Topic 10: Vector Analysis

Question 1

Overview of Spatial Analysis

Answer 1

Analyzing spatial data typically goes beyond mere measurement or data queries

Question 2

• Discuss vector data transformation operations:
• Buffering (proximity analysis)
• techniques and applications

Answer 2

• buffering: builds new objects around existing point, line, or polygons by identifying all areas within a specified distance
• can be produced as independent or overlapping
• for polygons, both exterior and interior buffers are possible

Question 3

Disucss vector data transformation operations:
- Overlay and extract operations
- feature types and overlay methods
- extract operations/ feature manipulation

Answer 3

• Combines the geometries of two new feature layers (with the same spatial reference) to create new data
• Three types: Point in polygon overlays, Line in polygon overlays, polygon on polygon overlays
• Feature manipulation: refers to the process of deriving new or updated geometries from two or more existing datasets (eg., clip, split, erase, select by attributes, dissolve, etc). Very related to overlays

Question 4

Pattern analysis
- dispersion, spatial autocorrelation, and clustering

Answer 4

• Pattern analysis: is the study of the spatial arrangements of point or polygon features
• point pattern analysis = point dispersion
• Spatial Autocorrelation metrics asses the relationship between proximity and similarity of the attribute feature

Question 5

a simple metric of point dispersion to identify whether a point pattern is regular (dispersed) or clumped (clustered) is the variance-to-mean ratio

Answer 5

• Ripley’s K
  if variance = mean, pattern is random
• if variance exceeds the mean >1, pattern is clustered
• if variance is less than the mean <1, pattern is regular

Question 6

Getis-Ord General G statistic

Answer 6

points or polygons, high/low clustering

Question 7

Positive, negative, zero spatial autocorrelation

Answer 7

• positive = features that are close in location are also similar in attributes
• negative = features that are close in location tend to be dissimilar in attributes
• zero = attributes are independent of location

Question 8

Tobler’s First Law of Geography

Answer 8

“all things are related, but nearby things are more related than distant things”
- early expression of auto correlation

Question 9

Measuring Spatial Autocorrelation

Answer 9

• Moran’s I is widely used measure of global spatial autocorrelation, which ranges from -1 to +1
• Negative = negative spatial autocorrelation
• Positive = positive spatial autocorrelation
• zero = spatial independence