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EAS 221 > Midterm 1, Deck pt 2 > Flashcards

Midterm 1, Deck pt 2 Flashcards

Data Collection to Raster Analysis

1
Q

accuracy

A

degree to which measurement is correct

how much you trust the data

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2
Q

precision

A

repeatability of the measurement and how small of a scale you are measuring (mm more precise than cm)

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3
Q

2 types of data collection

A

primary vs secondary

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4
Q

Primary data collection

A

a direct measurement, you collect yourself

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5
Q

secondary data collection

A

something collected in advance or using pre-existing data

always check metadata to make sure its trustworthy

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6
Q

T/F: secondary data collection is something we can control

A

True, we determine what our reolution, precision, accuracy is

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7
Q

examples of primary data collection

A

surveying land, GPS measurements (using satellites), taking air photos yourself, photogrammetry

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8
Q

examples of secondary data collection

A

scanning existing vector and raster data, DEMs, gazeteers, digitizing, heads up

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9
Q

Digitizing

A

converting geographic features of a map into digital format

digitizing tables time-consuming and impractical

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10
Q

digitizing always converts ____ pixels into ______ data

A

raster, vector

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11
Q

“Heads up” digitizing

A

digitize scanned maps/documents directly from a computer screen

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12
Q

4 steps of “heads up”

A

source data, georeference base data, digitize, edit

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13
Q

Georeferencing base data includes ____ or ____

R or R

A

rectify or register

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14
Q

rectify

A

rearrange locations to correspond to a specific reference system (coordinates)

map-world

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15
Q

register

A

rearrangement of locations in one data set to correspond to same locations in another data set

map-map

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16
Q

Linear transformation

A

everything moves by same amount, distortion is same across image

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17
Q

differential transformation

rubber sheeting

A

inconsistent stretch in image in different spots, Defining ground control points plotted as polynomial models

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18
Q

Ground control points should be …

A

easily identifiable, precise, discreet, well distributed, temporally consistent, and ideal for crosshairs

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19
Q

T/F: it is better to use more GCPs than a higher polynomial

A

True, it reduces error and keeps it less complex

just remeber to keep them well distributed or you’ll only have one detailed section

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20
Q

tie vs tic components in a GCP

A

Ground control (reference coordinates) vs Map locations (source coordinates)

you “tie” the new points down

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21
Q

Topology

A

relations used to validate the geometry of points, lines, and polygons

qualitative over quantitative (yes/no)

