CSDMS - User contributions [en]

2024 CSDMS meeting-003

2024-01-19T19:47:47Z

Bestkb: Created page with "{{CSDMS meeting personal information template-2024 |CSDMS meeting first name=Kelsea |CSDMS meeting last name=Best |CSDMS Pronouns=she, her |CSDMS meeting institute=Ohio State University |CSDMS meeting city=Columbus |CSDMS meeting country=United States |CSDMS meeting state=OH |CSDMS meeting email address=best.309@osu.edu }} {{CSDMS meeting select clinics1 2024 |CSDMS_meeting_select_clinics1_2024=3) Introduction to agent-based modeling for socio-environmental systems }} {{..."

{{CSDMS meeting personal information template-2024
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS Pronouns=she, her
|CSDMS meeting institute=Ohio State University
|CSDMS meeting city=Columbus
|CSDMS meeting country=United States
|CSDMS meeting state=OH
|CSDMS meeting email address=best.309@osu.edu
}}
{{CSDMS meeting select clinics1 2024
|CSDMS_meeting_select_clinics1_2024=3) Introduction to agent-based modeling for socio-environmental systems
}}
{{CSDMS meeting select clinics2 2024
|CSDMS_meeting_select_clinics2_2024=1) Fora.ai: A participatory modeling platform to reshape how we collaborate for climate and social impact
}}
{{CSDMS meeting select clinics3 2024
|CSDMS_meeting_select_clinics3_2024=5) Will not attend a clinic this day
}}
{{CSDMS meeting abstract yes no 2024
|CSDMS meeting abstract submit 2024=No
}}
{{CSDMS meeting abstract poster Epub 2024}}
{{CSDMS meeting abstract title temp2024}}
{{CSDMS meeting abstract template 2024}}
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Presenters-0633

2023-12-17T20:48:48Z

Bestkb: Created page with "{{Presenters temp |CSDMS meeting event title=CSDMS 2024: Coastlines, Critical Zones and Cascading Hazards: Modeling Dynamic Interfaces from Deep Time to Human Time |CSDMS meeting event year=2024 |CSDMS meeting presentation type=Clinic |CSDMS meeting first name=Kelsea |CSDMS meeting last name=Best |CSDMS meeting institute=The Ohio State University |Country member=United States |CSDMS meeting state=Ohio |CSDMS meeting email address=best.309@osu.edu |CSDMS meeting title pre..."

{{Presenters temp
|CSDMS meeting event title=CSDMS 2024: Coastlines, Critical Zones and Cascading Hazards: Modeling Dynamic Interfaces from Deep Time to Human Time
|CSDMS meeting event year=2024
|CSDMS meeting presentation type=Clinic
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=The Ohio State University
|Country member=United States
|CSDMS meeting state=Ohio
|CSDMS meeting email address=best.309@osu.edu
|CSDMS meeting title presentation=Introduction to agent-based modeling for socio-environmental systems
}}
{{Presenters presentation
|CSDMS meeting abstract presentation=Agent-based modeling (ABM) is a powerful simulation tool with applications across disciplines. ABM has also emerged as a useful tool for capturing complex processes and interactions within socio-environmental systems. This workshop will offer a brief introduction to ABM for socio-environmental systems modeling including an overview of opportunities and challenges. Participants will be introduced to NetLogo, a popular programming language and modeling environment for ABM. In groups, participants will have the hands-on opportunity to program different decision-making methods in an existing model and observe how outcomes change. We will conclude with an opportunity for participants to raise questions or challenges they are experiencing with their own ABMs and receive feedback from the group.
|CSDMS meeting youtube code=0
|CSDMS meeting participants=0
}}
{{Presenters additional material
|Working group member=Coastal Working Group, Critical Zone Focus Research Group, Human Dimensions Focus Research Group, Coastal Vulnerability Initiative, Modeling Platform Interoperability Initiative
}}

User:Bestkb

2023-11-07T20:44:39Z

Bestkb:

