crash cube: an application of map cube to hotspot discovery in vehicle crash data mark dietz, jesse...
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Crash Cube: An application of Map Cube to Hotspot Discovery in Vehicle Crash Data
Mark Dietz, Jesse VigCSCI 8715 Spatial Databases
University of Minnesota
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
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
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
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!
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
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
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
Spatial onlySpatio-
temporal
Thematic mapping
Color-coded continuous
surface
Statistically-based
ellipses
AnimationAlbum of
maps
AnimationAlbum
of maps
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
Key Concepts
Dimension Visualization
One non-spatial dimension Bar chart
One spatial dimension Geographic plot
One spatial and one non-spatial dimension
Album of geographic plots – one for each value of non-spatial dimension
Two non-spatial dimensions 2-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 dimensions Album 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
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
Validation – Spatial dimension
Plot of all crashes
Plot of all crashes aggregated in 16x16 grid
Spatial plots show hot spots in different ways
Validation – Single time dimension?
?
?
Time of Day shows rush hours and bar closing hot spots
Validation – Two time dimensions
TD and DW shows rush hours and bar closings hot spots confined to certain days
Validation – Spatial and time dimensions
Plot of all crashes on Sunday
Plot of all crashes on Friday
TD and Spatial shows different hot spots on Sunday vs. Friday
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
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