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In this work, we analyzed the locations of existing bus stops in the Halifax Regional Municipality (HRM) area and then calculated the walking distance in time in order to understand the accessibility for pedestrians. In doing so, we employed geographic information systems (e.g., spatial and network analysis tools) to generate accessibility models from bus stops at 5, 10, 15, and 20 min of walking distance. After generating the service area of bus stops on maps, we overlaid socioeconomic variables (i.e., age group, income, and bus-stop accessibility) to better understand the HRM public transport network. We found that population density was an important consideration in providing the number of bus stops in specific communities, which may be related to the facilities offered by the urban hierarchy. Furthermore, we established a relationship between transit stops and accessibility for people in age groups of between 0 and 19 (e.g., school-going children) and 65+ (i.e., the older population) so as to obtain an understanding of the time required for them to access the bus stops on foot. Overall, 77% of the population in the HRM was served by public transport within 5 min of walking; however, for the 65+ cohort, this number was higher (82%). A significant amount of the young population (23%) was served over longer distances than 10′ of walking. We summarize that our findings are critical for planners, practitioners, and researchers in order for them to understand the present transit system in place based on accessibility to the bus stops within walking distance.

Meteorological drought, driven by inadequate precipitation, has significant repercussions for water resources, agriculture, and human well-being. This study conducted an extensive assessment of meteorological drought in northern Bangladesh, employing remote sensing indices and machine learning techniques. The main aim was to evaluate meteorological drought occurrences in northern Bangladesh from 2010 to 2019, utilizing seven drought parameters and a machine learning model. Utilizing a Random Forest (RF) model, this study employed the Standardized Precipitation Index (SPI) as the dependent variable and seven remote sensing indices as independent variables. Through this methodology, the study assessed the significance of these indices generated by the model and integrated them, culminating in the creation of a meteorological drought distribution map spanning 2010 to 2019. This approach offers novel insights by probing the interplay and collective impacts of these indices, shedding light on previously unexplored aspects of regional drought patterns of northern Bangladesh. The major findings showed that precipitation strongly influenced both short-term and long-term meteorological drought episodes. Moreover, land surface-related indices, such as Evapotranspiration (ET) and Normalized Difference Water Index (NDWI), exhibited a more pronounced impact on short-term drought occurrences, while vegetation-related indices like Normalized Multi-band Drought Index (NMDI) and Normalized Difference Vegetation Index (NDVI) demonstrated greater influence over long-term drought events. During this timeframe, the Rajshahi division experienced frequent extreme and severe drought events. Moderate droughts and abnormally dry conditions were widespread. The Barind tract area consistently faced moderate to extreme droughts, with exceptions in 2011, 2014, and 2019. On average, over 5% of the region had extreme droughts, while more than 12% experienced severe droughts during this decade. Long-term drought indicators (SPI 6 and SPI 9) consistently showed higher frequencies of extreme and severe droughts compared to short-term indicators (SPI 1 and SPI 3), emphasizing the influence of prolonged rainfall deficits on extreme droughts and the relevance of longer time frames for severe drought dynamics. The RF model demonstrated strong performance with accuracy ranging from 81% to 95%. Low prediction errors (RMSE 6% to 31%) and high out-of-bag (OOB) accuracy ranging from 76% to 98% highlighted its accuracy. The F1 score consistently exceeded 76%, indicating high precision and recall. Cross-validation values ranged from 78% to 94%, affirming reliable generalization to new data. Incorporating the main findings, this study contributes valuable insights for the formulation of targeted drought mitigation strategies in northern Bangladesh. It is imperative to note that the scope of this study is confined to the northern region of Bangladesh, and generalizing these findings to other regions should be exercised with caution. Nevertheless, the research methodology and approach can serve as a model for future studies in related fields, advancing knowledge of how to assess droughts using remote sensing and machine learning methods.

This research intends to measure and characterize the level of rurality in the southwestern region of Bangladesh using an indexing approach from functional perspective. The findings of the study can be conducive for efficient decision making related to rural development policies and planning. Besides, operational delineation of administrative units, such as Unions and Wards, is essential for the formulation and effective implementation of evidence-based development initiatives. The study has utilized the latest available population census data published by Bangladesh Bureau of Statistics (BBS). The results reveal that Satkhira district exhibits a higher degree of rurality (RI = 4.23) compared to Khulna (RI = 3.58) and Bagerhat (RI = 4.14) districts. This variation among the three districts is ascribed to existing rural–urban disparities. The study underscores the primary influence of socio-economic factors on the rural–urban disparities, with a secondary role for demographic and infrastructural aspects. However, education's impact was notably limited in this context. Insights on intra-district disparities infer that Khulna district exhibits significantly greater rural–urban disparities than the other two. The study also highlights the clustering of non-rural areas along major rivers. This research will assist rural planners and policy makers in understanding the specific rural dynamics of the study area, essential for tailoring fit-to-context development strategies. Moreover, the study provides a basis for classifying the region into clusters to streamline development priorities and resource allocation. Furthermore, the article presents a transferable methodology for evaluating rurality and delineating rural regions in different contexts.

Land use land cover change (LULC) significantly impacts urban sustainability, urban planning, climate change, natural resource management, and biodiversity. The Chattogram Metropolitan Area (CMA) has been going through rapid urbanization, which has impacted the LULC transformation and accelerated the growth of urban sprawl and unplanned development. To map those urban sprawls and natural resources depletion, this study aims to monitor the LULC change using Landsat satellite imagery from 2003 to 2023 in the cloud-based remote sensing platform Google Earth Engine (GEE). LULC has been classified into five distinct classes: waterbody, build-up, bare land, dense vegetation, and cropland, employing four machine learning algorithms (random forest, gradient tree boost, classification & regression tree, and support vector machine) in the GEE platform. The overall accuracy (kappa statistics) and the receiver operating characteristic (ROC) curve have demonstrated satisfactory results. The results indicate that the CART model outperforms other LULC models when considering efficiency and accuracy in the designated study region. The analysis of LULC conversions revealed notable trends, patterns, and magnitudes across all periods: 2003–2013, 2013–2023, and 2003–2023. The expansion of unregulated builtup areas and the decline of croplands emerged as primary concerns. However, there was a positive indication of a significant increase in dense vegetation within the study area over the 20 years.