Spatial is Special

Geospatial Information is multidimensional (x,y), oftenprojected onto a flat surface, voluminous, representable atdifferent resolution/scale, with a lot to reveal about us!

Special methods are required to analyse geospatial data Procedures are usually complex and expensive (even if static)

Retrieval of large amounts of data for each analysis (even display), and long transactions for data manipulation requires special database, software, hardware

GISystem Can have the following architectures

1. Desktop GIS
2. Client Server 
3. Distributed
4. Web GIS

Client-Server Architecture (Desktop GIS + Network + Data Server)

• 3 Logical Layers in many Physical machines 
• Many clients and one server (data server)

Advantages:

• Consistency of data (there is only one version of data)
• Many Users (100 for example; usually below 300 due to limited connection objects)

Distributed Architecture (Desktop GIS + Network + Data Server + Application Server)

• 3 Logical Layers in many Physical machines
• Many clients and two servers (application, data server)

Advantages:

• Consistency of data (there is only one version of data) 
• Consistency of Analysis
• Many More Users (1000 for example)

Major Disadvantages of Client-Server and Distributed

• Maintenance of GIS software on client machines
• Limited Number of users: 1m, 10m simultaneous users!!
• Cost: dedicated administrator (team)
• System integration: GIS is not the only system in an organisation

WebGIS

1. No need to have the GIS software on client machines; just a web browser is needed for clients.
2. Platform-independency (desktop, mobile, Windows, Mac, Linux,...)
3. Users with no experience and expertise
4. Professionals can implement complex what-if scenarios
5. Low cost (no maintenance, no GIS admin, ...)
6. Once updated, all clients can access to the latest version of tools and data

Spatial databases Evolution

Why Relational Database is inefficient for geospatial data?

• Query : Return the shape of (contours) of France?
• Get all the boundaries of all objects constituting France (Mainland and the islands)

1. Queries on themes requires a knowledge of the spatial objects’ structure
2. Changing this structure implies a deep reorganization of the database and changing the query formulation

Why Relational Database is inefficient for geospatial data?

1. The bad performance of this approach, which requires in particular a considerable amount of relational tuples to represent spatial information
2. The impossibility of expressing geometric computations such as adjacency test, point query or window query using SQL

Why Relational Database is inefficient for geospatial data?

• Queries on themes requires a knowledge of the spatial objects’ structure
• Changing this structure implies a deep reorganization of the database and changing the query formulation
• The bad performance of this approach, which requires in particular a considerable amount of relational tuples to represent spatial information
• The impossibility of expressing geometric computations such as adjacency test, point query or window query using SQL

Geospatial Data Storage (part 1)

1- Geospatial data in text and binary files

• Text
• Simple import and export
• Usually in CSV (Comma Separated Values) format 
• GeoJSON

Binary

In most cases they provide little (or no) attribute support 
Limited Support for SRS, complex geometrires
Example: DXF (Autodesk Data eXchange Format)
BSON

Geospatial Data Storage (part 2)

2- Geospatial data in XML files KML, GML and GeoRSS

Geospatial Data Storage (part 3)

3- Geospatial data in GeoRelational Model (Loosely-Coupled approach)

• Georelational model is the one of the most widely used models of storage, processing and sharing geospatial data.
• geospatial data divided into two separate but related structures.
• The geometrical element of features stored in a binary or set of binary files and corresponding attributes stored in a table (relation in terms of RDBMS).
• There is a one-to-one relationship between rows in relational database and Features in binary files

Concurrency, security, recovery and ... problems in addition to limited file size and other issues

Geospatial Data Storage (part 4)

4- Use of Spatial Databases

• 4- A)Native Spatial Database
• Natively supports spatial data
• Spatial data-types and functions
• Integration of functions with query language (SQL)
• Support of spatial indexing (Accelerating retrieval of data)

Examples: 

PostGIS

SQL Server 

Oracle

DB2

MySQL

Geospatial Data Storage (part 4)

4- Use of Spatial Databases

4- B)Spatially-Enabled Database

• Use of relational database to store and manage geospatial data
• Attributes in columns of normal data-types such as text, int
• Geometry in BLOB (Binary Large Object)
• Handle all the geospatial functionality (such as query, analysis, indexing and ...) in Applications

Example:

Esri’s ArcSDE which is a middleware that can use SQL Server, Oracle, Informix, DB2 and PostgreSQL to store and manage geospatial data

Relational database systems to provide data for geospatial services

Advantages:

• support for geospatial data storage
• easy to use query language,
• standard query language (based on OGC simple feature),
• very good support from developer community,
• flexibility for modelling various kinds of relationships
• multiple users,
• support for transactions (short at DBMS and Long at Application)

Disadvantage:

• vertical scalability
• need for multiple joins for complex spatial queries,
• low performance for large volume of geospatial data