Spatial databases as models of reality
- the real world is too complex for our immediate and direct understanding
- we create "models" of reality that are intended to have some similarity with selected aspects of the real world
- databases are created from these "models" as a fundamental step in analyzing, managing and understanding the world
Spatial database
- is a collection of spatially referenced data that acts as a model of reality
- is a model of reality since it represents a selected set or approximation
- these selected phenomena are deemed important enough to represent in digital form
Database design
- in each organization or project only certain phenomena are important enough to collect and represent in a database
- database design consists of:
- determining how data are used in the organization or application
- identifying the phenomena which need to be recorded
- choosing an appropriate data representation for selected phenomena
- structuring the data so that they can be manipulated and retrieved as required
Fundamental database elements
- elements of reality modeled in a GIS database have two identities:
1. the element in reality - entity
2. the element as it is represented in the database - object
- a third identity important in cartographic applications is the symbol
- is used to depict the object/entity as a feature on a map or other graphic display
Entity
- a phenomenon in reality that is not further subdivided into phenomena of the same kind
Entity type
- any grouping of similar phenomena that should eventually get represented and stored in a uniform way
- roads for example? - provides convenient conceptual framework for describing phenomena at a general level
Object
- a digital representation of all or part of an entity
(Spatial) Object types
- the digital representation of entity types
- after identifying entity types
- next step of database design is to choose an appropriate method of representation for each of the entity types
Spatial object type classes
- 0-D - an object that has a position in space, but no length - point
- 1-D - an object having a length - linecomposed of two or more 0-D objects
- 2-D - an object having a length and width - area bounded by at least three 1-D line segment objects
- 3-D - an object having a length, width and height/depth - volume bounded by at least four 2-D objects
0-Dimensional spatial object types
- point
- node
1-Dimensional spatial object types
- line
- line segment
- string
- arc
- link
- directed link
- chain
2-Dimensional spatial object types
- area
- interior area
- simple polygon
- complex polygon
- pixel
- grid cell
Object classes
- the set of objects which represent a set of entities of one type
- e.g., the set of points representing the set of wells
Attributes
- a characteristic of an entity which has been selected for representation
- usually non-spatial
- however, some may be related to the spatial character of the phenomena under study
- e.g., area, perimeter
Attribute value
- the actual value of the attribute that has been measured (sampled) and stored in the database
- an entity type is almost always labeled and known by attributes
- e.g., a road usually has a name and is identified according to its class - alley, freeway
- often conceptually organized in attribute tables
- entries in each cell of the table represent the attribute value of a specific attribute for a specific entity
Summary - Database elements
- entity - real world thing
- entity type - similar things
- object - digital representation of entity
- object type - digital representation of entity type
- object classes - objects representing a set of similar entities
- attributes
- attribute values
Data model
- a conceptual description of a database defining entity types and associated attributes
- each entity type is represented by specific spatial objects
- the data model is a view of the database which the system can present to the user
- need not be related directly to the way the data are actually stored in the database
- e.g., census zones may be defined as being represented by polygons, but the program may actually represent the polygon as a series of line segments
Layers
- spatial objects can be grouped into layers
- also called overlays, coverages or themes
- one layer may represent a single entity type or a group of conceptually related entity types
- e.g. a layer may have only stream segments or may have streams, lakes, coastline and swamps
- depends on the system as well as the database model
- some spatial databases combine all entities into one layer
Objects and entities
- the objects in a spatial database are representations of real-world entities with associated attributes
- the power of a GIS comes from its ability to look at entities in their geographical context and examine relationships between entities
- thus a GIS database is much more than a collection of objects and attributes
Objects AS entities
- a spatial database is assembled from simple objects
- lines linked together form hydrologic or transportation networks
- points, lines or areas can be used to represent surfaces
Point entities
- city
- water or oil well
- soil sample site
- telephone pole
- house
- benchmark
Point data
- points are the simplest type of spatial object
- choice of entities which will be represented as points depends on the scale of the map/study
- e.g., on a large scale map - encode building structures as point locations
- e.g., on a small scale map - encode cities as point locations
Storing point data
- the coordinates of each point can be stored as two additional attributes
- information on a set of points can be viewed as an extended attribute table
- each row is a point
- each column is an attribute
- two of the columns are the coordinates
- each point is independent of every other point, represented as a separate row in the database model
Point data attribute table
- northing and easting are the y and x coordinates in the Universal Transverse Mercator (UTM) coordinate system
Line data
- line objects usually represent network entities
- infrastructure networks include:
- airline networks with hubs and routes
- transportation networks - highways, railroads
- utility networks - gas, electric, telephone, water
- natural networks include:
- river channels
- underground drainage systems
- other line objects include:
- geologic fault lines
Network characteristics
- a network is composed of:
nodes - junctions, ends of dangling lines
arcs - chains in the database model
Node valency
- valency of a node is the number of arcs at the node
- ends of dangling lines are "1-valent"
4-valent nodes are most common in street networks
3-valent nodes are most common in hydrology
Node valency
- a tree network has only one path between any pair of nodes, no loops or circuits are possible
- most river networks are trees
Network attributes
- some attributes relate one type of entity to another
- e.g., names of intersecting streets at a node (arc-node relationship)
- some attributes are associated with parts of arcs
- e.g., part of a railroad arc between two junctions may be inside a tunnel
- e.g., part of a highway arc between two junctions may need pavement maintenance
Network attributes
- many GISs attach these network attributes by splitting existing links and creating new nodes
- e.g., create a new arc for the stretch of railroad which lies inside the tunnel, plus 2 new nodes
- e.g. split a street arc at the house and attach the attributes of the house to the new (2-valent) node
Network attributes
- splitting arcs for attaching attributes can lead to impossibly large numbers of arcs and 2-valent nodes
- at a scale of 1:100,000, the US rail network has about 300,000 arcs
- the number of arcs would increase by orders of magnitude if new nodes had to be defined in order to locate bridges on links
Networks as linear addressing systems
- street networks often can be used to provide a local addressing system
- in a GIS, street addresses can be coded to specific geographic locations
- called address matching...
