Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

Abstract

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

Publication
ISPRS International Journal of Geo-Information
Sergio Rey
Sergio Rey
Director and Professor

My research interests include geographic information science, spatial inequality dynamics, regional science, spatial econometrics, and spatial data science.

Elijah Knaap
Elijah Knaap
Associate Director & Senior Research Scientist

My research interests include urban inequality, neighborhood dynamics, housing markets, spatial data science, regional science, and housing & land policy.

Wei Kang
Wei Kang
Assistant Professor

Wei Kang is an Assistant Professor at the University of North Texas with interests in spatial data science, housing, urban & neighborhood change, spatial inequality, and sustainability.

Levi John Wolf
Levi John Wolf
Senior Lecturer/Associate Professor

I work in spatial data science, building new methods and software to learn new things about social and natural processes.