Hey, this is Faiyad H Rishal. Currently a PhD student in Geography at the School of Environment, Society and Sustainability, University of Utah. As a geography major, I love people and the environment, also fond of music, traveling & sports!

Recent News

April 2026: Presented a poster at CSBS Research Day at the UofU on the use of machine learning and the NASA SWOT dataset to understand the water level in the Great Salt Lake Basin. Poster Link
January 2026: Started PhD in Geography at the University of Utah - School of Environment, Society and Sustainability. There, he will be working as a Graduate Reseach Assistant at the Earth and Water Observation and Sensing (EWOS) lab with Dr Sonam Sherpa.
September 2025: Joined Auburn University as an Instructor to conduct science labs for undergraduates. Also, jointly appointed by the Citizen Science GIS lab at the Department of Geosciences as a GIS Technician to work on geospatial projects.
August 2025: Graduated with an MS in Geography and Environmental Studies and a Graduate Certificate in GIS from Auburn University, Department of Geosciences.
July 2025: Attended the Esri User Conference (UC) in San Diego, CA (July 11–18) as a student assistant with 60 other student assistants from across the USA and abroad to help organize the world’s largest GIS conference with 20,000+ participants.
June 2025: Successfully defended MS thesis: Urban Heat and Existing Policy in Small and Medium-Sized Cities in South Asia: How Land Use Land Cover Change is Affecting the Heat Island Effect.
April 2025: Presented a poster at the AI@AU Conference (Auburn University) on Land Use Land Cover Change in South Asia using a Machine Learning Model on the Google Earth Engine Platform.
March 2025: Presented at the American Association of Geographers (AAG) Annual Meeting, Detroit, MI (March 24–28). Topic: Heat Vulnerability and Social Justice in Chattogram City, Bangladesh.
December 2024: Oral Presenter at the American Geophysical Union (AGU) Fall Meeting, Washington, DC (Dec 9–13). Paper: Urban Heat and Policy in South Asian Cities: How LULC Is Affecting the UHI Effect?
November 2024: Delivered a Lightning Talk at the American Geographical Society (AGS) Fall Symposium – Geography 2050, Columbia Climate School, NYC (Nov 21–22). Topic: Climate Policy Improvement for Social Justice in South Asia. YT Link

Photo Gallery - Geography in Action

This section portrays the pictures of my activities, from going on field trips to interviewing people to attending events in Bangladesh, India, Thailand, and the USA.

Education

2023-2025

Auburn University, AL, USA

May 2025

Auburn University, AL, USA

MS in Geography and Environmental Studies

Thesis: ‘Urbanization and Heat Island Effect in South Asia: Land Cover Change, Policy and Inequality’

Certificate in Geographic Information Systems (GIS)

Courses: Intro and Advanced GIS, Quant Method and Spatial Analysis, Aerial Photography and Remote Sensing, Urban Geo and Sustainability

2017-2021

Jahangirnagar University, Bangladesh

BSc in Geography and Environment

Activities: Volunteered at four university clubs. Initiated a project on Campus Sustainability. Convener of the Gold Awardee team of Duke of Edinburgh Int’l Award of JU.

Experience

2023-2025

Auburn University, AL, USA

2021-2023

Jahangirnagar University, Bangladesh

Graduate Teaching Assistant, Department of Geosciences

TA for Global Geography and Concept of Science. I delivered lectures, conducted labs, graded exams, and mentored students.

Research Assistant, Institute of Remote Sensing & GIS

Conducted research by reviewing the literature, writing reports, collecting field data, and creating maps using ArcGIS Desktop and Erdas Imagine, organized int'l conferences.

2021-2023

10 Minute School, Dhaka, Bangladesh

Geography Instructor (Project based)

Conducted research by reviewing the literature, writing reports, collecting field data, and creating maps using ArcGIS Desktop and Erdas Imagine, organized int'l conferences.

2020-2022

Fineanswer Consulting Ltd, Bangladesh

Team Lead, Fineanswer Study Abroad (Part-time)

Supervised teams of 4-10 people, leading operations, business development, and digital marketing while establishing the business foundation. Built partnerships with 15+ organizations.

