We propose the concept of situated spatial temporal data (spatio-temporal) analytic as a tool that support user’s ability to access data that of situational nature at location and time of the captured data (in-situ). The tool allows user to perform in-situ, trajectory data analytic tasks. Spatio-temporal visual analytic research to date has primarily focused on analytics in traditional computing paradigms and separated analytical processes of data representation and its context. With the advancement in Augmented Reality (AR), Head-Mounted Display (HMD), and sensor technologies, an emerging computing paradigm, i.e. situated analytics, enables access to spatio-temporal data nearly any time and place. Situated analytics involves inspecting the data in the context of the environment where it has been collected in. Although, situated spatio-temporal analytics has the potential to transform the way we engage with data comparing to traditional computing paradigms, it is necessary to explore the key design aspects of situated spatio-temporal analytics that enable in-situ analytic tasks and improve users’ analytic skills and experience. We empirically validated the potential of situated spatio-temporal analytics by comparing situated and non-situated analytics and found the situated group was more accurate compared to the non-situated group. We explored different user-generated spatio-temporal data visualization designs and interactions for an in-situ setting and proposed design recommendations to support in-situ data exploration and analytical activities. Based on design recommendations, we developed Situated Space-time Cube Analytics (SSCA) that utilizes two-dimensional (2D) and three-dimensional (3D) visualization, interactive data filtering, and embodied interaction. We conducted the SSCA prototype evaluation study to establish an understanding of in-situ data exploration activities, Visual Information Seeking Mantra (VISM) interaction taxonomy, and challenges in view visualization. From the SSCA evaluation study, we propose further design recommendations that reduces challenges found in the SSCA prototype and would improve the users’ exploration and interaction with the data. We used these design recommendations to develop Situated Spatio-temporal Multiple-Views Analytics (SSMA).