Storms,  Thematic Mapping, and KML - by Robert Innis

Storms,
Thematic Mapping

and KML
Dammit Jim, I’m a graphic designer, not a statistician.

This presentation is biased.

It’s an idea about connecting long term data to our locale using KML.

What makes a storm?
a clash of forces (cold and warm fronts, wind currents, moisture levels)

indications can be seen from a distance

drop rain, snow, lightning, hail (harbinger of doom or bounty)

[we’ll come back to this subject in a minute]

What is a thematic map?
a simple map made to reﬂect a particular theme about a geographic area.
Thematic maps can portray physical, social, political, cultural, economic, sociological,
agricultural, or any other aspects of a city, state, region, nation, or continent.

choropleth maps - shaded polygon maps; used to portray data collected for
already deﬁned units, such as states or countries or statistical reporting units;
most common

isarithmic maps - maps that depict smooth continuous phenomena such as precipitation or
ocean currents
dasymetric symbols - data that pertains to particular geographic areas and incorporates
ancillary data

hardiness zone dasymetric maps

Thematic Mapping and KML
Keyhole Markup Language (KML) is an XML-based language schema for expressing
geographic annotation and visualization for use in 2D and 3D Earth Browsers.

KML was developed for use with Google Earth, which was originally Keyhole Earth
Viewer, created by Keyhole, Inc, and acquired by Google in 2004. The name
quot;Keyholequot; is a homage to the KH reconnaissance satellites, the original eye-in-
the-sky military reconnaissance system ﬁrst launched in 1976.

Who is Bjørn Sandvik?
While a student of Geographic Information Systems at the University of
Edinburgh,
he wrote a dissertation, Using KML for Thematic Mapping.

He then created thematicmapping.org, an online mapping engine that generates a kmz ﬁle from
UN data.

He’s a project manager at United Nations Association (UNA) of Norway.

He sounds like a really nice guy.

<Folder>
<name>1987</name>
<TimeSpan>
<begin>1987</begin>
<end>1987-12-31</end>
creating a timespan for the folder </TimeSpan>
<Placemark>
<name>Santa Fe</name>
<Snippet maxLines=quot;2quot;>186572 (1987)</Snippet>
<styleUrl>#sharedStyle</styleUrl>
<Model id=quot;model_1595quot;>
<altitudeMode>relativeToGround</altitudeMode>
<Location>
<longitude>-105.9465104642608</longitude>
<latitude>35.36</latitude>
<altitude>1000</altitude>
</Location>
<Orientation>
name is what it means <heading>0</heading>
<tilt>0</tilt>
<roll>0</roll>
</Orientation>
<Scale>
<x>1865.72</x>
<y>1865.72</y>

KML is kinda
<z>1865.72</z>
</Scale>
<Link>
<href>sphere.dae</href>
</Link>
<ResourceMap>
</ResourceMap>
</Model>
</Placemark>
</Folder>
altitude can be absolute, relativeToSeaFloor, or
relativeToGround

<Folder>
<name>1987</name>
<TimeSpan>
<begin>1987</begin>
<end>1987-12-31</end>
</TimeSpan>
<Placemark>
<name>Santa Fe</name>
<Snippet maxLines=quot;2quot;>186572 (1987)</Snippet>
<styleUrl>#sharedStyle</styleUrl>
<Model id=quot;model_1595quot;>
<altitudeMode>relativeToGround</altitudeMode>
<Location>
<longitude>-105.9465104642608</longitude>
<latitude>35.36</latitude>
<altitude>1000</altitude>
</Location>
<Orientation>
<heading>0</heading>
<tilt>0</tilt>
<roll>0</roll>
</Orientation>
<Scale>
<x>1865.72</x>
<y>1865.72</y>

KML is XML-
<z>1865.72</z>
</Scale>
<Link>
<href>sphere.dae</href>
</Link>
<ResourceMap>
</ResourceMap>
</Model>
</Placemark>
</Folder>

longitude, latitude of Santa Fe

Scale–controls the scale of the Collada shape,
which is...

Collada (universal interchange format for 3D
ﬁles)sphere.dae

Proportional of infant mortality rates
Prism map scale map of populations

Bar map ofmap of fertility per capita
Prism internet users rates

Table 2.5.6. Real Personal Consumption Expenditures by Type of Expenditure, Chained Dollars

[Billions of chained (2000) dollars]

Warning: Do not add Chained Dollars

Bureau of Economic Analysis

Line

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1 Personal consumption expenditures

4770.3
4778.4
4934.8
5099.8
5290.7
5433.5
5619.4
5831.8
6125.8
6438.6
6739.4
6910.4
7099.3
7295.3
7561.4
7791.7
8029
8252.8
2 Food and tobacco

867.1
862.1
869.2
882.7
903.6
909.9
918.5
929.4
948.5
973
1003.7
1018.3
1030.7
1051.1
1080.7
1115.3
1156.1
1174.3
3 Food purchased for o-premise consumption (n.d.)

