Determining Time in Hotspots with the Help of FME
Upcoming SlideShare
Loading in...5
×
 

Determining Time in Hotspots with the Help of FME

on

  • 3,117 views

 

Statistics

Views

Total Views
3,117
Views on SlideShare
1,574
Embed Views
1,543

Actions

Likes
0
Downloads
12
Comments
0

5 Embeds 1,543

http://www.safe.com 1487
http://www.safe.com.local 48
http://staging.safe.com 4
http://tpau.safe.internal 2
http://translate.googleusercontent.com 2

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Determining Time in Hotspots with the Help of FME Presentation Transcript

  • 1. Time in HotspotBrett Lord-CastilloGIS ProgrammerSt Louis County Emergency Management April 10th, 2013
  • 2. A simple problem Given a set of designated crime “Hotspots” Find out how much time police vehicles spend in those hotspots  Which hotspots?  Which days?  What hours?  Which cars?
  • 3. The data Automatic Vehicle Location (AVL)  Car ID  Latitude/Longitude  Timestamp Every 30 seconds per vehicle Hundreds of thousands of records per day
  • 4. The data-complications No specific order Transmitters malfunction (and we lose a car) Receiver malfunctions (and we lose hours) 5 minute stopped = stops transmitting (But GPS Drift can bring it back on) Thirty seconds is rarely thirty seconds New dataset every day (And sometimes we even lose a day)
  • 5. Manual method Convert Latitude/Longitude to XY Point table Select by Location with Hotspots polygons Export and sum up number of returns Assume each return is 30 seconds -or- Manually assign times to each point Sum up time on all returns
  • 6. Why manual doesn’t work Very time consuming, especially with manually assigned times Have to redo selections for each dimension  By Hotspot  By Car  By Day of Week  By Time of Day
  • 7. The Workspace
  • 8. Not as complicated as it looks
  • 9. Initialize and collect recordswith a blocking transformer
  • 10. Inside the Loop: Three Date FormatsDay of the Week: String created by format %a (Name of day of week)Hour of Day: Number created by format %H (24-hour hour of day)epoch: Number created by format %s (seconds since epoch)_totaltime: Placeholder to assign time for each GPS return
  • 11. Get some more informationfme_feature_type: Keep track of which points are from this week_prevcar: Car ID from the last feature processed (We store this later)
  • 12. Assign the time this return is worth
  • 13. Set aside info for next feature
  • 14. Spatial FilterSelect by Location, Spatial Join,and Blocking Transformer all in one
  • 15. Output: Save Our Work(With fanout writer) Week1019_1025 Pings1019_1025
  • 16. Points for analysisAnd one sheet in an Excel Workbook
  • 17. Last two sheets in the workbook
  • 18. Outcomes
  • 19. Comparison First manual attempt took over 4 hours  (To process one day)  And assumed one point = 30 seconds  Single stat (total time by hotspot) FME ran entire week in under 30 minutes  Automated  Less assumptions  More dimensions (By Car, By Day, By Hour)
  • 20. Thank You! Questions? For more information:  Brett Lord-Castillo, blord-castillo@stlouisco.com  St Louis County Emergency Management  @blordcastillo  https://github.com/marigolds6