GEOG 222 II Flashcards
(128 cards)
1
Q
Intersection =
A
only location in both remain
-AND
2
Q
Union =
A
locations in either remain
-OR
3
Q
cookie cutter tool
A
clip
4
Q
like the opposite of the clip tool, what is left over
A
erase
5
Q
things to watch out for with intersection and overlay
A
common boundaries
spurious polygons
mixing up identify and intersect
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
7
Q
new polygon formed from common boundaries
A
spurious polygon
- not there in real life
- artifact
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
9
Q
raster
A
- space divided into small units
- space is tessellated
10
Q
tessellation
A
process to cover a surface through the repeated use of a single shape
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
12
Q
best raster shape
A
- lattice, grid, square, rectangle
- interlock, end at edge, fit screens
- easy to deal with mathematically
- efficient to store
13
Q
Information location, raster
A
- not explicit like coordinates
- recorded by cell location i.e. row 1, col 1
14
Q
continuous raster
A
- infinite values
- each cell has one value
15
Q
Raster issues
A
- grid cell size
- data storage
- only one attribute per layer
16
Q
why use raster
A
- data storage
- efficiency and processing speed
17
Q
types of raster encoding
A
- row by row, uncompressed
- run-length encoding
- boustrophedon
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
19
Q
boustrophedon
A
=how oxen ploughs the field
- right across bottom row
- left across second last row
- right …
20
Q
row by row encoding
A
- start at bottom left corner
- right on last row
- right on second last row
21
Q
Raster issues, multiple attributes
A
- stack grids
- raster calculator
22
Q
raster calculator
A
- operators (mathematical, boolean)
- functions
- queries
23
Q
Raster calculator, mathematical operators
A
-arithmetic: *, /, -, +
[raster1] + [raster2]
24
Q
Raster calculator, boolean operators
A
-AND, OR, NOT
[raster1] = 1 AND [raster2] = 4
-binary result
25
GPS segments
1. space (24 satellites, redundancy)
2. user segment (receivers)
3. control segment (ground stations)
26
control segment
- major stations check altitude, position, speed, health of satellites
- 'see' 11 at a time
- checked twice a day
27
measuring distance with GPS
-distance = time needed for radio signal transmitted from space to user
= travel t x speed of light
28
satellite clock features
- 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
29
satellite distance
10's of thousands of kms
30
Trilateration
```
number of satellites and data you can get
1= sphere
2 = circle
3 = points, intersect
4 = height, elevation
```
31
ground distance =
map distance x representative factor
32
why use network analysis
- control mobility and flow in discrete spaces
| - movement of goods, services, information
33
what is a network
- set of line segments connected at nodes
| - form paths and/or loops
34
Network links
-line segment connected to at least one other link
35
Network nodes
- junction of links
| - end points of links
36
network valency
-number of links at each node
37
Problems with routing
- shortest path
- traveling vendor
- vehicle routing problem
38
optimal route types
- shortest path
| - traveling vendor
39
shortest path
- find shortest path from origin through set of destinations
- user defined order
40
traveling vendor
- shortest tour from origin
- through destinations in any order
- back to origin
41
how shortest path works
= minimum cumulative impedance (opposition) between nodes
- build tree-like structure outward from source
- algorithm finds path of lowest cost
42
shortest path complexity based on number of nodes
-number of paths = n^3
43
Traveling vendor problem details
- most efficient order of stops
| - solved heuristically
44
heuristics
- algorithms designed to work quickly and come close to best answer w/o guaranteeing best answer
- logical, optimal
45
traveling vendor problem complexity
(n-1)!/2
46
Heuristic method
- start w/ feasible solution
- shuffle nodes
- recalculate
- repeat until satisfied solution not improving
47
Vehicle routing problem
- variation of TVP
| - given a fleet of vehicles and customers schedule routes and visits to minimize travel time
48
Network example, firestations
- Closest facility: firestations
- Incidents: house on fire
- Barriers: one-way streets, construction, etc.
