A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Abstract

Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.

Keywords:

• scale
• spatial autocorrelation
• user-generated data
• social media
• Twitter

Acknowledgements

Many fruitful discussions and valuable thoughts came to our minds in the course of discussions with colleagues. Therefore, we’d like to thank all staff and research affiliates from the GIScience research group at Heidelberg University and the Harvard Center for Geographic Analysis. You really helped a lot by steering us into the right direction. Special thanks go to Andreas Reimer (GIScience Heidelberg) for his valuable inspirations and for his help in creating appealing maps. We also thank Shih-Pei Chen (Max Planck Institute for the History of Science, Berlin) for cross-reading this article. Furthermore, this work has partially been funded by the Klaus Tschira Stiftung gGmbH as well as by the Graduate Funding Programme of the state of Baden-Württemberg.