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全文：没有最好的模型！通过多模式集合解决土地利用情景建模的局限性
Source: tandfonline.com
Article summary:
1. Models are widely used to predict and understand future land use changes, but have some important disadvantages.
2. Two main approaches exist for calculating transition potential: statistical tests or machine learning algorithms.
3. A new approach is proposed which takes a broader view of what constitutes a good model by evaluating and sensitivity-testing a wide range of alternatives.
Article analysis:
The article provides an overview of the current state of land use modelling, as well as proposing a new approach to determining transition potential. The article is generally reliable in its description of existing models and their limitations, providing citations to relevant research papers that support its claims. However, there are some areas where the article could be improved in terms of trustworthiness and reliability.
First, the article does not provide any evidence for its claim that manual trial-and-error based calibration can be “very tedious” or “nearly impossible” due to combinatorial problems. This claim should be supported with evidence from relevant studies or experiments conducted by other researchers in order to increase its credibility.
Second, while the article does provide citations for its claims about the importance of proximity to existing urban centres, accessibility through transport networks, and physical suitability of land for development, it does not explore any counterarguments or alternative perspectives on these topics. For example, it does not consider how technological advances such as autonomous vehicles may affect accessibility through transport networks in the future.
Finally, while the article does present a new approach to determining transition potential using R software and SIMLANDER modelling framework, it does not discuss any possible risks associated with this approach or how it might be improved upon in future research. This would help readers better understand both the strengths and weaknesses of this approach before deciding whether or not to adopt it in their own work.