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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow

Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow University of Texas at Austin Lectures 8 & 9 Feb 28, 2013 8 - Spatial Analysis 9 - Geocoding. Review. ArcInfo coverages (from Lecture 5).

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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow

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  1. Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow University of Texas at Austin Lectures 8 & 9 Feb 28, 2013 8 - Spatial Analysis 9 - Geocoding

  2. Review ArcInfocoverages(from Lecture 5) Created using ESRI’s ArcInfo software (prior to version 8) Older format (import/export as “.e00”) Set of files within a folder or directory called a workspace Files represent different types of topology or feature types • Coverages have geometry: Arcs (lines), Nodes (points), or Polygons, and associated attribute tables • Coverages also have Tics (spatial registration points), and may have Labels and Annotation INF385T(28437) – Spring 2013 – Lecture 5

  3. Inside a coverage… View from the operating system: INF385T(28437) – Spring 2013 – Lecture 8

  4. Coverage attribute table • Area and perimeter • Coverage_ and Coverage_ID INF385T(28437) – Spring 2013 – Lecture 5

  5. Labels vs. Annotation • Labels are based on one or more attributes of features. • Annotationis a way to store text to place on your maps independent of features. Each piece of text stores its own position, text string, and display properties. Annotation can also be linked to individual features, for positional or existence dependency. • If the exact position of each piece of text is important, you should store your text as annotation in a geodatabase. Annotation provides flexibility in the appearance and placement of your text because you can select individual pieces of text and edit them. • You can convert labels to create new annotation features. INF385T(28437) – Spring 2013 – Lecture 8

  6. Lecture 8 Spatial Analysis Outline (Tutorial Ch.9) Proximity buffers Site suitability example Basic apportionment (on your own) Advanced apportionment (on your own) Then… Geocoding (Tutorial Ch.7) INF385T(28437) – Spring 2013 – Lecture 8 6

  7. Lecture 8 Proximity buffers INF385T(28437) – Spring 2013 – Lecture 8

  8. Proximity buffers Points • Circular buffers with user supplied radius Lines • Looks like worm based on line feature INF385T(28437) – Spring 2013 – Lecture 8 8

  9. Proximity buffers Polygons • Extends polygons outward and rounds off corners • Created by assigning a buffer distance around polygon INF385T(28437) – Spring 2013 – Lecture 8 9

  10. Point buffer example Polluting company buffers • Added schools • Added population INF385T(28437) – Spring 2013 – Lecture 8 10

  11. Point buffer example Crimes near schools INF385T(28437) – Spring 2013 – Lecture 8 11

  12. Line buffer example Businesses within .25 miles of a selected street INF385T(28437) – Spring 2013 – Lecture 8

  13. Select features in buffer INF385T(28437) – Spring 2013 – Lecture 8 13

  14. Spatial join to count Join business points to buffer polygon INF385T(28437) – Spring 2013 – Lecture 8 14

  15. Polygon buffer example River buffer to analyze environmental conditions, flooding, etc. INF385T(28437) – Spring 2013 – Lecture 8

  16. Polygon buffer example Parcels within 150′ of selected property INF385T(28437) – Spring 2013 – Lecture 8

  17. Select features in buffer INF385T(28437) – Spring 2013 – Lecture 8 17

  18. Lecture 8 SITE Suitability INF385T(28437) – Spring 2013 – Lecture 8

  19. Locate new police station Criteria • Must be centrally located in each car beat (within a 0.33-mile radius buffer of car beat centroids) • Must be in retail/commercial areas (within 0.10 mile of at least one retail business) • Must be within 0.05 mile of major streets INF385T(28437) – Spring 2013 – Lecture 8

  20. Starting map Lake Precinct of the Rochester, New York, Police Department • Police car beats • Retail business points • Street centerlines INF385T(28437) – Spring 2013 – Lecture 8

  21. Create car beat centroids XY centroids for police beats INF385T(28437) – Spring 2013 – Lecture 8

  22. Buffer car beat centroids .33 mile buffer INF385T(28437) – Spring 2013 – Lecture 8 22

  23. Buffer retail businesses 0.1 mile buffer INF385T(28437) – Spring 2013 – Lecture 8 23

  24. Select major streets Select by attribute INF385T(28437) – Spring 2013 – Lecture 8 24

  25. Buffer major streets 0.05 mile buffer INF385T(28437) – Spring 2013 – Lecture 8 25

  26. Intersect buffers Can only intersect two at a time • Car beat and businesses • Streets INF385T(28437) – Spring 2013 – Lecture 8 26

