Concept, Design and Management

A spatial database system may be defined as a database system that offers spatial data types in its data model and query language, and supports spatial data types in its implementation, providing at least spatial indexing and spatial join methods.

Spatial database systems offer the underlying database technology for geographic information systems and other applications. We survey data modeling, querying, data structures and algorithms, and system architecture for such systems. The emphasis is on describing known technology in a coherent manner, rather than listing open problems.

Spatial Database Concept

In various fields there is a need to manage geometric, geographic, or spatial data, which means data related to space. The space of interest can be, for example, the two-dimensional abstraction of (parts of) the surface of the earth or a 3d-space representing a digital terrain model. At least since the advent of relational database systems there have been attempts to manage such data in database systems.

Characteristic for the technology emerging to address these needs is the capability to deal with large collections of relatively simple geometric objects, for example, a set of 100 000 polygons. Several terms have been used for database systems offering such support like pictorial, image, geometric, geographic, or spatial database system. The terms “pictorial” and “image” database system arise from the fact that the data to be managed are often initially captured in the form of digital raster images (e.g. remote sensing by satellites, or computer tomography in medical applications).

The term “spatial database system” has become popular during the last few years, and is associated with a view of a database as containing sets of objects in space rather than images or pictures of a space. Indeed, the requirements and techniques for dealing with objects in space that have identity and well-defined extents, locations, and relationships are rather different from those for dealing with raster images.

A spatial database therefore has the following characteristics:

(1)   A spatial database system is a database system.

(2)   It offers spatial data types (SDTs) in its data model and query language.

(3)   It supports spatial data types in its implementation, providing at least spatial indexing and efficient algorithms for spatial join.

Nobody cares about a special purpose system that is not able to handle all the standard data modeling and querying tasks. Hence a spatial database system is a full-fledged database system with additional capabilities for handling spatial data. Therefore spatial indexing is mandatory. It should also support connecting objects from different classes through some spatial relationship.

Spatial Database Design

A spatial database includes collections of information about the spatial location, relationship and shape of topological geographic features and the data in the form of attributes.  The design of the spatial database is the formal process of analyzing facts about the real world into a structured model. Database design is characterized by the following phases: requirement analysis, logical design and physical design. In other words, you basically need a plan, a design layout and then the data to complete the process.

Having a solid well designed spatial database is the key to performing good Spatial Analysis. The database can be complex and designed with expensive sophisticated software or can be merely a simple well organized collection of data that can be utilized in a geographic form.

Three main categories of spatial modeling functions that can be applied to geographic features within a GIS are: (1) geometric models, such as calculating the Euclidean distance between features, generating buffers, calculating areas and perimeters, and so on; (2) coincidence models, such as topological overlay; and (3) adjacency models (path finding, redistricting, and allocation). All three model categories support operations on spatial data such as points, lines, polygons, tins, and grids. Functions are organized in a sequence of steps to derive the desired information for analysis.

Almost all entities of geographic reality have at least a 3-dimensional spatial character, but not all dimensions may be needed. E.g. a highway pavement actually has a depth which might be important, but is not as important as the width, which is not as important as the length. Representation should be based on the types of manipulations that might be undertaken.  Map-scale of the source document is important in constraining the level of detail represented in a database.  E.g. on a 1:100,000 map individual houses or fields are not visible

Steps in database design

  1. Conceptual
    1. software and hardware independent
    2. describes and defines included entities
    3. identifies how entities will be represented in the database
    4. i.e. selection of spatial objects - points, lines, areas, raster cells
    5. requires decisions about how real-world dimensionality and relationships will be represented
    6. these can be based on the processing that will be done on these objects
    7. e.g. should a building be represented as an area or a point?
    8. e.g. should highway segments be explicitly linked in the database?
  2. Logical
    1. software specific but hardware independent
    2. sets out the logical structure of the database elements, determined by the data base management system used by the software
  3. Physical
    1. both hardware and software specific
    2. requires consideration of how files will be structured for access from the disk

Characteristics of a Good Database Design

In order that the GIS database provides the best service it should be:

o       Contemporaneous – the data should be updated regularly so as to yield information that pertains to the same time-frame for all its measured variables

o       Flexible and extensible so that additional datasets may be added as necessary for the intended applications

§         the categories of information and subcategories within them should contain all of the data needed to analyze or model the behavior of the resource using conventional methods and models

o       Positionally accurate – if for example the boundary between the residential and agricultural land has changed, this may be incorporated with ease.

o       Exactly compatible with other information that may be overlain with it

o       Internally accurate, portraying the nature of phenomena without error - requires clear definitions of phenomena that are included

o       Readily updated on a regular schedule

o       Accessible to whoever needs it

Spatial Database Management

Many factors influence a successful Geographic Information System (GIS) implementation. None however are more fundamental than having the right management strategies and software to implement these. The spatial database is the foundation by which all data is uniformly created and converted.  But maintaining the integrity and currency of the data is of fundamental importance. A classic mistake made by many organizations is thinking that a generic spatial database design will be sufficient for their needs. That is simply not the case. The spatial database is the end result of a series of processes that determine the specific functional requirements for the user and the key applications. Interoperability of data is also a critical area of concern in the development of spatial data information systems. As we move from newly created data to assimilation of all existing data, a properly designed spatial database is insurance for end user success. A good spatial database management software package should be able to:

  1. Scale and rotate coordinate values for "best fit" projection overlays and changes.
  2. Convert (interchange) between polygon and grid formats.
  3. Permit rapid updating, allowing data changes with relative ease.
  4. Allow for multiple users and multiple interactions between compatible data bases.
  5. Retrieve, transform, and combine data elements efficiently.
  6. Search, identify, and route a variety of different data items and score these values with assigned weighted values, to facilitate proximity and routing analysis.
  7. Perform statistical analysis, such as multivariate regression, correlations, etc.
  8. Overlay one file variable onto another, i.e., map superpositioning.
  9. Measure area, distance, and association between points and fields.
  10. Model and simulate, and formulate predictive scenarios, in a fashion that allows for direct interactions between the user group and the computer program.

Notes & Handouts

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