|Adjacency patterns: one viewpoint.
The dot density map.
It is interesting to consider how to use a GIS
to do some analysis of mapped information--using a bit of creative effort.
Dot density maps: layer of randomization, layer
of observation--scale change; absolute representation (1 dot represents
1000 people) and relative representation (1 dot represents 0.1% of the
population of the state). Use ArcView.
The concept of clustering is tied to scale.
Interesting reading on related topics: Mark Monmonier and Harm deBlij,
How to Lie with Maps, University of Chicago Press.
Equal Area Projections and dot density maps--one way to look for clustering
in geographic space.
Select a distribution that can usefully be represented as dot scatter--such
Then, choose polygonal nets of at least two different scales--such as state
Let the map with the smallest spatial units (counties) be used as the randomizing
scatter is spread around randomly within each unit.
View the scatter through polygons (states) that are larger than are those
of the randomizing layer (counties). Map
shows the results of removing the county boundaries; Map
2 shows the national picture with state boundaries; and, Map
3 views the dot scatter through the national border lens. The
clustering of dots at the state level means something; at the county level
it is merely random.
Select an equal
area projection (such as an Albers Equal Area Conic for the U.S.).
Because the underlying projection is an equal area projection, a unit square
(or other polygon) may be placed anywhere on the map and comparisons made
between one location and another. Indeed, urban or rural measures
might be held up to a value associated with similar polygon tossed out