Agent-based models as laboratories for spatially explicit planning policies

Agent-based modeling and simulation (ABMS) has been a part of geospatial sciences for over a decade. Most research activities so far have concentrated on either extending complexity theory to spatially explicit phenomena, or on designing computational models and software tools. Only a few of these activities have focused on using ABMS for spatially explicit modeling of real-world policy scenarios. In this paper we present a realistic application of ABMS to simulating alternative futures for a small community in Washington State, USA. We develop an ABMS assessment benchmark that comprehensively covers diverse aspects of a good operational agent-based model. Using an ABMS software tool—CommunityViz Policy Simulator—we generate future development scenarios in the municipality of Chelan, WA based on the County and the City Comprehensive Growth Plans. Simulation results are compared with Washington State projections for growth-management planning. The indication of the highest probability locations of urban growth in the studied community is crucial for environmental and economic planning and decisionmaking. Endangered salmon protection and recreational and retirement influxes of people from the Puget Sound metropolitan area have a direct impact on future growth of the region. The bottom-up microsimulation allows for interposition of individual decisions and actions into forecasting option generation. The ‘heterogeneity, adaptability, and tractability’ benchmark is instrumental in evaluating CommunityViz Policy Simulator and outlining possible challenges for future development of applied agent-based models.

Editorial for the Special Issue on Modelling and Simulation of Human-Environment Interactions

At the core of the Anthropocene lies human influence on the environment [...

Editorial for the Special Issue on Modelling and Simulation of Human-Environment Interactions

At the core of the Anthropocene lies human influence on the environment [...

Four types of topological measurements of data space.

<p>Four types of topological measurements of data space.</p

Sustainable Urban Land Use Allocation With Spatial Optimization

Sustainable urban form has been recognized as one of the major concerns of the planning practice. Current land use pattern trends with low-density, single-use, leapfrog urban growth on city outskirts call for a more efficient land use development strategies balancing economy, environmental protection, and social equity. In this paper, we present a new multiobjective spatial optimization model, which minimizes the conflicting objectives of open space development, infill and redevelopment, land use neighborhood compatibility, and cost distance to already urbanized areas. Land use allocation is restrained by a density based design constraint. Based on a hypothetical problem of 400 raster cells, we generate multiple exact compromise solutions with varying importance of allocation objectives. We discuss further model refinements and propose evaluating the prescribed patterns with multi agent geosimulation

The Volatility of Data Space: Topology Oriented Sensitivity Analysis

<div><p>Despite the difference among specific methods, existing Sensitivity Analysis (SA) technologies are all value-based, that is, the uncertainties in the model input and output are quantified as changes of values. This paradigm provides only limited insight into the nature of models and the modeled systems. In addition to the value of data, a potentially richer information about the model lies in the topological difference between pre-model data space and post-model data space. This paper introduces an innovative SA method called Topology Oriented Sensitivity Analysis, which defines sensitivity as the volatility of data space. It extends SA into a deeper level that lies in the topology of data.</p></div