GIS and Remote Sensing Projects

Here are some of the recent geospatial projects to answer environment related spatial questions. I’ve used tools like ArcGIS Pro, Google Earth Engine, QGIS, GeoDA, Python, and R to analyze, visualize, and map meaningful insights. From detecting LULC change using Random Forest machine learning model to running regression model, vulnerability assessments, or hotspot analysis — these projects reflect my passion for connecting data into real life solutions

Classify climate regions with Python - ESRI Tutorial

Tool Used: ArcGIS Pro (Python Notebook)
Skills Applied: Multidimensional Raster Analysis, Winkler Index Calculation, Climate Zoning
Dataset: NASA Daymet Daily Max/Min Temperature (2000–2020)
Course: Self-paced Project from Learn ArcGIS
Authors: Lain Graham, Josh Grail, Cole Walts, and Hong Xu (Esri)

Project Overview:
I tried the ESRI tutorial on Python Notebook in this project which mapped viticultural climate zoning in Napa County, California, using multidimensional climate data from NASA’s Daymet dataset. A 21-year subset (2000–2020) was defined for two key variables: daily maximum and minimum air temperature. The data was downloaded as .crf (Cloud Raster Format) files and processed in ArcGIS Pro using Python notebooks. Growing Degree Days (GDD) were calculated per pixel for each year to assess cumulative heat units. The resulting values were used to classify the region into six Winkler Index climate zones, a widely accepted framework in viticulture. A temporal chart was then generated to visualize how these zones shifted over time, providing insights into the ongoing impacts of climate change on Napa's wine industry.

Urbanization pattern using Machine Learning on Google Earth Engine

Tool Used: Google Earth Engine, ArcGIS Pro
Skills Applied: Random Forest Classification, LULC Change Detection, Remote Sensing, Land Surface Temperature Analysis
Dataset: Landsat Imagery (2000–2020)
Project Type: Independent Research (MS Thesis Work)

Project Overview:
This project analyzes Land Use Land Cover (LULC) change across nine South Asian cities over a 20-year period using the Random Forest machine learning algorithm on the Google Earth Engine platform. The selected cities represent diverse geographic and climatic contexts across five countries:

Bangladesh: Rajshahi, Chattogram
India: Ahmedabad, Bhubaneswar, Kanpur
Pakistan: Gwadar, Quetta
Nepal: Kathmandu
Afghanistan: Kabul
Satellite imagery was used to classify LULC into categories such as urban, vegetation, water, and bare land. The results revealed a substantial increase in built-up areas, often replacing natural land covers. Additionally, the study assessed the impact of urbanization on Land Surface Temperature (LST), highlighting spatial correlations between urban growth and thermal intensity. The findings emphasize the environmental trade-offs of rapid urban expansion in South Asia and inform future planning and climate adaptation efforts.

Spatiotemporal Hotspot and Outlier Analysis in New York City

Tool Used: ArcGIS Pro
Skills Applied: Space-Time Cube, Emerging Hotspot Analysis, Local Outlier Analysis, 3D Visualization
Dataset: 8 Years of Graffiti Incidents in NYC
Course: GEOG 6700 – Quantitative Method and Spatial Analysis, Auburn University

Project Overview:
In this project, I explored temporal and spatial patterns of urban graffiti incidents in New York using ArcGIS Pro’s space-time cube tools. By constructing a 3D space-time cube and performing Emerging Hotspot and Local Outlier Analysis, I identified evolving clusters and anomalies over time. This approach helped visualize not just where but when high or low incident patterns occurred, offering valuable insights for urban planning and policy.

Regression and Spatial Analysis of 911 Calls Using ArcGIS Pro

Tools Used: ArcGIS Pro
Skills Applied: Ordinary Least Squares (OLS), Explanatory Regression, Geographically Weighted Regression (GWR), Spatial Autocorrelation
Dataset: 911 Call Incident Data
Course: GEOG 6700 – Quantitative Methods and Spatial Analysis

Project Overview:
In this project, I analyzed the spatial distribution of 911 calls to understand the relationship between emergency incidents and socio-economic variables. Using Ordinary Least Squares (OLS), I first tested individual and multiple explanatory variables including population, job count, low education percentage, and distance to urban center. I then applied Explanatory Regression to evaluate multiple variable combinations and select the best-fitting model based on Adjusted R², AIC, and residual statistics.

To account for spatial variation, I used Geographically Weighted Regression (GWR), which revealed that the influence of population and other variables varied significantly across space. Compared to the global model, GWR improved the model's performance with a lower AIC and a higher Adjusted R² of 0.86, indicating strong explanatory and predictive power. The final spatial prediction map identified future high-risk areas for 911 calls, aiding in proactive emergency response planning.

Density and Heat Map Analysis Using ArcGIS and GeoDa

Tool Used: ArcGIS Pro, GeoDa
Skills Applied: Kernel Density Estimation, Heat Map Generation, Spatial Pattern Analysis
Dataset: Wildfire Incident Points
Course: GEOG 6700 – Quantitative Methods and Spatial Analysis

Project Overview:
This project demonstrates surface analysis techniques using ArcGIS and GeoDa. I created both simple density and kernel density surfaces to visualize concentrations of wildfire incidents. Additionally, I developed a heat map to highlight spatial intensity. Using GeoDa, I explored spatial clustering and autocorrelation, enhancing my understanding of spatial data distribution.

County-Level Spatial Clustering of Natural Resource Employment

Tool Used: GeoDa
Skills Applied: Local Indicators of Spatial Association (LISA), Cluster Analysis, Thematic Mapping
Dataset: County-wise Natural Resource Employment – Southeastern U.S.

