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