Abstract
One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.
Highlights
Geographic modeling is a fundamental methodology for understanding, simulating, and predicting geographic phenomena and processes within a certain context [1,2,3,4]
Input data preparation in geographic modeling is challenging due to the input data needed by geographic models often being obtained from distributed data sources and being syntactically and semantically heterogeneous [1,5,6,13,14]
They need to select and compose a set of applicable and compatible data pre-processing algorithms and their implementations, such as web services, to prepare needed input data. These data preparation steps often contain many operations that are repeated with a traditional manual method for most cases of geographic modeling. This means that considerable time, expertise, and effort are required to set up a new model application, which restricts the reproducibility of previous studies, for those non-expert stakeholders [5,6,13,14,15,16]
Summary
Geographic modeling is a fundamental methodology for understanding, simulating, and predicting geographic phenomena and processes within a certain context [1,2,3,4]. They need to select and compose a set of applicable and compatible data pre-processing algorithms and their implementations, such as web services, to prepare needed input data These data preparation steps often contain many operations that are repeated with a traditional manual method for most cases of geographic modeling. Based on artificial intelligence (AI) technologies such as ontology, logical reasoning, case-based reasoning (CBR), and AI planning, these methods aim to provide an automatic and intelligent way to discover data and the necessary pre-processing applications (e.g., web services) for geographic models [32,33,34,35,36,37,38] Using these methods, the time, expertise, and prior experience requirements for preparing model input data can be reduced significantly to ensure the efficiency and effectiveness of geographic modeling.
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