Publications
Investigating the Factors of Mega Infrastructure Project Delays: Dhaka Context
Abstract
Projects are short-term initiative that aim to produce a distinctive advantage, service or outcome, and infrastructure refers to the physical features that use the operation of a company, region, or nation, frequently involving the generation of public goods or the execution of production procedures. Mega infrastructure project, which costs at least 1 billion are a haphazardly organized collection of unconnected components with various uncertainty and unknown challenges. These various uncertainties and unknown challenges cause mega infrastructure project delays. Project delay increases the cost of the project and causes economic loss. For example, Bangladesh is currently running some mega infrastructure projects but facing project delays in some cases resulting financial loss for the government. To be more specific, MRT Line 6 project was selected as a case study that faced delay in completion. In this connection, this study tries to identify the specific reasons causing delays in completing MRT Line-6. This study followed a qualitative research approach to explore the factors through In Vivo Coding in the first cycle of coding and Pattern Coding in the second cycle of coding. Issues of the contractors, lack of knowledge and skills, design changes, low quality of planning and management, material collection, political issues, land acquisition, covid 19, safety and security concerns of foreign workers, traffic pressure in the city, institutional issues are the factors explored in this study that causes the delay of MRT Line 6. The study can contribute to understanding delay factors and applying knowledge for future infrastructure projects, particularly in developing countries, at the same time, it will provide valuable insights for policymakers to make better infrastructure decisions and ensure successful completion of project.
Land use and meteorological influences on dengue transmission dynamics in Dhaka city, Bangladesh
Abstract
Background: Dengue fever, a viral illness spread mostly by Aedes mosquitoes, continues to pose a substantial public health issue in Dhaka city, Bangladesh. In Dhaka, climatic and socio-demographic factors like population density affect the spread of dengue. The dengue indexes are greatest in the residential mixed zone. Numerous environmental parameters, such as temperature, relative humidity, rainfall, and the air pollution index, have been linked to mosquito larvae, and dengue prevalence is correlated with urbanization, decreased vegetation, and population expansion.
Methods: By using an extensive dataset that encompasses a range of years, we use spatial and temporal analytic methodologies to investigate the correlation between land use attributes, climatic variables, and the occurrence of dengue fever. To better understand the dynamics of dengue, the built environment and climatic factors are treated as independent variables in this study. ArcPy is a Python package that facilitates here for geographic data analysis and ArcMap 10.7 also used for visualizing spatial data.
Results: The results of our study demonstrate that land use significantly influences the spatial patterns of Dengue incidence in Dhaka city. The dengue hotspot Thana are identified and these are Badda, Jatrabari, kadamtali, Mir-pur, Mohammadpur, Sobujbagh, Shyampur, Tejgoan, Dhanmondi and Uttara. All of these areas’ population density and residential use as land use is higher than the other Thana of Dhaka city. There exists a significant correlation between climatic characteristics, such as temperature (0.25), rainfall (.803), specific humidity (0.74), relative humidity (0.76), wind speed (0.4) and Dengue incidence patterns. This research emphasizes the structural use and climatic relationship in Dengue epidemics, with climatic conditions playing a significant role as drivers of these variations.
Conclusions: This research demonstrates the complex relationship between land use, meteorological factors, and the spread of Dengue fever in Dhaka city. The results of this study have significant significance for several domains, including urban planning, public health measures, and vector control tactics. A comprehensive understanding of the temporal and geographical patterns of dengue transmission might aid in the development of accurate and effective prevention measures intended to lessen the effects of dengue in cities, such as Dhaka.
Predicting structure use with machine learning algorithm: Model validation approach for DAP data
Abstract
Machine learning techniques have been successfully applied in many fields, including urban planning. The focus of this article is to develop a machine learning model to automatically predict the use of structures. Automatic predictions can help mitigate the heavy load on urban planners in the early stages of decision-making and provide a quick preview of the scenario. In this study, building data from the Detail Area Plan of Dhaka were used. The number of floors and basements in a structure, the structure’s age, the number of dwelling units and the structure type were the independent variables for this research. Due to the dataset’s inclusion of both numeric and string data, the Decision Tree (DT) classifier was used for prediction. Python routines were used for data cleaning, model development, and model evaluation. The Scikit-learn Python package, primarily used for ML implementation, was utilized to develop the model. The model had an accuracy rate of 91% for predicting the use of institutional, education and research, mixed use, health facilities, under construction, and agriculture structures. Due to incomplete data, residential, restricted and special use, community facilities, miscellaneous, commercial, industrial, transportation and communication use of structures could not be reliably predicted. This model can aid in determining the use of a structure based on the characteristics of the structure (floor, basement, structure type, structure age, dwelling unit), based on historical data for that location. The model demonstrates the use of machine learning in urban planning.
Impact of Highway on Land Surface Temperature along the Peripheral Area (Conference Paper)
Abstract
Land cover is the surface cover of the ground, including waterbodies, vegetation, bare soil, and other physical features. The land surface temperature (LST) is the heat radiated from the surface of the Earth. According to prior studies, there is a strong relation between land cover and LST. LST has a proportional relationship with buildup area and bare soil and a disproportional relationship with vegetation and water bodies. This research examines the impact of highways on land cover and LST in its periphery and explores their relationship. It also determines whether highways affect LST in its periphery. Using Landsat-8 satellite image and the Dhaka Aricha Highway as the study area, due to the haphazard physical development in its periphery, this study found a significant correlation between land cover and land surface temperature. Built-up areas and bare soil are higher near the highway, while water bodies and vegetation increase with distance. The LST is higher near the highway and decreases with distance, for instance within both 500m and 1000m buffer the lowest temperature is 26.2126°C but this becomes 26.1104°C for both 1500m and 2000m. Consequently, this study claims that, the highway has an impact on the LST in its peripheral areas. However, the study only considered land cover, and further research is needed to examine other factors affecting LST in highway peripheral areas. Nonetheless, the study provides useful material for sustainable land use planning in highway peripheral areas.