  Genomic Epidemiology (genEpi) is a branch of public health that uses many
different data types including tabular, network, genomic, and geographic, to
identify and contain outbreaks of deadly diseases. Due to the volume and
variety of data, it is challenging for genEpi domain experts to conduct data
reconnaissance; that is, have an overview of the data they have and make
assessments toward its quality, completeness, and suitability. We present an
algorithm for data reconnaissance through automatic visualization
recommendation, GEViTRec. Our approach handles a broad variety of dataset types
and automatically generates coordinated combinations of charts, in contrast to
existing systems that primarily focus on singleton visual encodings of tabular
datasets. We automatically detect linkages across multiple input datasets by
analyzing non-numeric attribute fields, creating an entity graph within which
we analyze and rank paths. For each high-ranking path, we specify chart
combinations with spatial and color alignments between shared fields, using a
gradual binding approach to transform initial partial specifications of
singleton charts to complete specifications that are aligned and oriented
consistently. A novel aspect of our approach is its combination of
domain-agnostic elements with domain-specific information that is captured
through a domain-specific visualization prevalence design space. Our
implementation is applied to both synthetic data and real data from an Ebola
outbreak. We compare GEViTRec's output to what previous visualization
recommendation systems would generate, and to manually crafted visualizations
used by practitioners. We conducted formative evaluations with ten genEpi
experts to assess the relevance and interpretability of our results.
