Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model

Overmars, Koen P.; Verburg, Peter H.; Veldkamp, A.

doi:10.1016/j.landusepol.2005.09.008

Cited by 137 publications

(86 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This relates to the common practice of ignoring the diversity of decision-making strategies in regional land use models (e.g. Clarke et al 1997;Overmars et al 2007;Pijanowski et al 2002). In the conceptual framework proposed in this paper, the combination of individual agents, an agent typology and a probabilistic decision-making approach allow us to simplify and include the inherent variability of the population and decision-making in rural regions.…”

Section: Discussionmentioning

confidence: 99%

An agent-based approach to model land-use change at a regional scale

et al. 2009

Self Cite

View full text Add to dashboard Cite

Land-use/cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. A common approach to analyse and simulate LUCC as the result of individual decisions is agent-based modelling (ABM). However, ABM is often applied to simulate processes at local scales, while its application in regional studies is limited. This paper describes first a conceptual framework for ABM to analyse and explore regional LUCC processes. Second, the conceptual framework is represented by combining different concepts including agent typologies, farm trajectories and probabilistic decision-making processes. Finally, the framework is illustrated through a case study in the Netherlands, where processes of farm cessation, farm expansion and farm diversification are shaping the structure of the landscape. The framework is a generic, straightforward approach to analyse and explore regional LUCC with an explicit link to empirical approaches for parameterization of ABM.

show abstract

Section: Discussionmentioning

confidence: 99%

An agent-based approach to model land-use change at a regional scale

et al. 2009

Self Cite

View full text Add to dashboard Cite

show abstract

“…The product is a map containing values indicating the propensity for change. Numerous variations exist, for example in the amount of change that can be estimated by means of an external model [51], while the map of potential change can be obtained through methods enabling the incorporation of expert knowledge in order to depart from modeling strictly reproducing the patterns of change observed during the previous period [52,53].…”

Section: Training or Calibration Of The Modelmentioning

confidence: 99%

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Chang-Martínez

Mas

Valle

et al. 2015

IJGI

View full text Add to dashboard Cite

Spatially-explicit land cover land use change (LCLUC) models are becoming increasingly useful tools for historians and archaeologists. Such kinds of models have been developed and used by geographers, ecologists and land managers over the last few decades to carry out prospective scenarios. In this paper, we review historical models to compare them with prospective models, with the assumption that the ample experience gained in the development of models of prospective simulation can benefit the development of models having as their objective the simulation of changes that happened in the past. The review is divided into three sections: in the first section, we explain the functioning of contemporary LCLUC models; in the second section, we analyze historical LCLUC models; in the third section, we compare the former two types of models, and finally, we discuss the contributions to historical LCLUC models of contemporary LCLUC models.

show abstract

“…Model performance may differ when other regions with different development constraints and pressures is considered. Regional-scale development patterns are the physical manifestation of interacting socio-political decisions [81], environmental driving factors [82], and agent-based decision making [79]. Despite considerable progress, these processes have proven difficult to distill into a computer algorithm.…”

Section: Discussionmentioning

confidence: 99%

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

Pickard

Gray

Meentemeyer

2017

Land

View full text Add to dashboard Cite

Abstract:The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher's discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model's projections.

show abstract

Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model

Cited by 137 publications

References 26 publications

An agent-based approach to model land-use change at a regional scale

An agent-based approach to model land-use change at a regional scale

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

Contact Info

Product

Resources

About