{{Signup information member
|First name member=Kelsea
|Last name member=Best
|Picture member=Headshot2best.jpg
|Institute member=The Ohio State University
|Department member=Civil, Environmental & Geodetic Engineering
|City member=Columbus
|Postal code member=43207
|Country member=United States
|State member=Ohio
|Confirm email member=best.309@osu.edu
|Work phone member=6154785299
|Working group member=Coastal Working Group, Human Dimensions Focus Research Group, Coastal Vulnerability Initiative, Artificial Intelligence & Machine Learning Initiative
|Emaillist group member=yes
|Memberagreement=I have read and agree to the Privacy Policy
}}

File:Headshot2best.jpg

2023-11-07T20:44:34Z

Bestkb:

User:Bestkb

2023-11-07T20:43:55Z

Bestkb:

{{Signup information member
|First name member=Kelsea
|Last name member=Best
|Institute member=The Ohio State University
|Department member=Civil, Environmental & Geodetic Engineering
|City member=Columbus
|Postal code member=43207
|Country member=United States
|State member=Ohio
|Confirm email member=best.309@osu.edu
|Work phone member=6154785299
|Working group member=Coastal Working Group, Human Dimensions Focus Research Group, Coastal Vulnerability Initiative, Artificial Intelligence & Machine Learning Initiative
|Emaillist group member=yes
|Memberagreement=I have read and agree to the Privacy Policy
}}

2022 CSDMS meeting-043

2022-02-18T22:45:03Z

Bestkb:

{{CSDMS meeting personal information template-2022
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS Pronouns=she, her
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting select clinics1 2022
|CSDMS_meeting_select_clinics1_2022=4) Teaching with Jupyter NoteBooks
}}
{{CSDMS meeting select clinics2 2022
|CSDMS_meeting_select_clinics2_2022=4) Xarray for Scalable Scientific Data Analysis
}}
{{CSDMS meeting select clinics3 2022
|CSDMS_meeting_select_clinics3_2022=2) Rapid hypothesis testing and analysis with the open-source delta model pyDeltaRCM
}}
{{CSDMS meeting abstract yes no 2022
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2022
|CSDMS meeting abstract title=Comparing multiple methods of decision-making in an agent-based model of environmental migration
|Working_group_member_WG_FRG=Human Dimensions Focus Research Group
}}
{{CSDMS meeting abstract template 2022
|CSDMS meeting abstract=Environmental migration is an example of a complex coupled human and natural system with dynamics that operate across multiple spatial and temporal scales. Agent-based modeling (ABM) has demonstrated potential for studying such complex systems, especially where individual decision-making is an important component. In this work, we use an original ABM of environmental shock, livelihood opportunities, and migration decisions to study dynamics of environmental migration in rural Bangladesh. As ABMs are sensitive to the decision-making methods used, we present results utilizing multiple plausible decision-making methods for households deciding whether or not to send an internal migrant. We present results using both a simple economic method based on utility maximization as well as a more behaviorally complex method based on the Theory of Planned Behavior. We hypothesized that a more behaviorally complex decision method which incorporates social networks and community norms would more successfully reproduce the patterns of migration. However, using a pattern-oriented approach to reproduce two key patterns of migration from the empirical literature, we demonstrate that an economic model can reproduce our patterns of interest with high levels of success. For both decision methods, the level of community inequality in distribution of land ownership, which impacts the number of agricultural jobs available within the community, is critically important for patterns of migration outcomes. In this way, our model suggests that community-level inequality is has significant implications of migration dynamics in this study area.
}}
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2022 CSDMS meeting-043

2022-02-18T19:02:46Z

Bestkb:

