Unplanned industrialization is a major driver of agricultural land loss and environmental degradation in rapidly urbanizing peri-urban regions such as Narayanganj City Corporation. Identifying suitable locations for agro-industrial growth is essential for balancing economic expansion with ecological protection. This study enhances existing site-zoning research by integrating ensemble tree-based machine learning (Random Forest and XGBoost) with a GIS framework to quantitatively prioritize spatial determinants of agro-industrial suitability. Nine geospatial variables were assessed, including proximity to major and minor roads, rivers, waterbodies, settlements, agricultural land, watersheds, and terrain factors. Model performance was strong (RF accuracy: 93.94%, XGBoost accuracy: 93.76%; AUC > 0.94). A key novelty of this study lies in combining these ensemble models with SHAP explainability to explicitly reveal how each spatial factor drives suitability, offering transparent and interpretable insights rarely applied in agro-industrial zoning. The final suitability map shows that 38% of NCC is suitable for agro-industry, while only 13% is highly suitable. Approximately 26.7% is unsuitable, increasing to 62% when waterbodies are excluded from development consideration. Notably, only 8.2% of ML-identified suitable areas overlap with industrial zones designated in the NCC Detailed Area Plan, indicating a significant spatial and policy misalignment. The results further identify proximity to major roads, residential areas, and rivers as the most influential variables, providing planners with clear priorities for decision-making. By integrating interpretable ML with spatial policy comparison, this study offers a novel, data-driven framework for guiding environmentally responsible and context-specific agro-industrial expansion, while also highlighting institutional gaps and opportunities for improved land-use governance.

While studies undertaken worldwide revealed variations in how people travel during extreme weather events, no investigation has been carried out in Bangladesh to explore the impact of such weather events on people's choice of transport mode and modal shift. This study addresses this gap by determining the influence of seasonal and regional variability of weather events on mode choice and modal shift of Dhaka's commuters and non-commuters. In this regard, extreme heat, heavy rainfall, and waterlogged conditions were considered as extreme weather events. Results show that shifts from usual travel modes during extreme weather events are significantly influenced by demographics, situational factors, trip types, and travel priorities. Modal shifts during extreme weather are more likely among females, older adults, high-income individuals, and those prioritizing safety and convenience; while males, commuters, and vehicle owners are less likely to change usual travel modes. The availability of alternative routes, regular checking of weather updates, and waterlogging events further increase the likelihood of modal shift. These demographic, situational, and psychological factors should be considered in transportation planning to ensure equitable and sustainable mobility in all weather conditions.

The increasing importance of the informal market economy as a livelihood strategy in urban areas across developing countries, particularly in Bangladesh, secures an important position for the low- and middle-income people of a city. This study focuses on Bou Bazar market of Dhaka, Bangladesh, to examine the spatial, temporal, and managerial dynamics of a market characterized by a unique structure that combines both formal and informal elements of a market. Thus, it allows the investigation of a distinct type of market model, which has the potential to perform better than pure informal markets. The study involved Focus Group Discussion with different stakeholder groups, a series of interviews, and the utilization of different Participatory Rapid Appraisal (PRA) tools for data collection. Qualitative analysis was employed through clustering, grouping, and comparison within the collected data. Key findings include increased vendor diversity driven by growing demand, efficient use of public space with limited operating hours, improved vendor satisfaction through balancing their autonomy, external management with stakeholder collaboration and a discretionary policy of service fee which could not be assigned to any useful purpose other than exploitation. The study suggests that formal management of any informal market offers comparative advantages to ensure inclusive economic growth that can be a beneficial learning for other developing countries as well.

Extreme weather-induced vulnerability has acquired global prominence for posing frequent threats, resulting in significant socio-economic losses and infrastructure damage. As an unplanned city in the global south, Dhaka frequently grapples with such vulnerabilities due to a rapid rise in temperature and changes in precipitation patterns. Investigating zones susceptible to the combined effects of these extreme events is essential for robust planning and interventions to enhance the city’s resilience. However, no previous research examined this dual vulnerability. Therefore, this study aims to identify the sensitive zones to extreme heat and heavy rainfall (waterlogging) in the city by applying the advanced techniques of GIS, including a multi-criteria decision-making method (MCDA) and analytical hierarchy process on four spectral indices and other non-spectral, physical, hydrological, and geomorphological variables. The findings reveal that only 20% of the city exhibits low sensitivity to the combined effects, which are primarily concentrated in the northeastern and central part of the city. The remaining areas are moderate to high sensitive to extreme weather events, with around 49.39% being under high to very high susceptibility. This indicates that a major part of the city is highly susceptible to extreme weather events, suggesting a widespread vulnerability. The findings are useful in addressing extreme weather's potential to pose a substantial environmental obstacle and yield insights for the sustainable, efficient, and healthy development of the city.