485.7
484.9
483.9
488.9
503.4
507.1
513.2
518.3
528.4
548.8
566.7
578.6
586.6
598.7
618.4
646.3
673.1
688.3
4 Purchased meals and beverages (n.d.)1

289.6
289.4
295
304.3
309.4
310.8
312.3
317.7
327.8
335.3
348.8
351.8
358.2
368.7
380.7
390.7
405
409.9
5 Food furnished to employees (including military) (n.d.)

8.3
8.2
8.3
8.4
8.5
8.6
8.6
8.7
8.8
8.9
9.1
9.3
9.4
9.7
9.9
10.4
11.7
12.1

6 Food produced and consumed on farms (n.d.)

0.6
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.6
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.5
0.4
7 Tobacco products (n.d.)

87.7
81.4
84.9
83.2
84.2
85.5
86.7
86.7
84.3
79.5
78.5
78.1
76.3
74.1
72.3
69.3
68.2
66.5
8 Addenda: Food excluding alcoholic beverages (n.d.)

681.7
685.7
692.3
707
725.2
730
736.5
745.5
763.3
789.1
816.5
830.9
844.5
864.8
891.1
923.1
956.4
971.5
9 Alcoholic beverages purchased for

o-premise consumption (n.d.)

61.8
57.1
53.8
55.2
58.3
60
62.5
64.7
66.5
68.1
71.2
72.1
72.3
74.1
79.2
85.1
92.8
97.1

raw data is a bit
10 Other alcoholic beverages (n.d.)

41.2
40.8
42.3
40.4
38.6
37.2
35.8
35.1
35.8
36.4
37.5
37.2
37.9
38.8
39.2
39.7
41.2
42.4
11 Clothing, accessories, and jewelry

247.7
244.1
257.8
269.7
284.4
297.6
313.8
324.1
348.3
376
397
401
419.6
440.4
463.2
489
515.7
533
12 Shoes (n.d.)

30.4
29.5
30.3
31.5
33.7
35.4
37.5
38.7
41.3
44.7
47
48.1
50.5
52.1
53.9
55.4
58.2
59.8
13 Clothing and accessories except shoes2

157.7
159
168.6
175.6
184.5
191.7
200.9
207
221.5
237.7
250.4
255.3
267.4
281.6
296.5
316.8
336.1
353
14 Women's and children's (n.d.)

99
99.8
106.2
110.1
114.2
118.7
124.4
127
136.6
148
156.7
159.3
166.6
175
184.4
197.6
209.1
219.2
15
Men's and boys' (n.d.)

58.6
59.1
62.2
65.4
70.2
73
76.6
80
84.9
89.7
93.7
96
100.7
106.6
112.1
119.1
127
133.9
16
Standard clothing issued to military personnel (n.d.)
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.6
0.3
0.4
0.4
0.4
17
Cleaning, storage, and repair of clothing and shoes (s.)
14.2
13.3
13.4
13.1
13.1
13.6
13.6
14.5
15
15.5
15.7
15.2
14.8
13.9
13.8
13.8
14.1
13.7
18
Jewelry and watches (d.)

26.5
25.6
26.5
28.7
30.5
32.6
35.8
37.5
41.7
46.8
50.6
49.2
52.7
56.3
59.5
62.7
65.9
65.4
19
Other (s.)3

20
16.6
19
20.8
22.6
24.4
25.9
26.4
28.6
31.1
32.9
32.9
34
36.4
39.4
40.9
42.4
42.9
20
Personal care

68.5
68.3
70.8
72.3
75.1
79.3
82.8
88
90
91.6
93.4
92.7
94
97.1
102
105.4
107.3
109
21
Toilet articles and preparations (n.d.)

