Gis Exam 2 Review Questions

Front 1

attribute data types are:

Back 1

-character/string/text (algoma road)
-integers (2, 250, -4)
-floating (1,333)
-dates (02/21/2002)
-time (5:00PM

Front 2

Measurement scale do what?

Back 2

storing techniques to ease manipulation analysis display compulation

Front 3

Measurement scales include:

Back 3

-nominal (named, oak, fir)
-ordinal (ordered, good, bad)
-interval (ona scale with #'s)
-Ratio (has absolute zero)
-nominal and ordinal (categorical displayed
-interval and ratio (numberical computation

Front 4

The feature ID is:

Back 4

each row of the feature attribute table represents a map feature and it is linked to the location and geometry of that feature by a special and unique value which is called this_____

Front 5

what are raster data most suitable for?

Back 5

this data type is more suitable for representation of the variation and for the processing of continuous phenomena

Front 6

Examples of raster data continuous phenomena are:

Back 6

rainfall distribution, vegetation, change of elevation, wind
temperature

Front 7

Princile of raster data are:

Back 7

1) uses a grid to represent portions of the real world
2) each cell of the grid contains a value representing the magnitude of the phenomenon to model
3) the variation of the phenomenon is expressed by the change in values of the different cells

Front 8

Other names for raster data:

Back 8

grid, raster map, image, surface cover

Front 9

Data Representation include:

Back 9

-Point (single cell)
-Line (consecutive cells)
-Area (collection of adjacent cells)

Front 10

cell values are either ____ or _____

Back 10

integers or floating points

Front 11

integers do what:

Back 11

serve to represent categorical data such as land cover

Front 12

floating points do what:

Back 12

are used for the representation of continuous data, such as slope

Front 13

characteristics of raster data model are:

Back 13

-does not clearly seperate spatial and attribute data
-has strong association b/w grid and cell values = does not make much use of DBMS

Front 14

Data compression methods

Back 14

MrSID is one of the most popular tools used for rater data compression compatible with ESRI products

Front 15

chain coding is:

Back 15

represents boundary of regions, uses cardinal directions and cell (North, East, South, West)

Front 16

Block code is:

Back 16

uses square blocks to represent regions

Front 17

Map projecion and geometric trasnformation

Back 17

just like vector data, raster data need to be projected to real world coordinate system and/or re-projected (to be display with overlapping vector data)

Front 18

Rasterization is :

Back 18

vector to raster conversion

Front 19

Vectorizaton is :

Back 19

raster to vector conversion

Front 20

Data exploration is :

Back 20

data query + dtat analysis

Front 21

Vector data query is

Back 21

done on attribue data or on spartial data

Front 22

attribute data query is:

Back 22

in GIS it consists of retrieving data subsets from attribute data linked to spatial data.
It requires the use of logical expressions and boolean connectors

Front 23

logical expressions:

Back 23

components are-
logical operators and the operands

Front 24

boolean connectors are:

Back 24

used to connect locial expressions in query statements

Front 25

union uses:

Back 25

OR

Front 26

Intersection uses:

Back 26

AND

Front 27

Complement uses:

Back 27

NOT

Front 28

Boolena connectors :

Back 28

AND, Or, not, XOR

Front 29

Logical operators:

Back 29

=, =<, =>, <, >, <>

Front 30

Examples of operands in GIS:

Back 30

field, a number, a string, date

Front 31

Spatial data query:

Back 31

consists of retrieving data subsets from spatial features (on a map) linked to attribute data

Front 32

different ways of making feature selections:

Back 32

1) selection by cursor (using mouse or cursor to point at features of interest)
2) selectoin by graphic (using graphics such as lines, circles, box polygon to reach features of interest
3) selection by spaital relationship (based on spatial relationship (based on spatial relationships of features, the spatial relationships include - containment, intersect, proximity, adjacency

Front 33

Raster data query:

Back 33

it is determined by the type of value stored in the cells: integer or floating-point)

Front 34

recall in raster data query:

Back 34

an integer grid can have an attribute table (similar to the Feature Attribut table of vector data) called the Value Attrible Table or look up table

Front 35

Query by Cell Value

Back 35

perfomred on grid by the use of a logical expression referring to cell values

Front 36

graphic method consists of:

Back 36

selecting cells with the cursor, the mouse, or a graphic object (circle, square, polygon,etc)

Front 37

Network is:

Back 37

layer of connected linear features
-topologically based represenation of a line layer with attributes epressing the flow of objects (car, water, train, electricicty)

Front 38

characteristics of network is:

Back 38

-must have attribute info. necessary for specific applications
-must have topological info
-specific network applications are not always part of basic GIS package

Front 39

Overpass and underpass has two methods

Back 39

1) non-planar representation= continuous lines without a node at their intersections
2) Planar representation= there are two nodes at the intersection, one of them connects the areas representing the under pass and the other one connects the areas representing the overpass, an elevation value of 1 or 0 is assigned to each node

Front 40

allocation is:

Back 40

the study of the spatial distribution of resources (public facilities) through a network

Front 41

Location- allocation is:

Back 41

matching supply and demand using objectives and constrains
- requires :
supply (service at point location) , demand (point, line or polgon) and impedance (distance measures/time)