This document discusses using shapefiles and heatmaps to visualize geographic data in R. It describes how shapefiles contain polygon and attribute data for geographic regions in the Netherlands that can be read into R. It also explains how heatmaps can be used to explore the relationship between variables, where the value for each grid cell is calculated as the weighted average of nearby data points based on distance. Heatmaps provide an alternative to 3D graphs for visualizing relationships between more than two variables.

1. Factual
decision
making
Visualisation functions in R
Overview of heatmaps
Hoksan Yip | 17 juni 2013 | Rotterdam

2. Factual
decision
making 2
1 year ago ...
• Each postal code is
plotted as a dot
• Slow in plotting
• Switch needed to
Shapefiles

3. Factual
decision
making
Shapefiles
• Shapefiles of the Netherlands available at CBS site on
various levels (not on postal code level though)
• Consists of 3 files with the same name (different
extension):
• .shp contains the polygons of all shapes
• .shx is an index file of the regions
• .dbf contains statistical information of each region (from CBS)
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4. Factual
decision
making
DBF file
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5. Factual
decision
making
Polygons
• Set of points on the map
which forms the borders
of a region.
• Each region may have
several polygons
(Zeeland)
• Can be read into R
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6. Factual
decision
making
Levels
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2.716 Districts (wijk)
12.002 Neighbourhoods (buurt)
12 Provinces (provincie)
418 Municipals (gemeente)

7. Extensions: finding nearby municipals
• Given a point on the
map, we can select all
regions in the area.
• We can extract statistics
of each area per point.
• Problem: the radius is
fixed and is not relative to
the population density
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8. Extensions: finding nearby municipals – corrected distance
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9. Heatmaps part 2 – using on variable
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• Exploring the dependent
value within the area of
two input variables
• Hard to use 3-dimensional
graphs
• Similar to geographical
data, but here no regions
are specified.

10. Heatmaps part 2 – using on variable (2)
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• Idea: make use of grid areas.
• Value of the area is the weighted average of the nearby
observations.
• Weight dependent on the distance (the closer the
observation, the higher the weight):

11. Heatmaps part 2 – using on variable (3)
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• Result: