Spatial Databases and Information Systems

Spatial Databases and Information Systems

• Space and time, when considered separately, are incomplete. A union of the two preserves independent reality (Albert Einstein, ~1910).
• Spatial data is data associated with a location in space, or information about space itself (e.g., images).
• Spatial data can be composed of non-spatial and spatial data components.
• Spatial data is described using a spatial reference system (e.g., location, speed) and a coordinate system (e.g., Euclidean, polar).
• Geospatial data is a specific type of spatial data tied to the Earth's surface.

Spatial Data & Operations

• Spatial data encompasses data related to locations in space and data about space itself, including raster images.
• Spatial data is commonly described in terms of spatial references and coordinate systems (e.g. Euclidean, polar).

Examples of Spatial Data

• Simple spatial objects include points (e.g., a city), lines (e.g., road, boundary), regions (e.g., a forest).
• Spatially related collections of objects involve partitions (e.g., population density), networks (e.g., bus routes).

Spatial Data is Multidimensional

• Multidimensional data is made up of multiple dimensions, as opposed to single-dimensional data.
• Attributes can be viewed as dimensions of multidimensional space.
• Examples of multidimensional data include cities (name, population, coordinates, coverage, etc.).

Spatial Relationships

• Spatial objects are related to one another in space by vicinity, coverage, and connectedness (e.g., topological relationships).
• Spatial relationships are defined by attributes that describe these relationships.

Simple Spatial Relationships (No Holes and Connected 2D Objects)

• The universe (U) is the space surrounding a specific object (A).
• A geometric object and its interior (Aº), boundary (A), and exterior (A) are examined.

Spatial Operations on Data

• Finding the nearest point, identifying intersection points of lines, and finding nearest points on lines.

Examples of Spatial Data

• Data from surveys and censuses
• Satellite imagery
• Maps of waterways (rivers)
• Ecological data and locations of importance
• Site plans for machinery or buildings
• Information on weather and climate
• Medical imagery
• Animation data of bodies

Spatial Constraints

• Avoiding illogical modification and manipulation of spatial objects (e.g., translation, rotation).
• Supporting semantically consistent modification and animation, especially in combination with the spatial aspect.
• Using constraints for 2D and 3D representations

Spatial Constraints Encoding

• Ensuring the relationships between spatial objects are maintained during reshaping, by converting relationships into graphs and checking graph invariance.

Spatial Constraints in Data Reduction

• Converting/reducing an original object to another mesh that preserves functionality.

Spatial Constraints for Generating an Acceptable Track

• Ensuring generated shapes (e.g., polygons) fall within specific acceptable regions and have appropriate curves.

Spatial Constraints, Not!?

• Demonstrative slides with humour and/or relevance to the audience

Spatial Constraints for Generating Acceptable Movement

• Defining acceptable movement (based on space and time) in animations or simulations.

Spatial Data Manipulation

• Geometric selection (e.g., finding objects at a particular coordinate).
• Windowing (e.g., selecting objects within a defined region).
• Clipping (e.g., extracting parts of objects within a defined region).
• Attribute-based selection (e.g., selecting objects based on an attribute value, not spatial).

Spatial Data Quality

• Accuracy: The difference between the measured location and the true location.
• Precision: The level of detail in the data (e.g., resolution).
• Consistency: No internal conflicts or ambiguities.
• Completeness: Represents all necessary information.

Spatial Data Quality (Continued)

• Consistency: Absence of conflicts and clarity in the dataset.
• Compliance with specific topological rules, varying by dimension.

Spatial Data Representation

• Well-known text (WKT) representation: A textual way to encode different types of spatial data.
• Well-known binary (WKB) representation: A binary way to encode different types of spatial data.

Computer Systems with a Spatial Component

• Spatial component in computer systems, such as astronomy, circuit layouts, mechanical engineering, medical imagery, and bio-spatial models.
• Some software use spatial data, such as geographic information systems and spatial databases.

Spatial Databases

• Relational database management systems with spatial data functionality.
• Spatial data types, operations, and functions.

Examples of Spatial DBMS

• Oracle, Microsoft SQL Server, PostgreSQL (with extensions like PostGIS).

Necessary Supplementary Tools

• GIS software (e.g., QGIS)
• Data translation tools (e.g., GDAL, OGR)
• Web service API access tools (e.g., CuRL, WGET)
• JSON and GeoJSON editors

Spatial Queries in SQL

• Queries to retrieve/filter data based on spatial relationships (examples provided).

Spatial Enabled DBMS Overview

• Defining the role of spatial DBMSs
• Spatial data modeling
• Matching spatial models to DBMS
• Defining spatial data
• Implementing and using spatial support
• Providing examples of systems' usage

Spatial DBMS

• Data and query model
• Data definition language (DDL)
• Data control language (DCL)
• Data manipulation language (DML)
• Data imports and processing
• Query optimization
• Transaction management
• Management of spatial data and artefacts

The Spatial Data Model

• Types of spatial objects (e.g., points, lines, polygons).

Spatial Methods

• Methods for querying and analyzing spatial data, such as distance calculation and spatial relationships (e.g., overlaps, intersects).

Spatial Indexes

• Methods used when indexing spatial data (e.g., R-Trees, GiST).

Index Issues

• The size of the indexes, data access from disks and the basic units of transfer between memory and disk.

Indexing Properties

• Time, and space complexities, and dynamicity.

Types of Spatial Indexes

• R-Trees (proposed by Guttman in 1984).

R-Tree Properties

• Methods used in node splitting.
• Maximum/minimum entries limitations for disk space.

R-Tree Implementation in PostgreSQL

• Extensions such as SP-GiST
• Data structure types that can be utilized for querying, such as tries.

Partial Indexes

• Indexes created on subsets of relations based on specified conditions.

Expression Indexes

• Indexes that incorporate expressions or calculated values.

Multi-column Indexes

• Single indexes covering multiple columns.

Questions?

• A final slide, posing questions and thanking the audience