{{CSDMS meeting personal information template-2022
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS Pronouns=she, her
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting select clinics1 2022
|CSDMS_meeting_select_clinics1_2022=4) Teaching with Jupyter NoteBooks
}}
{{CSDMS meeting select clinics2 2022
|CSDMS_meeting_select_clinics2_2022=4) Xarray for Scalable Scientific Data Analysis
}}
{{CSDMS meeting select clinics3 2022
|CSDMS_meeting_select_clinics3_2022=2) Rapid hypothesis testing and analysis with the open-source delta model pyDeltaRCM
}}
{{CSDMS meeting abstract yes no 2022
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2022
|CSDMS meeting abstract title=Comparing multiple methods of decision-making in an agent-based model of environmental migration
|Working_group_member_WG_FRG=Human Dimensions Focus Research Group
}}
{{CSDMS meeting abstract template 2022
|CSDMS meeting abstract=Environmental migration is an example of such a complex coupled human and natural system with dynamics that operate across multiple spatial and temporal scales. Agent-based modeling (ABM) has demonstrated potential for studying such complex systems, especially where individual decision-making is an important component. In this work, we use an original ABM of environmental shocks, livelihood opportunities, and migration decisions to study dynamics of environmental migration in rural Bangladesh. As ABMs are sensitive to the decision-making methods used, we present results utilizing multiple plausible decision-making methods for households deciding whether or not to send an internal migrant. We present results using both a simple economic method based on utility maximization as well as a more behaviorally complex method based on the Theory of Planned Behavior. We hypothesized that a more behaviorally complex decision method which incorporates social networks and community norms would more successfully reproduce the patterns of migration. However, using a pattern-oriented approach to reproduce two key patterns of migration from the empirical literature, we demonstrate that an economic model can reproduce our patterns of interest with high levels of success. For both decision methods, the level of community inequality in distribution of land ownership, which impacts the number of agricultural jobs available within the community, is critically important for patterns of migration outcomes. In this way, our model suggests that community-level inequality is has significant implications of migration dynamics in this study area.
}}
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2022 CSDMS meeting-043

2022-02-17T19:31:41Z

Bestkb: Created page with "{{CSDMS meeting personal information template-2022 |CSDMS meeting first name=Kelsea |CSDMS meeting last name=Best |CSDMS Pronouns=she, her |CSDMS meeting institute=Vanderbilt..."

{{CSDMS meeting personal information template-2022
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS Pronouns=she, her
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting select clinics1 2022
|CSDMS_meeting_select_clinics1_2022=4) Teaching with Jupyter NoteBooks
}}
{{CSDMS meeting select clinics2 2022
|CSDMS_meeting_select_clinics2_2022=2) Image Segmentation using Deep Learning and Human-In-the-Loop Machine Learning - Part I
}}
{{CSDMS meeting select clinics3 2022
|CSDMS_meeting_select_clinics3_2022=3) Image Segmentation using Deep Learning and Human-In-the-Loop Machine Learning - Part II
}}
{{CSDMS meeting abstract yes no 2022
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2022
|CSDMS meeting abstract title=Comparing multiple methods of decision-making in an agent-based model of environmental migration
|Working_group_member_WG_FRG=Human Dimensions Focus Research Group
}}
{{CSDMS meeting abstract template 2022
|CSDMS meeting abstract=Environmental migration is an example of such a complex coupled human and natural system with dynamics that operate across multiple spatial and temporal scales. Agent-based modeling (ABM) has demonstrated potential for studying such complex systems, especially where individual decision-making is an important component. In this work, we use an original ABM of environmental shocks, livelihood opportunities, and migration decisions to study dynamics of environmental migration in rural Bangladesh. As ABMs are sensitive to the decision-making methods used, we present results utilizing multiple plausible decision-making methods for households deciding whether or not to send an internal migrant. We present results using both a simple economic method based on utility maximization as well as a more behaviorally complex method based on the Theory of Planned Behavior. We hypothesized that a more behaviorally complex decision method which incorporates social networks and community norms would more successfully reproduce the patterns of migration. However, using a pattern-oriented approach to reproduce two key patterns of migration from the empirical literature, we demonstrate that an economic model can reproduce our patterns of interest with high levels of success. For both decision methods, the level of community inequality in distribution of land ownership, which impacts the number of agricultural jobs available within the community, is critically important for patterns of migration outcomes. In this way, our model suggests that community-level inequality is has significant implications of migration dynamics in this study area.
}}
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2021 CSDMS meeting-052

2021-05-10T18:45:39Z

Bestkb:

