Scholarly Works, Geography

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Research articles, presentations, and other scholarship

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Navigating Disparities in Dental Health—A Transit-Based Investigation of Access to Dental Care in Virginia
Kim, Junghwan; Karki, Shashank; Brickhouse, Tegwyn; Vujicic, Marko; Nasseh, Kamyar; Wang, Changzhen; Zhang, Mengxi (2024-10-30)
Objective: To identify vulnerable areas and populations with limited access to dental care in Virginia, the study aimed (1) to calculate travel time and accessibility scores to dental care in Virginia using a transit-based accessibility model for all dental clinics and dental clinics participating in the Medicaid dental program and (2) to estimate factors associated with accessibility to dental clinics participating in the Medicaid dental program in Virginia. Methods: The study used building footprints as origins of transit trips to dental care services (or destinations). The study then computed transit-based origin–destination travel time matrices based on the detailed trip information, including in-vehicle and out-of- vehicle travel time. Accessibility scores were calculated by counting the number of dental clinics that can be reached within 60 min. Regression analysis was used to measure factors associated with accessibility scores to dental clinics participating in Medicaid. Results: Residents in smaller regions spent longer travel time to dental clinics by public transit compared with those who resided in larger regions. Medicaid participants also faced longer travel time compared with the general population. Residents spent more than three-fourths of the time waiting for public transit and walking to clinics regardless of where they live and what type of insurance they have. Associations between sociodemographic factors and accessibility scores to dental clinics participating in the Medicaid dental program varied across regions. Conclusions: Disparities in dental care accessibility exist depending on the size of regions and Medicaid participation in Virginia. The disparities in transit-based access to dental clinics and a disproportionate amount of time spent waiting for public transit and walking to dental clinics could be improved through tailored interventions taking into account the sociodemographic and geographic characteristics of each region.
Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models
Ma, Bingxin; Shao, Yang; Yang, Hequn; Lu, Yiwen; Gao, Yanqing; Wang, Xinyao; Xie, Ying; Wang, Xiaofeng (MDPI, 2024-10-10)
This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.
Toward Collaborative Adaptation: Assessing Impacts of Coastal Flooding at the Watershed Scale
Mitchell, Allison; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L.; McLaughlin, Daniel L. (Springer Nature, 2022-12)
The U.S. Mid-Atlantic coastal region is experiencing higher rates of SLR than the global average, especially in Hampton Roads, Virginia, where this acceleration is primarily driven by land subsidence. The adaptation plans for coastal flooding are generally developed at the municipal level, ignoring the broader spatial implications of flooding outside the individual administrative boundaries. Flood impact assessments at the watershed scale would provide a more holistic perspective on what is needed to synchronize the adaptation efforts between the neighboring administrative units. This paper evaluates flooding impacts from sea level rise (SLR) and storm surge among watersheds in Hampton Roads to identify those most at risk of coastal flooding over different time horizons. It also explores the implications of flooding on the municipalities, the land uses, and land covers throughout this region within the case study watershed. The 2% Annual Exceedance Probability (AEP) storm surge flood hazard data and NOAA’s intermediate SLR projections were used to develop flooding scenarios for 2030, 2060, and 2090 and delineate land areas at risk of combined flooding. Findings show that five out of 98 watersheds will substantially increase in inundation, with two intersecting multiple municipalities. They also indicate significant inundation of military, commercial, and industrial land uses and wetland land covers. Flooding will also impact residential land use in urban areas along the Elizabeth River and Hampton city, supporting the need for collaborative adaptation planning on hydrologically influenced spatial scales.
Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods
Karki, Shashank; Pingel, Thomas J.; Baird, Timothy D.; Flack, Addison; Ogle, J. Todd (MDPI, 2024-09-18)
Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.
