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Urban density is a central concern in urban design and public health, yet evidence on its relationships with health is dispersed across disciplines. This study used bibliometric methods to map the global research landscape on urban density and health, and to identify publication trends, themes, key contributors, and research priorities. A search of the Web of Science Core Collection in May 2025 combined terms for density, health, and urban context. English-language articles and reviews were screened, cleaned, and analysed. Performance analysis and science mapping techniques, including co-authorship, co-citation, and keyword co-occurrence, were conducted using VOSviewer. The dataset comprised 1,670 publications published between 1965 and 2025, with marked growth after 2019. Research output was concentrated in high-income countries, particularly the Global North, with no African country among the most productive contributors. Frequently co-cited sources showed a multidisciplinary field spanning public health, urban design, and physical activity research. Keyword analysis identified nine clusters. Built environment, walkability, and physical activity formed the core. Green space and metabolic health appeared as distinct but less prominent cluster. These findings show that density-health research has been organised mainly around built environment and movement-related pathways. Psychosocial, environmental exposure, and equity-related pathways remain less integrated. For urban health practice, the findings identify evidence gaps that are relevant to policy and planning. These patterns indicate a need for broader geographical coverage, stronger attention to non-mobility and psychosocial pathways, clearer and more comparable density measures, and deeper cross-disciplinary integration to generate evidence that can better inform healthy densification policies.

The study integrates machine learning algorithms with remote sensing data to enhance drought susceptibility predictions in northwestern Bangladesh's drought-prone regions under climate change scenarios. A preliminary analysis of 27 parameters was performed, leading to the identification of 13 significant predictors through Pearson correlation and Variance Inflation Factor (VIF) analysis to address multicollinearity, with NDVI, NDWI, precipitation, and the Vegetation Health Index identified as the most influential variables. Nine advanced machine learning models, including Adaptive Boosting, Bagging, Categorical Boosting, Decision Tree, Gradient Boosted Machine, LightGBM, Random Forest, Tree-Based Bagging, and Extreme Gradient Boosting, were developed and evaluated using ROC-AUC analysis, with CatBoost demonstrating exceptional performance by achieving the highest AUC value of 0.99. The model predicted that drought-prone areas range from 1.04% to 43.32% across various Upazilas, with Tanore, Godagari, and Paba identified as the most susceptible due to elevated land surface temperatures, groundwater depletion, and inadequate precipitation, whereas Bagha, Puthia, and Charghat exhibited the lowest drought susceptibility owing to favorable hydrological and environmental conditions. The Representative Concentration Pathways (RCPs) were employed to assess potential climate-driven drought susceptibility scenarios for the years 2040, 2060, 2080, and 2100, utilising projected precipitation and evapotranspiration data. Under precipitation-based analysis, regions at high risk of drought are anticipated to rise from 2.69% to 7.70%. In evapotranspiration-based scenarios, the area classified as highly susceptible to drought may expand by up to 37.67%. Vegetative health, precipitation variability, evapotranspiration intensity, and soil-water interactions influence drought susceptibility, aiding in evidence-based mitigation measures and sustainable agricultural transitions.

Land use involves a management and modification of natural environment. Regulating land use in an efficient way is an important term in the urban planning. This study is based on land use pattern of Rajshahi city corporation area in Bangladesh. This study seeks to find out the impact of change in land use pattern over the overall transportation system. Another major objective is to find out the change in road width and shrinkage of pedestrian ways due to the roadside informal activities. The study was conducted in several phases including questionnaire survey, field observation, and analysis of field data and interpretation of the data. The research will act as a base study for implementing public transport mode like tram, bus in Rajshahi city, Bangladesh. To understand the impact of informal activity in Rajshahi city, this study will approach as a guideline.

