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Open AccessReview

From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

by 1,2, Cheng-Zhi Qin 1,2,3,*, 1,2,3,4,5, 1,2, 1,2 and 1,2,3
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 376;
Received: 16 July 2019 / Revised: 9 August 2019 / Accepted: 23 August 2019 / Published: 28 August 2019
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. View Full-Text
Keywords: geographic modeling; input data preparation; intelligent geoprocessing; service composition geographic modeling; input data preparation; intelligent geoprocessing; service composition
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MDPI and ACS Style

Hou, Z.-W.; Qin, C.-Z.; Zhu, A.-X.; Liang, P.; Wang, Y.-J.; Zhu, Y.-Q. From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling. ISPRS Int. J. Geo-Inf. 2019, 8, 376.

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