CENTENNIAL COLLEGE SCHOOL OF ENGINEERING APPLIED SCIENCE VS

CENTENNIAL COLLEGE SCHOOL OF ENGINEERING & APPLIED SCIENCE VS 361 Introduction to GIS DATABASE CONCEPTS 1

GIS are driven by spatial data. . . Two basic spatial(coordinate/geometric) data model exist VECTOR: BASED ON GEOMETRY OF • Points • Lines • Polygons RASTER : based on geometry of • Grid cells (images, bitmaps, DEM) 2

Vector data model Discrete - Boundaries are well defined (x, y coordinates) 3

Point 4

Line : • Line stars and end at nodes –here Line #1 goes from node #2 to #1 • Vertices determines shape of line • Nodes and vertices are stored as coordinate pairs 5

Polygon 6

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POINT REPRESENTATION VECTOR DATA. . . • Nature of the feature: the reality suggests a point (it is not linear in nature, does not occupy an area): – GPS Location – hydrant – rare plant location – seep location… • Typically used for small scale representation. • Relative positioning and density of features is important. • Polygons can be represented as centroids --a special kind of point. 8

LINE REPRESENTATION VECTOR DATA. . . • Nature of the feature, the reality is the feature is linear: – Kinematic GPS Path – extent of rare plant community – seep drainage … • Used at all scales, although may appear as a point at extreme small scales. • Networks are typically represented as single lines with events associated with segments of the network, e. g. Capital Works Projects associated with street segments. • Networks typically have traces performed to 9 determine paths and routes.

POLYGON REPRESENTATION VECTOR DATA. . . • Nature of the feature, it occupies an area. – GPS Location – rare plant community – area of seepage • Typically used at mid to large scales (dependent on the size of the feature/polygon). • Often the relative location of a second feature is important, e. g. hydrants (points) located in a road allowance (polygon). • Polygons can be represented as centroids --a special kind of point. 10

Major types (formats) of vector data available in Arc. GIS • Arc. View shape file (shp, dbf, shx, together) • ESRI Geodatabase (personal, Arc. SDE) • ESRI Filegeodatabase (New for 9. 2 and above) • Arc. Info Coverage • CAD files (Auto. CAD d. WG, DXF, Micro. Station DGN) • Street map files • Spatial Database Engine (SDE) data • ASC 11 point coordinate data • Linear measure (route) data 11

Arc. View shapefiles • Preferred vector format in Arc. View • Display quickly • Fully editable (coordinate and tabular) in Arc. View • Simple in structure • Data sets are either point or line or polygon ESRI Geodatabase • Based on shapefile data model • Multiple data sets stored ina relational database file • Stored in MS Access database or higher-end database • Separate points, line, and polygon data sets are stored within the same Geodatabase ASC 11 coordinate data • Easy to obtain from a variety of sources - GPS - Traverse (survey) - Direct reading from maps 12

Characteristics of the vector data model: • + Features are positioned accurately • + Shape of features can be represented correctly • + Features are represented discretely (no fuzzy boundaries) • - Not good for representing spatially continues phenomena ex: precipitation measurement, forest cover, animal home range • - Potential complex data structure (specially for polygons can lead to long processing time for analytical operations) 13

Raster Data Model • The world is composed of cells/pixels arranged in a grid • Each cell/pixel is assigned a numeric value – Integer – (no decimal) – Floating-point (decimal) (However data may be represented by codes) • The size of the cell/pixel determines the resolution Every location given an object 14

DATA MODEL OF RASTER AND VECTOR REAL WORLD 1 2 3 4 5 6 7 8 GRID RASTER 9 10 VECTOR 15

Lake r ve Ri Pond Reality - Hydrography Lake r ve Ri Pond Reality overlaid with a grid 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 2 0 0 0 1 2 0 0 1 1 0 0 0 1 1 1 0 0 0 = No Water Feature 1 = Water Body 2 = River 0 Resulting raster Creating a Raster 16

