## 4 Discussion  
This research presents a model of LULC change that provides a plausible answer to our research question of where and how much new urban growth will occur in the urbanizing basins of Oregon. Our expectation was that this simple modeling method could be disseminated easily to stakeholders with at least some familiarity with the geospatial sciences and the characteristics of spatial data. It was our intention to reduce the number of decisions necessary to build and parameterize the model in order to produce maps relatively rapidly. Our final product is not as consistent as those produced through data gathered in a demanding iterative storyline process [15,18,21] or a well-designed role playing game [19,47]. Instead, we gathered qualitative data through an _ad hoc_ workshop and select interviews to develop a simple storyline, which is used elsewhere [48]. The maps can still prove useful as input in further scenario modeling. Considering uncertainty is inherently high in projecting future land conversions, the consistency of the maps is sufficient at the landscape scale. Other modeling approaches, like agent-based systems or cellular automata, may address complexity with more sophistication [49], but also produce a more complex message to explain, leading to additional time investment. At the workshop where results were presented, members unfamiliar with the process understood it quickly. We aimed for a simple, flexible model that facilitates communication about complex relationships among stakeholders with varying backgrounds [13]. Their comprehension led to questioning an underlying assumption used to produce the final map outputs. Unsurprisingly, it was an assumption based on our judgment: a similar proportion of urban land cover intensities in the future as in the current LULC map. This points out a problem our study shares with others. GIS modeling processes are difficult to fully parameterize with participatory data. This leads to the use of researcher discretion in modifying parameters or to assuming similar values across all scenarios [50]. We  
Figure 7: Example of grid cell raster mosaic phenomena depicting potential future land cover change. Exact arrangement of grid cells is determined randomly where there are more candidate pixels for transformation than are required. attempted to counteract this by relying on the opinion of at least some of the initial stakeholders for the validation of our results. This is similar to the \"social validation\" approach taken by others [47]. Their agreement that the maps are plausible at the landscape scale lends support to their viability as input in certain applications. Our consultation process left us with a narrative about increased urban growth and largely assumed other types of land conversions would be minimal to non-existent in our study area. Had various groups with differing perspectives been consulted over the course of a longer process, other factors addressing LULC change in rural areas could have potentially been identified. Even with the urban focus, other variables are likely important. Although deemed not important at the workshop, transportation networks could still be an important driver of land cover change [51]. Groundwater restriction zones, while having real consequences in rural lands, may not be a severe impediment to growth in urban land, since they are fed by surface supplies in our study area [52]. Additionally, stakeholders mentioned infrastructure access variables (e.g., water, sewer, gas lines) as having huge consequences on the location of new development, but data access has proven difficult. Using a few economic/demographic variables as parameters determining the amount of growth is simple and straightforward to communicate. This approach does not account for the dynamic nature of land supply and the spatially variable nature of demand. Our stakeholders pointed out that this is a major factor in urban planning. Our model assumes that as population grows, so will urban development.