Simple Agents, Complex Emergent City: Agent-Based Modeling of Intraurban Migration - Computational Approaches for Urban Environments - page 127

from parcel data. The ABM treats residential choice as primarily influenced
by distance and direction between movers and vacancies, updating the classic
intervening opportunity model with individual agents acting on real-world evidence.
Agent-based modeling has garnered a lot of attention for spatially explicit modeling
of urbanization and land use more broadly (Gimblett 2002 ;Parkeretal. 2003a ;
Irwin et al. 2009 ; see Batty 2008 ; O'Sullivan 2008 ). An agent-based model is a
computational system composed of semiautonomous software programs (termed
agents) that can represent entities ranging from atoms through households to cities.
Each agent in the system has its own resources, local context, knowledge, behavioral
rules, and goals. Importantly, agents interact with each other and their larger envi-
ronment. ABMs are increasingly used to understand urban issues such as growth and
sprawl, land use and transportation, and racial segregation and residential structure
because they explain how simple microbehavior leads to complex macro patterns
and processes (Torrens 2006 ; Fossett 2006 ; Salvini and Miller 2005 ; Miller et al.
2004 ). Using an ABM is important given the intractability of deriving analytical
solutions to a system of equations defined by real-world spatial data on thousands
of individuals outside of a simplifying mathematical approach or use of a statistical
model (Krzanowski and Raper 2001 ;Kwasnicki 1999 ). These approaches are
commonly used in part because they are powerful, but an ABM, by instantiating in
agents the underlying mathematical formulation of intervening opportunity theory,
allows exploration of the theory in a real-world context. Marrying mathematical
and statistical formalism with agent-based modeling is increasingly seen as a way
forward for theoretically derived and empirically tested models of human behavior
(Irwin et al. 2009 ).
The model developed here joins other related efforts that use ABM to understand
urban processes. There is a fast-growing body of research that applies this approach
to construct models centered on representing the decision-making processes of
individuals and their resultant mobility (Haase and Schwarz 2009 ; Torrens 2012 ;
Kennedy 2012 ;Parkeretal. 2003b ; Macy and Willer 2002 ;An 2012 ; Matthews
et al. 2007 ; O'Sullivan et al. 2012 ). These models vary broadly in their degree
of specificity and extent to which they are conceptually stylized models. Some
attempt to simulate classical urban residential processes and patterns, such as
monocentric cities and residential segregation (Benenson and Torrens 2004 ; Crooks
et al. 2008 ), with highly generalized and stylized models. Others build on these
simpler models via greater empirical specification, seeking to simulate urban
residential processes including gentrification (Jackson et al. 2008 ; Diappi and
Bolchi 2008 ; O'Sullivan 2002 ; Torrens and Nara 2007 ) and urban sprawl (Brown
et al. 2008 ; Fernandez et al. 2005 ; Loibl and Toetzer 2003 ). Other models go even
further by offering intricately detailed and data-rich explorations of urban processes
underlying complex residential choices within the urban sphere (Birkin and Wu
2012 ; Zaidi and Rake 2001 ).
This model is implemented in a spatially explicit agent-based model of land
change (Manson and Evans 2007 ). Agents are software objects, or semiautonomous
programs that have their own properties and routines, that exist in an environment
composed of raster and vector format layers. Importantly, agents update the