Spatial data or spatio-temporal data is graphic and image data that contains both time and space data. Spatio-temporal data consists of multidimensional information about objects, such as the location, shape, size distribution, and changes.
The traditional dichotomy is to classify spatio-temporal data into vector data and raster data. Spatio-temporal data has been used for location-based service (LBS) in traditional Geographic Information System (GIS), Global Positioning System (GPS), and Remote Sensing (3S) industries. As Internet of Things (IoT) and smart terminals become popular in various fields, perceptual spatio-temporal data emerges as a new type of data in this field. Perceptual spatio-temporal data extends from the single role of LBS to crossed roles for multi-dimension joint analysis and spatio-temporal pattern mining. Location technologies are advancing with the help of Artificial Intelligence (AI).
Spatio-temporal data types
- Vector data: digital map data and digital elevation model (DEM) data
- Raster data: remote sensing and panoramic image data
- Perceptual data: location data from smart terminals and laser point cloud data
Spatio-temporal data models
- Geometry model: complies with OpenGIS standards and supports 2D (x, y), 3D (x, y, z), and 4D (x, y, z, m) geometries.
- Raster model: consists of a matrix of cells or pixels that are organized into rows and columns, or a grid. Each cell contains a value that represents information such as temperature. Raster data can be digital aerial photographs, satellite images, digital images, or even scanned maps.
- Trajectory model: records the location information of a moving feature, such as a vehicle or a person.
- Business scenarios
- Commercial site selection based on the analysis of foot traffic and demographics
- Delivery personnel tracking or planning based on delivery trajectories or paths
- Message or advertisement push based on an LBS
- Analysis of spatial correlation based on customers and their active regions
- Traffic scenarios
- Real-time location services for public transportation, Internet plus transportation, and intelligent logistics
- Traffic flow statistics on roads and crossroads, analysis and prediction of flows of people and vehicles, and estimated time of arrival (ETA)
- Aggregation and analysis of vehicle departure and destination points
- Optimization on vehicle monitoring, scheduling, or designation
- Trajectory matching and analysis on similar trajectories or paths
- Transport capacity distribution or analysis and real-time thermodynamic diagrams of moving objects
- Dynamic management, monitoring and alerting of electronic fences
- Public security scenarios
- Special group tracking and child custody
- Special vehicle tracking and abnormal vehicle identification
- Alert push and danger warning
- Travel monitoring and management based on health QR codes
- Autopilot scenarios
- Storage, retrieval, analysis, and spatio-temporal pattern mining of laser point cloud data
- High-precision trajectory matching and planning of local paths
- Production and storage management of high-precision maps
Spatio-temporal engines of AnalyticDB for PostgreSQL
AnalyticDB for PostgreSQL provides two extension modules to efficiently store, index, query, analyze, and calculate spatial or spatio-temporal data.
- PostGIS: an open source spatial database engine that is developed by the PostgreSQL community
- Ganos: a spatio-temporal engine that is developed by Alibaba Cloud based on PostgreSQL
- Easy to use. You can smoothly switch between PostGIS and Ganos. Data can be migrated from a standalone PostgreSQL database to an AnalyticDB for PostgreSQL instance.
- Cost-effective. AnalyticDB for PostgreSQL uses the massively parallel processing (MPP) architecture. An AnalyticDB for PostgreSQL instance can process more spatio-temporal data than a standalone PostgreSQL database based on the same performance metrics and computing resources. AnalyticDB for PostgreSQL integrates with OSS to separate cold and hot data. This way, costs can be reduced without decreasing performance, especially for the storage and calculation of raster data and laser point cloud data.