  Due to the surge of spatio-temporal data volume, the popularity of
location-based services and applications, and the importance of extracted
knowledge from spatio-temporal data to solve a wide range of real-world
problems, a plethora of research and development work has been done in the area
of spatial and spatio-temporal data analytics in the past decade. The main goal
of existing works was to develop algorithms and technologies to capture, store,
manage, analyze, and visualize spatial or spatio-temporal data. The researchers
have contributed either by adding spatio-temporal support with existing
systems, by developing a new system from scratch for processing spatio-temporal
data, or by implementing algorithms for mining spatio-temporal data. The
existing ecosystem of spatial and spatio-temporal data analytics can be
categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big
spatio-temporal data processing infrastructures, and (3) programming languages
and software tools for processing spatio-temporal data. Since existing surveys
mostly investigated big data infrastructures for processing spatial data, this
survey has explored the whole ecosystem of spatial and spatio-temporal
analytics along with an up-to-date review of big spatial data processing
systems. This survey also portrays the importance and future of spatial and
spatio-temporal data analytics.
