1. CS 128/ES 228 - Lecture 12b1 Intro to Spatial Analysis (mostly 2D)

2. CS 128/ES 228 - Lecture 12b2 Some GIS Queries How big is the lake? What is the longest trail? How many fire hydrants on campus? Which dorms are within 100 m of an academic building? Where is the best place for a new dorm?

3. CS 128/ES 228 - Lecture 12b3 Types of queries Aspatial – make no reference to spatial data Which dorm has the highest occupancy rate? (we can already do) Spatial – make reference to spatial (and possibly attribute) data Which fire hydrant is closest to the chemistry labs? (we can sort of do)

4. CS 128/ES 228 - Lecture 12b4 “Simple” spatial queries How long is this line? “Tricky” if line is a bunch of line segments “Tricky” if distance isn’t Pythagorean How much area does this polygon cover? (Can we do this?) Is this point in this polygon? (Can’t do this!)

5. CS 128/ES 228 - Lecture 12b5 Conventional Distance The Pythagorean Theorem helps us compute “conventional” distances in the plane

6. CS 128/ES 228 - Lecture 12b6 “Alternative” distance “Manhattan” distance How many blocks (via a taxi cab) from A to B? A B What about one-way streets?

7. CS 128/ES 228 - Lecture 12b7 Area (by vector) Area of a rectilinearly aligned trapezoid is easy. A B C C*(A+B)/2

8. CS 128/ES 228 - Lecture 12b8 Area (by vector) For a polygon, add up the (signed) trapezoidal areas

9. CS 128/ES 228 - Lecture 12b9 Area (by Raster) Simply count the rasters inside the polygon or

10. CS 128/ES 228 - Lecture 12b10 Points in Polygon Send out a “ray” and count the crossings. ODD implies inside EVEN implies outside 3 Crossings => INSIDE 2 Crossings => OUTSIDE

11. CS 128/ES 228 - Lecture 12b11 Overlaying vector layers Spatial information (from layers) can be used to create new spatial information (i.e. new layers) Intersection Union Clipping (NOT) Geometrically (computationally) intensive

12. CS 128/ES 228 - Lecture 12b12 DIGRESSION: What are rasters? Vector layers with a single attribute datum?

13. CS 128/ES 228 - Lecture 12b13 Overlaying Rasters Simple Mathematics will often suffice But there is less information

14. CS 128/ES 228 - Lecture 12b14 Effective Overlaying via Reclassification Data is not always in a good format Codings are generally categorical, not mathematical Adding codings may not make sense Solution: RECLASSIFY

15. CS 128/ES 228 - Lecture 12b15 A Sample Reclassification Land Use Old value New value “Other ” new value Wetland714 Road1000 Lake1217 Forest1401

16. CS 128/ES 228 - Lecture 12b16 Buffering – another tool Buffering (building a neighborhood around a feature) is a common aid in GIS analysis

17. CS 128/ES 228 - Lecture 12b17 Putting it all together Siting a nuclear waste dump Build Layer A by selecting good geology Build Layer B by reclassifying population for high density Build Layer C by clipping B from A Build Layer D by buffering roads Build Layer E by intersecting C and D …

18. CS 128/ES 228 - Lecture 12b18 Where does it fit in? GIS holds data Spatial analysis causes us to view the data as information Combining queries turns that information into knowledge (It’s all a spectrum)

19. CS 128/ES 228 - Lecture 12b19 Conclusions A GIS without spatial analysis is like a car without a gas pedal. There are some things you can still do with it, but it’s hardly worth maintaining the vehicle.