Along with the historical background, characters and works of a particular period, this paper systematically summarizes the theory, method and technology of statistical analysis of spatial data (SASD), and divides the SASD into five periods: (1) The early gestation (before the quantitative revolution): Including German location theory in the early 19th century, and the early studies in ecology, geology, etc. (2) Quantitative revolution (1950s-1960s): Including mainly the direct application of classical statistics and mathematics, theoretical exploration, the understanding of spatial autocorrelation, and the birth of geostatistics. (3) Spatial statistics (1970s-1980s): Including systematic research on spatial autocorrelation, and the analysis of spatial point data, lattice data, and spatial continuous data. (4) Maturation and diffusion (1990s-2000s): With the help of computer, geographical information system (GIS) and spatial data collection technology, an in-depth study was conducted on large spatial databases and the spatial heterogeneity. It includes spatial data mining (SDM), e.g., GeoMiner, and local spatial statistics such as local indicators of spatial autocorrelation (LISA), geographical weighted regression (GWR), spatial scan statistics, and GeoDetector. On the other hand, with the maturity and systematization of SASD, many works of summary and application in many fields have emerged naturally. (5) Spatio-temporal big data (2010s and beyond): This is the most important trend of SASD at present. In other words, since the quantitative revolution, SASD has produced important new methods or technologies every 20 years or so. In the current era of spatio-temporal big data, several research directions are worthy of attention, i.e., spatio-temporal point pattern and process, data streams analysis, network analysis, outlier detection, and uncertainty. In summary, after more than 60 years of development since quantitative revolution, SASD has become an effective study field, with mature methods, technology, and remarkable social benefits. In the present period of spatio-temporal big data, the development of SASD requires the joint efforts of computer scientists, statisticians, geologists and many others, for the new major innovation in technologies and methods to appear.