District-level healthcare accessibility under flood-conditioned road network disruption in Pakistan - Ramla Khan; Martin Lauer; Muhammad Marwan (Spatial Information Research, 2026)

Adaptive spectral indices for mangrove and industrial area detection to strengthen blue carbon assessment in coastal regions - Dimas Danar Dewa; Ugay Sugarmansyah; Heri Apriyanto; Sherafina Reni Cahayanti; Nugroho Adi Sasongko; Warseno Warseno; Yanuar Iman Dwiananto; Anissa Dewi Suryaningtyas; Muhammad Abrar Rizqullah (Spatial Information Research, 2026)

Spatiotemporal distribution of cyclone-induced casualties in coastal Bangladesh (1970–2021): a comprehensive assessment - Tamanna Rahman; Muhammad Al-Amin Hoque; Sanjoy Roy; Biswajeet Pradhan (Spatial Information Research, 2025)
Bangladesh is highly vulnerable due to tropical cyclones, which frequently cause severe damages and fatalities in coastal regions of Bangladesh. This study investigates 41 major cyclonic storms from 1970 to 2021 to assess the spatial and temporal distribution of cyclone related fatalities in coastal Bangladesh. A total of 502,083 deaths and 111,885 injuries were recorded, with Chattogram, Patuakhali and Barishal districts experiencing the highest death rates. Notably, Sandwip upazila in Chattgram and Mirzaganj upazila in Patuakhali district had the highest mortality rates. Data were sourced primarily from newspapers and analyzed using ArcGIS at district and upazila levels. The findings illustrated significant spatial variations in cyclone impact, highlighting areas most at risk. The study highlights the importance of GIS based analysis in identifying vulnerable zones for targeted mitigation efforts. Public awareness, early warning systems, and disaster resilience strategies are essential for reducing cyclone related fatalities. Future research should focus on standardized data collection, catastrophe risk models, and spatiotemporal analysis to improve cyclone prediction and resilience planning.

Mapping seismic risk in Rangpur Division, Bangladesh: an integrated geospatial approach - Mehedi Hasan; Muhammad Al-Amin Hoque; Biswajeet Pradhan (Spatial Information Research, 2025)
Rangpur Division in Bangladesh lies near the active Dauki Fault, yet few studies have fully assessed its earthquake risk. Most research focuses separately on hazard or vulnerability, often ignoring mitigation and detailed spatial analysis. This study fills that gap by using a geospatial multi-criteria approach that integrates hazard, vulnerability, and mitigation indicators into one risk assessment model. Employing Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP), and Multi-Criteria Decision Analysis (MCDA) in Python, nine hazard criteria, seven vulnerability indicators, and five mitigation factors were modeled as weighted raster layers. These layers were combined to produce high-resolution maps and a seismic risk map with five risk levels from very high to very low. Model validation showed strong predictive ability (AUC = 0.857) and consistency (CR < 0.1). Results reveal that 21.43% of Rangpur is at very high risk, mainly due to proximity to fault lines and dense population, while only 10.62% shows strong mitigation capacity. This highlights the urgent need for better disaster preparedness, infrastructure improvement, and public awareness. The spatial outputs provide valuable guidance for policymakers on urban planning, risk reduction, and resilience-building in the region.

Mapping dengue susceptibility in Dhaka city: a geospatial multi-criteria approach integrating environmental and demographic factors - Muhammad Al-Amin Hoque; Md. Limon Sardar; Sharif A. Mukul; Biswajeet Pradhan (Spatial Information Research, 2025)
Dengue, a rapidly spreading mosquito-borne disease, poses a serious public health threat in tropical cities like Dhaka, Bangladesh—one of the world’s most densely populated megacities. In 2023 alone, Dhaka experienced its worst outbreak, recording 321,179 cases and 1,705 deaths. This study aims to assess dengue susceptibility across Dhaka using a geospatial Multi-Criteria Decision-Making (MCDM) approach. Fourteen environmental and demographic factors were selected, and thematic raster layers were developed and weighted using the Analytical Hierarchy Process (AHP). These layers were integrated to generate spatial dengue susceptibility maps, highlighting risk zones across the city. Findings reveal that southern and southeastern Dhaka, particularly under the South City Corporation, are highly susceptible based on environmental factors. Demographic analysis shows moderate to very high susceptibility in central and southern wards, with population density and proximity to waterlogged areas identified as key drivers. The model was validated through field surveys with 80 stakeholders, with 67.5% agreeing with the susceptibility classifications. This study provides a scalable and transferable framework for dengue risk assessment and can inform targeted interventions in other endemic regions. The results offer critical guidance for urban health planning, vector control, and resource allocation to mitigate dengue and similar vector-borne diseases.

Examining the effect of apartment attributes on their sale prices in Riyadh, Saudi Arabia - Ali Chikhmous; Muhammad Tauhidur Rahman (Spatial Information Research, 2024)

An effective deep learning model for ship detection from satellite images - Aaqib Mehran; Samabia Tehsin; Muhammad Hamza (Spatial Information Research, 2023)

Mining public opinion: a sentiment based forecasting for democratic elections of Pakistan - Asif Nawaz; Tariq Ali; Yaser Hafeez; Saif ur Rehman; Muhammad Rizwan Rashid (Spatial Information Research, 2022)