{{CSDMS meeting personal information template-2021
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=NASHVILLE
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.best@gmail.com
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting select clinics1 2021
|CSDMS_meeting_select_clinics1_2021=1) Git good with FAIR enough practices
}}
{{CSDMS meeting select clinics2 2021
|CSDMS_meeting_select_clinics2_2021=3) Decision Framing
}}
{{CSDMS meeting select clinics3 2021
|CSDMS_meeting_select_clinics3_2021=5) Communication & Engagement: Tips, Tricks, Traps and Opportunities
}}
{{CSDMS meeting abstract yes no 2021
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2021
|CSDMS meeting abstract title=Pattern-oriented modeling to test frameworks of environmental migration decisions in Bangladesh
|Working_group_member_WG_FRG=Human Dimensions Focus Research Group
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Jonathan
|CSDMS meeting coauthor last name abstract=Gilligan
|CSDMS meeting coauthor institute / Organization=Vanderbilt University
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
|CSDMS meeting coauthor email address=jonathan.gilligan@vanderbilt.edu
}}
{{CSDMS meeting abstract template 2021
|CSDMS meeting abstract=Environmental change interacts with human migration in complex ways and across multiple scales. This complexity makes agent-based modeling (ABM) a powerful tool to investigate environment-migration dynamics. Here, we present results from an original ABM of environmental migration in Bangladesh. The model simulates an origin community and how a stylized environmental shock to the community impacts labor opportunities and household decisions surrounding migration. Pattern-oriented modeling is a useful approach for evaluating ABM’s by assessing a model’s ability to reproduce multiple observed patterns of phenomena. We use a pattern-oriented approach to test our model’s ability to reproduce multi-level patterns of environmental migration from the literature. Previous work used machine learning methods to calibrate our ABM by identifying regions in parameter space that successfully reproduced the observed patterns. We demonstrated that a strictly income-based migration decision method was able to reproduce patterns of interest, but inconsistently. However, the pattern-oriented approach allows us to implement more complex, behaviorally driven decision-making methods of migration and evaluate their success. In this work, we will present preliminary results implementing and comparing different decision-making methods in our ABM based on existing theories including Theory of Planned Behavior, Protection Motivation Theory, and a mobility potential framework. Ultimately, we hypothesize that a hybrid framework of migration decision-making that includes community norms, social networks, and place attachment will most successfully be able to replicate known patterns of environmental migration.
}}
{{CSDMS meeting abstract figures
|CSDMS meeting abstract figure=best poster csdms 2021.pdf
}}
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File:Best poster csdms 2021.pdf

2021-05-10T18:45:30Z

Bestkb: Poster for 2021 CSDMS

Poster for 2021 CSDMS

2021 CSDMS meeting-052

2021-05-10T18:41:36Z

Bestkb:

2021 CSDMS meeting-052

2021-03-31T23:04:15Z

Bestkb:

2021 CSDMS meeting-052

2021-03-31T19:22:40Z

Bestkb:

{{CSDMS meeting personal information template-2021
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=NASHVILLE
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.best@gmail.com
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting select clinics1 2021
|CSDMS_meeting_select_clinics1_2021=1) Git good with FAIR enough practices
}}
{{CSDMS meeting select clinics2 2021
|CSDMS_meeting_select_clinics2_2021=3) Decision Framing
}}
{{CSDMS meeting select clinics3 2021
|CSDMS_meeting_select_clinics3_2021=5) Communication & Engagement: Tips, Tricks, Traps and Opportunities
}}
{{CSDMS meeting abstract yes no 2021
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2021
|CSDMS meeting abstract title=Pattern-oriented modeling to test frameworks of environmental migration decisions in Bangladesh
|Working_group_member_WG_FRG=Human Dimensions Focus Research Group
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Jonathan
|CSDMS meeting coauthor last name abstract=Gilligan
|CSDMS meeting coauthor institute / Organization=Vanderbilt University
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
|CSDMS meeting coauthor email address=jonathan.gilligan@vanderbilt.edu
}}
{{CSDMS meeting abstract template 2021
|CSDMS meeting abstract=Environmental change interacts with human migration in complex ways and across multiple scales. This complexity makes agent-based modeling (ABM) a powerful tool to investigate environment-migration dynamics. Here, we present results from an original ABM of environmental migration in Bangladesh. The model simulates an origin community and how a stylized environmental shock to the community impacts labor opportunities and household decisions surrounding migration. Pattern-oriented modeling is a useful approach for evaluating ABM’s by assessing a model’s ability to reproduce known patterns of phenomena. We use a pattern-oriented approach to test our model’s ability to reproduce multi-level patterns of environmental migration from the literature. Previous work combined pattern-oriented modeling with existing survey data and machine learning to parameterize and calibrate our ABM. We demonstrated that a strictly income-based migration decision method was able to reproduce patterns of interest, but inconsistently. However, the pattern-oriented approach allows us to implement more complex, behaviorally driven decision-making methods of migration and evaluate their success. In this work, we will present preliminary results implementing and comparing different decision-making methods in our ABM based on existing theories including Theory of Planned Behavior, Protection Motivation Theory, and a mobility potential framework. Ultimately, we hypothesize that a hybrid framework of migration decision-making that includes community norms, social networks, and place attachment will most successfully be able to replicate known patterns of environmental migration.
}}
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2021 CSDMS meeting-052