- Routes
49
Supply and demand
location/allocation
- locate service
- allocate demand
50
location/allocation goals
- minimize travel
| - maximize profit
51
Service area
- region w/i certain travelling time/distance
- polygons
- ex. pizza delivery area
52
service network
-streets w/i defined distance/ travel time
53
factors that affect extent of service area
- speed limit
- travel direction
- number of lanes
- traffic congestion
- slope of street
- weather
- time
54
impedance
cost associated w/ traversing a network link, stopping, turning, or visiting a centre
55
OD
Origin Destination
56
network
system of linear features that allows flow of objects
57
network analysis
investigate movement of goods, services, information
58
types of network analysis
- shortest path
- traveling vendor
- closest facility and location/allocation
59
what is a map
a form of communication
60
classifications for mapping areal data
- Chorochromatic
| - Choropleth
61
chorochromatic
- qualitative
- nominal
- no relative or absolute relationship
- presence/absence -- no greater meaning
62
choropleth
- quantitative
| - ordinal, interval, ratio data
63
mapping quantitative data
- Choropleth map
- proportional to some attribute (colour, shape, texture)
- range-graded
64
range-graded
data grouped into classes
65
key to successful mapping
classification
66
classification should
- have exhaustive classes (include all data)
- have mutually exclusive classes (no overlap in classes)
- facilitate display of spatial patterns
67
classification rules of thumb
```
2 : too few
3: simplistic
4-6: best
7: complicated
12: TOO MANY
```
68
fewer classes
-louder message
69
how we classify attributes
1. Natural breaks
2. Equal intervals
3. Quantile
4. SD
70
Natural breaks
- "Jenks"
- natural grouping inherent in data
- ArcMap identifies breaks that minimize w/i group variance, maximize btw group variance
71
Equal interval
- most common
- equal-sized subcategories
- number of classes specified
72
Quantile categories
- each class has equal number of features
- result can be misleading
- categories may contain widely different values
73
more classes
more information
| confuses message
74
Elevation is generated from
- existing contour maps
- stereo aerial photography
- satellite imagery
- laser, LIDAR
75
elevation relative to
sea level
76
DEM
digital elevation model
- raster grid w/ elevation values
- can be shown w/ greyscale or colours
77
isopleth
contour lines
| -connect points of equal elevation
78
contour interval
vertical distance btw contours
79
elevation increase over short distance
- steep
| - lots of isopleths close together
80
Aspect
- raster layer
- slope direction
- each cell has azimuth that slope faces
- 360º value
- flat area = -1
81
examples of aspect uses
- south facing slopes
| - solar illumination
82
elevation increases over long distance
- gradual
| - isopleths far apart
83
hill shading
- simulate interaction btw sunlight and surface
| - shading makes 2D look 3D
84
other cool features
```
draping
extrusion
TIN
illumination
line of sight
```
85
TIN
triangulated irregular network
86
illumination
where the illumination source is - where shadows will be cast
87
what do we need for spatial analysis
- data
| - software
88
how does internet include spatial analysis
-data is more prevalent than ever before
89
data availability
- internet GIS
- web services
- Government agencies
90
ArcGIS online examples
- perform analysis
- story map
- ArcGOS webb app builder
91
what makes ArcGIS online different
1. Access
2. Intuitive
3. GIL ?
92
GIL
geographic information literacy
| -understanding what youre doing
93
story map workflow
- select story
- choose template
- build
- share
94
what is story map
a form of communication!
- designed for non-technical ppl
- tell story of place, event, issue, trend, pattern in a geographic context
95
Key elements of story maps
text, video, photos, spreadsheets, GIS data, basemaps, allow for interactive use, queries, popups
96
types of overlay
- visual
| - topological
97
visual overlay
- examine areas of intersection btw 2+ maps
- superimpose layers
- see where they overlap
- features remain separate
98
topological overlay
- physical creation of a new data layer out of 2+ original layers
- enables further analysis on the result
99
topological overlay tools
- union
- clip
- intersect
100
Union
- polygons only
- all areas of both (OR)
- order doesn't matter
101
Clip
- 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
Intersect
- points, lines, or polygons
- attributes are retained from both layers
- "AND"
103
overlay for points
clip or intersect
104
cookie cutter
clip
105
trim input layer and keep both sets of attributes
intersect
106
how many census tracts are w/i City of Victoria municipal boundaries
clip
107
polygon overlay to keep both input and overlay features
union
108
if both sets of attributes are important.. clip or intersect?
intersect
109
raster data
- defines space as a grid of equally sized cells arranged in rows and columns
- each cell has attribute value and location coordinate
110
groups of cells with same value, raster
geographic features
111
vector data
- represent features as points, lines, polygons
- points are single co-ordinate pair
- lines, polygons, list of coordinates
112
vector data attributes
-associated w/ each feature (pt, line, polygon)
113
rasterization
- one attribute must be selected from vector
| - values automatically assigned, can be reclassified
114
important details in rasterization
- input field (attribute)
| - cell size
115
selecting cell size
-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
reasons for reclassifying
- replace values based on new info
- grouping like values to simplify data
- reclassify values to common scale
117
Boolean raster
displays only 0 and 1 values
118
example of networks
- streams/rivers
- roads
- flight paths
119
example of network analyst questions
- 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
network limitations
one way streets
barriers: accidents, road closures
time of day
121
quickest road impedence
time
122
finding the best route example
google maps
123
OD cost matrix
-examines impedance values from each origin to each destination
124
where to find column, row count
layer properties
125
area of a pixel
resolution ^2
126
buffer wizard for
buffers inside polygons
127
area of possible influence along a network from origins based on set criteria (t, length)
service area
128
evaluate route length from origins to destinations
cost matrix
