Recent Advancements in Geovisualization, with a Case Study on Chinese Religions
Producing high-quality, map-based displays for economic, medical, educational, or any other kind of statistical data with geographic covariates has always been challenging. Either it was necessary to have access to high-end software or one had to do a lot
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Abstract Producing high-quality, map-based displays for economic, medical, educational, or any other kind of statistical data with geographic covariates has always been challenging. Either it was necessary to have access to high-end software or one had to do a lot of detailed programming. Recently, R software for linked micromap (LM) plots has been enhanced to handle any available shapefiles from Geographic Information Systems (GIS). Also, enhancements have been made that allow for a fast overlay of various statistical graphs on Google maps. In this article, we provide an overview of the necessary steps to produce such graphs in R, starting with GISbased data and shapefiles and ending with the resulting graphs in R. We will use data from a study on Chinese religions and society (provided by the China Data Center at the University of Michigan) as a case study for these graphical methods.
1 Introduction Geographic visualization, short geovisualization, plays an important role in exploring and mining information from today’s huge collections of data with a geographic, that is, spatial context (MacEachren et al., 2004, 1999). Naturally, maps and, even more, interactive maps are an essential tool to extract, visualize, and comprehend complex spatial information (Andrienko and Andrienko, 1999; Andrienko et al., 2001; Roth, 2013). Geovisualization is closely related to exploratory spatial data analysis (ESDA), discussed in detail in Anselin (1994), Bivand (2010), and Symanzik (2014). In fact, Bivand (2010) indicates that “geovisualization is not separate from exploratory spatial data analysis, but rather constitutes the backbone of ESDA, joining up the large range of techniques proposed for examining spatial data in a shared and easily comprehended visualization framework.”
J. Symanzik () • X. Dai Department of Mathematics and Statistics, Utah State University, Logan, UT, USA e-mail: [email protected]; [email protected] S. Bao • M. Shui • B. She China Data Center, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected]; [email protected]; [email protected] © Springer International Publishing Switzerland 2016 Z. Jin et al. (eds.), New Developments in Statistical Modeling, Inference and Application, ICSA Book Series in Statistics, DOI 10.1007/978-3-319-42571-9_8
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In this article, we will discuss advances in geovisualization that make use of two types of maps, that is, linked micromap (LM) plots and overlays on Google maps. The data of interest are Chinese religions at the provincial level, introduced in Sect. 2. In Sect. 3, we discuss LM plots and demonstrate how those can be used for the effective visualization and exploration of the Chinese religions data. In the following Sect. 4, we describe how LM plots can be created interactively in the online Religion Explorer software. We briefly describe in Sect. 5 how Google maps can be used as an alternative way to visualize the Chinese religions data. This article is concluded by a brief discussion and outlook in Se
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