In this paper, Maceachren and Kraak (2001) concluded the research challenges in geovisualization. The first thing that catches my eye is the cartograph cube, which defines visualization in terms of map use. The authors argue that visualization is not the same thing as cartography. Visualization, same as communication is not just about making maps, but is also using them.
While the authors highlight the importance of scale issue, integrating heterogeneous data also present a challenge for geovisualization, because of the different categorization schemes and complex semantics that are applied in data creation. Similar conditions or entities are often represented with different attributes or measured with varying measurement systems. Therefore, the heterogeneity raises questions when we use data from different data producers: How to assess heterogeneity? How to make decisions about whether data may be combined? How to integrate multiple data sets if the same semantics are used differently?
Further, the emergence of geospatial big data, such as millions of conversations via location-enabled social media, stretches the limits of what and how we map. The potential of using geospatial big data as a data source for geovisualization requires developing appropriate methodologies. While this paper mainly discusses geovisualization of quantitative data. I am also curious about how to visualize qualitative spatial data. (QZ)