Search Results (274)

Urban health planning requires a multi-scalar understanding of the territory, capable of capturing socio-economic inequalities and health needs at the local level. In the case of Rome, current administrative subdivisions—Urban Zones (Zone Urbanistiche)—are too large and internally heterogeneous to serve as effective units for equitable health planning. This study presents a methodology for the territorial redefinition of Rome’s Municipality III, aimed at supporting healthcare planning through an integrated analysis of census sections. These were grouped using a combination of census-based socio-demographic indicators (educational attainment, employment status, single-person households) and real estate values (OMI data), alongside administrative and road network data. The resulting territorial units—21 newly defined Mesoareas—are smaller than Urban Zones but larger than individual census sections and correspond to socio-territorially homogeneous neighborhoods; this structure enables a more nuanced spatial understanding of health-related inequalities. The proposed model is replicable, adaptable to other urban contexts, and offers a solid analytical basis for more equitable and targeted health planning, as well as for broader urban policy interventions aimed at promoting spatial justice. Full article

Traffic crashes and congestion generate high social and economic costs, yet traditional traffic monitoring methods, such as police patrols, fixed cameras, and helicopters, are costly, labor-intensive, and limited in spatial coverage. This paper presents a novel Drone Routing and Scheduling with Flexible Multiple Visits (DRSFMV) framework, an optimization model for planning drone-based highway monitoring under realistic operational constraints, including battery limits, variable monitoring durations, recharging at a depot, and target-specific inter-visit time limits. A mixed-integer nonlinear programming (MINLP) model and a linearized version (MILP) are presented to solve the problem. Due to the NP-hard nature of the underlying problem structure, a heuristic solver, Hexaly, is also used. A case study using real traffic census data from three Southern California counties tests the models across various network sizes and configurations. The MILP solves small and medium instances efficiently, and Hexaly produces high-quality solutions for large-scale networks. Results show clear trade-offs between drone availability and time-slot flexibility, and demonstrate that stricter revisit constraints raise operational cost. Full article

This study investigates the critical link between child care accessibility and local labor force participation, addressing a gap in current research that often lacks local spatial granularity. While over half of the U.S. population resides in child care deserts, disproportionately affecting rural, low-income, and minority communities, the economic implications for local labor markets remain underexplored. Leveraging Michigan child care license data and Census tract-level demographic and employment characteristics, this research employs a spatial econometric approach to estimate the impact of geographic distance to child care facilities on labor supply using descriptive data. Our findings consistently demonstrate that increased distance to child care is significantly associated with reduced labor force participation. While female labor force participation is lower in areas with constrained access to child care, we also found that households with two parents are also less likely to have full labor force participation when access to child care is constrained. The cost-effective framework used here can be replicated to identify specific communities most impacted by child care-related employment disruptions. The analytical findings can be instrumental in targeting and prioritizing child care policy interventions. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

Understanding spatiotemporal mismatches between urban expansion and population dynamics is essential for guiding sustainable development in rapidly urbanizing regions. Using multi-source nighttime light (NTL) images and global settlement layers, this study investigates the settlement growth pattern and potential spatiotemporal mismatch with population distribution in Guangdong, China, from 1995 to 2019 at a 5-year interval. Specifically, population spatialization in urban and rural areas is separately mapped by adopting a population-based thresholding method, achieving strong agreement with the census record. Our analysis reveals distinct expansion patterns and mismatch conditions across Guangdong’s Core, Belt, and District subzones. The Core and District subzones primarily experienced infilling and edge-expansion urban growth, while the Belt subzone exhibited more dispersed spatial patterns. Notably, only 5 of 21 prefectures exhibited faster population growth than urban expansion, likely due to sustained migration driven by economic opportunities and advanced urbanization. Quantitatively, both urban expansion and population growth followed a Core, Belt, District order. Spatially, population-dominated areas were primarily clustered within 10 km of urban centers, while the District subzone extensively displayed overfilled settlements, indicating low-efficient land use. Temporally, urban growth relative to population in the Core subzone turned from slower pre-2000 to faster post-2000, followed by gradual deceleration, while the Belt subzone maintained balanced growth throughout the study period. The District subzone sustained faster urban growth from 2000 to 2019. Findings of the study provide an important reference for scientific urban planning and sustainable regional development, not only in Guangzhou but other rapidly urbanizing regions globally. Full article

