Comparing Inductive and Deductive Modeling of Land Use Decisions: Principles, a Model and an Illustration from the Philippines

Overmars, Koen P.; Groot, W.T. de; Huigen, Marco

doi:10.1007/s10745-006-9101-6

Cited by 47 publications

(46 citation statements)

References 25 publications

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“…For example, Rudel's sociological perspective on urban sprawl and tropical deforestation emphasizes the role of strategic actions by states and coalitions of interested parties in transforming landscapes, and identifies agents of change and the timing of transformative events (Rudel 2009). Overmars et al (2007a) formalize the explanation of human decision and action in the environmental field with the action-in-context (AiC) framework. The sociological and political economy approach can be especially powerful for land change research in combination with the economic approach (e.g., Walker et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Linking Land Change with Driving Forces and Actors: Four Conceptual Models

Hersperger¹,

Gennaio²,

Verburg³

et al. 2010

E&S

178

105

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ABSTRACT. Models in land change research are often chosen arbitrarily based on practical rather than theoretical considerations. More specifically, research on land change is often based on a research framework with three crucial elements -driving forces, actors, and land change -in an ad hoc and casespecific configuration. The lack of solid and widely applicable concepts about the conceptual link between these three elements can negatively affect individual research projects and hamper communication and generalizations beyond the individual project. We present four basic models for linking land change with driving forces and actors. These models are illustrated with examples from the research literature. Based on the main characteristics of the models and practical considerations, we propose guidelines for choosing among the four models for specific studies. More generally, we want to raise awareness that land change research is especially demanding with respect to conceptual backgrounds and that conceptual considerations will help improve the scientific quality of individual studies as well as their potential contribution towards generic theories of land change.

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Section: Introductionmentioning

confidence: 99%

Linking Land Change with Driving Forces and Actors: Four Conceptual Models

Hersperger¹,

Gennaio²,

Verburg³

et al. 2010

E&S

178

105

View full text Add to dashboard Cite

show abstract

“…The second activity, which we here call 'predictive modelling' (Shmueli 2010), instead presumes that a model describing the relationship of interest is already known, as are observed or estimated values of the relevant predictor variables, and therefore combines these to predict previously unknown values of the biodiversity response variable. The model used to make such predictions can be either an 'inductive model' derived through data analysis, in which case the activities of data analysis and prediction are integrally linked, or a 'deductive model' built directly from existing expert knowledge of the relationship between response and predictor variables (Corsi et al 2000;Overmars et al 2007;Tuanmu and Jetz 2014).…”

Section: Broad Roles Of Modelling In Biodiversity Assessmentmentioning

confidence: 99%

Biodiversity Modelling as Part of an Observation System

Ferrier

Jetz

Scharlemann

2016

The GEO Handbook on Biodiversity Observation Networks

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Modelling provides an effective means of integrating the complementary strengths of biodiversity data derived from in situ observation versus remote sensing. The use of modelling in biodiversity change observation, or monitoring, is just one of a number of roles that modelling can play in biodiversity assessment. These roles place different levels of emphasis on explanatory versus predictive modelling, and on modelling across space alone, versus across both space and time, either past-to-present or present-to-future. One of the most challenging, yet vitally important, applications of modelling to biodiversity monitoring involves mapping change in the distribution and retention of terrestrial biodiversity. Unlike many structural and functional attributes of ecosystems, most biological entities at the species and genetic levels of biodiversity cannot be readily detected through remote sensing. Estimating change in these levels of biodiversity across large spatial extents is therefore benefiting from advances in both species-level and community-level approaches to model-based integration of in situ biological observations and remotely sensed environmental data.

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“…A deductive (theory-driven) approach, on the other hand, is strong in explaining how and why land use will change (which is process resemblance), but is weak in spatial allocation of land-use change (Overmars, de Groot & Huigen, 2007). Overmars et al (2007) have shown that prediction of spatial location with deductive models is promising when compared to inductive models. For land-use change developments that follow a single dominant process it is expected that a deductive model will score only marginally less on spatial resemblance than an inductive model (see Fig.…”

Section: Lessons From Model Validationmentioning

confidence: 99%

Core Principles and Concepts in Land-Use Modelling: A Literature Review

Lantman¹,

Verburg

Bregt

et al. 2011

GeoJournal Library

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Simulation models of land use predict or describe land-use change over space and time. Recent overviews of land-use simulation models show an overwhelming amount of different types of models and applications (Heistermann, Muller & Ronneberger, 2006;Koomen, Stillwell, Bakema & Scholten, 2007;Verburg, Schot, Dijst & Veldkamp, 2004). Obviously, such models are simplifications of reality, but increasing computing power over the years has made it possible to incorporate more and more complexity in such models. This increased complexity, however, tends to obscure the theoretical foundations of land-use simulation models. This theoretical foundation relates to the core principles that are used to explain land-use change and the concepts that are applied to translate these principles into a functioning model of land-use change. An in-depth review of land-use change concepts, their underlying principles, applicability and translation into actual models does not exist to our knowledge. In this chapter we aim, therefore, to analyse the application of various theoretical concepts of land-use change that are used in modelling. This analysis is a first step to better understand the conceptual background of land-use change and the application of these concepts in computer simulation models. Based on this review we present some observations on important research issues in land-use modelling and suggest possible ways for further model improvement.Computer simulation models of land use are characterised in terms of core principles and applied concepts in Section 3.2. After this discussion of the theoretical background of current land-use models we dwell on two additional issues that are important for improving simulation models of land-use change. Lessons from model validation efforts are described in Section 3.3, while the importance of scale issues in land-use change is dealt with in Section 3.4. The main conclusion and discussion can be found in Section 3.5, whereas some themes for future research are suggested in Section 3.6. The latter suggestions are specifically focused on the Dutch LUMOS models.

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Comparing Inductive and Deductive Modeling of Land Use Decisions: Principles, a Model and an Illustration from the Philippines

Cited by 47 publications

References 25 publications

Linking Land Change with Driving Forces and Actors: Four Conceptual Models

Linking Land Change with Driving Forces and Actors: Four Conceptual Models

Biodiversity Modelling as Part of an Observation System

Core Principles and Concepts in Land-Use Modelling: A Literature Review

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