Geographic Information System (GIS) Dr. Taysir Hassan Abdel Hamid Lecture 2

Basic Data Models (Graphics) There are two types of GIS Data Models: (models used for graphic representation of geographic space) Vector Raster Note: A database structure need seldom be made to suit a data model. But a well prepared data model is vital for a successful GIS analysis. We will discuss database structures/models further in a separate presentation.

A look behind the scenes: Vector GIS data models Spaghetti model Topological model

The Spaghetti Model The spaghetti model is the most simple vector data model The model is a direct representation of a graphical image NO explicit topological information

Spaghetti Model Description: direct line for line translation of the paper map (often viewed as raw digital data) Pros: easy to implement, good for fast drawing Cons: storage and searches are sequential, storage of attribute data

Spaghetti model

Topology Branch of mathematics dealing with geometric properties Geometry of objects remain invariant under transformations Neighborhood relationships remain the same Topology is the distinguishing basis for more complicated vector models

Topological Vector Model Topological data models are provided with information that can help us in obtaining solutions to common operations in advanced GIS analytical techniques. This is done by explicitly recording adjacency information into the data structure, eliminating the need to determine it for multiple operations. Each line segment, the basic logical entity in topological data structures, begins and ends when it either contacts or intersects another line, or when there is a change in direction of the line.

Topological Vector Model Each line has two sets of numbers, a pair of coordinates and an associated node number. Each line segment has its identification number that is used as a pointer to indicate which set of nodes represent its beginning and ending.

Topological Vector Model Polygons also have identification codes that relate back to the link numbers. Each link in the polygon now is capable of looking left and right at the polygon numbers to see which two polygons are also stored explicitly, so that even this tedious step is eliminated. The Topological data model more closely approximates how we as map readers identify the spatial relationships contained in an analog map document.

Nevada Utah California Arizona

Identify the polygons

Create the Polygon Attribute Table (PAT) Poly-ID Name Population 1 California 33090214 2 Nevada 1818259 3 Utah 2135252 4 Arizona 4790311

Identify the nodes

Node table Node ID X-coord Y-coord 1 2 3 4 5 6 7 8

Identify the links (arcs, lines)

Simplify this

Create the topology! Links table FNode TNode LPoly RPoly 1 2 3 4 5 6 7 8 9 10 11

Nodes First Link# FNode TNode LPoly RPoly 1 2 3 6 4 5 7 8 9 10 11

Nodes First Link# FNode TNode LPoly RPoly 1 2 3 6 4 5 7 8 9 10 11

Polygons Link# FNode TNode LPoly RPoly 1 2 3 4 6 5 7 8 9 10 11

Polygons Link# FNode TNode LPoly RPoly 1 2 3 4 6 5 7 8 9 10 11

Identify the points

Link List Link# List of points 1 1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 11

Point Coordinates ID X-coord Y-coord 1 2 3 4 5 6 7 8 9 (etc)

Putting it all together Point-ID X-coord Y-coord 1 2 3 4 5 6 7 8 9 (etc) Poly-ID Name Population 1 California 33090214 2 Nevada 1818259 3 Utah 2135252 4 Arizona 4790311 Link# FNode TNode LPoly RPoly 1 2 3 4 6 5 7 8 9 10 11 Link# List of points 1 1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11 Node ID X-coord Y-coord 1 2 3 4 5 6 7 8

Putting it all together Poly-ID Name Population 1 California 33090214 2 Nevada 1818259 3 Utah 2135252 4 Arizona 4790311 Point-ID X-coord Y-coord 1 2 3 4 5 6 7 8 9 (etc) Link# FNode TNode LPoly RPoly 1 2 3 4 6 5 7 8 9 10 11 Link# List of points 1 1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11 Node ID X-coord Y-coord 1 2 3 4 5 6 7 8

Putting it all together Point-ID X-coord Y-coord 1 2 3 4 5 6 7 8 9 (etc) Poly-ID Name Population 1 California 33090214 2 Nevada 1818259 3 Utah 2135252 4 Arizona 4790311 Link# FNode TNode LPoly RPoly 1 2 3 4 6 5 7 8 9 10 11 Link# List of points 1 1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11 Node ID X-coord Y-coord 1 2 3 4 5 6 7 8

Putting it all together Point-ID X-coord Y-coord 1 2 3 4 5 6 7 8 9 (etc) Poly-ID Name Population 1 California 33090214 2 Nevada 1818259 3 Utah 2135252 4 Arizona 4790311 Link# FNode TNode LPoly RPoly 1 2 3 4 6 5 7 8 9 10 11 Link# List of points 1 1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11 Node ID X-coord Y-coord 1 2 3 4 5 6 7 8