Project Overview:
This map visualizes spatial clustering of natural resource employment across southeastern U.S. counties using LISA Cluster Analysis. High-High clusters represent counties with significantly high employment surrounded by similar counties, while Low-Low clusters indicate areas of consistently low employment. The map supports identifying regional economic patterns and can inform targeted development or conservation policies.

Heat Vulnerability Index Map of Chattogram City

This project showcases a map illustrating the heat vulnerability in Chattogram City Corporation. This is a mix of physical exposure (LST, NDVI, NDBI, NDWI) and social indicators (income, housing, age etc.). The map provides valuable insights into the areas most affected by heat vulnerability.

Air Pollution of Alabama and Correlation with Population using GIS Interpolation

This project explored the spatial relationship between PM2.5 air pollution levels and population density across counties in Alabama using ArcGIS Pro. Key GIS techniques included data integration, spatial interpolation using the Inverse Distance Weighting (IDW) method, and zonal statistics to analyze air quality data from the USEPA alongside population data from the US Census. The analysis produced several maps, including the spatial distribution of PM2.5 monitoring stations, interpolated pollution surfaces for 2018 and 2020, and a population density map categorized using the equal interval classification method. Results showed that while Montgomery had the highest PM2.5 levels (11.12 µg/m³), this did not consistently correlate with population density across other counties, indicating that additional factors like industrial activity and geography also influence air quality. A temporal comparison between 2018 and 2020 revealed minimal change in pollution levels during the COVID-19 pandemic, challenging assumptions about decreased human activity leading to cleaner air. The project highlights how GIS enables effective spatial analysis and visualization for environmental assessment, supporting urban planning and public health decision-making.

Correlation between LULC vs Temp: LST vs NDBI/NDVI/NDWI

This project includes nine scatter plots of comparing LST and MNDWI, NDVI, and NDBI across nine different cities. The scatter plots provide a comprehensive comparison of the relationships between LST and built up area, vegetation and waterbody. The indices were extracted from Google Earth Engine Platform and graphs were produced using R.

Spatiotemporal Hotspot Analysis of Land Surface Temperature in South Asia

Tools Used: Google Earth Engine (JavaScript), ArcGIS Pro
Skills Applied: MODIS LST Extraction, Space-Time Cube, Emerging Hotspot Analysis
Dataset: MODIS Land Surface Temperature (8-day, 1 km) | 2013–2023
Poster: GEOG 6700 – Quantitative Methods and Spatial Analysis

Project Overview:
Using MODIS data in Google Earth Engine, I extracted decadal LST values (2013–2023) and analyzed them in ArcGIS Pro with Space-Time Cube and Emerging Hotspot tools. This spatiotemporal analysis identified intensifying heat zones in India, Pakistan, and Afghanistan, while cooler trends were observed in Nepal, Bhutan, and Sri Lanka. The study provides regional insight into urban heat dynamics and supports climate resilience planning.

LULC Change Detection od Dhaka City using Landsat

Using Landsat 7 and 8 imagery, this study classified land cover in Dhaka in 2005 and 2020 with unsupervised classification in ERDAS Imagine. The analysis revealed a 24.69% increase in built-up area and a sharp decline in ecological land uses — including a 21.63% loss in vegetation, 37.56% in agricultural land, and 16.12% in water bodies. The classification achieved 91.6% overall accuracy.

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Journal Publications

2025

In Preparation

Rishal F.H., Mitra C., Byahut S. (2025). “Evaluation of Heat Action Plans to Minimize Social Inequality of Heat Risk in South Asian Cities”.

Rishal F.H., Mitra C., Islam S.T. (2025). “Land Use Land Cover Change impact on the Urban Heat Island effect in South Asia: Evidence from nine cities”.

2023

Hoque S., Rishal F.H. and Saika U. (2023). “Changing Perspectives in Urban Park Use and Management in Dhaka City, Bangladesh”. The Jahangirnagar Review, Part II: Vol. 47.

Read here

Rishal, F.H., Huda, K.M.S., Islam, M.N., Islam and S.T. (2023). “Post-disaster Recovery for Building Resilience: Bangladesh Perspective”. Lecture Notes in Civil Engineering, vol 283. Springer. Read here

2022

Islam, S.T., Akand, M.K., Islam, M.N., Samad, S. and Rishal, F.H. (2022), “Systems thinking approach for disaster resilient and climate smart agriculture in Bangladesh", International Journal of Disaster Resilience in the Built Environment. Read here

2020

Islam, S.T., Akand, M.K., Islam, M.N., Samad, S. and Rishal, F.H. (2022), “Systems thinking approach for disaster resilient and climate smart agriculture in Bangladesh", International Journal of Disaster Resilience in the Built Environment. Read here