1. Return to Outline Copyright © 2009 by Maribeth H. Price 6-1 Chapter 6 Spatial Joins

2. Return to Outline Copyright © 2009 by Maribeth H. Price 6-2 Outline GIS Concepts –What is a spatial join?What is a spatial join? –CardinalityCardinality –Types of spatial joinsTypes of spatial joins –Feature geometry and spatial joinsFeature geometry and spatial joins –Coordinate systems and distance joinsCoordinate systems and distance joins About ArcGIS –Choosing the join typeChoosing the join type –Setting up spatial joinsSetting up spatial joins

3. Return to Outline Copyright © 2009 by Maribeth H. Price 6-3 What is a spatial join?

4. Return to Outline Copyright © 2009 by Maribeth H. Price 6-4 Attribute joins Join tables on common field Joined table Destination tableSource table

5. Return to Outline Copyright © 2009 by Maribeth H. Price 6-5 Spatial joins citiesairportscities2 Destination feature class Source feature class Output feature class

6. Return to Outline Copyright © 2009 by Maribeth H. Price 6-6 Spatial Join Conditions Join two tables based on a common spatial relationship –One feature inside another –One feature closest to another

7. Return to Outline Copyright © 2009 by Maribeth H. Price 6-7 Unique values map by airport Graduated symbol map by distance A distance spatial join appends records based on which source feature (airport) is closest to the destination feature (city.)

8. Return to Outline Copyright © 2009 by Maribeth H. Price 6-8 An inside join appends the record of the source feature (geology) to the destination feature (septic) that falls inside.

9. Return to Outline Copyright © 2009 by Maribeth H. Price 6-9 Cardinality

10. Return to Outline Copyright © 2009 by Maribeth H. Price 6-10 Unique values map by airport What if you switch the destination to airports and the source to cities? Each airport is serving many cities and the cardinality is now one to many. The Rule of Joining is violated and no join is possible. If you could instead use Summarize to group the cities according to the airport they served, and summed the population, you would get a table like this. Now every airport has one record. You can do this with a summarized join.

11. Return to Outline Copyright © 2009 by Maribeth H. Price 6-11 Airports to cities One to many Many city records become a single record containing some statistics, which can then be joined to the airport. And you could map the airports based on population served.

12. Return to Outline Copyright © 2009 by Maribeth H. Price 6-12 Summarized joins Summarized joins are used to handle one-to- many relationships. They determine the spatial relationship, summarize the source features that match each destination feature, and then append the summarized statistics record to the destination feature. Airports and cities Counties and schools

13. Return to Outline Copyright © 2009 by Maribeth H. Price 6-13 Spatial Join Cardinality Simple joins –One-to-one or many-to-one cardinality Summarized joins –One-to-many or many-to-many The Rule of Joining applies to spatial joins also!

14. Return to Outline Copyright © 2009 by Maribeth H. Price 6-14 Simple spatial join One-to-one or many- to-one. No ambiguity in assigning fields.

15. Return to Outline Copyright © 2009 by Maribeth H. Price 6-15 Summarized join Destination table is counties. Source table is schools. Output table gives total schools located in each county. Count field is always generated automatically. User can optionally choose a statistic to calculate, for example, to sum the total number of students in each county.

16. Return to Outline Copyright © 2009 by Maribeth H. Price 6-16 Which attraction is closest to each hotel? How many attractions are closer to one hotel than another? Point to point joins Distance joins only Simple? or Summarized? Depends on the question being asked… Enforces a one to one cardinality… One to many cardinality…must use summarize

17. Return to Outline Copyright © 2009 by Maribeth H. Price 6-17 Types of spatial joins

18. Return to Outline Copyright © 2009 by Maribeth H. Price 6-18 SimpleSummarized Inside Distance Schools  Counties Which county is each school in? Hotels  Attractions Counties  Schools How many attractions are closest to each hotel? Which attraction is closest to each hotel? How far is it? How many schools in each of the counties? Hotels  Attractions

19. Return to Outline Copyright © 2009 by Maribeth H. Price 6-19 Feature geometry and spatial joins

20. Return to Outline Copyright © 2009 by Maribeth H. Price 6-20 Points to Polygons Join each county to the hospital that is nearest it. Each county features gets name of closest hospital and the distance.

