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ISPRS Int. J. Geo-Inf. 2016, 5(9), 151; doi:10.3390/ijgi5090151

Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

1
Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
2
Institute of Construction and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
3
Korea Land and Geospatial Informatix Corporation, 120, Giji-ro, Jeonju-si 54870, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Mahmoud R. Delavar and Wolfgang Kainz
Received: 13 June 2016 / Revised: 17 August 2016 / Accepted: 19 August 2016 / Published: 25 August 2016
(This article belongs to the Special Issue Location-Based Services)
View Full-Text   |   Download PDF [3053 KB, uploaded 25 August 2016]   |  

Abstract

Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method. View Full-Text
Keywords: clustering; LBSN; Twitter; MAUP; Moran’s I; Töpfer’s radical law clustering; LBSN; Twitter; MAUP; Moran’s I; Töpfer’s radical law
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, Y.; Kwon, P.; Yu, K.; Park, W. Method for Determining Appropriate Clustering Criteria of Location-Sensing Data. ISPRS Int. J. Geo-Inf. 2016, 5, 151.

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