CS 128/ES Lecture 12a1 Intro to Spatial Analysis (mostly 2D)

CS 128/ES Lecture 12a2 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?

CS 128/ES Lecture 12a3 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)

CS 128/ES Lecture 12a4 Time for some geometry!

CS 128/ES Lecture 12a5 “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!)

CS 128/ES Lecture 12a6 Conventional Distance The Pythagorean Theorem helps us compute “conventional” distances in the plane Of course ArcMap does it automatically

CS 128/ES Lecture 12a7 “Alternative” distance “Manhattan” distance How many blocks (via a taxi cab) from A to B? ArcMap can do this in a query/report A B What about one-way streets?

CS 128/ES Lecture 12a8 Not your mother’s “Distance” More complex distances require more complex analysis

CS 128/ES Lecture 12a9 Area (by vector) Area of a rectilinearly aligned trapezoid is easy. A B C C*(A+B)/2

CS 128/ES Lecture 12a10 Area (by vector) For a polygon, add up the (signed) trapezoidal areas

CS 128/ES Lecture 12a11 Area (by Raster) Simply count the rasters inside the polygon or How big is this?

CS 128/ES Lecture 12a12 Area (by ArcMap)

CS 128/ES Lecture 12a13 Points in Polygon Send out a “ray” and count the crossings. ODD implies inside EVEN implies outside 3 Crossings => INSIDE 2 Crossings => OUTSIDE

CS 128/ES Lecture 12a14 Overlaying vector layers Spatial information (from layers) can be used to create new spatial information (i.e. new layers)

CS 128/ES Lecture 12a15 Overlaying Layers (Intersection) Keep only those things that belong to both layers Example: Overlay my property with a hydrology layer Learn how much of my “land” is under water. What to do about the property boundary and the lake?

CS 128/ES Lecture 12a16 Overlaying Layers (Intersection) Keep any things that belong to either layer Example: Overlay state highways layer and local roads layer to create pavement layer Note: New Layer not actually created in this figure

CS 128/ES Lecture 12a17 Overlaying Layers (Clipping) Keep only those things from a given layer that lie within a specified boundary (often rectangular) Example: Consider only those roads that lie within Cattaraugus County Problem: What if a road crosses the boundary?

CS 128/ES Lecture 12a18 DIGRESSION: What are rasters? Vector layers with a single attribute datum?

CS 128/ES Lecture 12a19 Overlaying Rasters Simple Mathematics will often suffice But there is less information

CS 128/ES Lecture 12a20 Effective Overlaying via Reclassification Data is not always in a good format If raster pixels have different coverages, overlaying may be effectively impossible Codings are generally categorical, not mathematical Adding codings usually does not make sense Solution: RECLASSIFY

CS 128/ES Lecture 12a21 A Sample Reclassification Land Use Old value New value “Other ” new value Wetland714 Road1000 Lake1217 Forest1401

CS 128/ES Lecture 12a22 Buffering – another tool Buffering (building a neighborhood around a feature) is a common aid in GIS analysis

CS 128/ES Lecture 12a23 Using Buffers to Select Select the features Save the features as a layer (Export)

CS 128/ES Lecture 12a24 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 … See also: Figure 6.9, p. 121

CS 128/ES Lecture 12a25 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)

CS 128/ES Lecture 12a26 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.