Raster Data Model • Origin is set explicitly • Cell size is always known • Cell references (row/column locations) are known • Cell values are referenced to row/column location • Values represent numerical phenomena or index codes for non –numerical phenomena 17

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Characteristics of the raster data model • Rectangular grid of square cells • - Shape of discrete polygonal features generalized by cells • + Continuous (surface) data represented easily • + Simple data structure • Raster data are good at representing continues phenonena eg. . - Wind speed - Elevation, slope, aspect - Chemical concentration - Likelihood of existence of a certain species - Electromagnetic reflectance (Photography or Satellite imagery) 20

Types of Raster Data • Remotely Sensed Images – Satellite • Landsat (http: //landsat. usgs. gov/) • AVHRR (http: //edc. usgs. gov/products/satellite/avhrr. html) • SPOT (http: //www. spot. com/) – Digital Elevation Models (DEMs) • U. S. Geologic Survey (USGS) DEMs • LIDAR (light detection and ranging) • Multibeam sonar (acoustics for capturing depth information) – Digital Orthophotos 21

Types of Raster Data – Images • Digital Raster Graphics (DRGs) – Scanned USGS topographic maps (http: //topomaps. usgs. gov/drg/) • Graphic Image Files – historic aerial photos, scanned paper maps – – . tif (Tagged Image File Format) sid (Lizard. Tech Mr. SID). img (ERDAS Imagine). jpg (Joint Photographic Experts Group). Many packages work on RECTIFYING these images photograph’s scale is not constant across image 22

Raster Data Formats – ESRI Grids • Proprietary format • Discrete – integer (whole number) • Continuous – floating point (number with decimals) 23

Raster Resolution RASTER DATA. . . • Expressed as the ground pixel measured in cm (aerial) or m (satellite). – 15 cm resolution or ground pixel means that each pixel in the image corresponds to 15 cm on the ground. Dependent on source scale and scan rate: • • • 1: 6, 000 scale photography (inch) 1100 dots per inch (dpi) scan rate (What is the RS? ) 1” = 0. 0254 m x 6, 000 = 152. 4 m (1” of the image represents 154. 4 m on the ground) 152. 4 m / 1100 = 0. 138 m; or 13. 8 cm (RS) each pixel of image represent 13. 8 cm on the ground 24

Raster Resolution RASTER DATA. . . • Resolution: • Refers to how accurately the location and shape of map feature can be depicted (presented) at a given scale • Large Scale Maps have better resolution because the reduction is less • As Scale becomes smaller, more and more features become too small to display 25

Raster Resolution RASTER DATA. . . • Accuracy: • Accuracy of a raster image relates to positional location of a pixel relative to its true position. Accuracy is a combination of resolution, map scale (drafting skills, thickness of lines) 26

Raster Resolution RASTER DATA. . . Scale The image scale or map scale as it is sometime called refers to the relative difference in size or distance between the image and the features represented on the ground. This difference is written as a ratio of image distance over ground distance. For example, a scale of 1: 100, 000 (one to one hundred thousand) means 1 centimeter on the map equals 100, 000 centimeters (1 km) on the ground. The following is a list of scales and equivalent ground distances for three distances measured on an image. 27

Raster Resolution RASTER DATA. . . Image scale 1: 10, 000 1: 40, 000 1: 100, 000 1: 500, 000 1 mm on image 10 m 40 m 100 m 500 m 3 mm on image 30 m 120 m 300 m 1, 500 m 5 mm on image 500 m 2, 000 m 5, 000 m 25, 000 m One often refers to a scale as being larger or smaller than another scale. This can be confusing, especially since scales are often referred to solely by their denominator. For example a scale of 1: 100, 000 (one to one hundred thousand) may be called a scale of 100, 000 when it is actually a ratio of 1/100, 000. A scale of 1: 100, 000 is smaller than a scale of 1: 40, 000 because the number 1/100, 000 is smaller than 1/40, 000 (or as it is often stated, a scale of 100, 000 is smaller than a scale of 40, 000). Another way to look at this is to think of a lake on an image with a scale of 1: 100, 000 and another with a scale of 1: 40, 000. The lake will be larger on the 1: 40, 000 image because the scale is larger 28

