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2017/2018 > GEOG 222 II > Flashcards

GEOG 222 II Flashcards

1
Q

Intersection =

A

only location in both remain

-AND

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2
Q

Union =

A

locations in either remain

-OR

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3
Q

cookie cutter tool

A

clip

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4
Q

like the opposite of the clip tool, what is left over

A

erase

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5
Q

things to watch out for with intersection and overlay

A

common boundaries
spurious polygons
mixing up identify and intersect

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6
Q

common boundaries

A
  • may be able to see that the line looks thicker

- zoomed in there may be a new polygon from lines crossing, not quite lining up

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7
Q

new polygon formed from common boundaries

A

spurious polygon

  • not there in real life
  • artifact
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8
Q

intersect vs identify

A
  • both calculate geometric intersection of input layers
  • intersect = AND - only in common features, based on input layer, order doesn’t matter
  • identity = all features of first layer + those that overlap w/ identity layer, order matters
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9
Q

raster

A
  • space divided into small units

- space is tessellated

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10
Q

tessellation

A

process to cover a surface through the repeated use of a single shape

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11
Q

Raster shape

A
  • any reasonable geometric shape that can be connected to create a continuous surface
  • squares, triangles, hexagons
  • not circles - dont interlock
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12
Q

best raster shape

A
  • lattice, grid, square, rectangle
  • interlock, end at edge, fit screens
  • easy to deal with mathematically
  • efficient to store
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13
Q

Information location, raster

A
  • not explicit like coordinates

- recorded by cell location i.e. row 1, col 1

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14
Q

continuous raster

A
  • infinite values

- each cell has one value

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15
Q

Raster issues

A
  • grid cell size
  • data storage
  • only one attribute per layer
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16
Q

why use raster

A
  • data storage

- efficiency and processing speed

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17
Q

types of raster encoding

A
  • row by row, uncompressed
  • run-length encoding
  • boustrophedon
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18
Q

discrete raster

A
  • limited, non-continuous numbers
  • classes, eg. soil class
  • pixels w/ same value = same class
  • similar to polygons, eg. a group of 0’s is a water body
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19
Q

boustrophedon

A

=how oxen ploughs the field

  • right across bottom row
  • left across second last row
  • right …
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20
Q

row by row encoding

A
  • start at bottom left corner
  • right on last row
  • right on second last row
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21
Q

Raster issues, multiple attributes

A
  • stack grids

- raster calculator

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22
Q

raster calculator

A
  • operators (mathematical, boolean)
  • functions
  • queries
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23
Q

Raster calculator, mathematical operators

A

-arithmetic: *, /, -, +

[raster1] + [raster2]

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24
Q

Raster calculator, boolean operators

A

-AND, OR, NOT
[raster1] = 1 AND [raster2] = 4
-binary result

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25
Q

GPS segments

A
  1. space (24 satellites, redundancy)
  2. user segment (receivers)
  3. control segment (ground stations)
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26
Q

control segment

A
  • major stations check altitude, position, speed, health of satellites
  • ‘see’ 11 at a time
  • checked twice a day
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27
Q

measuring distance with GPS

A

-distance = time needed for radio signal transmitted from space to user
= travel t x speed of light

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28
Q

satellite clock features

A
  • 12 hours to orbit earth
  • 4 atomic clocks aboard each satellite
  • 1 billionth of a second precision
  • radio antenna sends signal to E at speed of light
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29
Q

satellite distance

A

10’s of thousands of kms

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30
Q

Trilateration

A 
number of satellites and data you can get 
1= sphere
2 = circle 
3 = points, intersect
4 = height, elevation
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31
Q

ground distance =

A

map distance x representative factor

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32
Q

why use network analysis

A
  • control mobility and flow in discrete spaces

- movement of goods, services, information

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33
Q

what is a network

A
  • set of line segments connected at nodes

- form paths and/or loops

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34
Q

Network links

A

-line segment connected to at least one other link

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35
Q

Network nodes

A
  • junction of links

- end points of links

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36
Q

network valency

A

-number of links at each node

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37
Q

Problems with routing

A
  • shortest path
  • traveling vendor
  • vehicle routing problem
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38
Q

optimal route types

A
  • shortest path

- traveling vendor

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39
Q

shortest path

A
  • find shortest path from origin through set of destinations
  • user defined order
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40
Q

