Spatial and Geographical data

Today, many applications deal with information that is related to locations, shapes, maps, and geographical features. A normal database cannot efficiently handle such location-based information. That’s where spatial data and spatial databases come into the picture. 

Spatial Data

Spatial data is data that represents objects in physical space — anything that has a shape, size, or position on Earth.

Examples:

• A building shown on a digital map
• A road, river, or boundary line
• A satellite image
• The outline of a country, state, or city

Normal indexes (like B-Trees or hashing) cannot efficiently answer questions such as:

“Find all buildings that lie inside this area”
“Show all roads that intersect this route”
“Which houses are within 5 km of this point?”

To handle such queries, databases need special spatial index structures like R-trees.

Types of Spatial Data

1. Raster Data

Raster = grid of pixels (like a photo or image).
Each pixel has an associated value (color, elevation, temperature, etc.)

Examples:

• Satellite images
• Scanned maps
• Digital photos
• Heatmaps showing rainfall or temperature

Think of raster as an image divided into tiny squares.

2. Vector Data

Vector = geometric shapes that represent real-world objects.

Examples:

• Points → Location of ATM, hotel, shop
• Lines → Roads, rivers, pipelines
• Polygons → Buildings, countries, lakes
• 3D shapes → Cylinders, cuboids used in 3D modeling

Vector data is more precise and structured than raster data.

Spatial Data in Computer-Aided Design (CAD)

CAD is used to design:

• Machines
• Cars
• Buildings
• Aircraft
• Electronics (like integrated circuits)

Problems with old CAD systems:

• Data stored in memory during editing
• Everything had to be saved back to one big file
• Even if you needed only a small part, you had to read the whole file
• Large models (like an entire aircraft) couldn’t fit into memory

Solution: Object-Oriented Databases

OODBMS represent every component as an object.

Example:

• Engine → object
• Wing → object
• Inner parts → objects connected to each other

This makes the design more organized, efficient, and scalable.

Spatial Data in Geographic Information Systems (GIS)

GIS stores and manages geographical data, such as:

• Road maps
• Land usage maps
• Elevation maps
• Political boundaries
• Land ownership information
• Soil type maps
• Rainfall distribution maps

GIS data differs from CAD data:

• CAD focuses on designing objects
• GIS focuses on earth-related information like maps and satellite images

Applications

Microsoft SQL Server

Since SQL Server 2008, it supports spatial data types and spatial indexes.

You can store:

• Points
• Lines
• Polygons
  And run queries like:

Find all locations within 10 km of this point.

CouchDB (with GeoCouch plugin)

CouchDB is a document-based NoSQL database.
GeoCouch plugin adds:

• Spatial indexing
• Geo-queries
• Location-based search

Neo4j

Neo4j is a graph database.
It handles spatial data mainly for:

• Pathfinding
• Routing (like GPS navigation)
• Graph-based map analysis

Example:

Find the shortest route between two locations.