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Background Street layout is a key urban design element that influences a neighbourhood’s street connectivity, providing opportunities for physical activity, social interaction, and risk of environmental exposures. However, its effects across different socioeconomic contexts and varying durations of exposure to the neighbourhood are less explored. Methods This study aims to address these gaps by employing ordinal logistic regression models to examine the associations between neighbourhood street patterns and residents’ physical and mental health. It also investigates whether these associations vary by neighbourhood socioeconomic status (SES) and length of residence. Results The findings showed that grid street patterns were positively associated with self-assessed physical health, particularly in low SES neighbourhoods and amongst longer-term residents. However, no significant associations were observed between street pattern and self-assessed mental health. Conclusions Our findings underscore the importance of considering both the social context and duration of residence when investigating the influence of urban design on health.

This study examined spatial and temporal variations and developed models to assess the density and severity of road traffic accidents (RTAs) in Washington, D.C., using accident data from January 2017 to April 2020. Descriptive statistics revealed that accident severity was highest during the morning peak (Severity Index = 0.77), while accident frequency peaked between 12 and 5 AM, particularly at 3 AM (4.62 accidents/hour). Weekly accidents rose from Monday to Friday, and seasonally, the summer months showed the highest crash counts. Spatial analysis using severity-weighted Kernel Density Estimation identified high-risk hotspots in central neighborhoods, disproportionately affecting pedestrians and cyclists. Regression analyses compared a Classical Regression Model (CRM) with two spatial econometric models: the Spatial Lag Model (SLM) and Spatial Error Model (SEM). For accident density, the SLM achieved the best performance (R2 = 0.837, RMSE = 4.67, MAPE = 22.7%), outperforming both CRM and SEM, while confirming significant spatial autocorrelation (ρ = 0.40). Similarly, for accident severity, the SLM provided the strongest explanatory power (R2 = 0.542, RMSE = 0.106, MAPE = 16.1%), with a robust lag dependence (ρ = 0.55). Across models, road density, commercial activity, and population density were positively associated with crash risk, whereas higher per capita income reduced severity outcomes. These findings demonstrate the superiority of spatial econometric approaches in capturing accident clustering and provide actionable insights for policymakers to prioritize interventions across high-risk locations and time periods.

Life, livelihood, nature, and the onset of extreme climate change continue to pose challenges for the coastal residents of Dacope Upazila in Khulna district, Bangladesh, almost daily. As Dacope is one of the key areas affected by climate change, it is highly vulnerable to these changes. Recognizing the hardships faced by the community and the need for targeted support, this study evaluates the impacts of climate change on various livelihood assets using the Livelihood Vulnerability Index (LVI), LVI-IPCC, and Livelihood Effect Index (LEI) methods. Data were collected through a semi-structured household questionnaire survey covering nine unions within Dacope Upazila, with a sample of 400 households. Secondary data was obtained from the NASA Power website. Results show that Sutarkhali Union is the most vulnerable, with an LVI score of 0.584. Both the LVI-IPCC and LEI approaches ranked Sutarkhali as the most vulnerable. This study provides insight into living conditions and the factors that make the livelihoods of Dacope Upazila’s residents vulnerable to climate change. Consequently, it offers valuable information for policymakers and development practitioners to design effective interventions for building resilience in coastal communities.

Despite the growing progression of regional economic integration as a driving factor for national development, the spatial dynamics of industrial interdependence in Bangladesh's southern coastal zone remain insufficiently theorized and empirically underexplored. The research addresses this gap by examining the functional correlation, dependable characteristics, and structural linkages among industries across four strategically significant districts (Khulna, Satkhira, Bagerhat, and Narail) through an integrated spatial-analytical framework. Employing a composite weighted index for formal regionalization alongside Reilly's Law of Retail Gravitation, the research delineates influence areas (IA) and unserved areas (UA) at the upazila level to assess intra-regional economic reach and service accessibility differentials. Input-output interdependence is contextualized within a GIS-based spatial representation, while Local Indicators of Spatial Association (LISA) and Moran's I statistic are applied to detect local clustering patterns, spatial outliers, and distributional asymmetries in industrial concentration. The findings reveal considerable spatial heterogeneity: Koyra (Khulna) and Shyamnagar (Satkhira) exhibit the highest influence areas, whereas Daulatpur registers the lowest, reflecting structural deficits in transport connectivity and economic infrastructure. At the district level, Narail records the lowest unserved area share (0.96%), attributable in part to its favourable positioning along the Padma Bridge corridor. LISA analysis identifies the absence of significant high-high clusters, indicating a fragmented rather than cohesive pattern of industrial development, with Khulna emerging as a high-influence, high-unserved-area outlier characterized by disproportionately low industrial concentration relative to its functional importance. These spatial imbalances are further explained through a multi-factor analysis encompassing transport networks, vocational infrastructure, agricultural land use, and market density. The research concludes that targeted policy interventions, including upazila-scale industrial decentralization, infrastructure investment, and skills development, are necessary to foster inter-regional industrial interdependence and mitigate spatial disparities in southern Bangladesh.

Cyclones, storm surges, sea-level rise, tidal flooding, and limited adaptive capacity due to high population pressure and flat terrain make coastal Bangladesh highly vulnerable. This study assesses long-term climatic variability and household-level social vulnerability (SV) in Dacope Upazila, Khulna District, Bangladesh. Primary data were collected through semistructured household surveys using random sampling, while secondary climate data (1981–2021) were obtained from the NASA POWER database. The Mann–Kendall test was applied to evaluate the statistical significance of trends in rainfall and temperature, and Sen's slope method was used to estimate trend magnitude. Results indicate a downward trend in precipitation for January and February, with Z values of −0.096 and −0.042, respectively. Annual temperature also shows a decreasing trend (Z = −0.422). The rainfall anomaly index reveals that since 2015, values exceeding 1.3 indicate annual precipitation shifted from dry to extremely humid conditions. Future projections using global climate models suggest that maximum temperature may increase by 1–2 °C. SV was assessed using principal component analysis based on 28 indicators. The SV index score (+9.772) indicates high societal sensitivity to climate change. These findings can support policymakers in developing effective climate adaptation and disaster risk reduction strategies.

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Cities worldwide are quickly developing due to population increase, economic development, and infrastructural improvements in the 21st century. MRT may transform urban mobility and the landscape by providing reliable, efficient, and sustainable transportation. This study seeks to identify the impact of newly established metro stations on the urban landscape and property values. The study focuses on Uttara South and North, two sizable metro stations in the capital city of Dhaka, Bangladesh. Shannon’s Entropy was applied to measure land-use dispersion and detect spontaneous development. Mann-Kendall test analysis was conducted to identify factors contributing to expansion. The Hedonic Pricing Model (HPM) was also employed to assess the impact of proximity to metro stations on residential property values. Additionally, a SWOT analysis was performed. Data was gathered through a questionnaire survey, Key Informant Interviews, and the Landsat 8 satellite. Results show moderate dispersion in entropy values, suggesting growth is structured. The HPM identified eight attributes contributing to the increase in property prices. With p-values below 0.05, each of the eight attributes showed statistical significance. Uttara North Metro Station’s entropy value rose from 1.20 in 2020 to 1.23 in 2024. Likewise, the entropy value of Uttara South Metro Station increased from 1.12 in 2020 to 1.18 in 2024. The Kendall tau value (1) shows upward trends, indicating that increasing population and housing demand are among the significant factors contributing to expansion. This research advocates affordable housing and inclusionary zoning rules to share Transit-Oriented Development (TOD) benefits evenly across socioeconomic levels to foster socio-economic diversity.