Quantifying the scale effect in geospatial big data using semi-variograms

Lei Chen, Yong Gao, Di Zhu, Yihong Yuan, Yu Liu

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

The scale effect is an important research topic in the field of geography. When aggregating individual-level data into areal units, encountering the scale problem is inevitable. This problem is more substantial when mining collective patterns from big geo-data due to the characteristics of extensive spatial data. Although multi-scale models were constructed to mitigate this issue, most studies still arbitrarily choose a single scale to extract spatial patterns. In this research, we introduce the nugget-sill ratio (NSR) derived from semi-variograms as an indicator to extract the optimal scale. We conducted two simulated experiments to demonstrate the feasibility of this method. Our results showed that the optimal scale is negatively correlated with spatial point density, but positively correlated with the degree of dispersion in a point pattern. We also applied the proposed method to a case study using Weibo check-in data from Beijing, Shanghai, Chengdu, and Wuhan. Our study provides a new perspective to measure the spatial heterogeneity of big geo-data and selects an optimal spatial scale for big data analytics.

Original languageEnglish (US)
Article numbere0225139
JournalPloS one
Volume14
Issue number11
DOIs
StatePublished - Nov 1 2019
Externally publishedYes

Bibliographical note

Funding Information:
This study is funded by the National Science Foundation of China (#41971331 and #41625003). YG and YL received these two fundings respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank the anonymous reviewers and the academic editor for their constructive comments, which greatly improved the content and clarity of this paper. Ximeng Cheng and Xin Yao helped with the research design and edited an early draft of this paper. Mr. Lei Zhang helped improving the grammar and style of this work.

Publisher Copyright:
© 2019 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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