Modeling landowner interactions and development patterns at the urban fringe
Authored by Jennifer Koch, Monica A Dorning, Berkel Derek B Van, Scott M Beck, Georgina M Sanchez, Ashwin Shashidharan, Lindsey S Smart, Qiang Zhang, Jordan W Smith, Ross K Meentemeyer
Date Published: 2019
DOI: 10.1016/j.landurbplan.2018.09.023
Sponsors:
United States Geological Survey (USGS)
United States National Science Foundation (NSF)
US Fish and Wildlife Service
Platforms:
No platforms listed
Model Documentation:
ODD
Model Code URLs:
Model code not found
Abstract
Population growth and unrestricted development policies are driving
low-density urbanization and fragmentation of peri-urban landscapes
across North America. While private individuals own most undeveloped
land, little is known about how their decision-making processes shape
landscape-scale patterns of urbanization over time. We introduce a
hybrid agent-based modeling (ABM) cellular automata (CA) modeling
approach, developed for analyzing dynamic feedbacks between landowners'
decisions to sell their land for development, and resulting patterns of
landscape fragmentation. Our modeling approach builds on existing
conceptual frameworks in land systems modeling by integrating an ABM
into an established grid-based land-change model FUTURES. The
decision-making process within the ABM involves landowner agents whose
decision to sell their land to developers is a function of heterogeneous
preferences and peer-influences (i.e., spatial neighborhood
relationships). Simulating landowners' decision to sell allows an
operational link between the ABM and the CA module. To test our hybrid
ABM-CA approach, we used empirical data for a rapidly growing region in
North Carolina for parameterization. We conducted a sensitivity analysis
focusing on the two most relevant parameters spatial actor distribution
and peer-influence intensity and evaluated the dynamic behavior of the
model simulations. The simulation results indicate different
peer-influence intensities lead to variable landscape fragmentation
patterns, suggesting patterns of spatial interaction among landowners
indirectly affect landscape-scale patterns of urbanization and the
fragmentation of undeveloped forest and farmland.
Tags
Agent-based model
Social networks
Agent-based modeling
Simulations
Land-use
Integrated modeling
Urbanization
growth
Scenarios
Decisions
Cover
Homo-economicus
Land systems science
Willingness to sell
Scenario simulations