  27. Site suitability result Map showing possible sites for police station INF385T(28437) – Spring 2013 – Lecture 8 27

  28. Spatial Analysis Summary Proximity buffers (Tutorial exercise 9-1) Site suitability example (Tutorial exercise 9-2) Basic apportionment (optional) Advanced apportionment (optional) Assignments: 9-1, 9-2 (9-3 optional) Next up today - Geocoding INF385T(28437) – Spring 2013 – Lecture 8 28

  29. INF385T(28437) – Spring 2013 – Lecture 8

  30. Lecture 8 Basic apportionment INF385T(28437) – Spring 2013 – Lecture 8

  31. Apportionment example Population by voting district • You want to know the population of a voting district but only have census tracts • Voting districts and census tracts are not contiguous • Approximate the population of voting using census tracts and blocks INF385T(28437) – Spring 2013 – Lecture 8 31

  32. Population by voting district Start with census tracts INF385T(28437) – Spring 2013 – Lecture 8

  33. Population by voting district Overlay voting districts (not contiguous with tracts) INF385T(28437) – Spring 2013 – Lecture 8 33

  34. Population by voting district Better to use block centroids for population • Smaller than tracts INF385T(28437) – Spring 2013 – Lecture 8

  35. Spatially join centriods Join centroids to voting districts INF385T(28437) – Spring 2013 – Lecture 8 35

  36. Other simple apportionments Population by • Neighborhoods • Zip Codes • Historic sites • Others? INF385T(28437) – Spring 2013 – Lecture 8

  37. Census data to apportion • Short form SF1 data (tract, block group, block) • Population • Age • Race • Housing Units • Others? • Long form SF3 data (tract and block group) • Educational attainment • Income • Poverty status • Others? INF385T(28437) – Spring 2013 – Lecture 8 37

  38. Lecture 8 Advanced apportionment INF385T(28437) – Spring 2013 – Lecture 8

  39. Advanced Apportionment Chapter 9 example • Police want to know the number of under-educated persons in their car beats • Under-educated data is located SF3 tables, census tracts or block groups (not car beat polygons) INF385T(28437) – Spring 2013 – Lecture 8 39

  40. Data to apportion Car beats Census tracts Beats and tracts • Not contiguous INF385T(28437) – Spring 2013 – Lecture 8 40

  41. Beats and tracts zoomed Tracts clearly cut across beats INF385T(28437) – Spring 2013 – Lecture 8 41

  42. Tract attribute table Tracts contain undereducated data • No high school degree INF385T(28437) – Spring 2013 – Lecture 8 42

  43. Math of apportionment Simple census data (e.g. population) is not a problem • Can use block centroids Problem • Block centroids don’t contain undereducatedpopulation • Tracts contain thisinformation INF385T(28437) – Spring 2013 – Lecture 8 43

  44. Math of apportionment Tract 360550002100 Car beats 261 and 251 INF385T(28437) – Spring 2013 – Lecture 8 44

  45. Math of apportionment One approach • Assume that the target population is uniformly distributed across the tract • You could split undereducated population up by the fraction of the area of the tract in each car beat • What if, however, the tract has a cemetery, park, or other unoccupied areas? Then the apportionment could have sizable errors INF385T(28437) – Spring 2013 – Lecture 8 45

  46. Math of apportionment A better approach • Use a block-level, short-form census attribute as the basis of apportionment • Assume that the long-form attribute of interest is uniformly distributed across the short-form population (accounts for unoccupied areas) • One limitation of the block-level data is that the break points for age categories do not match those of the educational attainment data (persons 25 or older) • The best that can be done with the block data is to tabulate persons aged 22 or older • Close enough for approximation INF385T(28437) – Spring 2013 – Lecture 8 46

  47. Math of apportionment Of the 26 blocks making up the tract, the 13 that lie in car beat 261 have 1,177 people aged 22 or older. The other 13 blocks in car beat 251 have 1,089 such people for a total of 2,266 for the tract. Tract 360550002100 has 39 block centroids that span 2 beats INF385T(28437) – Spring 2013 – Lecture 8 47

  48. Math of apportionment Apportionment assumes that the fraction of undereducated people aged 25 or older is the same as that for the general population aged 22 or older • This fraction, called the weight, is 1,177 ÷ 2,266 = 0.519. For the other car beat, the weight is 1,089 ÷ 2,266 = 0.481 • Thus, we estimate the contribution of tract 36055002100 to car beat 261’s undereducated population to be (1,177 ÷ 2,266) × 205 = 106. For car beat 251, it is (1,089 ÷ 2,266) × 205 = 99 INF385T(28437) – Spring 2013 – Lecture 8 48

  49. Math of apportionment Eventually, by apportioning all tracts, we can sum up the total undereducated population for car beats 261 and 251 INF385T(28437) – Spring 2013 – Lecture 8 49

  50. Lecture 8 Background steps INF385T(28437) – Spring 2013 – Lecture 8

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