Spatial Data Visualization with Linked Micromaps – An Example of Chinese Dependency Ratios

Authors: Shiyang Ruan*, George Mason University, David Wong, George Mason University
Topics: Geographic Information Science and Systems, Population Geography, China
Keywords: Geo-Visualization, Dependency Ratio, Micromaps
Session Type: Paper
Day: 4/14/2018
Start / End Time: 2:00 PM / 3:40 PM
Room: Grand Chenier, Sheraton, 5th Floor
Presentation File: No File Uploaded


Over the past several decays, spatial data have been increasing in volume and complexity. (Geo)visualizing these data efficiently and effectively is always a challenge. Many visualization tools for spatial data have been developed, and the Linked Micromaps have not been widely used for geo-visualization. Different from choropleth maps, Linked Micromaps reveal geographic patterns by sorting the statistics and linking the sorted values with a series of low-resolution maps.

The Linked Micromaps framework has been used most often to visualize single variables. Using the framework for multiple-variable setting has not be explored thoroughly. In this particular application of the framework, we use a particularly type of data which may be loosely labeled as component variables. Within a measurement scheme, these component variables are related to each other and they together form an aggregated variable. The concern is to visualize both the aggregated and component variables. Examples of this type of measurement schemes more relevant to geographical research are plenty: total GDP and components of GDP from different economic sectors of different states or provinces of a country; total crime incidences and different types of crimes across different parts of a city. This research modified the Micromaps framework to visualize this type of data schemes. The aggregated variable is Total Dependency Ratio, which is composed of the Youth Dependency Ratio and the Elderly Dependency Ratio. The data used in illustration are from the 2010 Chinese population census. The intent is to reveal the relationships among the three variables in a spatial context.

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