Final answer:
Spatial big data differs from traditional spatial data in scale, temporal resolution, storage, and processing needs, with big data requiring advanced systems for analysis and management.
Step-by-step explanation:
The question concerns the differences between traditional spatial data and spatial big data, particularly in terms of Scale of data, Temporal resolution, Geographic coverage, and Data storage requirements. Traditional spatial data often involves smaller datasets that are manageable with conventional GIS (Geographic Information Systems) software, with sufficient temporal resolution for specific, localized studies. In contrast, spatial big data encompasses extremely large datasets, often collected at high velocities and representing extensive geographic coverages that require advanced computer algorithms, significant storage capacity, and processing power to manage.
Geographers and GIS specialists use spatial statistics to analyze and interpret these data, with a focus on understanding both spatial and temporal patterns and relationships. The field has evolved with the advent of big data, demanding sophisticated methods to address the challenges of scale and complexity. As such, spatial big data is often characterized by the need for supercomputing resources, specialized software, and techniques to handle the high volume, variety, and velocity of data.