This study investigates the relationships between air quality, social vulnerability, and health outcomes at the census tract-level in Harris County, Texas. Spatial and regression analyses were conducted using sociodemographic data, air quality indicators, including PM2.5, diesel particulate matter (DPM), nitrogen dioxide (NO₂), and ozone, and health metrics, such as coronary heart disease, chronic obstructive pulmonary disease (COPD), asthma, and stroke prevalence. The results indicated variability in sociodemographic challenges, air pollution, and health outcomes. Social vulnerability strongly correlated with increased prevalence of respiratory and cardiovascular diseases, notably COPD, asthma, and stroke. The air quality metrics showed significant geospatial variability: PM2.5 and NO₂ were concentrated centrally near transportation corridors, DPM was elevated near eastern industrial regions, and ozone peaked in western parts of the county, potentially due to atmospheric transport and photochemical processes. PM2.5 exposure significantly correlated with increased cardiovascular and respiratory health outcomes, particularly at elevated concentrations. In contrast, ozone demonstrated a plateauing effect, increasing the health risks but with a diminishing impact at higher concentrations. The correlations between social vulnerability and air quality were modest, suggesting homogenous distributions of PM2.5, NO₂, and DPM across socioeconomically diverse areas, whereas ozone exposure slightly increased with higher social vulnerability. The findings pointed to the complexity of spatial relationships between socioeconomic status, air pollution, and health, highlighting the need for additional monitoring and targeted interventions to improve health outcomes in socio-demographically and economically challenged communities. Full article

In the European Union, 75% of the residential building stock is estimated to have energy inefficiencies, which increases the probability of falling into energy poverty. Poor thermal conditions reduce the quality of life of dwelling occupants. Renovating the residential building stock is essential to reduce energy consumption, CO₂ emissions, and energy poverty in cities. This study aims to assess and map the energy efficiency and energy poverty of residential buildings in Seville at the urban district and census tract level. A total of 45,908 dwellings were evaluated using data from the Energy Performance Certificates database and demographic and economic information from national and official databases. The analysis considers dwelling typology, year of construction, average household income, and geographic location at the district and census tract level. The results show that Seville’s residential building stock performs poorly, with 83% and 92% of dwellings rated “E” or lower for energy consumption and CO₂ emissions, respectively. The findings of this GIS-based study help identify urban areas with less efficient buildings and higher energy poverty risk, providing valuable information to develop targeted renovation strategies and reduce the climate impact of Seville’s residential building stock. Full article

Water management is a long-standing source of dispute between the riparian states of Karnataka and Tamil Nadu. Recently, these disputes have intensified due to impacts from climate change and Bangalore’s rapid growth to megacity status. Despite well-defined national water governance instruments, competition between state actors and limited access to reliable hydrometric data have led to a fragmented regulatory regime, allowing unchecked exploitation of surface and groundwater resources. Meanwhile, subsidised energy for groundwater pumping incentivises the unsustainable irrigation of high-value, water-intensive crops, resulting in overextraction and harm to aquatic ecosystems. Here, we employ a water–energy–food–environment (WEFE) nexus approach to examine the socio-political, economic, and environmental factors driving unsustainable irrigation practices in the Cauvery River Basin (CRB) of Southern India. Our methodology integrates spatially explicit analysis using digitised irrigation census data, theoretical energy modelling, and crop water demand simulations to assess groundwater use patterns and energy consumption for irrigation and their links with governance and economic growth. We analyse spatio-temporal irrigation patterns across the whole basin (about 85,000 km²) and reveal the correlation between energy access and groundwater extraction. Our study highlights four key findings. First, groundwater pumping during the Rabi (short-rain) season consumes 24 times more energy than during the Kharif (long-rain) season, despite irrigating 40% less land. Second, the increasing depth of borewells, driven by falling water table levels, is a major factor in rising energy consumption. Third, energy input is highest in regions dominated by paddy cultivation. Fourth, water pumping in the Cauvery region accounts for about 16% of India’s agricultural energy use, despite covering only 4% of the country’s net irrigated area. Our study reinforces the existing literature advocating for holistic, catchment-wide planning, aligned with all UN Sustainable Development Goals. Full article

