Spatial Big Data
Definition
Spatial big data refers to geolocated datasets that are massive in volume, velocity, and variety—satellite image streams, IoT sensors, mobile traces, LIDAR point clouds, and nationwide vector tiles. Handling them requires distributed storage, spatial indexing, streaming pipelines, and parallel analytics. Challenges include data quality, privacy, governance, and costs.
Application
National mapping agencies manage petabyte imagery archives; cities ingest telemetry from fleets; commerce analyzes foot traffic; and environmental science processes global climate indicators. Platforms use cloud object stores, data lakes, and tiling schemes to serve many users.
FAQ
Why are traditional row‑store databases insufficient by themselves?
They struggle with petabyte rasters and rapid writes. Object stores and purpose‑built engines handle parallel reads, while columnar formats and spatial tiles speed queries.
What indexing strategies enable fast spatial lookups at scale?
Space‑filling curves (Hilbert/Quadkeys), R‑trees on partitions, and time‑indexed shards support bounding‑box and time queries efficiently.
How does privacy risk grow with scale and linkage?
Combining datasets can re‑identify individuals even after aggregation. Governance must enforce minimization, consent, and differential privacy where appropriate.
What cost controls keep big spatial programs sustainable?
Lifecycle policies, caching hot tiles, spot compute for batch jobs, and deleting intermediates after audits keep storage and compute bills in check.