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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
Q

orientation

A

can we travel in a given direction

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26
Q

containment

A

is x within y (or vice versa)

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27
Q

arc-node topology

A

table defining each node that make up a line (arc)

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28
Q

arc-poly topology

A

table defining arcs that make up each polygon

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29
Q

what are the rows and columns for attribute data tables

A

records and fields respectively

all records have same fields, one record per observation/entity

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30
Q

4 data types of fields

A

characters, integers, floats, BLOBs

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31
Q

characters

A

text or numbers formatted as text “strings”

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32
Q

Integers

A

numbers without decimals

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33
Q

floats

A

numbers with decimals

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34
Q

BLOBs

A

Binary Large OBjects
attachments that are not characters, integers, or floats

references, photos, media, etc

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35
Q

Measurement scale of attribute data

acronym

A

NOIR

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36
Q

T/F: Raster attribute data tables are detailed

A

False, you can only have basic raster tables

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37
Q

What is the feature attribute table in regards to table joins

A

the target table, stores spatial infromation

dbf files

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38
Q

flat files

A

one large file with all the data in it

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39
Q

pros/cons of flat files

A

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)

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40
Q

Primary key

A

attribute in the target table that can uniquely identify a record

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41
Q

relations

A

joining of tables

42
Q

why are joined tables better than a flat file

A

they reduce the likelihood of error

43
Q

what are the 4 cardinalities

target:join

A

1:1, 1:many, many:1, Many:Many

44
Q

1:1

A

one record in target table relates to one record in join table

for every house there is one owner

45
Q

1:M

A

one target record relates to multiple join records

for every subdivision there are multiple houses

46
Q

M:1

A

multiple target records relate to one join record

multiple houses are in one subdivision

47
Q

M:M

A

any combintion of the other 3 relations

48
Q

outer join

A

keep all records, keep all inputs in a joined table

49
Q

inner join

A

keep only matching records, leaves no empty entries

50
Q

Structured Query Language

SQL

A

language to talk about data, provides syntax

51
Q

Query

A

Question posed to database in form of a SELECT statement

52
Q

SQL used for a query: records, table, field

A

SELECT, FROM, WHERE

53
Q

Comparison operators

A

find matches and thresholds, look only for what you need, symbols

<, >, =, not equal

54
Q

Logical operators

A

Boolean membership functions

AND, OR, NOT, XOR

55
Q

FROM is always the ____ ____ table

A

feature attribute

56
Q

AND does what to the result sets and includes what in a venn diagram

A

narrows results, and is the middle only

57
Q

OR does what to result sets and includes what in a venn diagram

A

widens results and includes all of the venn diagram

58
Q

NOT does what to the results and looks like what in a venn diagram

A

increases precision and includes only one side with no middle

59
Q

XOR does what to results and looks like what in a venn diagram

A

satisfies either x or y but not both, includes both sides of venn diagram without the middle

60
Q

Spatial Joins

A

match records from a join layer to a target layer

.shp files

61
Q

spatial joins by distance

A

attributes of nearest join feature added to target layer along with distance

always 1:1 - you can only have one closest thing

62
Q

spatial joins by CONTAINS

A

polygon as target, points as join, attribute table would list points in each polygon record

63
Q

Spatial join by IS_WITHIN

A

points as target, polygon as join, for each point record it would list the polygon it is within

64
Q

is there a change to output layer geometry with spatial joins

A

No

65
Q

how are overlays different from spatial joins

A

geometry of the output changes

66
Q

UNION overlay

A

polygon overlay, keeps all features from both datasets (OR)

67
Q

INTERSECT overlay

A

Input can be anything but INTERSECT feature must be polygon, features common to all inputs kept, (AND)

68
Q

IDENTITY overlay

A

input anything, IDENTITY feature must be polygon, keeps all inputs and changes attributes that intersect identity feature

69
Q

T/F: dimensionality and extent of output matches input always

A

True, for all three overlays

70
Q

Minimum Mappable Unit

A

arbitrarily decided, smallest entity you want to be represented, a threshold

71
Q

ELIMINATE tool

A

slivers < MMU are merged with largest neighbour or largest boundary

non-overlay

72
Q

CLIP tool

A

Changes extents of the inputs to fit an area of interest (AOI)

non-overlay

73
Q

SPLIT tool

A

split a vector feature into set of contiguous smaller features

non-overlay

74
Q

DISSOLVE tool

A

change geometry, turn smaller features into larger ones

(merge municipalities into counties, or counties into states)

non-overlay

75
Q

Buffering

A

creates polygons around pts, lines, plygns based on a buffer distance from those features

76
Q

Raster analysis includes ____ surfaces and ____ gradients

A

continuous, concentration

77
Q

continuous surfaces and examples

A

things that exist everywhere

elevation, temperature, land cover

78
Q

concentration gradients and examples

A

phenomena that extends from a point, varying as it distances

smoke, crime stats, pollination, flooding

79
Q

Logical operators

A

Boolean conditions, selecting based on criteria

80
Q

Arithmetic operators

A

+, -, *, /, tan, sin

81
Q

“overlay” operators

A

logical/arithmetic operators on multiple datasets, stacking raster layers together based on pixels

needs column-row coincidence

82
Q

what is column-row coincidence

A

when pixels line up, boundaries line up with boundaries perfectly

83
Q

T/F: in overlay operations, output pixels will all have data even if not all layers have data for a pixel

A

False. If anything has no data, then that pixel will have no data in the output

84
Q

Geometric operators

A

projection, resampling (change in spatial resolution), warping (rubber-sheeting)

85
Q

map algebra

A

uses a raster calculator of operators

remember input is always same form as input

86
Q

3 Scopes of Raster analysis

A

local, focal, zonal

87
Q

local scope

A

operations performed on a cell-by-cell basis, no influence from neighbouring pixels, calculate through layers not across

True or False, yes or no, 0 or 1

88
Q

Focal scope

moving window

A

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
Q

will the output be smaller or larger than the input in a focal raster analysis

A

smaller, because you lose the outside layer(s) of pixels

90
Q

Low-pass filter is a ____ filter

A

smoothing

91
Q

what does a low-pass filter do

A

allows low frequency variation to remain, removes high variation and turns it into gradation instead of hard breaks

92
Q

Low-pass filters take the ____

A

average, and place it in the center of the kernel

93
Q

Median filters do what

A

eliminate extreme values, and remove striping

94
Q

median filters take the ____

A

median and put it in the center

95
Q

high-pass filters

A

highlight variation, are a sharpening filter

96
Q

two types of high-pass filters

A

laplace and sobel

97
Q

laplace filters

A

accentuate differences in values between neighbours, by accentuating center pixel, depressing cardinal directions, and diagonals as 0

raises the contrast

98
Q

sobel filters

A

look for vertical edges/boundaries between pixels or horizontal edges

99
Q

zones in raster analysis are …

A

groups of cells with the same value (code) and gaps are represented by no data cells

can be contiguous or not

100
Q

zonal raster analysis

A

finds some stat from each zone and applies it to every corresponding zone cell

EAS 221 (7 decks)

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