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


Autoři: Lei Chen aff001;  Yong Gao aff001;  Di Zhu aff001;  Yihong Yuan aff002;  Yu Liu aff001
Působiště autorů: Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, China aff001;  Department of Geography, Texas State University, San Marcos, Texas, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225139

Souhrn

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.

Klíčová slova:

Cell cycle and cell division – Data processing – Human mobility – Polynomials – Simulation and modeling – Social media – Urban areas – Remote sensing imagery


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Článek vyšel v časopise

PLOS One


2019 Číslo 11