Transcript Simple Thematic Mapping with Stata

Simple Thematic Mapping
in Stata
3rd German Stata Users
Group Meeting
Berlin, 8 April 2005
Maurizio Pisati
University of Milano Bicocca – Italy
[email protected]
Thematic maps

Thematic maps represent the spatial
distribution of one or more variables of interest
within a given geographical unit
Thematic maps

Examples:
–
–
A sociologist could use a choropleth map (a.k.a.
shaded map) to show how the percentage of
families below the poverty line varies across the
states or the provinces of a given country
A police officer could be interested in analyzing a
dot map showing the locations of drug markets
within a given city
Software for thematic mapping


Usually, to produce state-of-the-art thematic
maps one has to resort to specialized software
(e.g., ArcView, MapInfo)
In some cases, however, it is possible to
exploit the graphical engine of a generalpurpose statistical package to draw simple but
effective thematic maps
Stata’s mapping capabilities


Up until version 7, Stata offered very limited
mapping capabilities
On the other hand, the graphical engine
introduced in Stata 8 is quite flexible and
makes it possible to draw several kinds of
maps in a relatively simple manner
The tmap package

The tmap package is a suite of Stata programs
designed to draw five kinds of thematic map:
–
–
–
–
–
Choropleth maps
Proportional symbol maps
Deviation maps
Dot maps
Label maps
The tmap package



Choropleth, proportional symbol, and deviation
maps are intended to depict area data
Dot maps are suitable for representing point
data
Label maps can be used to show data of both
types
The tmap package

The tmap package exploits the possibility –
offered by the new Stata graphical engine – to
overlay a large number of different graphs,
each of which is used to create a distinct
element of the desired map
The tmap package

Specifically:
–
–
graph twoway area is used to draw the outlines
of the geographical areas of interest and to fill them
with the appropriate colors
graph twoway scatter is used to plot the
proper symbols or labels when required
Required datasets

To use tmap, one typically needs two Stata
datasets:
–
–
The master dataset
The boundary dataset
Master dataset


The master dataset is intended to store the
spatial data to be represented
It is a standard cases-by-variables dataset
whose rows represent the geographical areas
or locations objects of analysis
Master dataset: example
id
land
turnout
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Baden-Wuerttemberg
Bayern
Bremen
Hamburg
Hessen
Niedersachsen
Nordrhein-Westfalen
Rheinland-Pfalz
Saarland
Schleswig-Holstein
Brandenburg
Mecklenburg-Vorpommern
Sachsen
Sachsen-Anhalt
Thueringen
Berlin
62.6
57.1
61.3
68.7
64.6
67.0
56.7
62.1
55.5
66.5
56.4
70.6
59.6
56.5
53.8
68.1
Boundary dataset

The boundary dataset is intended to store the
geographical boundaries of the whole
geographical unit of interest R or of its subareas Ai (i = 1,…,n)
Boundary dataset

The boundary dataset must always include
the following three variables:
–
–
–
_ID, which contains the numeric identifier of R or of
each sub-area Ai
_X, which contains the x-coordinates of the polygon
or polygons that make up R or each sub-area Ai
_Y, which contains the y-coordinates of the polygon
or polygons that make up R or each sub-area Ai
Boundary dataset

If one or more of the sub-areas Ai are “islands”,
i.e., are completely surrounded by the territory
of another sub-area, then the boundary
dataset must include an additional variable:
–
_ISLAND, which takes value 1 when the
corresponding sub-area is an “island”, and value 0
otherwise
Boundary dataset

The boundary dataset must always be sorted
by variable _ID
Boundary dataset

Each polygon included in the boundary
dataset must be defined by 1+k+1 records,
each of which corresponds to a proper pair of
(x,y) coordinates
Boundary dataset

The first record denotes the beginning of a new
polygon and corresponds to a missing
coordinate pair (.,.)
Boundary dataset


