1. Administrative Stuff ECE 297

2. Administration Milestone 0: –Submit by Friday at 5 pm –Demo in lab this week –Write your name on the board when ready to be marked  first on board, first to be marked

3. Administration Milestone 1: –Plan to release today (course website) –Submit by Monday, Feb. 2 @ 5 pm –9% of final mark 6% auto-graded –4% functionality –2% runtime (pass / fail speed tests) 3% by TA –Coding style, SVN use and log messages, created unit tests, can answer questions about code –TA will ask questions the week of the submission (Feb. 2)

4. Administration Tutorial: more advanced STL containers – (linked list) – (binary tree) –iterators  let you walk through these data structures All tutorial rooms / times will happen –Friday 9 – 11: both GB 412 & SF 2202 Team selection –Select by Friday night –Head TA will be setting up wiki pages & shared directories over weekend

5. OpenStreetMap and Graphs ECE 297

6. OpenStreetMap Open geographic database of the world –Created by regular people contributing data

7. OSM Data (lattitude,longitude) points (OSM nodes) –Each also has a unique integer id Lists of points (OSM ways) that form –A piece of a road –A shoreline –A park boundary –etc. Optional tags on these points and ways –key,value pairs –e.g. name = Yonge –e.g. natural = water Documented at http://wiki.openstreetmap.org/wiki/Main_Page http://wiki.openstreetmap.org/wiki/Main_Page

8. Big Data! Can download all the data for the planet, in xml format –planet.osm: 498 GB file! Hard to work with! Have extracted & downloaded 3 cities –Toronto.osm (383 MB) –Hamilton –Cairo

9. OSM Trade-offs Positive –Powerful: lots of data, few restrictions –XML is text  human readable Negative –Data is low-level  can be hard to interpret –Data can be somewhat inconsistent (e.g. few absolute rules on names  entered by volunteers) –Big (383 MB for Toronto) –Slow to load (45 seconds for Toronto)

10. Solution: Parse, Reduce, Refine osm2bin program –Written by course TA (Jeff Cassidy) –Reads in raw OSM data –Applies consistency checks –Organizes data –Writes out only essential data in exact binary form needed to load our (libstreetsdatabase) classes toronto.bin: 38 MB –Can load this data back in very fast (1 s) to initialize the same classes This is called class serialization We use a library called boost serialization

11. API layers in milestone 1.osm OSM2BIN toronto.osm This is the api you will build  higher level, more full featured You will talk to this api  need to understand it You are given this header  need to write the implementation (.cpp files) for all the functions in it

12. Organizing the Map Data The lower-level API you’ll talk to

13. What Will We Do with the Data? Milestone 1: Use The provided API (StreetsDatabaseAPI.h) to implement various functions. E.g. vector find_intersection_street_names ( string intersection_name); Milestone 2: Graphics – use the provided API and m1 functions to visualize the map. Milestone 3: Pathfinding – find paths through the map between 2 intersections Milestone 4: Courier company (Traveling Salesman) – find a path through n points on the map.

14. How would you represent a street map? As a graph! –G = (V, E) –Vertices (V)? –Edges (E)? Intersections Street Segments (City blocks)

15. Map as a Graph

16. libstreetsdatabase How to identify each vertex? –unique integer id  small, fast 0 to getNumberOfIntersections() - 1 ; ~166,000 for Toronto –unique string name  convenient for end user 0 1 2 3 4 Bloor & Yonge Bay & College Bloor & Bay College & Spadina DVP & unknown(20)

17. libstreetsdatabase How to identify each edge (StreetSegment)? –unique integer id  small, fast 0 to getNumberOfStreetSegments() - 1 ; ~215,000 for Toronto –no unique string name 0 1 2 3 4 Bloor & Yonge Bay & College Bloor & Bay College & Spadina DVP & unknown(20) 0 1 2 3 4 5