2021-03-30T15:19:31Z

Bestkb:

2021 CSDMS meeting-052

2021-02-09T16:08:24Z

Bestkb:

2021 CSDMS meeting-052

2021-02-09T16:01:11Z

Bestkb: Created page with "{{CSDMS meeting personal information template-2021 |CSDMS meeting first name=Kelsea |CSDMS meeting last name=Best |CSDMS meeting institute=Vanderbilt University |CSDMS meeting..."

2020 CSDMS meeting-052

2020-03-12T16:27:33Z

Bestkb:

{{CSDMS meeting personal information template-2020
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting select clinics1 2020
|CSDMS_meeting_select_clinics1_2020=3) Promote diversity and inclusion
}}
{{CSDMS meeting select clinics2 2020
|CSDMS_meeting_select_clinics2_2020=4) Landlab
}}
{{CSDMS meeting select clinics3 2020
|CSDMS_meeting_select_clinics3_2020=4) Modeling in the cloud
}}
{{CSDMS scholarships yes no
|CSDMS meeting scholarships=Yes
}}
{{CSDMS meeting abstract yes no 2020
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2020
|CSDMS meeting abstract title=An Agent-Based Model of Migration and Drought-Induced Crop Loss in Bangladesh
}}
{{CSDMS meeting abstract template 2020
|CSDMS meeting abstract=One possible human response to climate change and other environmental stresses is migration. However, migration is complex, multi-causal phenomenon, and the complexity of human migration poses a challenge for researchers who aim to study the effects of environmental changes on population mobility. This project aims to understand how changing environmental conditions and livelihood opportunities impact migration decisions in coastal Bangladesh. An original agent-based model (ABM) that combines stylized environmental change dynamics with livelihood is developed to understand how these dynamics impact migration decisions as well as what feedbacks may exist between them. The ABM is constructed such that agents represent households, consisting of individuals, within a single origin community. At each step of the model, an agent will first assess the expected utility of its different options within the community, including doing nothing, seeking employment internal to the community, and investing in non-agricultural livelihood options. After assessing livelihood options internal to the community, households with sufficient wealth and a sufficient number of family members will decide whether or not to send a household member as a migrant, also based on expected utility of a migration trip. The model’s representation of natural processes will be simulated in the form of drought, modeled stochastically, that impacts crop yields and crop-associated income. In this initial version of the ABM, agent decision-making is based on simple utility maximization. Future work will incorporate more complex decision-making theories into the model, as well as different destination locations and the possibility of return migration.
}}
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2020 CSDMS meeting-052

2020-02-18T18:15:49Z

Bestkb: Created page with "{{CSDMS meeting personal information template-2020 |CSDMS meeting first name=Kelsea |CSDMS meeting last name=Best |CSDMS meeting institute=Vanderbilt University |CSDMS meeting..."

