale of cultivated land management played a moderating role in the impact of cultivated land fragmentation on ecological efficiency, with differences observed between topographical types. The scale of management can offset part of the negative impact of cultivated land fragmentation on the ecological efficiency of cultivated land use. (3) Regarding the impact of cultivated land fragmentation on the ecological efficiency of cultivated land use, cultivated land management scale changes play a complete mediating role. These findings help provide policy implications to improve the ecological efficiency of cultivated land use. Policy support should be strengthened by promoting moderate-scale cultivated land operations, enhancing the comprehensive remediation of cultivated land fragmentation, and developing skilled farmers for long-term environmental sustainability.

Impact of COVID-19 health precautions on asymptomatic Streptococcus pyogenes carriage in palestinian children: a pre- and post-pandemic study

BackgroundStreptococcus pyogenes (Group A Streptococcus, GAS) is a significant pathogen that causes diverse infections, ranging from pharyngitis to severe invasive diseases. Asymptomatic carriage in children is pivotal for transmission. The COVID-19 pandemic’s health measures, including mask wearing and enhanced hand hygiene, likely influenced GAS transmission dynamics. This study evaluated the impact of these precautions on the prevalence of asymptomatic pharyngeal GAS carriage among schoolchildren in the southern West Bank, Palestine.MethodsThis cross-sectional study was conducted in two phases: pre-COVID-19 (November 2019–January 2020) and post-COVID-19 (November 2023–April 2024). Throat swabs were collected from 701 children (345 pre-COVID-19, 356 post-COVID-19) via cluster sampling. The samples were tested with the ABON Strep A rapid test and confirmed by culture. Sociodemographic, health, and household data were also collected. The statistical analyses included descriptive statistics, chi-square tests, and binary logistic regression.ResultsThe prevalence of asymptomatic pharyngeal GAS carriage declined from 15.7% pre-COVID-19 to 10.4% post-COVID-19 (p = 0.038). Significant reductions were observed among urban residents (23.5–10.1%, p = 0.003) and those from medium socioeconomic backgrounds (16.0–9.1%, p = 0.008). Compared with urban residents, rural residents had lower GAS carriage rates (adjusted OR = 0.505, p = 0.023). Carriage rates also decreased among children with frequent sore throats (17.6–7.3%, p = 0.007) and those using private wells (52.5–14.9%, p < 0.001). Higher BMI was a significant risk factor (adjusted OR = 17.68, p < 0.001), whereas frequent tooth brushing (adjusted OR = 0.055, p < 0.001) and hand washing (adjusted OR = 0.367, p < 0.001) were protective factors.ConclusionsCOVID-19-related health precautions were correlated with a significant reduction in asymptomatic GAS carriage among Palestinian children. These findings suggest that public health measures, such as mask wearing and hand hygiene, can influence the transmission of respiratory pathogens. Ongoing surveillance and targeted interventions are essential for managing GAS infections, particularly in resource-limited settings.

Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?

The integration of AI and ML into energy forecasting is crucial for modern energy management. Federated Learning (FL) is particularly noteworthy because it enhances data privacy and facilitates collaboration among distributed energy resources. It enables model training across multiple locations while minimizing reliance on centralized servers and data transfers. However, FL faces significant security challenges, particularly from adversarial attacks that can undermine the models' integrity and reliability. This paper addresses these security concerns by evaluating the effectiveness of Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) in distributed load forecasting. We conducted a comparative analysis using two publicly available datasets, household data and smart grid data, for short-term load forecasting. Our study finds that DFL is more robust against adversarial attacks and artificial noise compared to CFL. Specifically, adversarial model poisoning attacks impact only the targeted client in DFL, while CFL is more broadly affected. For instance, in the presence of attacks, CFL's average client Mean Absolute Error (MAE) increased from 0.084 to 0.78 kWh for Dataset 1 and from 0.475 to 11.159 MW for Dataset 2, whereas DFL maintained relatively low error rates. Conversely, artificial noise had a less pronounced effect on CFL compared to DFL. Additionally, we introduce Decentralized Random Layer Aggregation (DRLA) to enhance DFL's robustness, offering further insights into improving FL methodologies in the energy sector. Our findings also indicate that only a single layer of the neural network is required for local updates during the aggregation process in DFL.

Pairwise Accelerated Failure Time Regression Models for Infectious Disease Transmission in Close-Contact Groups With External Sources of Infection.

