Points can represent "fixed" entities
In this case, points are qualitatively similar to polygons/lines
The goal here is, taking location fixed, to model other aspects of the data
Examples:
Point data are not only a different geometry than polygons or lines...
... Points can also represent a fundamentally different way to approach spatial analysis
[Source]
[Source]
Distribution of points over a portion of space
Assumption is a point can happen anywhere on that space, but only happens in specific locations
Point Pattern Analysis
Describe, characterize, and explain point patterns, focusing on their generating process
Two routes (today):
Use polygon boundaries and count points per area
[Insert your skills for choropleth mapping here!!!]
But, the polygons need to "make sense" (their delineation needs to relate to the point generating process)
If no polygon boundary seems like a good candidate for aggregation...
...draw a hexagonal (or squared) tesselation!!!
Hexagons...
(Arbitrary) aggregation may induce MAUP (see Lecture 4)
Points usually represent events that affect only part of the population and hence are best considered as rates (see Lecture 4)
Estimate the (continuous) observed distribution of a variable
[Source]
Probability of finding observations at a given point in space
Cluster is a hard to define term
Huge literature spanning spatial analysis, statistics and computer science. Today, we'll look at...
D
ensity
B
ased
S
patial
C
lustering of
A
pplications with
N
oise
DBSCAN
(Additional) Pros:
(Additional) Cons:
Geographic Data Science'16 - Lecture 8 by Dani Arribas-Bel is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.