Cartographic and GIS Data Structures Overview Map as

Cartographic and GIS Data Structures

Overview • Map as an Abstraction of Space • Database Management system • Methods of representing geographic space – Raster Model – Vector Model

Map as an Abstraction of Space • Spatial features can be represented as point, lines, areas, or surfaces • Some phenomena or objects are selected for inclusion, others are not spatial features and there attributes are simplified, aggregated, and classified • When we want to enter this data into a GIS, certain decisions need to be made based upon how the data can be entered into a computer (geocoding vs. drawing) • How do you get simple spatial concepts into the computer (e. g. , a map which identifies a lake within an island, surrounded by ocean, covered by forest on north side, and a cleared beach on the other side) • Inside, surrounded, by, north, south

What is a Database? • A database is a set of computer files that stores information in an organized, structured format • The information is organized in records and fields • Information in a database is related so questions can be asked such as: • List all of the courses that are 4000 level or higher • List the name and address for all people whose last names begin with "T"

Database-continue • 4 basic types of computer database Structures for management of data: hierarchical, network, relational, and object oriented • Database Records and Fields • Record: a small group of related data items (the logical unit of a database) • Field: An individual item of data (contain information that describe records)

Methods of representing geographic space Vector Raster

The diagram below shows how real-world objects can be represented on a computer monitor by x, y coordinates. The coordinate pairs 1, 5 3, 5 5, 7 8, 8 and 11, 7 represent a line (road) The coordinate pairs 6, 5 7, 4 9, 5 11, 3 8, 2 5, 3 and 6, 5 represent a polygon (lake). The first and last coordinates of the polygon are the same; a polygon always closes.

Raster Models • Raster - from the Greek word meaning "to rake" • Quantizes or divides space into discrete packets (cells), each representing a part of the whole • Cells are of equal size square, rectangular, hexagon, triangles • Loose the ability to represent exact locations (e. g. , point represented as single cell) • Zero dimensional object rep. with 2 D feature • Lines represented as a series of connected cells • Multiple cells joined at edges or corners, usually with only 1 or 2 neighbors, 1 D objects represented in 2 D • Areas represented as a series of connected cells • 2 D objects represented in 2 D, cells distort area and shape - stairs-stepped appearance

Raster Models-continue • Two general ways of associating attribute data with raster entities • 1. store an attribute for every grid cell problem is redundancy in storage • 2. link cells to RDBMS • Permits more than one attribute to be associated for a single cell • Only have to store attributes once • Cell value linked to attribute table • Essentially many to one - "many cells being linked to one record in separate attribute table"

Generic structure for a grid Grid extent Rows Grid cell Resolution Columns

Geographic Representations • CELLS: a representation of geographic data based on rows and columns (e. g. . continuous surface data such as elevation or temperature, and categorical representations derived from vector data) • PIXELS: a group of independent points with a color value but no other associated data (e. g. . scanned documents, orthophotography, satellite images)

• Like the vector data model, the raster data model can represent discrete point, line and area features. • A point feature is represented as a value in a single cell, a linear feature as a series of connected cells that portray length, and an area feature as a group of connected cells portraying shape.

• Because the raster data model is a regular grid, spatial relationships are implicit. Therefore, explicitly storing spatial relationships is not required as it is for the vector data model.

Vector Models • Features represented in basically the same way as an analog map, permits more precise representation than raster model, permits "empty space”, variations of the vector model • Spaghetti models • Simplest of vector data structures • Does not explicitly store spatial relationships (topology), essentially X, Y coordinates, and which should be connected by lines • Doesn’t really "know" if points and connected lines form a line entity or poly entity • Topological models • Recognizes the concept of an entity • Stores spatial relationship information explicitly associated with each entity, most common in GIS

Feature Geometry

To keep track of many features, each is assigned a unique identification number or tag. Then, the list of coordinates for each feature is associated with the feature’s tag. The objects you see in a vector theme are actually saved in theme table

Vector Data: Advantages • Data can be represented at its original resolution and form without generalization. • Graphic output is usually more aesthetically pleasing (traditional cartographic representation) • Since most data, e. g. hard copy maps, is in vector form no data conversion is required. • Accurate geographic location of data is maintained. • Because it recognizes entities, model allows for efficient encoding of topology, and as a result more efficient operations that require topological information, e. g. proximity, network analysis.

Vector Data: Disadvantages • The location of each vertex needs to be stored explicitly • For effective analysis, vector data must be converted into a topological structure. This is often processing intensive and usually requires extensive data cleaning. • Topology is static, and any updating or editing of the vector data requires re-building of the topology • Algorithms for manipulative and analysis functions are complex and may be processing intensive • Often, this inherently limits the functionality for large data sets, e. g. a large number of features. • Continuous data, such as elevation data, is not effectively represented in vector form. Usually substantial data generalization or interpolation is required for these data layers

Raster Data: Advantages • Due to the nature of the data storage technique data analysis is usually easy to program and quick to perform. • The inherent nature of raster maps, e. g. one attribute maps, is ideally suited for mathematical modeling and quantitative analysis. • Discrete data, e. g. forestry stands, is accommodated equally well as continuous data, e. g. elevation data, and facilitates the integrating of the two data types. • Grid-cell systems are very compatible with raster-based output devices, e. g. electrostatic plotters, graphic terminals. • Also compatible with digital satellite imagery.

Raster Data: Disadvantages • The cell size determines the resolution at which the data is represented. • Processing of associated attribute data may be cumbersome if large amounts of data exists. • Raster maps normally reflect only one attribute or characteristic for an area. • Since most input data is in vector form, data must undergo vector-to-raster conversion. • Most output maps from grid-cell systems do not conform to high-quality cartographic needs.

Vector Representation

Vector to Raster

Raster Representation

The mixed pixel problem

Vector Vs. Raster

Exercise • Compare between Raster and Vector Model for representing geographic features; illustrate by figures