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Bangladesh is highly disaster-prone, with drought being a major hazard which significantly impacts water, food, health, livelihoods, and migration. In seeking to reduce drought vulnerabilities and impacts while improving responses, existing literature pays limited attention to community-level views and actions. This paper aims to contribute to filling in this gap by examining how an indigenous group, the Santal in Bangladesh’s northwest, responds to drought through local strategies related to water, food, and migration which in turn impact health and livelihoods. A combination of quantitative data through a household survey and qualitative data through participatory rural appraisal is used. The results suggest that the Santal people have developed and applied varied mechanisms for themselves to respond to drought. The categories of responses found are water collection and storage, crop and livestock selection, and migration. These responses might not be enough to deal with continuing droughts, yielding lessons for Bangladesh and beyond.

Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support in-season research and decision-making regarding crop loss estimation, yield estimation, and grain pricing. The USDA mostly relies on field survey and farmers’ reports for the ground truth to train image classification models, which is one of the major reasons for the delayed release of CDL. This research aims to use trusted pixels as ground truth to train classification models. Trusted pixels are pixels which follow a specific crop rotation pattern. These trusted pixels are used to train image classification models for the classification of in-season Landsat images to identify major crop types. Six different classification algorithms are investigated and tested to select the best algorithm for this study. The Random Forest algorithm stands out among selected algorithms. This study classified Landsat scenes between May and mid-August for Iowa. The overall agreements of classification results with CDL in 2017 are 84%, 94%, and 96% for May, June, and July, respectively. The classification accuracies have been assessed through 683 ground truth data points collected from the fields. The overall accuracies of single date multi-band image classification are 84%, 89% and 92% for May, June, and July, respectively. The result also shows higher accuracy (94–95%) can be achieved through multi-date image classification compared to single date image classification.

A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.

In Bangladesh, cities are conventionally classified based on population size and revenue collection. This conventional city classification system neglects the spatial characteristics inherit in cities. Providing a more comprehensive city classification system is essential for the country’s future budget allocation and infrastructure development. Five spatial features: city size (area), urban form (AWMPFD), the ratio of built-up and non-built-up areas, urban growth rate, and total night lights intensity for 331 cities of Bangladesh are derived from remote sensing data. This study classifies these cities into six classes using a hierarchical clustering algorithm based on five selected spatial characteristics. The six categories are named for their levels of spatial development, with Cluster 1 being the highest level and Cluster 6 being the lowest level. The share of employment in the primary sector (agriculture) gradually rises from Cluster 1 to Cluster 6. In contrast, the employment share of the service sector follows a reverse trend from Cluster 2 to Cluster 6. Both per capita income and expenditure is higher for the large cities of Cluster 2 than for the metropolitans of Cluster 1. Comparisons across the six classes with non-spatial attributes validate the classification system. Findings also reveal that remote sensing derived spatial information can explain non-spatial characteristics of cities. Therefore, remote sensing derived spatial attributes of cities can be used for city classification where census data are scarce.

This paper aims to investigate the perceptions of neighborhood conditions and their effect on urban active transportation (UAT) in the context of a city in the Global South. We analyze data from a survey of commuters in the city of Rajshahi, Bangladesh. Concretely, we are interested in cycling and walking. A probabilistic model of mode use is estimated using disaggregate data collected in Rajshahi through a face-to-face survey in 2017. The study reveals that similar to other regions in the world, students are more likely to use active transportation compared to other socio-demographic groups, and that motorized vehicle ownership is associated with lower probabilities of active transportation. Furthermore, the probabilities of choosing active modes at different neighborhood-level conditions were calculated based on the derived model for both students and non-students by residential and non-residential land-use types. In addition to the duration of the trip, the perceived neighborhood-level characteristics are critical for active transportation. Improving neighborhood conditions and their perception by the public can enhance the attractiveness of active travel, more specifically cycling for longer commutes. Based on the study findings, the paper discusses strategies to promote the use of active transportation in the context of a country in the Global South.

This paper shares the findings of an active transportation (AT) study conducted in the context of a city in a developing country. First, a list of AT indicators was developed based on the literature review and expert opinion survey. Second, a face-to-face survey was conducted to collect information on individuals' socio-demographic characteristics, travel behavior, AT mode choice, and their perceptions regarding the AT conditions in their neighborhoods. Analysis of the survey results suggests that several socio-demographic characteristics are associated with AT use. For example, young adults and individuals with low income are the primary users of AT. Also, results suggest that students are mainly active commuters. The ratio of AT use increases with the number of bicycles in the household. Individuals tend to walk more when travel duration is less than 10 minutes. Educational areas are perceived as safer and convenient areas for using active modes of transport compared to other land-use types. Also, a higher proportion of active commuters perceive local roads to be safer from vehicular traffic compared to main roads. Many sectors such as planning, transportation, health, and education as well as non-government organizations will be benefited from this study.

Comprehensive studies that employ larger data sets to explore the degree and conditions under which households depend on mangrove ecosystems account for only a negligible number in Asia's context. We contribute to these lacunae by analysing households' livelihood dependence on the Sundarbans of Asia. Specifically, we seek to: (i) appraise the livelihood strategies around the Sundarbans, (ii) analyse the conditions and determinants of household dependence on the Sundarbans, and (iii) explore perceptions surrounding livelihood dependence. An extensive survey of 1188 directly and indirectly dependent households drawn from 35 villages was conducted using structured interviews to address these topics. Twenty focus group discussions complemented this data. Using logistic regression, we analysed household dependence as a function of socioeconomic attributes. We suggest that socioeconomic factors have a substantial relationship with resource extraction and, therefore, policy prescriptions should focus on coordinating less-extractive diversification activities such as ecotourism to reduce the impact on the Sundarbans.