Implications for spatial non-stationarity and the neighborhood effect averaging problem (NEAP) in green inequality research: evidence from three states in the USA
Gyanwali, Sophiya; Karki, Shashank; Jang, Kee Moon; Crawford, Thomas W.; Zhang, Mengxi; Kim, Junghwan (Springer, 2024-09-04)
Recent studies on green space exposure have argued that overlooking human mobility could lead to erroneous exposure estimates and their associated inequality. However, these studies are limited as they focused on single cities and did not investigate multiple cities, which could exhibit variations in people’s mobility patterns and the spatial distribution of green spaces. Moreover, previous studies focused mainly on large-sized cities while overlooking other areas, such as small-sized cities and rural neighborhoods. In other words, it remains unclear the potential spatial non-stationarity issues in estimating green space exposure inequality. To fill these significant research gaps, we utilized commute data of 31,862 people from Virginia, West Virginia, and Kentucky. The deep learning technique was used to extract green spaces from street-view images to estimate people’s home-based and mobility-based green exposure levels. The results showed that the overall inequality in exposure levels reduced when people’s mobility was considered compared to the inequality based on home-based exposure levels, implying the neighborhood effect averaging problem (NEAP). Correlation coefficients between individual exposure levels and their social vulnerability indices demonstrated mixed and complex patterns regarding neighborhood type and size, demonstrating the presence of spatial non-stationarity. Our results underscore the crucial role of mobility in exposure assessments and the spatial non-stationarity issue when evaluating exposure inequalities. The results imply that local-specific studies are urgently needed to develop local policies to alleviate inequality in exposure precisely.
Pastoralist livelihood diversification and social network transition: a conceptual framework
Baird, Timothy D. (Frontiers, 2024-07-19)
Around the world, many pastoralists are diversifying their livelihoods by incorporating alternative income generating activities. Much scholarship has examined the causes of this trend, however, less has been written about the consequences of diversification, especially how it may affect the structure and function of pastoralists’ social networks. This perspective presents a conceptual framework for a pastoralist social network transition, driven by livelihood diversification, and its effects on resilience at household and community scales.
NetPointLib: Library for Large-Scale Spatial Network Point Data Fusion and Analysis
Kang, Yunfan; Lyu, Fangzheng; Wang, Shaowen (ACM, 2024-07-17)
Network-constrained events, including for example traffic accidents and crime incidents, are widespread in urban environments. Understanding spatial patterns of these events within network spaces is essential for deciphering the underlying dynamics and supporting informed decision-making. The fusion and analysis of networkconstrained point data pose significant computational challenges, particularly with large datasets and sophisticated algorithms. In this context, we introduce NetPointLib, a computationally efficient library designed for processing and analyzing large-scale event data in network spaces. NetPointLib utilizes the capabilities of highperformance computing (HPC) environments including ROGER supercomputer, ACCESS resources, and the CyberGISX platform, providing a scalable and accessible framework for conducting network point data fusion and pattern analysis and supporting computational reproducibility. The library encompasses several algorithmic implementations, including the network local K function and network scan statistics, to enable researchers and practitioners to identify spatial patterns within network-constrained data. This is achieved by harnessing the computational power of HPC resources, facilitating advanced spatial analysis in an efficient and scalable manner.
New pathways for women’s empowerment in pastoralist Maasai households, Tanzania
Baird, Timothy D.; Woodhouse, Emily; McCabe, J. Terrence; Barnes, Paul; Terta, Felista; Runda, Naomi (Elsevier, 2024-07)
Despite the extensive scholarship on women's empowerment and gender equality in the Global South, few studies have examined how changing livelihoods create new challenges and opportunities for women seeking access to intra-household decision-making. Here we examine pastoralist Maasai women's access to a range of household-level decisions that span more longstanding and more recent aspects of changing social and economic life. Our team conducted a mixed-methods data collection in 10 Maasai communities in northern Tanzania in 2018 and 2022. We (1) interviewed groups of women and men (n = 18) to identify key types of household decisions and the factors affecting women's access to them; and (2) conducted a survey of married women (n = 321) to identify individuals' perceptions of access to intra-household decision-making and other characteristics. We applied an information theoretic approach to model selection of fitted cumulative link mixed effects models. Our findings show that newer sources of human, social, and physical capital for women, including school-based education, land tenure, and community group membership, are associated with access to more contemporary decision types, including income generation, children's schooling, and children's health care. Alternatively, we find fewer pathways to decision-making for more longstanding decision types, including livestock management and children's marriage. Notably, agricultural land has a complex relationship with decision-making wherein basic access to land is associated with lower access to decision-making, but land tenure is associated with greater access. This study shows how marginalized women can leverage changing social and economic contexts to gain greater access to intra-household decision-making.