User:Bestkb

2020-02-18T18:13:32Z

Bestkb:

{{Signup information member
|First name member=Kelsea
|Last name member=Best
|Institute member=Vanderbilt University
|Department member=Earth and Environmental Science
|Postal address 1 member=723A McFerrin Ave
|City member=Nashville
|Postal code member=37206
|Country member=United States
|State member=Tennessee
|Confirm email member=kelsea.best@gmail.com
|Work phone member=6154785299
|Working group member=Coastal Working Group, Human Dimensions Focus Research Group, Coastal Vulnerability Initiative, Artificial Intelligence & Machine Learning Initiative
|Emaillist group member=yes
|Memberagreement=I have read and agree to the Privacy Policy
}}

2019 CSDMS meeting-012

2019-03-22T15:29:46Z

Bestkb:

{{CSDMS meeting personal information template-2019
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting scholar and pre-meeting2019
|CSDMS meeting pre-conference2019=Quantifying Uncertainty in Earth Systems
}}
{{CSDMS meeting select clinics1 2019
|CSDMS_meeting_select_clinics1_2019=3) Pangeo - Scalable Geoscience Tools Python
}}
{{CSDMS meeting select clinics2 2019
|CSDMS_meeting_select_clinics2_2019=4) Model Calibration with Dakota
}}
{{CSDMS meeting select clinics3 2019
|CSDMS_meeting_select_clinics3_2019=2) Deep Neural Networks Classification
}}
{{CSDMS scholarships yes no
|CSDMS meeting scholarships=Yes
}}
{{CSDMS meeting abstract yes no 2019
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2019
|CSDMS meeting abstract title=Environmental Migration and Land Inundation: An Agent-Based Modeling Approach
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Jonathan
|CSDMS meeting coauthor last name abstract=Gilligan
|CSDMS meeting coauthor institute / Organization=Vanderbilt University
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
|CSDMS meeting coauthor email address=jonathan.gilligan@vanderbilt.edu
}}
{{CSDMS meeting abstract template 2019
|CSDMS meeting abstract=Migration is a complex phenomenon that is impacted by economic, social, cultural, and environmental factors. This complexity makes it challenging to study, and especially challenging to understand how future environmental and climatic change may affect mobility and population movements. Agent-based modeling (ABM) is a promising but underutilized method for studying environmental impacts on human migration because of its ability to study connections between large-scale dynamics and individual decision-making. This work presents initial results from an original ABM that simulates household migration decisions in Bangladesh under environmental pressure. The model seeks to understand how riverbank erosion and land inundation impact mobility patterns under varying decision-making frameworks and livelihood opportunities. The model stochastically simulates inundation of land bordering a river. Agents whose land is flooded seek employment in a local labor market. Agents who cannot find employment within the community then face the decision to migrate or remain in the community with limited economic opportunity. We compare several different decision frameworks of varying complexity, in which the probability of choosing to migrate depends upon: household wealth alone, wealth, age, and household size, and finally using the Theory of Planned Behavior, which incorporates interactions between individual-level variables and community-level phenomenon, such as social norms, which are mediated through each agent’s social network. This model serves as a starting point to begin to test how different decision-making frameworks and environmental scenarios may produce different dynamics in the response to environmental stress.
}}
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2019 CSDMS meeting-012

2019-03-22T15:06:57Z

Bestkb:

2019 CSDMS meeting-012

2019-03-01T15:08:45Z

Bestkb:

2019 CSDMS meeting-012

2019-01-17T15:36:55Z

Bestkb:

2019 CSDMS meeting-012

2019-01-17T15:36:37Z

Bestkb: Created page with "{{CSDMS meeting personal information template-2019 |CSDMS meeting first name=Kelsea |CSDMS meeting last name=Best |CSDMS meeting institute=Va |CSDMS meeting city=Nashville |CS..."