• Grid of cells called pixels • Two dimensional • Each pixel has a discrete value, I. e. grey scale value 29

Raster and Vector Summary • Vectors have advantage of accuracy but not good with continuous fields • Vectors were used first - digitizing • Earliest include ASCII (x, y coordinates but got too large) then binary took over. • Raster not good with lines or points but good with continuous coverage areas. • Raster has the mixed pixel problem. 30

Vector Advantages • Requires less disk storage space • Topological relationships are readily maintained • Graphical output more closely resembles hand-drawn maps • Preferred for network analysis Vector Disadvantages • More complex data structure • Not as compatible with remotely sensed data • Software and hardware often more expensive • Some spatial analysis procedures may be more difficult • Overlaying multiple vector maps is often time consuming 31

ATTRIBUTE DATA GIS DATABASE CONCEPTS. . . www. colorado. edu/geography/gcraft/notes/datacon. html Read and make notes. . . 3. Organizing Attribute Data a. Flat Files b. Hierarchical Files c. Relational files – Very Important 4. Representing Relationship 5. Topological Relationship – very Important 32

Attribute data are stored in database tables Tables are composed of: 1. Fields 2. Records 33

Relational files. . . • Connect different files or tables (relations) without using internal pointers or keys. Instead a common link of data is used to join or associate records • A "matrices of tables" is used to store the information • The tables have a common link they may be combined by the user to form new inquires and data output 34

Arc. GIS uses tabular data formats from dbase, ASC 11 text, and INFO files Tables are stored on the disk as • . dbf files • . txt files or • binary files in INFO directories Each vector data source has an attribute table 35

Tables can be linked and joined (“Related”) by use of common values in fields 36

Relationship between map and tabular data. . • One to one between feature and records • When selection is made, both the record and the feature are selected 37

Different types data that may have attribute tables in Arc. GIS Vector • Point attribute • Polygon attribute • Line attribute • Node attribute • Text attribute • Route and event • CAD attribute Raster • Value attribute table 38

Topology. . . . It is defined as the mathematics of connectivity or adjacency of points or lines that determines spatial relationships in a GIS The topological data structure logically determines exactly how and where points and lines connect on a map by means of nodes (topological junctions). example to see how connections are coded into a database The first step is to record the location of all "nodes, " that is endpoints and intersections of lines and boundaries 39

Based upon these nodes, "arcs" are defined • These arcs have endpoints, but they are also assigned a direction indicated by the arrowheads. • The starting point of the vector is referred to as the "from node" and the destination the "to node. " • It is possible to use this information to establish routes from node to node or place to place. Thus, if one wants to move from node 3 to node 1 40

"polygons" are defined by arcs. To define a given polygon, trace around its area in a clockwise direction recording the component arcs and their orientations Finally, for each arc, one records which polygon lies to the left and right side of its direction of orientation. If an arc is on the edge of the study area, it is bounded by the "universe. " 41

Questions about connectivity and location. . . What polygons adjoin polygon A? first look to see what arcs define polygon A, then we check to see what other polygons are defined by these arcs in their negative orientation What is the shortest route from node 3 to node 2? Trace all arc paths that lead from node 3 to node 2, sum their lengths by calculating distances from node list 42

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. 43

Terminology • Point: x, y coordinate identifying a geographic location • Link (line, arc): an ordered set of points with a node at the beginning and end of it • Node: the beginning and end of link (often defined where 3 or more lines connect) • Polygon: two or more links connected at the nodes, contains a point inside to identify the polygons attributes 44