From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread
Song, Zhenlei; Zhang, Zhe; Lyu, Fangzheng; Bishop, Michael; Liu, Jikun; Chi, Zhaohui (MDPI, 2024-06-13)
In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual’s behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform “Susceptible—Exposed—Infectious—Removed” (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions.
Generative AI tools can enhance climate literacy but must be checked for biases and inaccuracies
Atkins, Carmen; Girgente, Gina; Shirzaei, Manoochehr; Kim, Junghwan (Springer Nature, 2024-04)
In the face of climate change, climate literacy is becoming increasingly important. With wide access to generative AI tools, such as OpenAI’s ChatGPT, we explore the potential of AI platforms for ordinary citizens asking climate literacy questions. Here, we focus on a global scale and collect responses from ChatGPT (GPT-3.5 and GPT-4) on climate change-related hazard prompts over multiple iterations by utilizing the OpenAI’s API and comparing the results with credible hazard risk indices.Wefind a general sense of agreement in comparisons and consistency in ChatGPT over the iterations. GPT-4 displayed fewer errors than GPT-3.5. Generative AI tools may be used in climate literacy, a timely topic of importance, but must be scrutinized for potential biases and inaccuracies moving forward and considered in a social context. Future work should identify and disseminate best practices for optimal use across various generative AI tools.
Designing Virtual Pathways for Exploring Glacial Landscapes of Glacier National Park, Montana, USA for Physical Geography Education
Gielstra, Dianna; Moorman, Lynn; Kelly, Jacquelyn; Schulze, Uwe; Resler, Lynn M.; Cerveny, Niccole V.; Gielstra, Johan; Bryant, Ami; Ramsey, Scott; Butler, David R. (MDPI, 2024-03-05)
Virtual field trips in physical geography transcend our human limitations regarding distance and accessibility, allowing students to experience exemplars of physical environments. These experiences can be critical for students to connect to the physical world beyond traditional classroom formats of communicating themes and features in physical geography. To maximize the learning potential of these experiences, designers must engage in a translational process to take resources and content from the physical world and migrate it to an online, virtual format. However, these virtual learning experiences need to account for how learners learn; and should draw heavily on the foundations of educational research and field sciences, while highlighting the awe and beauty of the natural landscape itself. Crafting these spatial stories of the natural world with learning elements requires careful and intentional design to maximize the perception of physical features, patterns, and processes at the landscape scale. To help field-trip developers comprehend the workflows used to create perceptible, rich environments that spur students’ learning, we propose a development process (TECCUPD) as a guide to navigate the intersection of education and science, using an example of geodiversity and alpine glacial landscapes found in Glacier National Park, Montana.
The Relationship Between the Saharan Air Layer, Convective Environmental Conditions, and Precipitation in Puerto Rico
Miller, Paul W.; Ramseyer, Craig A. (American Geophysical Union, 2024-01-04)
The Saharan Air Layer (SAL) is a hot, dry, and dust-laden feature that advects large concentrations of dust across the Atlantic annually to destination regions in the Americas and Caribbean. However, recent work has suggested the SAL may be a contributing factor to high-impact drought in the Caribbean basin. While the SAL's characteristic dust loadings have been the focus of much previous research, fewer efforts have holistically engaged the co-evolution of the dust plume, its associated convective environment, and resultant rainfall in Caribbean islands. This study employs a self-organizing map (SOM) classification to identify the common trans-Atlantic dust transport typologies associated with the SAL during June and July 1981–2020. Using the column-integrated dust flux, termed integrated dust transport (IDT), from MERRA-2 reanalysis as a SAL proxy, the SOM resolved two common patterns which resembled trans-Atlantic SAL outbreaks. During these events, the convective environment associated with the SAL, as inferred by the Gálvez-Davison Index, becomes less conducive to precipitation as the SAL migrates further away from the west African coast. Simultaneously, days with IDT patterns grouped to the SAL outbreak typologies demonstrate island-wide negative precipitation anomalies in Puerto Rico. The SOM's most distinctive SAL outbreak pattern has experienced a statistically significant increase during the 40-year study period, becoming roughly 10% more frequent over that time. These results are relevant for both climate scientists and water managers wishing to better anticipate Caribbean droughts on both the long and short terms.