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22
Q

topographical relations

4

A

connectivity, adjacency, orientation, containment

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23
Q

connectivity

A

are two points connected

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24
Q

adjacency

A

are x and y next to each other

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25
orientation
can we travel in a given direction
26
containment
is x within y (or vice versa)
27
arc-node topology
table defining each node that make up a line (arc)
28
arc-poly topology
table defining arcs that make up each polygon
29
what are the rows and columns for attribute data tables
records and fields respectively ## Footnote all records have same fields, one record per observation/entity
30
4 data types of fields
characters, integers, floats, BLOBs
31
characters
text or numbers formatted as text "strings"
32
Integers
numbers without decimals
33
floats
numbers with decimals
34
BLOBs
Binary Large OBjects attachments that are not characters, integers, or floats ## Footnote references, photos, media, etc
35
Measurement scale of attribute data | acronym
NOIR
36
T/F: Raster attribute data tables are detailed
False, you can only have basic raster tables
37
What is the feature attribute table in regards to table joins
the target table, stores spatial infromation ## Footnote dbf files
38
flat files
one large file with all the data in it
39
pros/cons of flat files
Pro: convenient for simple, ltd data sets cons: bad for complex data sets, data redundancy (repeats of fields), and data consistency (different data types/spelling/formatting)
40
Primary key
attribute in the target table that can uniquely identify a record
41
relations
joining of tables
42
why are joined tables better than a flat file
they reduce the likelihood of error
43
what are the 4 cardinalities | target:join
1:1, 1:many, many:1, Many:Many
44
1:1
one record in target table relates to one record in join table | for every house there is one owner
45
1:M
one target record relates to multiple join records ## Footnote for every subdivision there are multiple houses
46
M:1
multiple target records relate to one join record ## Footnote multiple houses are in one subdivision
47
M:M
any combintion of the other 3 relations
48
outer join
keep all records, keep all inputs in a joined table
49
inner join
keep only matching records, leaves no empty entries
50
Structured Query Language | SQL
language to talk about data, provides syntax
51
Query
Question posed to database in form of a SELECT statement
52
SQL used for a query: records, table, field
SELECT, FROM, WHERE
53
Comparison operators
find matches and thresholds, look only for what you need, symbols | <, >, =, not equal
54
Logical operators
Boolean membership functions | AND, OR, NOT, XOR
55
FROM is always the ____ ____ table
feature attribute
56
AND does what to the result sets and includes what in a venn diagram
narrows results, and is the middle only
57
OR does what to result sets and includes what in a venn diagram
widens results and includes all of the venn diagram
58
NOT does what to the results and looks like what in a venn diagram
increases precision and includes only one side with no middle
59
XOR does what to results and looks like what in a venn diagram
satisfies either x or y but not both, includes both sides of venn diagram without the middle
60
Spatial Joins
match records from a join layer to a target layer | .shp files
61
spatial joins by distance
attributes of nearest join feature added to target layer along with distance ## Footnote always 1:1 - you can only have one closest thing
62
spatial joins by CONTAINS
polygon as target, points as join, attribute table would list points in each polygon record
63
Spatial join by IS_WITHIN
points as target, polygon as join, for each point record it would list the polygon it is within
64
is there a change to output layer geometry with spatial joins
No
65
how are overlays different from spatial joins
geometry of the output changes
66
UNION overlay
**polygon** overlay, keeps all features from both datasets (OR)
67
INTERSECT overlay
Input can be anything but INTERSECT feature must be **polygon**, features common to all inputs kept, (AND)
68
IDENTITY overlay
input anything, IDENTITY feature must be polygon, keeps all inputs and changes attributes that intersect identity feature
69
T/F: dimensionality and extent of output matches input always
True, for all three overlays
70
Minimum Mappable Unit
arbitrarily decided, smallest entity you want to be represented, a threshold
71
ELIMINATE tool
slivers < MMU are merged with largest neighbour or largest boundary ## Footnote non-overlay
72
CLIP tool
Changes extents of the inputs to fit an area of interest (AOI) ## Footnote non-overlay
73
SPLIT tool
split a vector feature into set of contiguous smaller features ## Footnote non-overlay
74
DISSOLVE tool
change geometry, turn smaller features into larger ones | (merge municipalities into counties, or counties into states) ## Footnote non-overlay
75
Buffering
creates polygons around pts, lines, plygns based on a buffer distance from those features
76
Raster analysis includes ____ surfaces and ____ gradients
continuous, concentration
77
continuous surfaces and examples
things that exist everywhere ## Footnote elevation, temperature, land cover
78
concentration gradients and examples
phenomena that extends from a point, varying as it distances | smoke, crime stats, pollination, flooding
79
Logical operators
Boolean conditions, selecting based on criteria
80
Arithmetic operators
+, -, *, /, tan, sin
81
"overlay" operators
logical/arithmetic operators on multiple datasets, stacking raster layers together based on pixels ## Footnote needs column-row coincidence
82
what is column-row coincidence
when pixels line up, boundaries line up with boundaries perfectly
83
T/F: in overlay operations, output pixels will all have data even if not all layers have data for a pixel
False. If anything has no data, then that pixel will have no data in the output
84
Geometric operators
projection, resampling (change in spatial resolution), warping (rubber-sheeting)
85
map algebra
uses a raster calculator of operators ## Footnote remember input is always same form as input
86
3 Scopes of Raster analysis
local, focal, zonal
87
local scope
operations performed on a cell-by-cell basis, no influence from neighbouring pixels, calculate through layers not across ## Footnote True or False, yes or no, 0 or 1
88
Focal scope | moving window
have a focus (kernel) and take some stat and place it in your center pixel of the focus, then move over and do it over and over again
89
will the output be smaller or larger than the input in a focal raster analysis
smaller, because you lose the outside layer(s) of pixels
90
Low-pass filter is a ____ filter
smoothing
91
what does a low-pass filter do
allows low frequency variation to remain, removes high variation and turns it into gradation instead of hard breaks
92
Low-pass filters take the ____
average, and place it in the center of the kernel
93
Median filters do what
eliminate extreme values, and remove striping
94
median filters take the ____
median and put it in the center
95
high-pass filters
highlight variation, are a sharpening filter
96
two types of high-pass filters
laplace and sobel
97
laplace filters
accentuate differences in values between neighbours, by accentuating center pixel, depressing cardinal directions, and diagonals as 0 ## Footnote raises the contrast
98
sobel filters
look for vertical edges/boundaries between pixels **or** horizontal edges
99
zones in raster analysis are ...
groups of cells with the same value (code) and gaps are represented by no data cells ## Footnote can be contiguous or not
100
zonal raster analysis
finds some stat from each zone and applies it to every corresponding zone cell

EAS 221 (7 decks)

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