traveling vendor

A
  • shortest tour from origin
  • through destinations in any order
  • back to origin
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41
Q

how shortest path works

A

= minimum cumulative impedance (opposition) between nodes

  • build tree-like structure outward from source
  • algorithm finds path of lowest cost
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42
Q

shortest path complexity based on number of nodes

A

-number of paths = n^3

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43
Q

Traveling vendor problem details

A
  • most efficient order of stops

- solved heuristically

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44
Q

heuristics

A
  • algorithms designed to work quickly and come close to best answer w/o guaranteeing best answer
  • logical, optimal
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45
Q

traveling vendor problem complexity

A

(n-1)!/2

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46
Q

Heuristic method

A
  • start w/ feasible solution
  • shuffle nodes
  • recalculate
  • repeat until satisfied solution not improving
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47
Q

Vehicle routing problem

A
  • variation of TVP

- given a fleet of vehicles and customers schedule routes and visits to minimize travel time

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48
Q

Network example, firestations

A
  • Closest facility: firestations
  • Incidents: house on fire
  • Barriers: one-way streets, construction, etc.
  • Routes
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49
Q

Supply and demand

A

location/allocation

  • locate service
  • allocate demand
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50
Q

location/allocation goals

A
  • minimize travel

- maximize profit

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51
Q

Service area

A
  • region w/i certain travelling time/distance
  • polygons
  • ex. pizza delivery area
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52
Q

service network

A

-streets w/i defined distance/ travel time

53
Q

factors that affect extent of service area

A
  • speed limit
  • travel direction
  • number of lanes
  • traffic congestion
  • slope of street
  • weather
  • time
54
Q

impedance

A

cost associated w/ traversing a network link, stopping, turning, or visiting a centre

55
Q

OD

A

Origin Destination

56
Q

network

A

system of linear features that allows flow of objects

57
Q

network analysis

A

investigate movement of goods, services, information

58
Q

types of network analysis

A
  • shortest path
  • traveling vendor
  • closest facility and location/allocation
59
Q

what is a map

A

a form of communication

60
Q

classifications for mapping areal data

A
  • Chorochromatic

- Choropleth

61
Q

chorochromatic

A
  • qualitative
  • nominal
  • no relative or absolute relationship
  • presence/absence – no greater meaning
62
Q

choropleth

A
  • quantitative

- ordinal, interval, ratio data

63
Q

mapping quantitative data

A
  • Choropleth map
  • proportional to some attribute (colour, shape, texture)
  • range-graded
64
Q

range-graded

A

data grouped into classes

65
Q

key to successful mapping

A

classification

66
Q

classification should

A
  • have exhaustive classes (include all data)
  • have mutually exclusive classes (no overlap in classes)
  • facilitate display of spatial patterns
67
Q

classification rules of thumb

A 
2 : too few
3: simplistic 
4-6: best 
7: complicated
12: TOO MANY
68
Q

fewer classes

A

-louder message

69
Q

how we classify attributes

A
  1. Natural breaks
  2. Equal intervals
  3. Quantile
  4. SD
70
Q

Natural breaks

A
  • “Jenks”
  • natural grouping inherent in data
  • ArcMap identifies breaks that minimize w/i group variance, maximize btw group variance
71
Q

Equal interval

A
  • most common
  • equal-sized subcategories
  • number of classes specified
72
Q

Quantile categories

A
  • each class has equal number of features
  • result can be misleading
  • categories may contain widely different values
73
Q

more classes

A

more information

confuses message

74
Q

Elevation is generated from

A
  • existing contour maps
  • stereo aerial photography
  • satellite imagery
  • laser, LIDAR
75
Q

elevation relative to

A

sea level

76
Q

DEM

A

digital elevation model

  • raster grid w/ elevation values
  • can be shown w/ greyscale or colours
77
Q

isopleth

A

contour lines

-connect points of equal elevation

78
Q

contour interval

A

vertical distance btw contours

79
Q

elevation increase over short distance

A
  • steep

- lots of isopleths close together

80
Q

Aspect

A
  • raster layer
  • slope direction
  • each cell has azimuth that slope faces
  • 360º value
  • flat area = -1
81
Q

