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.
- 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 as population.
- Then, choose polygonal nets of at least two different scales--such as state and county boundaries.
- Let the map with the smallest spatial units (counties) be used as the randomizing layer--the dot 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 1 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 randomly.
How to Lie with Maps, University of Chicago Press. |