Many important questions in infectious disease epidemiology involve associations between covariates (e.g., age or vaccination status) and infectiousness or susceptibility. Because disease transmission produces dependent outcomes, these questions are difficult or impossible to address using standard regression models from biostatistics. Pairwise survival analysis handles dependent outcomes by calculating likelihoods in terms of contact interval distributions in ordered pairs of individuals. The contact interval in the ordered pair is the time from the onset of infectiousness in to infectious contact from to , where an infectious contact is sufficient to infect if they are susceptible. Here, we introduce a pairwise accelerated failure time regression model for infectious disease transmission that allows the rate parameter of the contact interval distribution to depend on individual-level infectiousness covariates for , individual-level susceptibility covariates for , and pair-level covariates (e.g., type of relationship). This model can simultaneously handle internal infections (caused by transmission between individuals under observation) and external infections (caused by environmental or community sources of infection). We show that this model produces consistent and asymptotically normal parameter estimates. In a simulation study, we evaluate bias and confidence interval coverage probabilities, explore the role of epidemiologic study design, and investigate the effects of model misspecification. We use this regression model to analyze household data from Los Angeles County during the 2009 influenza A (H1N1) pandemic, where we find that the ability to account for external sources of infection increases the statistical power to estimate the effect of antiviral prophylaxis.

Can migraine be triggered by unemployment in France? A Bi-Probit/Multinomial probit approach

ABSTRACT Migraine is a major public health issue with massive human, économic and social costs, particularly for women. However, the effects of the labour market on this disease are to date little studied. Because unemployment may trigger migraine due to induced stress, we present in this empirical study estimates of the impact of unemployment on migraine using French household data. The causal impact of unemployment is established using instrumental variable methods. We use a rich database (Health and Professional Itinerary Survey) that allows to measure the presence of migraine, mental health and labour market variables, including working conditions. We show that unemployment increases the probability of migraine among women, with an important marginal effect of 14.4% points. We also show that although the issue is unemployment, inactivity must also be included and instrumented, which leads to estimate a generalization of the bi-probit model in which the first step is a multinomial probit. From a public policy perspective, the results show that prevention and care policies must be put in place and target unemployed women.

Quantifying age-specific household contacts in Aotearoa New Zealand for infectious disease modelling.

Accounting for population age structure and age-specific contact patterns is crucial for accurate modelling of human infectious disease dynamics and impact. A common approach is to use contact matrices, which estimate the number of contacts between individuals of different ages. These contact matrices are frequently based on data collected from populations with very different demographic and socio-economic characteristics from the population of interest. Here we use a comprehensive household composition dataset based on Aotearoa New Zealand census and administrative data to construct a household contact matrix and a synthetic population that can be used for modelling. We investigate the behaviour of a compartment-based and an agent-based epidemic model parametrized using these data, compared with a commonly used contact matrix that was constructed by projecting international data onto New Zealand's population. We find that using the New Zealand household data, either in a compartment-based model or in an agent-based model, leads to lower attack rates in older age groups compared with using the projected contact matrix. This difference becomes larger when household transmission is more dominant relative to non-household transmission. We provide electronic versions of the synthetic population and household contact matrix for other researchers to use in infectious disease models.

Machine learning and public policy: Early detection of physical violence against children

Physical violence against children is a widespread and grossly underreported phenomenon with substantial short and long-term negative consequences. In Latin America and the Caribbean, 43% of children under the age of 15 experience corporal punishment at home, yet reporting rates are alarmingly low. This paper aims to demonstrate how household data can be considered for a future predictive analytics model in Argentina. Based on the 2019–20 MICS survey we apply machine learning techniques to predict physical violence against children (understood as physical discipline) at the household level in Argentina. The scope and potential benefits of using predictive models in this context are assessed, as well as the main limitations and risks. The results suggest that, by analyzing the situation of the 30% of households with the highest risk scores, 43 out of 100 households in which children experience physical violence could be identified at an early stage.

Local Agglomeration and Household Mortgage Debt

Using detailed household data, we find that households working in locally agglomerated economies have high mortgage loans and are more likely to have mortgage loans. Channel testing shows that local agglomeration increases housing and mortgage demand as well as mortgage supply. The increase in mortgage loans is driven by purchases of larger houses and smaller down payments. Mechanism analyses document that local agglomeration affects mortgage loans by increasing career upward potential and providing downside protection. Specifically, we find that the impact of local agglomeration is stronger for skilled employees with enhanced prospects. In addition, local agglomeration weakens the negative relation between unemployment and mortgage loans, which supports the downside-protection role of local agglomeration. These results hold under instrumental variables analysis, difference-in-difference analysis, and a set of robustness checks. Overall, our findings highlight the importance of local labor market composition in household mortgage debt. This paper was accepted by Kay Giesecke, finance. Funding: F. F. Chen acknowledges the financial support provided by the Fundamental Research Funds for the Central Universities; the MOE Project of Key Research Institute of Humanities and Social Science in University [Grant 22JJD790094]; and the 111 Project [Grant B18033]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03486 .