This study examines access to rental housing in the metropolitan area of Las Palmas de Gran Canaria, linking it to socio-economic inequalities and the increasing precarization. In recent years, housing affordability has worsened due to rising rents, stagnant wages, and speculative dynamics—particularly those linked to tourism and platform-based economies. Drawing on official data from the State Reference System for Rental Housing Prices (SERPAVI) and income statistics at the census tract level, this research quantifies housing affordability and spatial disparities through indicators such as economic effort rates. The analysis identifies patterns of exclusion and urban fragmentation, showing that large sectors of the population—especially those earning the minimum age—face severe barriers to accessing adequate housing. The findings highlight the insufficiency of current public policies and propose the expansion of social rental housing and stricter rental market regulation as necessary steps to ensure fairer urban conditions. Full article

This paper analyzes the 1/3 Financial Rule, a method of allocating income equally among debt repayment, savings, and living expenses. Through mathematical modeling, game theory, behavioral finance, and technological analysis, we examine the rule’s potential for supporting household financial stability and reducing bankruptcy risk. The research develops theoretical foundations using utility maximization theory, demonstrating how equal allocation emerges as a solution under standard economic assumptions. The game-theoretic analysis explores the rule’s effectiveness across different household structures, revealing potential strategic advantages in financial decision-making. We investigate psychological factors influencing financial choices, including cognitive biases and neurobiological mechanisms that impact economic behavior. Technological approaches, such as AI-driven personalization, blockchain tracking, and smart contract applications, are examined for their potential to support financial planning. Empirical validation using U.S. Census data and longitudinal studies assesses the rule’s performance across various household types. Stress testing under different economic conditions provides insights into its adaptability and resilience. The research integrates mathematical analysis with behavioral insights and technological perspectives to develop a comprehensive approach to household financial management. Full article

Multiple definitions have been used to identify individuals who are high system users (HSUs), through economic costs, frequency of use, or length of stay for inpatient care users. However, no definition has been validated to be representative of those residing in rural communities, who face unique service accessibility. This paper identifies an HSU definition for rural Canada that is inclusive of various levels of rurality, longitudinal patient experiences, and types of hospitalizations experienced. This study utilized the 2011 Canadian Census Health and Environment Cohort (CanCHEC) linkage profile to assess hospitalization experiences between 1 January 2009 and 31 December 2013. A range of common HSU indicators were compared using Cox proportional hazards modelling for multiple periods of assessment and types of admissions. The preferred definition for rural HSUs was individuals who are in the 90th percentile of unplanned hospitalization episodes for 2 of 3 consecutive years. This approach is innovative in that it includes longitudinal hospital experiences and multiple types of hospitalizations and assesses an individual’s rurality as a point of context for analysis, rather than a characteristic. These differences provide an opportunity for community characteristic needs assessment and subsequent adjustments to policy development and resource allocation to meet each rural community’s specific needs. Full article

In this study, we investigate the residential stability and mobility patterns of Black single mothers compared to White single mothers. Using data from the Panel Study of Income Dynamics from 1970 to 2015, linked to the U.S. Census for contextual characteristics, our multilevel linear probability models reveal substantial racial disparities. Black single mothers have a lower probability of remaining in non-poor neighborhoods rather than migrating to poor neighborhoods relative to White single mothers. Conversely, Black single mothers possess a higher probability of remaining in poor neighborhoods instead of moving to non-poor ones in relation to White single mothers. When economic resources are allowed to vary between Black and White single mothers, even higher-income Black single mothers cannot convert these resources into remaining in or migrating to non-poor neighborhoods at the same rate as White single mothers. Full article