Advances in tropical climatology – a review
Moraes, Flávia D. S.; Ramseyer, Craig A.; Miller, Paul W.; Trepanier, Jill C. (Informa, 2024-02-12)
Understanding tropical climatology is essential to comprehending the atmospheric connections between the tropics and extratropical latitudes weather and climate events. In this review paper, we emphasize the advances in key areas of tropical climatology knowledge since the end of the 20th century and offer a summary, assessment, and discussion of previously published literature. Among the key areas analyzed here, we explore the advances in tropical oceanic and atmospheric variability, such as El Niño – Southern Oscillation and the Madden-Julian Oscillation, and how those teleconnection events have helped us to better understand variabilities in tropical monsoons, tropical cyclones, and drought events. We also discuss new concepts incorporated into the study of tropical cyclones, such as rapid intensification, and how those studies are evolving and helping scientists to better prepare and predict hurricanes. Regarding tropical aerosols, we discuss how satellite-based dust detection has improved the comprehension of Saharan dust as a driver of drought in locations far from the dust source region while simultaneously altering tropical cyclone variability. Finally, our review shows that there have been significant advances in tropical hydroclimatic studies in order to better investigate monsoons, flooding, and drought, helping scholars of tropical climatology to better understand its extreme events.
Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding
Nourali, Zahra; Shortridge, Julie E.; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L. (MDPI, 2024-01-11)
Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding.
Local studies provide a global perspective of the impacts of climate change on Indigenous Peoples and local communities
Reyes-García, Victoria; García-Del-Amo, David; Porcuna-Ferrer, Anna; Schlingmann, Anna; Abazeri, Mariam; Attoh, Emmanuel M. N. A. N.; Vieira da Cunha Ávila, Julia; Ayanlade, Ayansina; Babai, Daniel; Benyei, Petra; Calvet-Mir, Laura; Carmona, Rosario; Caviedes, Julián; Chah, Jane; Chakauya, Rumbidzayi; Cuní-Sanchez, Aida; Fernández-Llamazares, Álvaro; Galappaththi, Eranga K.; Gerkey, Drew; Graham, Sonia; Guillerminet, Théo; Huanca, Tomás; Ibarra, José T.; Junqueira, André B.; Li, Xiaoyue; López-Maldonado, Yolanda; Mattalia, Giulia; Samakov, Aibek; Schunko, Christoph; Seidler, Reinmar; Sharakhmatova, Victoria; Singh, Priyatma; Tofighi-Niaki, Adrien; Torrents-Ticó, Miquel (2024-01-08)
Indigenous Peoples and local communities with nature-dependent livelihoods are disproportionately affected by climate change impacts, but their experience, knowledge and needs receive inadequate attention in climate research and policy. Here, we discuss three key findings of a collaborative research consortium arising from the Local Indicators of Climate Change Impacts project. First, reports of environmental change by Indigenous Peoples and local communities provide holistic, relational, placed-based, culturally-grounded and multi-causal understandings of change, largely focused on processes and elements that are relevant to local livelihoods and cultures. These reports demonstrate that the impacts of climate change intersect with and exacerbate historical effects of socioeconomic and political marginalization. Second, drawing on rich bodies of inter-generational knowledge, Indigenous Peoples and local communities have developed context-specific responses to environmental change grounded in local resources and strategies that often absorb the impacts of multiple drivers of change. Indigenous Peoples and local communities adjust in diverse ways to impacts on their livelihoods, but the adoption of responses often comes at a significant cost due to economic, political, and socio-cultural barriers operating at societal, community, household, and individual levels. Finally, divergent understandings of change challenge generalizations in research examining the human dimensions of climate change. Evidence from Indigenous and local knowledge systems is context-dependent and not always aligned with scientific evidence. Exploring divergent understandings of the concept of change derived from different knowledge systems can yield new insights which may help prioritize research and policy actions to address local needs and priorities.