Putting it all together Point-ID X-coord Y-coord 1 2 3 4 5 6 7 8 9 (etc) Poly-ID Name Population 1 California 33090214 2 Nevada 1818259 3 Utah 2135252 4 Arizona 4790311 Link# FNode TNode LPoly RPoly 1 2 3 4 6 5 7 8 9 10 11 Link# List of points 1 1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11 Node ID X-coord Y-coord 1 2 3 4 5 6 7 8

The definition of Topology The spatial relationships can be interpreted identification of connecting lines along a path definition of the areas enclosed within these lines identification of contiguous areas In digital maps, these relationships are depicted using ‘Topology’ Topology = A mathematical procedure for explicitly defining spatial relationship Topology is the description of how the spatial objects are related with spatial meaning

Topological data models Three types of topological concepts Arc, Node and polygon topologies Arc Arcs have directions and left and right polygons (=contiguity) Node Nodes link arcs with start and end nodes (=connectivity) Polygon Arcs that connect to surround an area define a polygon (=area definition)

Terms and concepts Connectivity - from and to nodes Contiguity - Polygon Enclosure Adjacency - from Direction To Node Arc Right Polygon Left Polygon From Node

Topology errors There are different types of topological errors and they can be grouped according to whether the vector feature types are polygons or polylines. Topological errors with polygon features can include unclosed polygons, gaps between polygon borders or overlapping polygon borders. A common topological error with polyline features is that they do not meet perfectly at a point (node). This type of error is called an undershoot if a gap exists between the lines, and an overshoot if a line ends beyond the line it should connect to

Raster representation: Bathymetry East Pacific Rise near 9-10N is currently our best-studied section of fast-spreading mid-ocean ridge Decades of investigation by ridge geologists and geophysicists, as wellchemists and biologists. wealth of observational data, results and data-driven theoretical (often numerical) studies that are very much under-utilized research scientists and educators. (state several reasons different formats, standards, availablility, tools incompatible or incomplete, some in their infancy, etc.) Situation is improving but much data, results, and related theoretical models still exist either in an inert, non-interactive form (e.g. journal publications) or as unlinked and currently incompatible computer data or algorithms. Infrastructure needed not just for ready access to data but linkage of disparate data sets (data with data) AND data with models quantitatively evaluate hypotheses, refine numerical simulations, and explore new relations between observables

Spatial Encoding - RASTER POINT 1 5 5 3 AREA 1 3 3 1 1 2 LINE 1 1 1

Spatial Encoding - VECTOR * a single node with NO area POINT - x, y - x1, y1 - x2, y2 . - xN, yN LINE * a connection of nodes (vertices) beginning with a “to” and ending with a “from” (Arcs) Area (Polygons) * a series of arc(s) that close around a “label” point - x1, y1 - x2, y2 . - xN, yN (closure Point)

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

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.

Generic structure for a grid Grid extent Grid cell s w o R Resolution Columns

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 to Raster 32 34 34

Raster Representation 32 34 34

Vector Vs. Raster

PRO AND CONS OF RASTER MODEL raster data is more affordable simple data structure very efficient overlay operation cons topology relationship difficult to implement raster data requires large storage not all world phenomena related directly with raster representation raster data mainly is obtained from satellite images and scanning

PRO AND CONS OF VECTOR MODEL more efficient data storage topological encoding more efferent suitable for most usage and compatible with data good graphic presentation cons overlay operation not efficient complex data structure

Tabular data Tabular data is information describing a Raster or Vector? While any feature type can be represented using either model, discrete features, such as customer locations, pole locations or others, and data summarized by area such as postal code areas or lakes, are usually represented using the vector model. Continuous categories, such as soil type, rainfall, or elevation, are represented as either vector or raster. Tabular data Tabular data is information describing a map feature. For example, a map of customer locations may be linked to demographic information about those Customers Tabular data for use in a GIS can be purchased already packaged with spatial data or it can be found in your own organization.

Attribute data The “I” in GIS GIS are often split into two components Coordinate information (describes object geometry or spatial information) Attribute information (describes other non-spatial properties associate with it) Often referred as tabular data as they are presented in tabular form

Databases - cont. GIS data components - spatial & non-spatial Bolstad, 2005 GIS data components - spatial & non-spatial

Attribute Information Presentation In GIS, attribute information are typically entered, analyzed, and presented using a database management system (DBMS)

DBMS Functions DBMS incorporates a special set of software tools to manage the GIS non-spatial tabular data Efficient data storage Data retrieval Data indexing Data reporting

Example of a real-world problem Imagine that you are a retail analyst who wants to create a GIS to help with decisions about a new store location. You have collected together maps, population statistics, aerial photographs and post-coded customer information.

End of Today’s Lecture