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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow University of Texas at Austin Lectures.

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Presentation on theme: "Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow University of Texas at Austin Lectures."— Presentation transcript:

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, Spatial Analysis 9 - Geocoding

2 ArcInfo coverages (from Lecture 5) Created using ESRIs 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 Review

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

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

5 Labels vs. Annotation Labels are based on one or more attributes of features. Annotation is 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. 5 INF385T(28437) – Spring 2013 – Lecture 8

6 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) 6 INF385T(28437) – Spring 2013 – Lecture 8 Lecture 8

7 PROXIMITY BUFFERS Lecture 8 7 INF385T(28437) – Spring 2013 – Lecture 8

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

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

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 12 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. 15 INF385T(28437) – Spring 2013 – Lecture 8

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

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

18 SITE SUITABILITY Lecture 8 18 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 19 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 20 INF385T(28437) – Spring 2013 – Lecture 8

21 Create car beat centroids XY centroids for police beats 21 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 28 INF385T(28437) – Spring 2013 – Lecture 8

29

30 BASIC APPORTIONMENT Lecture 8 30 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 31 INF385T(28437) – Spring 2013 – Lecture 8

32 Population by voting district Start with census tracts 32 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 34 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? 36 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? 37 INF385T(28437) – Spring 2013 – Lecture 8

38 ADVANCED APPORTIONMENT Lecture 8 38 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) 39 INF385T(28437) – Spring 2013 – Lecture 8

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

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 dont contain undereducated population Tracts contain this information INF385T(28437) – Spring 2013 – Lecture 8 43

44 Math of apportionment Tract 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 Tract has 39 block centroids that span 2 beats 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. 47 INF385T(28437) – Spring 2013 – Lecture 8

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 = For the other car beat, the weight is 1,089 ÷ 2,266 = Thus, we estimate the contribution of tract to car beat 261s 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 BACKGROUND STEPS Lecture 8 50 INF385T(28437) – Spring 2013 – Lecture 8

51 Background steps 1.) Download census data Download census block and tract polygons from the census Web sites for the county containing the administrative area polygons Download the short-form census data for blocks that are the basis of apportionment, in this case the population of age 22 and greater Download the long-form census attribute(s) at the tract level that you wish to apportion to the administrative area, in this case the population aged 25 or greater with less than high school education 51 INF385T(28437) – Spring 2013 – Lecture 8

52 Background steps 2.) Create new tract layer That intersects administrative boundaries If a tract is only partially inside the administrative area, you must include the entire tract for apportionment to work correctly An example tract is the southerly-most tract in Tutorial9- 3.mxd INF385T(28437) – Spring 2013 – Lecture 8 52

53 Background steps 3.) Prepare block centroids Create a new centroid point layer for blocks Clip the centroids with the new intersected tract layer Join census short-form data to the clipped block centroids This is the layer that is the basis for apportionment INF385T(28437) – Spring 2013 – Lecture 8 53

54 Background steps 4.) Sum the short-form census attributes in age categories to create Age22Plus in the clipped block centroids table This step is unique to this problem Also, this table has a new TractID attribute which concatenates FIPSSTCO & TRACT2000 to create an ID matching the Tracts map layer INF385T(28437) – Spring 2013 – Lecture 8 54

55 Background steps 5.) In the attribute table for block centroids, sum the field for persons aged 22 or older by TractID to create a new table, SumAge22Plus. This table provides the denominator for the weight used in apportionment INF385T(28437) – Spring 2013 – Lecture 8 55

56 APPORTIONMENT STEPS Lecture 8 56 INF385T(28437) – Spring 2013 – Lecture 8

57 Apportionment steps 1.)Intersect tracts and car beats to create new polygons that each have a tract ID and car beat number INF385T(28437) – Spring 2013 – Lecture 8 57

58 Apportionment steps 2.) Spatially join the new layer of tracts and car beats with the block centroids to assign all the tract attributes (including the attribute of interest: undereducated population) and car beat attributes to each blocks centroid INF385T(28437) – Spring 2013 – Lecture 8 58

59 Apportionment steps 2.) INF385T(28437) – Spring 2013 – Lecture 8 59

60 Apportionment steps 3.) Join SumAge22Plus to block centroids to make the apportionment weight denominator, total population aged 22 or older by tract, available to each block centroid INF385T(28437) – Spring 2013 – Lecture 8 60

61 Apportionment steps 3.) Export the join as a precaution INF385T(28437) – Spring 2013 – Lecture 8 61

62 Apportionment steps 4.) For each block centroid, create new fields to store apportionment weight and apportioned undereducated population values, then calculate these values INF385T(28437) – Spring 2013 – Lecture 8 62

63 Apportionment steps 4.) Calculate values INF385T(28437) – Spring 2013 – Lecture 8 63

64 Apportionment steps 5.) Sum the apportionment weights by tract as a check for accuracy (they should sum to 1.0 for each tract) INF385T(28437) – Spring 2013 – Lecture 8 64

65 Apportionment steps 5.) Each tract that is totally within car beats will have weights summing to 1. Those partially within car beats sum to less than 1 INF385T(28437) – Spring 2013 – Lecture 8 65

66 Apportionment steps 5.) Sum the undereducated population per car beat INF385T(28437) – Spring 2013 – Lecture 8 66

67 Join apportionment results The last task is to join the table containing undereducated population by car beat to the car beats layer, then symbolize the data for map display INF385T(28437) – Spring 2013 – Lecture 8 67

68 Finished map INF385T(28437) – Spring 2013 – Lecture 8 68

69 Summary Proximity buffers Site suitability example Basic apportionment Advanced apportionment 69 INF385T(28437) – Spring 2013 – Lecture 8


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