1. Crash Cube: An application of Map Cube to Hotspot Discovery in Vehicle Crash Data Mark Dietz, Jesse Vig CSCI 8715 Spatial Databases University of Minnesota

2. Outline Motivation Problem definition Identifying crash “hot spots” through visualization Key Challenges Related Work Limitations of Related Work Contribution – Crash Cube Validation Case study: Crash Cube applied to crashes in Houston 1999 Conclusions and Future Work

3. Motivation Automobile crashes kill 1.2 million annually 50 million more injured City and road design – major factors in crashes Identifying “hot spots” benefits: City planners and traffic engineers Insurance companies Drivers

4. Problem Definition Given: Locations and times of vehicle crashes Other dimensions also possible Find: Visualization of the data across spatial and temporal dimensions Objective: Make hot spots easy to recognize by manual inspection

5. Key Challenges What is the appropriate visualization? Shape and scale of hot spots unknown How to filter? Some hot spots only visible with certain filter Visualization two dimensions But the crash data is higher dimension!

6. Statistical detection – out of scope Types of visualization Purely spatial Thematic mapping Color-coded continous surface Statistically-based elliptical objects Spatio-temporal Animation Album of maps Related Work – Hot spot detection

7. Related Work – Data cube and Map Cube Data cube – aggregate operator on N dimensions Creates 2^N aggregations for each possible combination of dimensions Map cube – extension of data cube to spatial data Has been applied to: Census data Traffic data

8. Limitations of Related Work Purely spatial visualizations – not temporal Animation – only shows most prominent hot spots Album of maps – limit number of visualizations –Creator must know desired visualizations Map Cube – not applied to crashes

9. Contribution – Crash Cube Album of maps – more visualizations than related work 2^N visualizations instead of 2 or 3 Aggregates data on 2^N combinations Spatial Time Time of Day (TD) Day of Week (DW) Month of Year (MY) Drilldown and rollup Find desired visualization easily

10. Key Concepts DimensionVisualization One non-spatial dimensionBar chart One spatial dimensionGeographic plot One spatial and one non-spatial dimension Album of geographic plots – one for each value of non-spatial dimension Two non-spatial dimensions2-D plot with each axis representing a non-spatial dimension One spatial and two non-spatial dimensions Album of geographic plots – one for each combination of values of the non-spatial dimension Three non-spatial dimensionsAlbum of matrices – one for each value of the third dimension One spatial and three non-spatial dimensions Album of geographic plots – one for each combination of values of the non-spatial dimension

11. Approach For each combination of dimensions Create a table with Fields for non-spatial dimensions OGIS Point representing the crash location Value of the aggregation Create visualization of table as graph or map Answer following types of queries What are the temporal hot spots? What are the spatial hot spots? How do crashes vary throughout the days of the week? Case study: crashes from Houston 1999

12. Validation – Spatial dimension Plot of all crashes Plot of all crashes aggregated in 16x16 grid Spatial plots show hot spots in different ways

13. Validation – Single time dimension ? ? ? Time of Day shows rush hours and bar closing hot spots

14. Validation – Two time dimensions TD and DW shows rush hours and bar closings hot spots confined to certain days

15. Validation – Spatial and time dimensions Plot of all crashes on SundayPlot of all crashes on Friday TD and Spatial shows different hot spots on Sunday vs. Friday

16. Conclusions Crash cube aggregations can reveal hotspots in both space and time Different cube dimensions reveal different types of hotspots Rollup and drilldown allow user to explore dataset without prior knowledge Album allows careful inspection of data Example: Sunday vs. Friday visualization

17. Future Work Alternate aggregation functions Sum of fatalities Sum of vehicles involved in crashes Average number of fatalities per 1000 crashes Additional aggregation levels By county By street By year By month over several years Incorporate spatial visualizations from related work into Crash Cube framework