The primary objective is to investigate whether active transportation options like e-bikes and e-scooters can replace short vehicle trips (distance 0-10 Miles).

I have used the NextGen NHTS Origin-Destination data 2022, which provides aggregated trip counts by distance from different zonal units to another in the USA. Using this data, I aim to identify the non-linear effects of Built Environment (BE) elements at the city or zone level that affect the rate of short trips (below 10 miles). I have cover the entire Contiguous United States (CONUS), comprising over 500 zones. While my main focus is on BE variables, which I collected from the Smart Location Database, I have also used sociodemographic data from the American Community Survey as control variables. My model is expected to improve the understanding of the non-linear effect of BE on the short trip rate, characteristics of cities that promote short trips, and broaden the discussion of the X-minute city.

Platform Used: Jupyter Notebook, ArcGIS, Code: [GitHub]

The main objective was to design and train a deep learning classifier that takes an X-ray image as the input and assigns the input image to one of the four classes: COVID-19 positive, Normal, Lung Opacity, and Viral Pneumonia.

Trained, tested, and validated a dataset comprising approximately 21,165 x-ray images using the ResNet50 architecture to build a prediction model. The model exhibited an accuracy of around 80.76%. This was achieved through a training process using 10 steps per epoch for 30 epochs and validated with 16 steps. The model's accuracy was determined through image detection processes, and its performance was validated by constructing a confusion matrix.

Platform used: Jupyter Notebook, Code: [GitHub]

The primary objective was to identify a suitable location for green infrastructures in the region.

For stormwater runoff analysis, the SCS-CN method was employed, with key measures such as Land Use Land Cover (LULC), Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI) determined using Google Earth Engine. Additional considerations include the Urban Heat Island Effect, park proximity, stormwater runoff, and image analysis, which will be integrated into a Multi-Criteria Suitability Analysis to comprehensively evaluate the area's environmental and urban planning needs through green infrastructure.

Platform used: Google Earth Engine, ArcGIS, Code: [LST, NDBI, NDWI, LULC]

The main objective of the research is to assess the impact of ecosystem services and disservices by tracking change over a 15-year span (2005-2020) using satellite image analysis, specifically in wards 23 and 29 of Khulna City Corporation (KCC).

Reconnaissance surveys and data collection through questionnaires and checklists were conducted. Then, Landsat satellite imagery from 2005 and 2020 was analysed to assess changes in vegetation, built-up areas, and water bodies. The satellite imagery calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). Finally, field data and satellite image analysis were combined using GIS mapping techniques, and the ecosystem services and disservices were assessed.

Related Publication: [Link]

The main objective is to investigate the impact of COVID-19 on key sectors in Bangladesh through GIS mapping and proposes a 10-sector crisis response management plan and committee to address the crisis and prepare for the post-COVID-19 period.

The patterns and structures of different studies related to the COVID-19 scenario were examined worldwide. The works included studying steps and measures taken by other countries during the pandemic and for the future. This paper has put up an overall GIS-based map output of the public health sector's scenario from COVID-19 dispersal to the steps taken to prevent the pandemic in the country. Analyzing the situation in Bangladesh using graphs and charts from various secondary data sources, different spatial outputs were generated. Finally, proposals for new feasible policies and an efficient framework were developed to prevent this pandemic and also potential problems like this in the future.

Related Publication: [Link]

The study's main objective is to explore the existing traffic flow of Khulna City, examine how this traffic flow is affected by road characteristics, and analyze the relationship between road characteristics and traffic flow through statistical analysis.

According to the objectives, the data were collected through different types of surveys such as road geometrics data using tape, moving observation survey, volume count survey, and vehicle speed survey. These data were collected at peak and off-peak hours to easily differentiate traffic flow patterns at different intersections at different times. The data were analyzed in many ways, and a relationship was established between road characteristics and traffic flow. Through Regression and multiple regression models, it was shown that there is a significant relationship between traffic flow and road geometry.

Platform Used: ArcGIS, SPSS

The main objectives of the study were to determine the distance between the two shopping centers and to determine which shopping center is more influential.

Jogipole Union is a remote area of Khulna City considered as rural as a rural growth center having fewer or average facilities. According to the basic need approach, people need properly distributed facilities like education, health, religion, shopping centres, etc., whether it is a village or not. Shopping behavior analysis is a major field for determining the coverage of shopping centers among a large area where a countable number of village people are connected. This study split the overall area into two major centres, Teligati Bazar and Fulbarigate Bazar. People in this region mainly avail themselves of their daily services from these two centers. This study determined the market boundary of these two centers and concluded which center is more attractive.  By Reilly’s law of retail gravitation, distances of shopping centers, and attractiveness of shopping centers, have been determined. So, by analyzing this behavior, it would be easy to make a clear concept about the overall condition of the study area regarding which shopping center is more influential and accessible by the people. The findings showed that for daily items, people go to the nearby market, which is the Teligati Bazar, but for luxurious products, they choose Fulbarigate Bazar, which is a bit far from the locality. This creates a tendency, pattern, shopping, and travel behavior among the locals regarding which market to choose and which mode of travel to obtain when.

Platform Used: ArcGIS