IPBES VA Chapter 4 - Literature & case study review on outcomes in protected areas and indigenous and community conserved areas (ICCAs)
Baird, Timothy D.; McCabe, J. Terrence; Woodhouse, Emily (2024)
Atmospheric Flash Drought in the Caribbean
Ramseyer, Craig A.; Miller, Paul W. (American Meteorological Society, 2023-09-13)
Despite the intensifying interest in flash drought both within the U.S. and globally, moist tropical landscapes have largely escaped the attention of the flash drought community. Because these ecozones are acclimatized to receiving regular, near-daily precipitation, they are especially vulnerable to rapid-drying events. This is particularly true within the Caribbean basin where numerous small islands lack the surface and groundwater resources to cope with swiftly developing drought conditions. This study fills the tropical flash drought gap by examining the pervasiveness of flash drought across the pan-Caribbean region using a recently proposed criterion based on the Evaporative Demand Drought Index (EDDI). The EDDI identifies 46 instances of widespread flash drought “outbreaks” in which significant fractions of the pan-Caribbean encounter rapid drying over 15 days and then maintain this condition for another 15 days. Moreover, a self-organizing maps (SOM) classification reveals a tendency for flash drought to assume recurring typologies concentrated in either the Central American, South American, or Greater Antilles coastlines, though a simultaneous, Caribbean-wide drought is never observed within the 40-year (1981-2020) period examined. Further, three of the six flash drought typologies identified by the SOM initiate most often during Phase 2 of the Madden-Julian Oscillation. Collectively, these findings motivate the need to more critically examine the transferability of flash drought definitions into the global tropics, particularly for small water-vulnerable islands where even island-wide flash droughts may only occupy a few pixels in most reanalysis datasets.
The effects of projected climate change on crop water availability in the US Caribbean
Moraes, Flavia D. S.; Ramseyer, Craig A.; Gamble, Douglas (IWA Publishing, 2023-04)
Anthropogenic climate change affects small islands, and farming systems in the Caribbean are vulnerable to climate change due to their high dependence on rainfall. Therefore, this work evaluated how temperature and precipitation projections affect water crop needs in Puerto Rico and St. Croix. We used Daymet data to create a baseline climatology (1981-2010) and the Coupled Model Intercomparison Project Phase 6 (CMIP6) to create future climatologies (2041-2070 and 2071-2100). A water budget model estimated the water deficit, and the crop risk (CROPRISK) model determined crop suitability for sweet pepper, banana, and plantain. Results indicated an increase in water stress after 2041 for most of the region from June to August, except for western Puerto Rico, where it will occur from January to March. For sweet pepper, banana, and plantain, the most water-stressed season is projected to be January-July. November will be the only month during which all crops are projected to be highly suitable through the end of the 21st century. These findings suggested that Puerto Rico and St. Croix crop water stress may be more sensitive to changes in temperature than precipitation.
Evaluation of predicted loss of different land use and land cover (LULC) due to coastal erosion in Bangladesh
Islam, Md Sariful; Crawford, Thomas W.; Shao, Yang (Frontiers, 2023-04)
Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988-2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments.
Pandemic Simulator: An Agent-Based Framework with Human Behavior Modeling for Pandemic-Impact Assessment to Build Sustainable Communities
Weligampola, Harshana; Ramanayake, Lakshitha; Ranasinghe, Yasiru; Ilangarathna, Gayanthi; Senarath, Neranjan; Samarakoon, Bhagya; Godaliyadda, Roshan; Herath, Vijitha; Ekanayake, Parakrama; Ekanayake, Janaka; Maheswaran, Muthucumaru; Theminimulle, Sandya; Rathnayake, Anuruddhika; Dharmaratne, Samath; Pinnawala, Mallika; Yatigammana, Sakunthala; Tilakaratne, Ganga (MDPI, 2023-07-17)
It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they are highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

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