This work builds upon the research agenda for cartography and Big Data, specifically linking to data-based artworks in a geovisual analytics context. Art is a medium that potentially affords easily-assimilated, complex and flexible representations of data. The generation of such artworks using two fractal-based methods is initially described, supported by the example of New Zealand cities and city streets. On one hand, the use of head/tail breaks to extract "natural cities" and within them, "natural streets" captures emergent organic hierarchies based on size as well as producing shapes of aesthetic value. On the other hand, attribute and geometric parameters associated with spatial data can be used to build fractally-generated "objects of beauty" such as the Barnsley fern leaf (in effect becoming a multivariate symbol such as the Chernoff face). These are the building blocks of the artwork, which finally undergoes a style transfer process (using the convolutional neural network-based Google Deep Dream). Since the artwork is explicitly built on data, it would be possible to place this display in a linked and brushed geovisual analytics tool. This paper ends with a discussion of the possibilities of art-enabled geovisual analytics.