Simple Urban Simulation Atop Complicated Models: Multi-Scale Equation-Free Computing of Sprawl Using Geographic Automata

Abstract

Abstract:Reconciling competing desires to build urban models that can be simple and complicated is something of a grand challenge for urban simulation. It also prompts difficulties in many urban policy situations, such as urban sprawl, where simple, actionable ideas may need to be considered in the context of the messily complex and complicated urban processes and phenomena that work within cities. In this paper, we present a novel architecture for achieving both simple and complicated realizations of urban sprawl in simulation. Fine-scale simulations of sprawl geography are run using geographic automata to represent the geographical drivers of sprawl in intricate detail and over fine resolutions of space and time. We use Equation-Free computing to deploy population as a coarse observable of sprawl, which can be leveraged to run automata-based models as short-burst experiments within a meta-simulation framework.

Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata

“…The distinguishing characteristic of CA is that large‐scale, complex spatial patterns emerge from its simple small‐scale transition rules, which is consistent with complex theories (Clarke & Gaydos, ; Batty & Torrens, ). For decades, CA models have been used to simulate LUCCs at various local or regional scales (Dietzel & Clarke, ; Li & Yeh, ; Lin et al, ; Liu et al, ; Torrens et al, ), such as in the regional‐scale GEOMOD model (Estoque & Murayama, ), the continental‐scale DynaCLUE model (Verburg & Overmars, ), and the continental‐ to global‐scale LandSHIFT model (Schaldach et al, ). CA models can also be used to simulate global‐scale LUCCs based on land area demand constraints predicted by global assessment models.…”

Coupling Land Use Change Modeling with Climate Projections to Estimate Seasonal Variability in Runoff from an Urbanizing Catchment Near Cincinnati, Ohio

“…The choice of factors was determined based on the literature review [19,20,22,25,26] and statistical analysis. For the purposes of this analysis, factor variables included: (1) proximity to roads; (2) slopes below 25 percent (no restrictions based on the slope factor were applied to transitions to cropland, woodland, barren land and wetlands); (3) proximity to streams and water bodies; (4) proximity to protected natural areas and open space; and (5) proximity to growth areas, defined as areas that experienced substantial growth in population and employment between 1990 and 2000.…”

“…Alterations of a watershed's hydrological characteristics due to urban development can significantly impact peak discharges, volume, and frequency of floods [13,16]. Over the past two decades, cellular automata (CA) models of urban simulation found numerous applications in practically every research area in the field of urban planning [18,19]. Researchers focus on the CA models in their explorations of the urban space because, for the most part, CA models are capable of conducting a number of previously intractable research tasks, such as modeling of spatial dynamics, simulation of micro-levels interactions, and capacity to predict emergent patterns [18,20].…”

“…Researchers focus on the CA models in their explorations of the urban space because, for the most part, CA models are capable of conducting a number of previously intractable research tasks, such as modeling of spatial dynamics, simulation of micro-levels interactions, and capacity to predict emergent patterns [18,20]. Torrent et al [19] developed a meta-simulation framework capable of running simulations incorporating both coarse scale (e.g., population growth) and fine scale (e.g., space and time) sprawl driving factors. Batty [20] explores a host of urban simulations models conceptually rooted in the complexity theory demonstrating their applicability to bottom-up stochastic temporal dynamics and phenomena associated with the processes of urban spatial evolution.…”

Global and Local Reduced Models for Interacting, Heterogeneous Agents

“…The interactions may be deterministic or stochastic in nature and could be based on simple heuristics or detailed rules. The versatility of agent-based models has contributed to their broad appeal across disciplines such as disease outbreaks and response [1][2][3][4] , city parking 5 , the modeling of urban sprawl 6,7 , the assessment of the impacts of shared autonomous vehicles 8,9 , finance 10,11 , social interactions 12,13 , and the evaluation of changes in flood risk caused by climate change 14 to name a few. This paper discusses alternative, data-driven ways to obtain reduced models of the agent behavior.…”