Geographic Data Science

D ata science is an emerging area of work concerned with the task of extracting useful information and gaining insight from large data collections. Methods that scale to big data in terms of volume, variety, velocity, and veracity are of particular interest in data science. Data science methods and approaches address all stages of transition from data to knowledge and action, including data acquisition, cleaning and processing, information extraction, integration and representation, data analysis, and knowledge extraction and explanation. Visualization of data is essential for human understanding of the subject under analysis, analytical reasoning about it, and generating new knowledge. Interactive visual interfaces support the human cognitive processes by allowing analysts to look at a subject from different perspectives and at different scales and levels of detail, link diverse pieces of information, and direct and control the work of computational analytical tools. Therefore, visual analytics approaches play an important role in data science. More specifically, geographic data science deals with data that incorporates spatial and, often, temporal elements. In 2010, Gennady Andrienko and his colleagues defined the research agenda for spatiotemporal visual analytics, pointing out the unique properties of space and time that necessitate specific approaches to analyzing data with spatial and temporal components.1 Thus, spatial and temporal dependence (autocorrelation) enable interpolation and extrapolation, which can be used to fill gaps in incomplete data and derive plausible estimates beyond the areas and/or time periods represented in available data; the integration of information of different types and from different sources using references to common locations and/or time units; spatial and temporal inference; and many other operations. The effects of the spatial and temporal dependences are not absolute, however. Geographic space consists of places with diverse properties, and spatial dependence is weakened by this heterogeneity and by natural or artificial barriers that often exist between places. In 2017, a strategic paper by Alan MacEachren called for geo-visual analytics approaches for defining and characterizing places based on multiple heterogeneous and interconnected data types and sources. Time can be considered a linearly ordered set of moments or intervals as well as a system of recurring time cycles: daily, weekly, annual, and domainspecific cycles. Therefore, temporal dependence is more complex than just a correlation between close time moments along a timeline because it also includes correlations between corresponding positions in different time cycles. For example, there may be more similarity between the mornings of different days than between the morning and noon time of the same day. As with barriers in space, temporal dependence could also be interrupted by various events. Thus, we must properly take into account both the existence of spatial and temporal dependences and the possibility of distortion or interruption of these dependences.