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Storms, 
Thematic Mapping, and KML - by Robert Innis
 

Storms, 
Thematic Mapping, and KML - by Robert Innis

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Storms, 
Thematic Mapping, and KML - by Robert Innis Storms, 
Thematic Mapping, and KML - by Robert Innis Presentation Transcript

  • Storms, Thematic Mapping
  • 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?
  • 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?
  • 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
  • water use choropleth map
  • hardiness zone dasymetric maps
  • Thematic Mapping and KML
  • 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?
  • 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 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...
  • 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!!)
  • 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
  • retail space per capita vs. personal savings per capita
  • total hours worked full-time and part-time (in millions of hours)
  • total hours worked full-time and part-time (in millions of hours)
  • Personal debt as a percentage of disposable personal income
  • 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