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CS 128/ES 228 - Lecture 10b1 Geospatial Attribute Data.

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Presentation on theme: "CS 128/ES 228 - Lecture 10b1 Geospatial Attribute Data."— Presentation transcript:

1 CS 128/ES 228 - Lecture 10b1 Geospatial Attribute Data

2 CS 128/ES 228 - Lecture 10b2 We Lied! Earlier this semester we claimed that data was either spatial, i.e. said where something was, or attribute, i.e. told you something about an object. In fact, on the exam, we accepted “non- spatial data” as part of the definition of attributed data. BUT, it’s not that simple…

3 CS 128/ES 228 - Lecture 10b3 Some attribute data is tied to a location, not an object Estimate of phytoplankton distribution in the surface ocean: global composite image of surface chlorophyll a concentration (mg m-3) estimated from SeaWiFS data (Source: NASA Goddard Space Flight Center, Maryland, USA and ORBIMAGE, Virginia, USA).

4 CS 128/ES 228 - Lecture 10b4 Spatial Data – A Few Definitions Spatial data: Data that have some form of spatial or geographical reference that enables them to be located in two or three-dimensional space. -- Heywood, Cornelius & Carver, p. 289 Spatial data: Data that relate to the geometry of spatial features. -- Chang, Introduction to Geographical Information Systems, p. 4 Spatial data: Any information about the location and shape of, and relationships among, geographic features. This includes remotely sensed data as well as map data. -- The GIS Dictionary, http://www.geo.ed.ac.uk/agidict/welcome.html, searched 3/27/2007 http://www.geo.ed.ac.uk/agidict/welcome.html

5 CS 128/ES 228 - Lecture 10b5 A Compromise Geospatial Attribute Data Data about a non-spatial entity that is intrinsically tied to a given location

6 CS 128/ES 228 - Lecture 10b6 Examples of Geospatial Attribute Data Rainfall Snow depth Land use Crime rates Average income level Population statistics

7 CS 128/ES 228 - Lecture 10b7 What is special about this data? Data sets are generally very large Turning such data into information (or knowledge) can be tricky (or worse!) Dimensionality becomes an issue

8 CS 128/ES 228 - Lecture 10b8 Dimensionality Paper maps are generally two- dimensional While color can be used as a third dimension, it is more often used for attribute display

9 CS 128/ES 228 - Lecture 10b9 Sometimes 2-D works Source: U.S. Census Bureau, 2005 American Community Survey (American FactFinder)

10 CS 128/ES 228 - Lecture 10b10 More fine-grained 2-D Image from: http://www.csc.noaa.gov/products/nchaz/htm/lidtut.htm

11 CS 128/ES 228 - Lecture 10b11 What’s the Weather Like in Merry Old England? Source

12 CS 128/ES 228 - Lecture 10b12 When 2-D tends to work “Planar” area being mapped One piece of data for each position Minimal problem locating data in “space” No “time” dimension

13 CS 128/ES 228 - Lecture 10b13 What about Time? Traditionally described as a “fourth” dimension, time adds a “third” dimension to GIS data. This creates problems converting the data to information and knowledge. 2-D maps usually don’t cut it.

14 CS 128/ES 228 - Lecture 10b14 Solutions to the “Time Dilemma”: 1. Graphs Source: National Weather Service http://newweb.erh.noaa.gov/ahps2/hydrograph. php?wfo=buf&gage=oln n6&view=1,1,1,1,1,1

15 CS 128/ES 228 - Lecture 10b15 More graphing http://www.pmel.noaa.gov/tao/disdel/disdel.html Tropical Ocean Array Buoys in Pacific Ocean Monitor Conditions Monitor El Niňo

16 CS 128/ES 228 - Lecture 10b16 Custom Graphs from TOA Monthly Wind Speed data for the buoy I selected 1977-2007

17 CS 128/ES 228 - Lecture 10b17 Also available as… Downloadable data file Formatting can be an issue But if you add it you your GIS, it’s yours! Location: 8S 165E 16 Aug 1991 to 16 Mar 2007 ( 188 times, 2 blocks) Gen. Date Mar 28 2007 Units: Winds (M/S), W. Dir (DEG), - 99.9 = missing, (1,1) is NE at sqrt(2) m/s Time: 1200 16 Aug 1991 to 1200 16 Aug 1996 (index 1 to 61, 61 times) Depth (M): -4 -4 -4 - 4 QUALITY YYYYMMDD HHMM UWND VWND WSPD WDIR SD 19910816 1200 -5.0 0.7 5.6 278.1 22 19910916 1200 -2.9 -1.4 4.8 243.7 22 19911016 1200 -2.7 -0.1 3.4 268.2 22 19911116 1200 -0.2 2.1 4.3 354.3 22 19911216 1200 -0.5 1.7 3.3 344.0 22 19920116 1200 1.8 1.3 4.2 53.8 22 19920215 1200 4.4 0.3 5.3 86.2 22 19920316 1200 4.0 1.0 5.3 75.7 22

18 CS 128/ES 228 - Lecture 10b18 Solutions to the “Time Dilemma”: 2. Multiple Images Really just a set of 2-D images shown side- by-side or in sequence Source:http://commons.wikimedia.org/wiki/Image:ElectoralCollegeYYYY-Large.png

19 CS 128/ES 228 - Lecture 10b19 Items of note Each of the images here is a separate map, no longer associated with a GIS Each map actually contains summary information as well as traditional map elements

20 CS 128/ES 228 - Lecture 10b20 Solutions to the “Time Dilemma”: 3. Animation http://encarta.msn.com/encyclopedia_761567360_1/Animation.html Animation: motion pictures created by recording a series of still images— drawings, objects, or people in various positions of incremental movement— that when played back no longer appear individually as static images but combine to produce the illusion of unbroken motion.

21 CS 128/ES 228 - Lecture 10b21 My Daily Habit – Doppler Data Animation

22 CS 128/ES 228 - Lecture 10b22 More Weather From England http://www.xcweather.co.uk/

23 CS 128/ES 228 - Lecture 10b23 Watch My Friends Ride Across The Country http://stats.raceacrossamerica.org/2006/animation/ A similar site, with elevation profiles, exists for the Tour de France, but it only animates during the race

24 CS 128/ES 228 - Lecture 10b24 Get Seasick? http://www.pmel.noaa.gov/tao/jsdisplay/

25 CS 128/ES 228 - Lecture 10b25 What if there is a real third dimension? Actual images (video) But these can only show “transparent” or “discrete” attribute data Flyovers/fly-throughs help Virtual reality But most users don’t have the equipment to “view” this

26 CS 128/ES 228 - Lecture 10b26 And in the movies… (Screen snapshot of) Animation of tornado- monitoring “buoys” from the Warner Brothers film Twister Source: http://www.vfxhq.com/1996/twister.html

27 CS 128/ES 228 - Lecture 10b27 Conclusions about geospatial data It’s abundant It’s important Display is a challenge Technologies only get better

28 CS 128/ES 228 - Lecture 10b28 Great Data Sets Abound Census bureau USGS Weather Service Scientific labs (esp. government funded)


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