{{CSDMS meeting personal information template-2019
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=Va
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting scholar and pre-meeting2019
|CSDMS meeting pre-conference2019=Quantifying Uncertainty in Earth Systems
}}
{{CSDMS meeting select clinics1 2019
|CSDMS_meeting_select_clinics1_2019=1) Morphological Modeling - Delft3D Flex Mesh
}}
{{CSDMS meeting select clinics2 2019
|CSDMS_meeting_select_clinics2_2019=4) Model Calibration with Dakota
}}
{{CSDMS meeting select clinics3 2019
|CSDMS_meeting_select_clinics3_2019=2) Deep Neural Networks Classification
}}
{{CSDMS scholarships yes no
|CSDMS meeting scholarships=Yes
}}
{{CSDMS meeting abstract yes no 2019
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2019}}
{{CSDMS meeting abstract template 2019}}
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2018 CSDMS meeting-062

2018-03-20T15:14:32Z

Bestkb:

{{CSDMS meeting personal information template-2018
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting scholar and pre-meeting
|CSDMS meeting pre-conference=Bootcamp
|CSDMS meeting post-conference=No
}}
{{CSDMS meeting select clinics1 2018
|CSDMS_meeting_select_clinics1_2018=4) Sediment Experimentalist Network
}}
{{CSDMS meeting select clinics2 2018
|CSDMS_meeting_select_clinics2_2018=3) Data for natural hazards
}}
{{CSDMS meeting select clinics3 2018
|CSDMS_meeting_select_clinics3_2018=1) Downscaling climate models for regional applications
}}
{{CSDMS scholarships yes no
|CSDMS meeting scholarships=Yes
}}
{{CSDMS meeting abstract yes no 2018
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2018
|CSDMS meeting abstract title=Machine Learning to Identify Drivers of Internal Migration in Coastal Bangladesh
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Jonathan
|CSDMS meeting coauthor last name abstract=Gilligan
|CSDMS meeting coauthor institute / Organization=Vanderbilt University Earth and Environmental Science
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Hiba
|CSDMS meeting coauthor last name abstract=Baroud
|CSDMS meeting coauthor institute / Organization=Vanderbilt University Civil and Environmental Engineering
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Brooke
|CSDMS meeting coauthor last name abstract=Ackerly
|CSDMS meeting coauthor institute / Organization=Vanderbilt University Political Science
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Bishawjit
|CSDMS meeting coauthor last name abstract=Mallick
|CSDMS meeting coauthor institute / Organization=Technical University Dresden
|CSDMS meeting coauthor town-city=Dresden
|CSDMS meeting coauthor country=Germany
}}
{{CSDMS meeting abstract template 2018
|CSDMS meeting abstract=As climate change and environmental variability increase pressure on vulnerable communities, migration is one possible adaptation strategy. However, the decision to migrate is complex, and environmental factors are rarely the sole drivers of that decision. Rather, the decision to migrate is often influenced by a combination of economic, social, political, and environmental pressures. This is especially true in coastal communities in Bangladesh, where temporary migration has long been a method of livelihood diversification, and researchers are trying to understand how environmental factors influence existing migration flows. This work addresses a gap in current research by beginning to investigate how different “push” and “pull” drivers of migration might have distinctive variables that contribute to the ultimate decision to move or stay. In this study, random forest classification models are applied to a dataset consisting of household surveys from more than 1,200 households in southwestern Bangladesh to directly assess key variables that influence five types of migration in coastal communities: temporary migration within a village due to environmental stress, migration for education, migration for healthcare, migration for trade or commerce, and migration to visit relatives. This work demonstrates that these types of migration do have different drivers, which yields insights into the complex motivations that impact the decision to migrate. However, livelihood variables and individual aspirations were key for all investigated forms of migration. In the process, this work demonstrates that random forest models could be a powerful method for improving predictive accuracy of migration models to better inform migration policy and planning.
}}
{{blank line template}}

2018 CSDMS meeting-062

2018-03-20T15:11:29Z

Bestkb:

