Arika Ligmann-Zielinska

Spatial optimization as a generative technique for sustainable multiobjective land-use allocation

In this paper, we examine the applicability of spatial optimization as a generative modelling technique for sustainable land‐use allocation. Specifically, we test whether spatial optimization can be used to generate a number of compromise spatial alternatives that are both feasible and different from each other. We present a new spatial multiobjective optimization model, which encourages efficient utilization of urban space through infill development, compatibility of adjacent land uses, and defensible redevelopment. The model uses a density‐based design constraint developed by the authors. The constraint imposes a predefined level of consistent neighbourhood development to promote contiguity and compactness of urban areas. First, the model is tested on a hypothetical example. Further, we demonstrate a real‐world application of the model to land‐use planning in Chelan, a small environmental amenity town in the north‐central region of the State of Washington, USA. The results indicate that spatial optimization is a promising method for generating land‐use alternatives for further consideration in spatial decision‐making.

Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest

Land use/cover change (LUCC) modeling is an important approach to evaluating global biodiversity loss and is the topic of a wide range of research in ecology, geography and environmental social science. This paper reports on development and assessment of maps of change potential produced by two spatially explicit models and applied to a Tropical Deciduous Forest in western Mexico. The first model, DINAMICA EGO, uses the weights of evidence method which generates a map of change potential based on a set of explanatory variables and past trends involving some degree of expert knowledge. The second model, Land Change Modeler (LCM), is based upon neural networks. Both models were assessed through Relative Operating Characteristic and Difference in Potential. At the per transition level, we obtained better results using DINAMICA. However, when the per transition susceptibilities are combined to compose an overall change potential map, the map generated using LCM is more accurate because neural networks outputs are able to express the simultaneous change potential to various land cover types more adequately than individual probabilities obtained through the weights of evidence method. An analysis of the change potential obtained from both models, compared with observed deforestation and selected biodiversity indices (species richness, rarity, and biological value) showed that the prospective LUCC maps tended to identify locations with higher biodiversity levels as the most threatened areas as opposed to areas that had actually undergone deforestation. Overall however, the approximate assessment of biodiversity given by both models was more accurate than a random model.

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.

Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change

The growing body of knowledge on modeling land use systems points to epistemic uncertainty as one of the challenging obstacles in development and application of agent-based models (ABMs). To decrease outcome uncertainty, sensitivity analysis (SA) is performed as part of model verification and validation. Oftentimes, however, it is inadequately addressed, partly because of the lack of tools and techniques that focus on an explicit evaluation of ABM dynamics. The nonlinear processes, inherent in such models, necessitate longitudinal SA with time path investigation of input–output relationships of endogenous variables. In response to the outlined deficiencies, this study investigates the potential of time-dependent global sensitivity analysis (time-GSA) in examining the dynamics of outcome uncertainty of a simple ABM of land use change. Specifically, we apply first and total order sensitivity indices to decompose variance of output landscape fragmentation, apportioned to model inputs for multiple time steps and multiple realizations of the ABM. We focus the analysis on selected complex systems characteristics including preference uncertainty, path dependence, access to information, and magnitude of interactions and feedbacks. We conclude that the factor sensitivity measures vary significantly during model execution. Consequently, a static snapshot of ABM sensitivity, taken at the end of the simulation, is inadequate when deciding on factor prioritization and reduction. Assuming that ABM dynamics is a result of factor interaction, we observe a distinct time lag of nonlinearity, which unfolds after the formation of the seeds of development. Therefore, we argue for further application of time-GSA in ABM as one of the visual quantitative techniques contributing to evaluation of ABM nonlinearity.

Exploring normative scenarios of land use development decisions with an agent -based simulation laboratory

Abstract Suburbia and exurbia have an undeniable appeal to many urban dwellers. At the same time, they are characterized by an ineffective and fragmented residential patchwork of developed and undeveloped tracts. This research addresses a question of whether other arrangements of land, ameliorating the negative effects of current growth in the suburban fringe, are feasible from the perspectives of planning agencies and property developers. In order to answer this research question, the study employs two loosely coupled land use models: multiobjective land use allocation (MOLA) and an exploratory agent-based modeling (ABM) of residential development. The aligned modeling methodology has a number of advantages. Firstly, it combines top-down and bottom-up modeling. Such an approach is an attempt to represent society from two standpoints: institutions on one side (like zoning regulations of local planning agencies) and individual agents on the other (like developers). Secondly, the framework combines both static form (MOLA) and dynamic process (ABM). The MOLA model is equipped with mechanisms that encourage both compact and alternative residential land use arrangements. The outcomes of this model are used as zoning regulations in the ABM to examine the impact of regional-scale top-down urban growth plans on agent disutility which reflects the competitiveness of the local property market. Selected MOLA plans are further relaxed using different distance buffers. The findings point to a complex disutility–fragmentation relationship. Under the simulated planning situation, a potentially acceptable solution for planners and developers involves a relatively high compactness of development, which could satisfy agents’ overall disutility.