The 1742–1743 plague epidemic significantly impacted Santa Fe, a key city along the Royal Road (Camino Real), a vital colonial trade route connecting Buenos Aires and Lima. This study examines the epidemic’s demographic and socio-economic consequences in Santa Fe, focusing on excess mortality and its disproportionate impact on marginalized groups, such as Indigenous and African-descended populations. Utilizing parish records, census data, and archival sources, the research quantifies mortality rates during the epidemic and highlights the role of Santa Fe’s strategic position in facilitating the rapid spread of the disease. The findings underscore the intersection of systemic vulnerabilities and territorial dynamics shaped by Bourbon reforms, which exacerbated the epidemic’s effects. Furthermore, this study explores the resilience of local communities, emphasizing how cultural and religious practices contributed to recovery. By situating Santa Fe’s experience within the broader context of the Royal Road, this research provides a nuanced understanding of the complex interplay between epidemics, social structures, and demographic change in colonial Latin America, offering valuable insights for contemporary public health strategies. Full article

We examined neighborhood-level demographic, economic, and social characteristics and food and health-services access to gauge the vulnerability of older-adult neighborhoods in New York State (NYS), which is understudied and is significant given the rapid aging of populations worldwide. We conducted descriptive ecological analyses using data from the American Community Survey, historical redlining maps, Social Capital Instruments, U.S. Department of Agriculture food access atlas, ESRI businesses, and Social Determinants of Health. We compared census tracts classified as having high and low levels of older-adult population; among those identified as high-older-adult neighborhoods, we then examined tracts with high and low levels of adult population living alone and in poverty. Our results showed that NYS neighborhoods with large shares of the older adult population are generally faring well in terms of their socioeconomic status, social capital, lack of social isolation, and health services access. However, the older-adult neighborhoods with larger shares of the population living alone and in poverty fare worse, living in areas with poorer socioeconomic status, lower social capital, and considered medically underserved. NYS older adult communities are projected to increase by 2030. Resources should be invested in such areas with vulnerable groups so populations may age in equitable and accessible communities. Full article

The topic of the mosaic patterns of the settlement of ethnic minorities in US cities has been studied in depth. Many works proceed from the optics of studying inequality, which, in the American context, is often explained by overlapping patterns of ethnoracial discrimination. In this regard, groups at the intersection of inequality patterns are of great interest—for example, non-white immigrants and their descendants. This article analyzes the settlement of two such groups—Haitians and Jamaicans—at the state, county, and census tract levels in the two main population centers for the following two groups: New York and Miami metropolitan areas. The authors pose the question of whether Haitians and Jamaicans fundamentally differ from African Americans in terms of settlement patterns and socio-economic status. It is established that these two population groups are highly concentrated on a nationwide scale (in the states of Florida and New York) and in some counties (comprising metropolitan areas of New York and Miami). The authors carried out a spatial correlation (LISA) analysis to identify areas of concentration of Haitians and Jamaicans in the two above-mentioned metropolitan areas. Three distinct Haitian areas, three mixed Haitian/Jamaican, and one distinct Jamaican area are found in New York. In the Miami metropolitan area, three mixed Haitian/Jamaican areas are identified, as well as one Haitian area. Calculation of the Darden–Kamel Composite Socio-Economic Index was used to assess the socio-economic status of these areas and to compare it with that in the areas of concentration of African Americans. The analysis revealed that the areas of concentration of Haitians and Jamaicans are relatively disadvantaged in their socio-economic status, but to a lesser extent than those of African Americans. The study also established the following pattern: in the New York metropolitan area, the higher socio-economic status is a feature of the mixed Haitian/Jamaican areas of concentration in the central city, while in Miami, the higher socio-economic status is observed in the Jamaican areas in the suburbs. Beyond these empirical findings, this article offers a contextual perspective on inequality among non-white migrant groups as they settle near established African American communities. The novelty of our approach to the research problem relates to the idea that communities of African Americans, Haitians, and Jamaicans are quite variable in time and space—in particular, due to the non-simultaneous settlement of these groups in the metropolitan areas under consideration. Our most important conclusion is the discovery of the relationship between the stage of the beginning of the integration of the groups considered into urban communities and their socio-economic status at that moment. The later the group began integration, the fewer discriminatory practices it had to face, which led to a higher socio-economic status. Full article