The 2nd to (k+1)th records denote the k nodes
of the polygon and correspond to the k
coordinate pairs (x,y) that define such nodes
These records must be arranged so as to
correspond to consecutive nodes
Boundary dataset

The last record denotes the end of the polygon
and corresponds to a coordinate pair which is
an exact replica of the first node of the polygon
Boundary dataset: example
A3
A2
A1
A1
Boundary dataset: example
+-------------------------+
| _ID
_X
_Y
_ISLAND |
|-------------------------|
|
1
.
.
0 |
|
1
10
10
0 |
|
1
10
30
0 |
|
1
18
30
0 |
|
1
18
10
0 |
|
1
10
10
0 |
|
1
.
.
0 |
|
1
22
10
0 |
|
1
22
30
0 |
|
1
30
30
0 |
|
1
30
10
0 |
|
1
22
10
0 |
|-------------------------|
|
2
.
.
0 |
|
2
10
30
0 |
|
2
10
50
0 |
|
2
30
50
0 |
|
2
30
30
0 |
|
2
10
30
0 |
|-------------------------|
|
3
.
.
1 |
|
3
22
48
1 |
|
3
28
48
1 |
|
3
28
42
1 |
|
3
22
42
1 |
|
3
22
48
1 |
+-------------------------+
<- Polygon 1: start
<- Polygon 1: end
<- Polygon 2: start
<- Polygon 2: end
<- Polygon 3: start
<- Polygon 3: end
<- Polygon 4: start
<- Polygon 4: start
Boundary dataset: mif2dta



mif2dta is a simple Stata program that converts
MapInfo Interchange Format boundary files into Stata
boundary datasets
mif2dta converts any given pair of files
rootname.mif and rootname.mid into a new pair of
Stata datasets: rootname-Coordinates.dta (the
boundary dataset) and rootname-Database.dta (the
master dataset)
Optionally, mif2dta also computes the coordinates of
the centroids of the geographical areas of interest
Boundary dataset: shapefiles

To convert a shapefile into a Stata boundary
dataset:
–
–
Convert the shapefile of interest into the proper pair
of MIF files (e.g., using the freeware DOS program
shp2mif.exe)
Use mif2dta to convert the MIF files into the
corresponding Stata master and boundary datasets
tmap choropleth

tmap choropleth represents the spatial
distribution of area data by means of
choropleth maps, i.e., maps where each of n
sub-areas is colored (or shaded) according to a
discrete scale based on the value taken on by
a quantitative variable of interest in that subarea
tmap choropleth


The number of classes that make up the
discrete scale must be between 2 and 9
The corresponding class breaks can be based
on four different criteria:
–
–
–
–
Quantiles
Equal intervals
Standard deviates
Custom
tmap choropleth: example
tmap choropleth spd2,
id(id) map(Germany-Coordinates.dta)
clmethod(quantile) clnumber(4)
palette(Reds)
title("Pct. votes for SPD at latest election")
subtitle("(Two-party share)")
legpos(5)
///
///
///
///
///
///
tmap propsymbol

tmap propsymbol represents the spatial
distribution of area data by means of
proportional symbol maps, i.e., maps where
the value taken on by a quantitative variable of
interest in each of n sub-areas is represented
by a symbol whose size is proportional to the
value itself
tmap propsymbol: example
tmap propsymbol spd2,
x(x_coord) y(y_coord)
map(Germany-Coordinates.dta)
scolor(red) sshape(O) ssize(1.2)
ocolor(white) fcolor(sand)
title("Pct. votes for SPD at latest election")
subtitle("(Two-party share)")
note("Symbol size proportional to variable value", span)
///
///
///
///
///
///
///
tmap deviation

