A similarity-based automatic data recommendation approach for geographic models

ABSTRACT

The complexity of geographic modelling is increasing; hence, preparing data to drive geographic models is becoming a time-consuming and difficult task that may significantly hinder the application of such models. Meanwhile, a huge number of data sets have been shared and have become publicly accessible through the Internet. This study presents a data similarity-based approach to automatically recommend available data sets to fulfil the data requirements of geographic models. Unified description factors are adopted to provide a consistent description of public data sets and input data requirements of geographic models. Five elementary data similarities between them, specifically content, spatial coverage, temporal coverage, spatial precision, and temporal granularity similarities, are calculated. An overall similarity is estimated from aggregating the elementary data similarities. Thereafter, the candidate data for running the models are recommended in the order of overall data similarity. As a case study, the approach has been applied to recommend data from the China National Data Sharing Platform of Earth System Science to drive the population spatialization model (PSM). The approach has successfully recommended the most related data sets to run PSM. The result also suggests that the data recommendation approach can facilitate the intelligent identification of geographic data and the building of links between the open data sets.

KEYWORDS:

• Geographic model
• public data
• automatic recommendation
• data similarity
• data sharing

Acknowledgements

This work was supported by the Natural Science Foundation of China (No. 41371381, 41431177 and 41631177), the National Special Program on Basic Works for Science and Technology of China (No. 2013FY110900), the Public and Basic Geological Project of Guizhou Province, China (No. [2014]23), the National Basic Research Program of China (No. 2015CB954102), the Natural Science Research Program of Jiangsu (No.14KJA170001) and the National Key Technology Innovation Project for Water Pollution Control and Remediation (No.2013ZX07103006). The authors are grateful for the support from the China Scholarship Council. We would like to thank the editors and the anonymous reviewers for very helpful suggestions that improved the article.

Disclosure statement

No potential conflict of interest was reported by the authors.​​​

Additional information

Funding

This work was supported by the Natural Science Foundation of China (number 41371381, 41431177 and 41631177); the National Special​​​ Program on Basic Works for Science and Technology of China (number 2013FY110900); the Public and Basic Geological Project of Guizhou Province, China (number [2014]23); the National Basic Research Program of China (number 2015CB954102); the Natural Science Research Program of Jiangsu (number 14KJA170001) and the National Key Technology Innovation Project for Water Pollution Control and Remediation (number 2013ZX07103006). The authors are grateful for the support from the China Scholarship Council.​​​