The Barind Tract, straddling the Bangladesh-India border, is among the most drought-prone agricultural landscapes in South Asia, yet prior remote-sensing drought assessments have treated its Bangladeshi and Indian sides separately, using inconsistent sensors, indices, and time periods. This study applies a harmonized MODIS NDVI, Vegetation Condition Index (VCI), and CHIRPS precipitation framework in Google Earth Engine to directly compare drought dynamics in Rajshahi District, Bangladesh, and Maldahh District, India, over 2013-2023. Mann-Kendall trend tests, Sen's slope estimation, and Pearson correlation were applied to district-level annual time series extracted via Earth Engine zonal statistics. Maldahh showed a statistically significant increasing trend in both NDVI and VCI (Mann-Kendall p = 0.029, Sen's slope = 0.0035 NDVI units/year), while Rajshahi's positive trend narrowly missed significance (p = 0.087-0.120). Neither district showed a significant precipitation trend, and NDVI-precipitation correlation was weak and non-significant in both districts, most notably in Maldahh (r = 0.080, p = 0.815) compared with Rajshahi (r = 0.446, p = 0.169). Both districts' only VCI-classified drought years (VCI < 35) clustered in 2013-2014, with Maldahh adding 2016, and no drought years occurred in either district thereafter. These results indicate that vegetation greening in this landscape is substantially decoupled from rainfall variability. We attribute Maldahh's significant greening trend to its artificial water supply from the Mahananda, Kalindri, Punarbhaba, and Tangon river-and-canal irrigation system combined with active plantation programmes, while Rajshahi's weaker, non-significant trend is attributed to rapid urban expansion converting green space to built-up land alongside heavy dependence on deep-tubewell groundwater extraction. The findings suggest that transboundary drought early-warning systems relying on rainfall thresholds alone may misrepresent actual vegetation-level drought risk on either side of this border, and that coordinated canaldischarge and groundwater monitoring is needed alongside satellite vegetation monitoring.

In Bangladesh, most of the areas are rural. That is the reason for selecting a rural area to know about their problems and finding out a way to improve their condition. The study area is a village named Bhetupara which is located at Mohanpur upazila in Rajshahi district. The villagers are mainly engaged in agricultural activity. Housing is a basic need for mankind. The study is based on rural housing. The objective of the study is to know about their housing condition and needs and how it relates to decrease in cultivated land. Housing condition represents basically the condition of utility facilities in the village. The condition is shown by chart and data is collected from field survey. The study is based on both quantitative and qualitative data. For collection of data, Participatory Rural Appraisal (PRA) method and questionnaire are used. The used PRA tools are social map, transect and cause-effect diagram. The maps and diagram are prepared by focus group discussion with key informant. Social map and transect are used to know the existing condition and facilities and cause-effect diagram is used to know how housing affecting decrease in cultivated land and what will be the situation in future. This study reflects the dream of the people by including their recommendation. It will provide a base for development authority or planner to facilitate these rural people according to their expectation.

Transportation planning traditionally relies on quantitative measures such as traffic volumes, travel demand, and network performance. Although these approaches provide essential technical insights, they often fail to capture how people perceive and experience urban space. This study explores the role of human perception in transportation planning through emotional mapping and spatial cognition analysis in Meherpur District, Bangladesh. A total of 313 newspaper-reported incidents from 2019–2024 were georeferenced and classified into seven emotional categories: anger, happiness, love, sadness, disgust, fear, and surprise. Sixteen participants interpreted the spatial distribution of these emotion points and developed perception-based spatial patterns. The resulting maps were digitized and analyzed using GIS overlay techniques to identify collective perception structures. The findings reveal the emergence of common cognitive nodes, perception corridors, and high-consensus spatial zones despite individual differences in interpretation. Comparison with the proposed Circular Road alignment demonstrates a notable correspondence between collective perception patterns and planned transportation infrastructure. The study highlights the potential of integrating emotional geography and spatial cognition into transportation planning to support more human-centered and socially responsive decision-making.