21. Return to Outline Copyright © 2009 by Maribeth H. Price 6-21 Note on polygon distances In measuring distances for polygons, the center of the polygon is used. For each of the counties, the centroid of the county is closest to the hospital. If the county contains a hospital, the distance is zero. Pennington County has three hospitals all with a distance of zero, so one is randomly chosen to match.

22. Return to Outline Copyright © 2009 by Maribeth H. Price 6-22 Points to Lines In this example we wish to evaluate impact of septic systems on various streams based on distance. Streams is the destination. Each point represents one or more septic systems. We find the number of septic points closest to each stream segment, and summarize the totals.

23. Return to Outline Copyright © 2009 by Maribeth H. Price 6-23 Feature types Every join involves two geometry types –Points to points –Polygons to lines –Lines to points, etc. Each combination offers two possible join types. –One is usually a simple join, the other is summarized.

24. Return to Outline Copyright © 2009 by Maribeth H. Price 6-24 Note In the next examples, we break with our usual convention and put the destination layer on the right. This is done to match the convention used in the ArcGIS join menu. The destination table is shown in boldface.

25. Return to Outline Copyright © 2009 by Maribeth H. Price 6-25 Geometry Type Join TypeExample Points to Points Simple distanceFind the hospital closest to each town. Summarized distance Find all the towns closer to one hospital than to any other hospital. Lines to Points Simple distance Find the water main closest to the proposed building site. Summarized inside Find the total voltage of all electric lines meeting at a substation. Polygons to Points Simple inside Find the soil type that underlies each gas station. Simple distance Find the lake that is closest to each campground. Several possible combinations

26. Return to Outline Copyright © 2009 by Maribeth H. Price 6-26 Points to Points Simple distance – Find the source feature that is the closest to the destination feature. –Find the hospital closest to each town. Summarized distance –Summarize the attributes of all the source features that are closer to the destination feature than to any other. –Find all the towns closer to one hospital than to any other hospital. Destination: hospitals

27. Return to Outline Copyright © 2009 by Maribeth H. Price 6-27 Polygons to Points Simple distance –Find the county that is closest to each hospital and give the hospital the county attributes. Simple inside –Find the county that each hospital is inside and give the hospital that county’s attributes. Destination: hospitals

28. Return to Outline Copyright © 2009 by Maribeth H. Price 6-28 Polygons to Lines Simple distance –Find the park that is closest to each road and give the road the park attributes. Summarized inside –Give the interstate the total population of all the counties that it crosses.

29. Return to Outline Copyright © 2009 by Maribeth H. Price 6-29 Beware and think… Some choices don’t make sense for particular layers. Finding the closest county to each river has no meaning here. Finding the county a river is inside does have meaning…BUT …some rivers cross county lines. We’ll return to this issue in the next chapter. Destination layer: rivers

30. Return to Outline Copyright © 2009 by Maribeth H. Price 6-30 Polygons to Polygons Simple inside –Give each urban area the attributes of the county that it falls inside. Summarized inside –Give each county the summarized attributes of the urban areas that fall inside it. Notice that in this case we need to switch the destination layer for the join to make sense.

31. Return to Outline Copyright © 2009 by Maribeth H. Price 6-31 Polygons to Polygons Simple inside –Find the park that each lake is inside and give the lake the attributes of the park. Summarized inside –Give the park the total area of all the lakes that fall inside it. Notice that in this case we need to switch the destination layer for the join to make sense. Also notice that some lakes do not fall cleanly inside one park or another. Not all joins are capable of giving valid results. Think!

