12 - Spatial data management

Question 1

Generalizing methods (5)

Answer 1

• Coord thinning and smoothing
• aggregating
• classifying
• recoding
• resampling

Question 2

Common errors in spatial data (6)

Answer 2

• missing entities
• duplicate entities
• mis-located entities
• missing labels
• duplicate labels
• artefacts of digitizing

Question 3

Snapping distance def

Answer 3

The distance a GIS uses to search for the closest vertex and / or segment you are trying to connect when you digitise.

Question 4

How to eliminate slivers

Answer 4

1. Poly - deletes longest arc and label point
2. Line - arc merge with its longest neighbour - shares pseudo node

Question 5

Non-topo editing def

Answer 5

spatial editing, BUT no topology gets
defined in the editing process.

Question 6

Non-topo editing examples

Answer 6

1. editing existing features
2. creating new features
3. edge matching
4. generalizations

Question 7

Non-topo errors def

Answer 7

Variety of basic editing operations that can modify simple features and create new features from existing features

Question 8

editing existing features (NON-topo)

Answer 8

• extend/ trim
• delete/ move
• reshape
• split

Question 9

Buffer def

Answer 9

Create boundaries around a feature at an equal distance in all directions

Question 10

Union def

Answer 10

Combine features from different layers into one

Question 11

Intersect def

Answer 11

Create a new feature from the intersection of overlapped features in different layers

Question 11

Edge matching def

Answer 11

the process to determine which edges (lines) should be linked among candidates

Question 12

Simplify

Answer 12

Point remove & bend simplify

Question 13

Smooth

Answer 13

Paek (inside) & Bezier interpolation

Question 14

Geometric transformation def

Answer 14

To make digitized, scanned or imported data usable, we must
convert the newly digitized map/data into a projected
coordinate system

Question 15

Map projection vs geometric transformations

Answer 15

• Map projection converts data sets from 3D format (Earths sphere) into 2D planar coordinates (Cartesian graticule)
• Geometric transformation converts data sets from 2D digitizer
  units/Pixels into 2D planar coordinates (Cartesian graticule)

Question 16

Geometrical transformation def

Answer 16

the process of using a set of control
points & transformation equations to register a digitized map, satellite
image or aerial photo into projected coordinates

Question 17

Geometric transformation reasons

Answer 17

(1) improve spatial reliability
(2) Make geographical data sets compatible with other data sets

Question 18

Examples of geometric transformations

Answer 18

a) Registering
b) Rubbersheeting
c) Re-projecting
d) Scaling
e) Translating

Question 19

Geometric transformations

Answer 19

1. Equiarea
2. Similarity
3. Affine
4. Projective

Question 20

Resampling methods

Answer 20

1. Nearest Neighbor
2. Bilinear interpolation
3. Cubic convolution

Question 21

Nearest Neighbor def

Answer 21

fills each pixel of the new image with the nearest pixel value from the original image

Question 22

Bilinear interpolation def

Answer 22

uses the average of the four nearest pixel values from three linear interpolations

Question 23

Cubic convolution def

Answer 23

uses the average of the 16 nearest pixel values from five cubic polynomial interpolations

Question 24

RMSE def

Answer 24

• measures deviations between coordinate values on a map and
  coordinate values from an independent source of higher accuracy for identical points
• Measures the displacement between actual and estimated locations of control points

Question 25

Higher accuracy data sources

Answer 25

• GPS
• Other digital/hardcopy map data
• Survey data