Impact of proximity-adjusted preferences on rank-order stability in geographical multicriteria decision analysis

This paper presents a new approach to deriving preferences assigned to evaluation criteria in geographical multicriteria decision analysis. In this approach, the preferences, expressed by numeric weights, are adjusted by distance measures derived from the explicit consideration of a locational structure. The structure is given by locations of decision options and high importance reference objects. The approach is demonstrated on the example of a house selection case study in San Diego, California. The results show that proximity-adjusted preferences for the evaluation criteria can alter significantly the rank order of decision options. Consequently, the explicit modeling of spatial preference variability may be needed in order to better account for decision-maker’s preferences.

A Framework for Sensitivity Analysis in Spatial Multiple Criteria Evaluation

The paper presents a framework for sensitivity analysis (SA) in spatial multiple criteria evaluation (S-MCE). The framework focuses on three aspects of S-MCE: spatiality, scope, and cardinality. Spatiality stresses the importance of spatial criteria and spatial weights that should be explicitly considered in GIS-based MCE. Scope relates to the extent of SA, ranging from local one-at-a-time criterion examination to global testing of interdependencies among the multiple criteria model components. Cardinality addresses the duality of motivation for performing SA, namely, single-user learning and group consensus building. The framework organizes the existing SA techniques according to spatiality and scope and can be used as a conceptual guide in selecting SA techniques fitting a task at hand.

The impact of risk-taking attitudes on a land use pattern: an agent-based model of residential development

One of the biggest challenges in modeling residential development involves identifying a theory-driven representation of human behavior. In this paper, an agent-based modeling experiment that tests different conceptions of risk-explicit decision making is reported upon. The decision rules used in the model are representative of risk-taking and risk-averse attitudes. The goal of this experiment is to assess spatial consequences of employing different attitude utility functions, which reflect peoples simplified psychological frames of reference for land use decision making. The experiments are performed on an artificial landscape, which is being developed by competing agents equipped with several land-related objectives and utilizing various configurations of risk-bearing attitudes. The subsequent land patterns are compared using selected spatial metrics. The results suggest that an attitude toward risk may significantly influence landscape patterns. Further investigation of psychological drivers that stand behind land development decisions is suggested. Such knowledge constitutes an important step toward improving our ability to model behaviorally grounded residential location decision making.

Methods for translating narrative scenarios into quantitative assessments of land use change

In the land use and land cover (LULC) literature, narrative scenarios are qualitative descriptions of plausible futures associated with a combination of socio-economic, policy, technological, and climate changes. LULC models are then often used to translate these narrative descriptions into quantitative characterizations of possible future societal and ecological impacts and conditions. To respect the intent of the underlying scenario descriptions, this process of translation needs to be thoughtful, transparent, and reproducible. This paper evaluates the current state of the art in scenario translation methods and outlines their relative advantages and disadvantages, as well as the respective roles of stakeholders and subject matter experts. We summarize our findings in the form of a decision matrix that can assist land use planners, scientists, and modelers in choosing a translation method appropriate to their situation. Assessments of land use and land cover change often employ narrative scenarios.Detailed evaluation of policy actions and outcomes requires quantitative model output.We review methods of translating narrative scenarios into model-based assessments.A summary table provides guidance for choosing a method suitable for the situation.

Spatially-explicit sensitivity analysis of an agent-based model of land use change

The complexity of land use and land cover (LULC) change models is often attributed to spatial heterogeneity of the phenomena they try to emulate. The associated outcome uncertainty stems from a combination of model unknowns. Contrarily to the widely shared consensus on the importance of evaluating outcome uncertainty, little attention has been given to the role a well-structured spatially explicit sensitivity analysis (SSA) of LULC models can play in corroborating model results. In this article, I propose a methodology for SSA that employs sensitivity indices (SIs), which decompose outcome uncertainty and allocate it to various combinations of inputs. Using an agent-based model of residential development, I explore the utility of the methodology in explaining the uncertainty of simulated land use change. Model sensitivity is analyzed using two approaches. The first is spatially inexplicit in that it applies SI to scalar outputs, where outcome land use maps are lumped into spatial statistics. The second approach, which is spatially explicit, employs the maps directly in SI calculations. It generates sensitivity maps that allow for identifying regions of factor influence, that is, areas where a particular input contributes most to the clusters of residential development uncertainty. I demonstrate that these two approaches are complementary, but at the same time can lead to different decisions regarding input factor prioritization.