32. Return to Outline Copyright © 2009 by Maribeth H. Price 6-32 More options These examples are only a few of the possible combinations—the rest are shown in your text. If this seems complicated, don’t worry. ArcGIS figures out the two possibilities for you and presents you with a choice…one usually makes sense after a little thought.

33. Return to Outline Copyright © 2009 by Maribeth H. Price 6-33 Coordinate systems and distance joins

34. Return to Outline Copyright © 2009 by Maribeth H. Price 6-34 Source coordinate system Look again at the join of hospitals to counties. What happens if the source layers are in a GCS?

35. Return to Outline Copyright © 2009 by Maribeth H. Price 6-35 Distance join units Distances are given in stored map units Decimal degrees cannot be easily converted to miles or km because the conversion factor varies with latitude Better to use a projected coordinate system… The source data was in a GCS with units of decimal degrees.

36. Return to Outline Copyright © 2009 by Maribeth H. Price 6-36 Distance joins and the CS Using a GCS or distorted projection may yield incorrect results. A B C A B C Use source data with a projection that conserves distance! Distance join with GCS sourceDistance join with UTM source

37. Return to Outline Copyright © 2009 by Maribeth H. Price 6-37 Beware GCS Projected on the fly The data frame coordinate system may be different from the source data coordinate system. Setting the data frame CS is not enough to fix the problem. You must project the source data using the Project tool (Chapter 11) and do the join again. SD State Plane

38. Return to Outline Copyright © 2009 by Maribeth H. Price 6-38 About ArcGIS Chapter 6. Spatial Joins

39. Return to Outline Copyright © 2009 by Maribeth H. Price 6-39 Choosing the join type

40. Return to Outline Copyright © 2009 by Maribeth H. Price 6-40 How to join Right-click destination table Set Join type to spatial Choose source table Choose join type Specify output file

41. Return to Outline Copyright © 2009 by Maribeth H. Price 6-41 Two choices Based on the two geometries and the destination, ArcMap picks the possible two join types. You just need to pick the right one. Usually one is simple and one is summarized.

42. Return to Outline Copyright © 2009 by Maribeth H. Price 6-42 Choosing summary statistics Unlike the Summarize command that pairs stats with specific fields, in joins you only pick the type(s) of statistics. A new stats field will be generated for every numeric field in the table. Choose only what you need—or your output may have too many fields!

43. Return to Outline Copyright © 2009 by Maribeth H. Price 6-43 Setting up spatial joins

44. Return to Outline Copyright © 2009 by Maribeth H. Price 6-44 Setting up a join Sketch the layers How do I want the output layer/table to look? Which is the destination layer? Is this a distance join or an inside join? What is the cardinality? Do I need a simple join or a summarized join?

45. Return to Outline Copyright © 2009 by Maribeth H. Price 6-45 Example #1 Find all congressional districts that have had more than 10 earthquake deaths.

46. Return to Outline Copyright © 2009 by Maribeth H. Price 6-46 How do I want the output to look? Which is the destination layer? Is this a distance join or an inside join? What is the cardinality? Do I need a simple join or a summarized join?

47. Return to Outline Copyright © 2009 by Maribeth H. Price 6-47 Example #2 Develop a pollution risk index for rivers based on the total number of people in the adjacent counties.

48. Return to Outline Copyright © 2009 by Maribeth H. Price 6-48 How do I want the output to look? Which is the destination layer? Is this a distance join or an inside join? What is the cardinality? Do I need a simple join or a summarized join?

49. Return to Outline Copyright © 2009 by Maribeth H. Price 6-49 Example #3 Create a table showing the volcano closest to each city in the US

50. Return to Outline Copyright © 2009 by Maribeth H. Price 6-50 Students can find volcano closest to their city. How do I want the output to look? Which is the destination layer? Is this a distance join or an inside join? What is the cardinality? Do I need a simple join or a summarized join? Or can they? Look at the distances…