examples of aspect uses

A
  • south facing slopes

- solar illumination

82
Q

elevation increases over long distance

A
  • gradual

- isopleths far apart

83
Q

hill shading

A
  • simulate interaction btw sunlight and surface

- shading makes 2D look 3D

84
Q

other cool features

A 
draping
extrusion
TIN
illumination
line of sight
85
Q

TIN

A

triangulated irregular network

86
Q

illumination

A

where the illumination source is - where shadows will be cast

87
Q

what do we need for spatial analysis

A
  • data

- software

88
Q

how does internet include spatial analysis

A

-data is more prevalent than ever before

89
Q

data availability

A
  • internet GIS
  • web services
  • Government agencies
90
Q

ArcGIS online examples

A
  • perform analysis
  • story map
  • ArcGOS webb app builder
91
Q

what makes ArcGIS online different

A
  1. Access
  2. Intuitive
  3. GIL ?
92
Q

GIL

A

geographic information literacy

-understanding what youre doing

93
Q

story map workflow

A
  • select story
  • choose template
  • build
  • share
94
Q

what is story map

A

a form of communication!

  • designed for non-technical ppl
  • tell story of place, event, issue, trend, pattern in a geographic context
95
Q

Key elements of story maps

A

text, video, photos, spreadsheets, GIS data, basemaps, allow for interactive use, queries, popups

96
Q

types of overlay

A
  • visual

- topological

97
Q

visual overlay

A
  • examine areas of intersection btw 2+ maps
  • superimpose layers
  • see where they overlap
  • features remain separate
98
Q

topological overlay

A
  • physical creation of a new data layer out of 2+ original layers
  • enables further analysis on the result
99
Q

topological overlay tools

A
  • union
  • clip
  • intersect
100
Q

Union

A
  • polygons only
  • all areas of both (OR)
  • order doesn’t matter
101
Q

Clip

A
  • points, lines, or polygons
  • keeps all INPUT features
  • order matters
  • overlay layer must be a polygon
  • cookie cutter
  • attributes not combined, only info from input layer is retained
102
Q

Intersect

A
  • points, lines, or polygons
  • attributes are retained from both layers
  • “AND”
103
Q

overlay for points

A

clip or intersect

104
Q

cookie cutter

A

clip

105
Q

trim input layer and keep both sets of attributes

A

intersect

106
Q

how many census tracts are w/i City of Victoria municipal boundaries

A

clip

107
Q

polygon overlay to keep both input and overlay features

A

union

108
Q

if both sets of attributes are important.. clip or intersect?

A

intersect

109
Q

raster data

A
  • defines space as a grid of equally sized cells arranged in rows and columns
  • each cell has attribute value and location coordinate
110
Q

groups of cells with same value, raster

A

geographic features

111
Q

vector data

A
  • represent features as points, lines, polygons
  • points are single co-ordinate pair
  • lines, polygons, list of coordinates
112
Q

vector data attributes

A

-associated w/ each feature (pt, line, polygon)

113
Q

rasterization

A
  • one attribute must be selected from vector

- values automatically assigned, can be reclassified

114
Q

important details in rasterization

A
  • input field (attribute)

- cell size

115
Q

selecting cell size

A

-big enough to be efficient
-small enough to capture required detail
-same as other raster layers
consider:
-resolution, size/memory of database, response time, analysis to be preformed
-never finer than input data

116
Q

reasons for reclassifying

A
  • replace values based on new info
  • grouping like values to simplify data
  • reclassify values to common scale
117
Q

Boolean raster

A

displays only 0 and 1 values

118
Q

example of networks

A
  • streams/rivers
  • roads
  • flight paths
119
Q

example of network analyst questions

A
  • quickest way from pt A to pt B
  • which houses are w/i 5 min of a fire station
  • what areas do a business cover
120
Q

network limitations

A

one way streets
barriers: accidents, road closures
time of day

121
Q

quickest road impedence

A

time

122
Q

finding the best route example

A

google maps

123
Q

OD cost matrix

A

-examines impedance values from each origin to each destination

124
Q

where to find column, row count

A

layer properties

125
Q

area of a pixel

A

resolution ^2

126
Q

buffer wizard for

A

buffers inside polygons

127
Q

area of possible influence along a network from origins based on set criteria (t, length)

A

service area

128
Q

evaluate route length from origins to destinations

A

cost matrix

2017/2018 (7 decks)

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