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PHIL BARTIE AND SIMON KINGHAM
Media Mapping:
Using Georeferenced Images and Audio to provide supporting
information for the Analysis of Environmental Sensor Datasets.
Slide 1 of 1095
Author Backgrounds
Phil Bartie
PhD Candidate Geospatial Research Centre (GRCNZ), University of Canterbury, NZ [LBS, Viewsheds, Speech UI]
Associate Professor Simon Kingham
Geography Department, University of Canterbury, NZ [Sustainable Transport, Air Pollution, GeoHealth]
www.OpenStreetMap.org
The BIG Picture
Origins of this Research
Problem encounteredOverview of Desired SolutionFit of Existing Tools
Implementation of Solution -Mobile Application -Desktop Application
Usage ExamplesConclusions
http://www.datenform.de/map14_1000.jpg
Origin of this Research
An air quality monitoring project being run in the New Zealand cities of Christchurch and Auckland funded by New Zealand Transport Agency
Monitor personal exposure to air pollution in daily commute
1 month of field trials per city (2009)
Different modes of transport
Data Collected= Air quality, location, time, situational notes
Problem
The analysis of temporal datasets in GIS is often hampered by a lack of supporting relevant information on local conditions at the time of data capture.
(e.g. A passing vehicle may be the cause of a noted spike in airborne particulate matter, but without the supporting situational information the spike may never be explicitly explained.)
Air Quality Samplers
GRIMM – PM1, PM2.5, PM10 [1 sample every 6 seconds]
Langan –CO, Temperature[1Hz]
3007 – Particle counter[1Hz]
Environmental datasets
=> Time stamped log files
Desired Solution Overview
Two parts:
1) An automated method to allow researchers to capture co-located situational (context) data while on daily commute
2) A tool to assist researchers in using the capture datasets during the analysis phase
Restrictions/ Limitations
[Overall]Cheap solution– minimal budget (~80hrs
programming)Field ready within short timeframeEasy to use
[Mobile Data Capture Tool] RobustSmall and lightweightBattery life to capture data for at least 90 minutes
Ways to Capture Context
Written notesAccess to supporting data from sensor
networksVideoAudio
Multimedia files provide an extra information channel when linked to GIS... (Cartwright et al. 2007)
Media Mapper (Red Hen Systems)
Links multimedia files to a map pointGeotagged filesSpatially enabled document retrieval system
One way relationship between location and filesTemporal content of media file is not spatially
attributed
www.openstreetmap.org
CamNav Mapper (BlueGen Ltd)
Standard video cameraAudio channel used to store GPS location
information as binary data (like modem)
Video => see where on MapClick on Map => see corresponding video
GeoMobSense (Kanjo et al. 2007)
Toolkit for smartphones to enable them to be used as data loggers
Ability to records sound levels using phone’s microphone, and add additional environmental sensors
Phone screen used to display current values
Fails to capture a spatially attributed audio & video feed for later analysis (eg only records sound level not audio, no image capture)
MESSAGE consortium
The MESSAGE consortium (Polak and Hoose 2008) have undertaken a number of projects using mobile phones as personal environmental sensors and data loggers.
Sensors included carbon monoxide, carbon dioxide, traffic volume, nitrogen levels
Data can be fed in real time to a data centre to reveal city wide trends
No facility to store audio/video with location for future retrieval
Bi-Directional Search Tools
Jaejun (2002) - “A video geographic information system for supporting bi-directional search for video data and geographic information”
Zeiner (2005) – “Video documentation of urban areas.”
No facilities to link to other temporal datasets(eg air pollution datasets)
xTri-
Custom Solution
High level of integration between mapping, charting/graphing, video & audio
Co-located synchronized set of sensors sampling the local environment during data capture (i.e. mobile)
Small lightweight robust capture device
Good battery life performance
Nokia N82 Smartphone
Built in high sensitivity Assisted-GPS Programmable in C, Java, Python, Flash 2GB micro-SD card included 5MP high quality camera Good battery life Able to run multiple custom applications
simultaneously (if required)
Mobile Application Attempt 1
Use Python – rapid development
Record video file 15fps (with audio track)GPS data log fileSync video and GPS using playhead position
time (tightly coupled video and location every
second)Video file tightly compressed using MP4 (70MB/hr)Depleted fully charged battery in 1hr
http://sourceforge.net/projects/pys60/
Custom Python S60 application to:
Sound recording all the time 8kHz Photo every 3 seconds GPS, Cell tower logged every 3 seconds
with the play head position in the sound file
Sound – taking ‘spatial’ notes, listen for buses, cars etc
Approx 60MB /hr for sound file
320 by 240 – Front Camera
Mobile Application Attempt 2
Easy to Use
•4 sensor kits + 4 phones•SIS install restricted to phone by IMEI•Right Soft Key to Launch DataLogger•www.symbiansigned.com
“Industrial Handbags”
“Campbell Live” March 2009 http://www.3news.co.nz/Scientists-embark-on-air-pollution-study/tabid/817/articleID/93564/cat/84/Default.aspx
Data Capture Code Overview
On GPS UpdateEvent
Capture Photo
Write Log File
Record Audio
AT=S.current_position()
S=audio.Sound.open(filename)S.record()
PT=Phone Python time
PT,AT, GPS
Filename = PT.jpg
Set descriptio
n
•Visualize data on map, linked by time•Sound file forms base time through which all other data streams are linked
Data Analysis Tool (Desktop)
DirectSound
ZedGraphPiccoloOGRPROJ
C# .NET
(NZMG)
Higher quality air in pedestrian precinct – Map
Air quality drops when entering multi-storey car park
Conclusion
Was it useful? Yes How was it useful? - easy to capture supporting information which proved useful in analysis
phase to help explain data trends (worked as intended) - consistent, reliable - audio track useful for taking georeferenced notes - GPS + time log useful for exporting to other applications
Could it be improved? Yes How? - video rather than stills - multi-thread application to continue capturing images when no GPS updates
(eg inside buildings) - multiple cameras / wide angle lens - log sensor data to phone using Bluetooth link - more powerful query tools in desktop application
Acknowledgements
Kreepa Shrestha, and Woodrow Pattinson who carried out the extensive field trials.
Justin Harrison for setting up and supporting the environmental sensor equipment.
This research would not have been possible without funding support from the Geospatial Research Centre (NZ) and New Zealand Transport Agency.
References
Blueglen Ltd (2009) CamNavMapper. Retrieved 20 May 2009 from http://www.blueglen.com/prod_camnav_single.htm
Cartwright W, Peterson MP, Gartner GF (2007) Multimedia cartography, Springer Verlag
Jaejun YOO, Joo T, Park JH, Lee J (2002) A video geographic information system for supporting bi-directional search for video data and geographic information. Proceedings of International Symposium 2002
Kanjo E, Benford S, Paxton M, Chamberlain A, Fraser DS, Woodgate D, Crellin D, Woolard A (2007) MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones Personal and Ubiqui-tous Computing
Polak J, Hoose N (2008) Mobile Environmental Sensing System Across Grid Environments
Red Hen Systems (2009) MediaMapper. Retrieved 20 May 2009 from http://www.afds.net/mediamapper.html
Zeiner H, Kienast G, Derler C, Haas W (2005) Video documentation of urban ar-eas. Computers, Environment and Urban Systems 29: 653-668
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