Park audits can inform neighbourhood-scale urban design strategies that support health. However, interpreting park audit data is time-consuming and requires specialised expertise. Recent advances in large language models suggest potential for assisting with structured data interpretation, but their application to park audits remains unexplored. This study examined whether a large language model (ChatGPT-5) can assist in interpreting park audit data from public parks in Dhaka City, Bangladesh. Using a modified park audit tool, 59 parks were audited, of which 26 met the predefined completeness threshold and were included in the analysis. ChatGPT-5 and human experts received identical park audit datasets and instructions for interpretation. Outputs were evaluated using content analysis and a pre-defined scoring framework to assess accuracy and comprehensiveness. Paired t-tests compared ChatGPT-5 outputs with the expert benchmark. ChatGPT-5 showed no statistically significant difference from the expert benchmark in overall accuracy in this sample (p = 0.13). The unadjusted analysis showed a higher recommendation-match count for ChatGPT-5 (p < 0.05), but this result should be interpreted as exploratory because multiple criteria were tested. No statistically significant differences were detected across accuracy domains or other comprehensiveness criteria. These findings suggest that ChatGPT-5 may assist with preliminary interpretation of structured park audit data. However, the results do not establish equivalence with expert assessment. Expert review remains necessary to assess feasibility, contextual relevance, and alignment with local planning standards.

Background Built environment metrics are widely used to assess walkability, but they often assume that conditions remain stable throughout the year. Under snow and ice conditions, accumulated and prolonged snowfall disrupts active behaviours and changes how people move through urban environments. Nevertheless, many built environment metrics do not fully consider these snow- and ice-related challenges. Methods A systematic search of Scopus, PubMed, Web of Science, and ScienceDirect identified peer-reviewed English-language studies reporting relationships between built environment metrics and active behaviour under snow and ice barriers. Searches combined terms related to snow and ice, built environment, and active and sedentary behaviour outcomes, with the final search on 31 July 2025. From 1,501 records, 14 studies met the inclusion criteria. Data on study characteristics, exposure and outcome measures, and key results were extracted and synthesised narratively due to heterogeneity in study designs and measures. Results The included studies were conducted primarily in Canada and Sweden, with others from China, Japan, Finland, and the United States. Walking was the most frequently investigated active behaviour outcome (50%), followed by total physical activity (29%) and cycling (21%). Most quantitative studies reported positive associations between built environment metrics and physical activities under snow and ice barriers, with fewer reporting null results. Qualitative studies consistently described supportive influences. Active transportation infrastructure (57%) and snow- and ice-specific adaptations (64%) were the most frequently examined metrics, both showing predominantly positive relationships with active behaviours. Conclusions Snow- and ice-specific adaptations and active transportation infrastructure appeared to be the built environment domains most consistently relevant to active behaviour under snow and ice conditions. Evidence for composite walkability measures and less frequently studied metrics, including land use mix, residential density, destination accessibility, public transportation accessibility, aesthetics, and public open space or greenspace access, remained limited. Objective, dynamic, and longitudinal snow-sensitive metrics are needed to assess how winter maintenance, sidewalk snow clearance, cycling infrastructure, covered pathways, and other interventions can reduce snow-related barriers to active behaviour and support year-round walkability.

Road traffic accidents remain a significant public safety issue in Bangladesh, causing high mortality rates and eco nomic losses.With high death and economic tolls. While measures have been taken to make roads safer, the rise in the number of cars on the road coupled with the lack of proper infrastructure is aiding accidents.The aim of this study is to predict the severity of road traffic accident based on machine learning methods namely LightGBM and SHAP in Bangladesh. The purpose of this study is to find out the number and type of factors which different low and normal severity incidents from fatal incidents have, and to find out how the identified factors can be used for traffic safety improvement. This study has employed a data set on the occurrence of road traffic accidents using FIR data from three districts of Rajshahi, Natore and Pabna in Bangladesh. Data were carefully preprocessed, with missing values addressed and data normalized. The LightGBM model was applied to classify the severity of accidents and SHAP was applied to understand how the individual features contributed to the model’s prediction. The LightGBM model shows great performance, with an AUC of 0.87 for fatal and low/normal severity classes. Type of collision, vehicle type, vulnerable road users and environmental conditions turned out to be the most prominent factors affecting the severity.Overall, the study highlights the potential of machine learning in predictive modeling for improving road safety. These findings can help policy makers prioritize the interventions that are needed for safety, by focusing on the factors that are most likely to lead to death, thus minimizing the number of lives lost on the roads in Bangladesh.