Lifestyle and Cardiometabolic Risk Factors Associated with Impoverishment Due to Out-of-Pocket Health Expenditure in São Paulo City, Brazil.

The rise in obesity and related chronic noncommunicable diseases (NCDs) during recent decades in Brazil has been associated with increases in the financial burden and risk of impoverishment due to out-of-pocket (OOP) health expenditure. Thus, this study investigated trends and predictors associated with impoverishment due to health expenditure, in the population of São Paulo city, Brazil, between 2003 and 2015. Household data from the São Paulo Health Survey (n = 5475) were used to estimate impoverishment linked to OOP health expenses, using the three thresholds of International Poverty Lines (IPLs) defined by the World Bank at 1.90, 3.20, and 5.50 dollars per capita per day purchasing power parity (PPP) in 2011. The results indicated a high incidence of impoverishment due to OOP disbursements for health care throughout the period, predominantly concentrated among low-income individuals. Lifestyle choices referring to leisure-time physical activity (OR = 0.766 at $3.20 IPL, and OR = 0.789 at $5.50 IPL) were linked to reduction in the risk for impoverishment due to OOP health expenditures whilst there were increases in the probability of impoverishment due to cardiometabolic risk factors referring to obesity (OR = 1.588 at $3.20 IPL, and OR = 1.633 at $5.50 IPL), and diagnosis of cardiovascular diseases (OR = 2.268 at $1.90 IPL, OR = 1.967 at $3.20 IPL, and OR = 1.936 at $5.50 IPL). Diagnosis of type 2 diabetes mellitus was associated with an increase in the probability of impoverishment at only the $1.90 IPL (OR = 2.506), whilst coefficients for high blood pressure presented lack of significance in the models. Health policies should focus on interventions for prevention of obesity to ensure the financial protection of the population in São Paulo city, Brazil, especially targeting modifiable lifestyle choices like promotion of physical activity and reduction of tobacco use.

Housing Market Segmentation as a Driver of Urban Micro-Segregation? An In-Depth Analysis of Two Viennese Districts

The concept of segregation analyses the unequal distribution of social groups between neighbourhoods. It rests on two assumptions: that of homogeneous neighbourhoods and of a market liberal housing system. Both assumptions are applicable the context of American cities, but they display severe limitations when applied to the European context. Vienna’s housing market is particularly highly segmented, not only throughout the city as a whole but also within neighbourhoods. In the densely built-up area, residential buildings of different segments with different underlying rent regulations and entry barriers can be found side by side. Therefore, buildings are expected to show varying tenant and owner structures, which undermines the idea of a homogeneous neighbourhood. Against this background, we analyse at the micro scale small neighbourhoods defined by 100 m grid cells in a case study of two inner-city Viennese districts (districts 6 and 7) characterised by a particularly vivid housing-transformation and commodification dynamic. Using a novel and fine-grained dataset combining building information with the socio-economic data of households, we investigate the patterns and dynamics of income inequality and income segregation, as well as the relationship between housing market segments and socio-economic patterns. As data comprise two cross-sections for the years 2011 and 2020/21, changes in the neighbourhoods during the house-price boom period are also considered. This leads us to ask the question: How do housing market segmentation and its related changes affect income inequality and segregation at the micro scale? Our analysis delivers two main results: Firstly, we show the existence of marked social variation and related dynamics at the micro scale, even within a small urban area. Secondly, we show that the spatial distribution of housing market segments has a strong impact on income inequality in the neighbourhood.

Development of a Smart Metering System for Efficient Energy Consumption

Smart energy concepts are based on the utilization of emerging integrated microprocessors and communication technologies for monitoring energy consumption. In the utilization of electricity, energy management is a core problem, especially for cost savings to the benefit of electricity customers. In modern electricity generation, transmission and consumption, energy management has occupied the center of attention for sustainable techno-economic motives. The present-day power sector has an immense role to play in the overall economic performance of the global community, thus, requiring the application of efficient and reliable metering systems for the measurement of energy consumption. The application of a smart metering system depends on the effectiveness of the embedded smart communication interface in the utility service provider system and the energy consumers’ metering devices based on energy supply reliability and consumption security. Therefore, this study focuses on the design and development of an efficient smart energy meter using the advancements provided by the Internet of Things (IoTs). The system was tested based on the collection of real-time data and subsequently analyzed for performance evaluation through a case study of household data collection.