{{CSDMS meeting personal information template-2018
|CSDMS meeting first name=Kelsea
|CSDMS meeting last name=Best
|CSDMS meeting institute=Vanderbilt University
|CSDMS meeting city=Nashville
|CSDMS meeting country=United States
|CSDMS meeting state=Tennessee
|CSDMS meeting email address=kelsea.b.best@vanderbilt.edu
|CSDMS meeting phone=6154785299
}}
{{CSDMS meeting scholar and pre-meeting
|CSDMS meeting pre-conference=Bootcamp
|CSDMS meeting post-conference=No
}}
{{CSDMS meeting select clinics1 2018
|CSDMS_meeting_select_clinics1_2018=4) Sediment Experimentalist Network
}}
{{CSDMS meeting select clinics2 2018
|CSDMS_meeting_select_clinics2_2018=3) Data for natural hazards
}}
{{CSDMS meeting select clinics3 2018
|CSDMS_meeting_select_clinics3_2018=1) Downscaling climate models for regional applications
}}
{{CSDMS scholarships yes no
|CSDMS meeting scholarships=Yes
}}
{{CSDMS meeting abstract yes no 2018
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title temp2018
|CSDMS meeting abstract title=Machine Learning to Identify Drivers of Internal Migration in Coastal Bangladesh
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor country=United States
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Bishawjit
|CSDMS meeting coauthor last name abstract=Mallick
|CSDMS meeting coauthor institute / Organization=Technical University Dresden
|CSDMS meeting coauthor town-city=Dresden
|CSDMS meeting coauthor country=Germany
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Brooke
|CSDMS meeting coauthor last name abstract=Ackerly
|CSDMS meeting coauthor institute / Organization=Vanderbilt University Political Science
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Hiba
|CSDMS meeting coauthor last name abstract=Baroud
|CSDMS meeting coauthor institute / Organization=Vanderbilt University Civil and Environmental Engineering
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Jonathan
|CSDMS meeting coauthor last name abstract=Gilligan
|CSDMS meeting coauthor institute / Organization=Vanderbilt University Earth and Environmental Science
|CSDMS meeting coauthor town-city=Nashville
|CSDMS meeting coauthor country=United States
|State=Tennessee
}}
{{CSDMS meeting abstract template 2018
|CSDMS meeting abstract=As climate change and environmental variability increase pressure on vulnerable communities, migration is one possible adaptation strategy. However, the decision to migrate is complex, and environmental factors are rarely the sole drivers of that decision. Rather, the decision to migrate is often influenced by a combination of economic, social, political, and environmental pressures. This is especially true in coastal communities in Bangladesh, where temporary migration has long been a method of livelihood diversification, and researchers are trying to understand how environmental factors influence existing migration flows. This work addresses a gap in current research by beginning to investigate how different “push” and “pull” drivers of migration might have distinctive variables that contribute to the ultimate decision to move or stay. In this study, random forest classification models are applied to a dataset consisting of household surveys from more than 1,200 households in southwestern Bangladesh to directly assess key variables that influence five types of migration in coastal communities: temporary migration within a village due to environmental stress, migration for education, migration for healthcare, migration for trade or commerce, and migration to visit relatives. This work demonstrates that these types of migration do have different drivers, which yields insights into the complex motivations that impact the decision to migrate. However, livelihood variables and individual aspirations were key for all investigated forms of migration. In the process, this work demonstrates that random forest models could be a powerful method for improving predictive accuracy of migration models to better inform migration policy and planning.
}}
{{blank line template}}

2018 CSDMS meeting-062

2018-03-19T19:05:54Z

Bestkb:

2018 CSDMS meeting-062

2018-02-28T02:35:52Z

Bestkb: Created page with "{{CSDMS meeting personal information template-2018 |CSDMS meeting first name=Kelsea |CSDMS meeting last name=Best |CSDMS meeting institute=Vanderbilt University |CSDMS meeting..."

User:Bestkb

2018-02-28T02:33:43Z

Bestkb: Created page with "{{Signup information member |First name member=Kelsea |Last name member=Best |Institute member=Vanderbilt University |Department member=Earth and Environmental Science |Postal..."

{{Signup information member
|First name member=Kelsea
|Last name member=Best
|Institute member=Vanderbilt University
|Department member=Earth and Environmental Science
|Postal address 1 member=107 West End Place
|City member=Nashville
|Postal code member=37205
|Country member=United States
|State member=Tennessee
|Confirm email member=kelsea.b.best@vanderbilt.edu
|Working group member=Coastal Working Group, Coastal Vulnerability Initiative
|Emaillist group member=yes
}}