Address matching
- is the process of finding the location of a building on a street network from its street address
- example - find the location of 124 White Street
- the relevant side of the street block contains house numbers from 100 to 198
- house 124 is likely to be 1/5 of the way along that link
Using matched addresses
- points are located on the network by arc number and distance from beginning of arc
- can be more useful than the (x,y) coordinates since it links the points to a location on the network
Using matched addresses
- provides an answer to the problem of assigning attributes to parts of links
- keep matched entities (houses, tunnels) in separate tables
- link them to the network by arc number and distance from node
- one distance for point entities
- two for extended entities like tunnels (start and end locations)
- GIS can compute the (x,y) coordinates of the entities if needed
- arcs need not be permanently split
Area data
- is represented on area class maps and choropleth maps
- boundaries may be defined:
- by natural phenomena, e.g. lake
- by man, e.g. forest stands, census zones
Environmental and natural resource zones
- examples include
- land cover data - forests, wetlands, urban
- geological data - rock types
- forestry data - forest "stands", "compartments"
- soil data - soil types
- boundaries are defined by the phenomenon itself
- e.g. changes of soil type
- almost all junctions are 3-valent
Socio-economic zones
- includes census tracts, ZIP codes, etc.
- boundaries defined independently of the phenomenon, then attribute values are enumerated
- boundaries may be culturally defined, e.g. neighborhoods
- frequent 4-valent nodes
Land records
- land parcel boundaries, land use, land ownership, tax information
- many 4-valent nodes
Which is which?
a. natural areas?
b. socio-economic zones?
c. land parcels?
Summary: Spatial objects and data models necessitate Data Structures
point data - coordinates as attributes
line data - networks, nodes, links, valency, address matching
area data - natural vs artificial boundaries
surfaces - points, contour lines, TINs
volumes
Burrough, P. A., 1986. Geographical Information Systems for Land Resources Assessment, Clarendon Press, Oxford. See chapter 2 for a review of database models.
Codd, E. F., 1981. "Data Models in Database Management," ACM SIGMOD Record 11(2):112-114. Explains the nature of data models, their role in constructing databases.
DCDSTF - Digital Cartographic Data Standards Task Force. 1988. "The proposed standard for digital cartographic data," The American Cartographer 15(1). Summary of the major components of the proposed US National Standard.
Duecker, K. J., 1987. "Geographic Information Systems and Computer-Aided Mapping," American Planning Association Journal, Summer 1987:383-390. Compares database models in GIS and computer mapping.
Goodchild, M. F., 1992. "Geographical Data Modeling," Computers and Geosciences 18(4):401-408. Required reading on reserve.
Healy, R. G., 1991. "Database Management Systems" in Maquire, D., Goodchild, M., and Rhind, D. (Eds.). GIS: Principles and Applications, Wiley, New York. Helpful overview of hierarchical, network, and relational database management systems.
Mark, D.M., 1978. "Concepts of Data Structure for Digital Terrain Models," Proceedings of the Digital Terrain Models (DTM) Symposium, ASP and ACSM, pp. 24-31. A comprehensive discussion of DEM database models.
Marx, R. W., 1986. "The TIGER System: Automating the Geographic Structure of the United States Census," Government Publications Review 13:181-201. Issues in the selection of a database model for TIGER.
Nyerges, T. L. and K. J. Dueker, 1988. Geographic Information Systems in Transportation, Federal Highway Administration, Division of Planning, Washington, D. C. Database model concerns in transportation applications of GIS.
Peuquet, D.J., 1984. "A conceptual framework and comparison of spatial data models," Cartographica 21(4):66-113. An excellent review of the various spatial data models used in GIS.
Robinson, A., R. Sale, J. Morrison, and P. Muehrcke, 1984. The Elements of Cartography, (5th ed.), John Wiley and Sons, New York. Useful survey of cartographic terminology and models.
Unwin D., 1981. Introductory Spatial Analysis, Methuen, London. A spatial analysis perspective on spatial data models.