tmap deviation represents the spatial
distribution of area data by means of deviation
maps, a particular kind of proportional symbol
maps where:
–
–
symbol size expresses the absolute deviation of the
quantitative variable of interest from its mean or
median
symbol fill expresses the sign of the deviation
(positive or negative)
tmap deviation: example
tmap deviation spd2,
x(x_coord) y(y_coord)
map(Germany-Coordinates.dta)
scolor(blue) sshape(O) ssize(1.2)
ocolor(white) fcolor(bluishgray)
title("Pct. votes for SPD at latest election")
subtitle("(Two-party share)")
note(`"`"Solid circles denote positive deviations from the mean"'"'
`"`"Hollow circles denote negative deviations from the mean"'"'
`"`"Circle size proportional to absolute value of deviation"'"',
span)
///
///
///
///
///
///
///
///
///
///
tmap label


tmap label is an auxiliary program that
allows the user to superimpose onto a base
map the values taken on by a numeric or string
variable at different locations
This program can be used, for example, to plot
sub-area names or to represent the spatial
distribution of a given quantitative variable of
interest in numeric form
tmap label: example
tmap label state,
x(x_coord) y(y_coord)
map(Germany-Coordinates.dta)
lcolor(red) llength(30) lsize(0.8)
ocolor(sienna) fcolor(eggshell)
title("Länder")
///
///
///
///
///
tmap dot

tmap dot represents the spatial distribution of
point data by means of dot maps, i.e., maps
where the locations at which some “events” of
interest have occurred are indicated by
symbols whose color and/or shape can vary
according to the type of “event”
tmap dot: example
tmap dot,
x(x) y(y)
map("MilanoOutline-Coordinates.dta")
by(type)
fcolor(stone)
title("Location of police stations")
subtitle(`"`"Milano, 2004"'"')
legtitle("Police force", size(*0.7))
legbox(lc(black))
///
///
///
///
///
///
///
///
tmap choropleth: more examples
tmap choropleth murder if conterminous,
id(id) map(Us48-Coordinates.dta)
palette(Blues) ocolor(white) bcolor(navy)
title(`"`"Murders per 100,000 population & Pct. pop. with high school diploma"'"',
color(white) span) subtitle("United States 1994", color(white))
legbox(lc(white) fc(navy) margin(medsmall))
legpos(5) legcol(white)
legtitle("Murder rate", color(white) size(*0.8))
addplot(deviation hsdip if conterminous, x(x) y(y) sc(red) ssi(0.8))
note(`"`"Circles represent pct. pop. with high school diploma"'"'
`"`"Solid circles denote positive deviations from the mean"'"'
`"`"Hollow circles denote negative deviations from the mean"'"'
`"`"Circle size proportional to absolute value of deviation"'"',
color(white) span)
///
///
///
///
///
///
///
///
///
///
///
///
///
tmap choropleth: more examples
tmap choropleth winner if conterminous,
id(id) map(Us48-Coordinates.dta)
clmethod(unique)
palette(Custom) colors(`"`"203 24 29"'"' navy)
title(US Presidential Elections 2004)
subtitle(Pct. votes for Bush)
legpos(5) legsize(1.2)
legtitle("Winner", size(*0.8)) legcount
addplot(label votebushpct if conterminous,
x(x) y(y) lc(gs14) ls(0.9))
///
///
///
///
///
///
///
///
///
tmap and spatial data analysis


tmap can be used to display results produced
by other Stata programs, e.g., spatial data
analysis programs
What follows is an example of a choropleth
map + LISA cluster map (Anselin 1995) created
using a combination of tmap and a modified
version of spatlsa
European Parliament Elections 2004 - Lombardia
Pct. votes for Northern League
Ackowledgments

The color schemes used in tmap
choropleth were designed by Dr. Cynthia A.
Brewer, Department of Geography, The
Pennsylvania State University, University Park,
Pennsylvania, USA. The color schemes are
used with Dr. Brewer’s permission and are
from the ColorBrewer map design tool
available at ColorBrewer.org
Ackowledgments

I wish to thank Nick Cox and Ian S. Evans for
helping improve the first release of tmap

The second release owes much to ideas and
suggestions by Nick Cox and Vince Wiggins
Any remaining errors and limitations are mine