41.1
40.8
41
43
45.4
47.9
50.9
53.8
54
54.6
55
53.9
54
56.1
58.3
60.8
64.3
66.8
22
Barbershops, beauty parlors, and health clubs (s.)
27.4
27.5
30.1
29.4
29.7
31.3
31.8
34.2
36
37
38.4
38.8
40
41
43.6
44.5
43.4
42.8
23
Housing

802.2
820.1
832.7
841.8
869.3
887.5
901.1
922.5
948.8
978.6
1006.5
1033.7
1042.1
1051.9
1083.8
1118.4
1154.6
1171.7
24
Owner-occupied nonfarm dwellings--space rent (s.)4
551.6
567.4
575.5
583.9
604.8
615.9
626.7
642.4
663.7
688.2
712.2
740.2
748.9
764.9
793.9
821.9
851.7
858.7
25
Tenant-occupied nonfarm dwellings--rent (s.)5
199.9
201.7
203.8
204.9
209.9
216.5
217.1
218.8
222.8
226.7
227.5
230.7
228.8
220.4
220.6
223.6
227.7
235.7
26
Rental value of farm dwellings (s.)

9.6
9.6
9.6
9.6
9.6
9.6
9.7
10
10.2
10.5
10.7
10.8
10.9
11
11
10.9
10.9
10.9
27
Other (s.)6

40.9
41.2
43.8
43.3
45
45.5
47.7
51.4
52.2
53.2
56
52
53.5
55.9
58.6
62.4
64.8
66.9
28
Household operation

485
485.7
501.3
526.8
553.1
572.8
597.5
621.3
650.5
686.3
719.3
723.8
740.5
765.9
800.3
824.1
844.1
868.1
29
Furniture, including mattresses and bedsprings (d.)
41.8
41
41.4
43.8
46.4
48.7
51.8
56
59.2
63.2
67.6
68.3
71.6
73.9
79.5
84.7
88.1
90.7
30
Kitchen and other household appliances (d.)7
22.2
22
23.1
23.9
24.2
25.1
25.3
25.2
26.6
28.9
30.4
31
32.7
34.6
38
39.2
40.2
39.6
31
China, glassware, tableware, and utensils (d.)
17.8
17.6
18.7
19.8
21.1
22.5
23.9
25
26.3
28.9
31
31.9
34.5
36.8
38.8
40.8
45.6
48.2
32
Other durable house furnishings (d.)8
39.2
37.3
38.4
41.2
45.2
47.9
51
54.3
57.6
63.1
67.3
68.6
72.1
77.7
85.2
90.6
100.1
104.6
33
Semidurable house furnishings (n.d.)9
20.1
20.8
22.7
23.8
25.2
26.5
28.1
29.5
31.6
34.2
36.5
37.4
39.9
44.5
49.1
53
59.9
66.1
34
Cleaning and polishing preparations, and miscellaneous household supplies and paper products (n.d)
46.2
45.9
47.3
50.1
53.4
54.5
56
58.1
59.4
61.8
61.6
62.2
63.5
35
Stationery and writing supplies (n.d.)
17.2
17.5
17.8
18.1
18.4
17.8
17.3
16.9
17.3
18.2
19
18.3
18.3
18.8
19.7
20.5
21.5
22.3
36
Household utilities
174.5
177.7
176.7
185.4
189.9
193.3
199.3
198
201
204.5
209.9
207.3
212.3
215.4
215.5
217.9
213.6
218.5
37
Electricity (s.)
81.1
83.5
82
86.9
88.8
90.2
91.8
91.7
98
98.8
102.3
100
104.6
105.7
108
113.2
110.6
112
38
Gas (s.)
36.6
38.1
39.1
40.6
40.5
40.4
43.8
42.8
38.7
39.3
41
40.8
40.3
41.8
41.3
40.3
38.5
39.8
39
Water and other sanitary services (s.)
41.1
40.5
39.7
41.6
43.5
45.2
46.6
47.7
48.4
50
50.8
51.3
51.9
52.5
51.8
52.1
53.4
53.8
40
Fuel oil and coal (n.d.)
16.7
16.6
17
17.4
18.2
18.7
18.4
16.9
16
16.4
15.8
15.2
15.5
15.4
14.6
13.2
12.4
13.7
41
Telephone and telegraph (s.)
58.3
61
67.5
71.1
75.5
79.5
86.5
96.4
104
114.3
125.1
131
131.2
133.5
139.7
140.8
142.7
146
42
Domestic service (s.)
14.3
13.3
14.3
14.9
15.4
16
15.7
16.2
18.3
16.8
17.4
16.2
15.5
16.7
17.3
17.1
17.5
17.9
43
Other (s.)10
39.7
37.2
37.6
38.7
42.3
44.3
45.4
47.4
50.6
52.8
53.6
51.7
49.9
48.9
51.3
52.8
53
53.9
44
Medical care
905.9
932.1
971.5
988
1003.1
1028.9
1053.7
1085.7
1129.7
1164.7
1218.3
1279.6
1353.2
1411
1457
1503.8
1546.4
1591.8
45
Drug preparations and sundries (n.d.)11
90.1
91.7
92.3
95.2
99.2
105.6
112.7
122.8
138.1
154.3
169.4
184.2
195.5
208.6
218.3
223.9
233.1
239.1
46
Ophthalmic products and orthopedic appliances (d.)
17.3
15.3
15.3
15.3
16.3
16.4
17.9
19.7
20.8
21.4
22.1
20.1
21.1
21.5
22
22.4
22.8
24.7
47
Physicians (s.)12
194.7
198.1
204.3
198.4
196.5
200.1
204.9
210.6
218.7
224.6
236.8
249.7
269.8
288.1
302.2
317.5
331.4
340.7
48
Dentists (s.)
53.5
52.4
54.2
54.2
55.3
56.7
57.1
58.1
59.2
60
61.8
64.2
66.4
65.9
67.5
67.8
69.1
69.1
49
Other professional services (s.)13
94.8
104.2
115.6
125.9
132.7
142.3
148.6
151.1
155.5
157.2
161.6
169.9
178.1
187.2
196.6
204.3
210.4
221.6
50
Hospitals and nursing homes14
386.4
399.4
414.4
421.9
426.5
434.1
441.6
453.6
462.6
469.4
482.6
504
529.3
541.5
549.8
564
576.2
594
51
Hospitals
317.3
328.8
341
346.6
350
353.3
357.8
365.7
374.9
383
396
416.8
440.9
452.2
461.1
473.7
484.6
501.2
52
Nonproﬁt (s.)
214.7
224.4
235.3
239.2
238.6
237.6
239.7
245.1
251.7
259.4
267.1
275.8
288.1
297
305.7
315
323.9
335.7
53
Proprietary (s.)
38.9
38.6
37.6
36.1
36.4
38.4
41.2
42.9
42.2
41.9
43.1
48.1
54.7
57.2
57
59.7
62.6
64.9
54
Government (s.)
64.1
66.1
68.3
71.6
75
77.3
76.9
77.7
81.1
81.7
85.7
93
98.2
98
98.5
99.1
98.2
100.7

