Modeling Land-Use Decision Behavior with Bayesian Belief Networks

Abstract

ABSTRACT. The ability to incorporate and manage the different drivers of land-use change in a modeling process is one of the key challenges because they are complex and are both quantitative and qualitative in nature. This paper uses Bayesian belief networks (BBN) to incorporate characteristics of land managers in the modeling process and to enhance our understanding of land-use change based on the limited and disparate sources of information. One of the two models based on spatial data represented land managers in the form of a quantitative variable, the area of individual holdings, whereas the other model included qualitative data from a survey of land managers. Random samples from the spatial data provided evidence of the relationship between the different variables, which I used to develop the BBN structure. The model was tested for four different posterior probability distributions, and results showed that the trained and learned models are better at predicting land use than the uniform and random models. The inference from the model demonstrated the constraints that biophysical characteristics impose on land managers; for older land managers without heirs, there is a higher probability of the land use being arable agriculture. The results show the benefits of incorporating a more complex notion of land managers in land-use models, and of using different empirical data sources in the modeling process. Future research should focus on incorporating more complex social processes into the modeling structure, as well as incorporating spatiotemporal dynamics in a BBN.

Encoding Dependence in Bayesian Causal Networks

“…When used to generate predictive maps, spatial dependency structure may not be well-represented by a simple aggregation of independent joint distributions (Grêt- Regamey and Straub, 2006). In real-world problems like land-use decision-making, representing interactions between neighboring regions properly and reliably is crucial (Aalders, 2008;Grêt-Regamey et al, 2013). Goodchild (1992) has formulated representation tests as criteria for spatially-explicit models that require spatial locations associated with BN network nodes to be explicitly considered in the modeling process and in any extended coupling of a model's attributes, such as in any meta-analysis or model ensembles.…”

Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector

“…The relative merits of probabilistic and deterministic modelling approaches have been discussed extensively in the literature, e.g. Aalders (2008), Clark (2005), Voorspools (2005), Wiggering et al (2006). The inherent uncertainties associated with GHG estimation suggest a probabilistic modelling approach is appropriate.…”

A Modeling Approach for Farmland Protection Zoning Considering Spatial Heterogeneity: A Case Study of E-Zhou City, China

“…This advantage also allows us to combine the dynamic change of farmland in the study area (prior knowledge) with suitability conditions of land use parcels (new evidence) when optimizing the spatial allocation of primary farmland. In addition, as a non-parametric statistical tool, BNs contain both qualitative and quantitative variables [37], making it an appropriate tool for optimizing farmland protection zones using both types of biophysical and socioeconomic data from different scales.…”