Data Aggravation Data Localization
vs.
Since 1984 the Consumer Price Index (CPI) has undergone signiﬁcant “opportunistic adjustments”
to make sure the rate of inﬂation does not appear too great. –
”False Statistics in the CPI” Harpers, Aug. 2008; see also the Pollyanna Eect

Census Data data is collected every 7 years and the in-between years are extrapolated up or down.

Gross Domestic Product (GDP) does not include the health, culture, and welfare of people.

vs.
Local experts collecting data locally.

Reinforcing the fact that an entire local population is involved.

Economic reactions and preparedness are more eective when derived from the
local level.
Posting it like a local forecast above our heads reinforces the immediacy of the data.

What if...
we learned to watch the skies
how can we make complex subjects more tangible?

1. make it personal: hang a black cloud above everyone's head so they may tend to act more locally

2. make it visible and geographically relevant -
use Google Earth so people will recognize their surroundings (and it’s 3D!!)

What if...
we learned to watch the skies
how can we make complex subjects more tangible?

1. make it personal: hang a black cloud above everyone's head so they may tend to act more locally

2. make it visible and geographically relevant -
use Google Earth so people will recognize their surroundings (and it’s 3D!!)

3. compress the timeline - put long-term data into a format that can be immediately recognizable

4. pick the data cross/storm
yes this is the biased part, but hey it's just an idea

which brings us back
to the subject of storms...

which brings us back
to the subject of storms... location.
and

it’s in our nature to watch the skies
each geography has dierent topography
longer distances to watch the approach, and more response time, but fewer areas of shelter

what a storm brings to the mountains/plains/high desert environment may dictate dierent responses;
storms bringing bountiful rain, but lightning on the plain you may want to avoid

Google Earth and the weather layer – Rain may be welcome to New Mexico,
but can cause ﬂooding along the Mississippi River.

retail space per capita
vs.
personal savings per capita

total hours worked
full-time and part-time
(in millions of hours)

Personal debt as a percentage of
disposable personal income

Credits
en.wikipedia.org
www.thematicmapping.org
www.galisteobasinphotoproject.com

Greg Mac Gregor: Galiseo Storm, Highway 41.
Guitar Solo 1, Neil Young, Dead Man Soundtrack (an awesome ﬁlm by Jim Jarmusch

Storms,  Thematic Mapping, and KML - by Robert Innis

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