AGILE Conference - Castelló (2014)
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A comparative study on VGI and professional noise data Irene Garcia Joaquín Torres Luis Rodríguez Joaquín Huerta AGILE 2014 Session 1: Data Capture and Mapping 4th June 2014
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Transcript of AGILE Conference - Castelló (2014)
A comparative study on VGI andprofessional noise data
Irene GarciaJoaquín TorresLuis RodríguezJoaquín Huerta
AGILE 2014 Session 1: Data Capture and Mapping
4th June 2014
Background• Every 4 years, university commissions noise pollution study in
Campus
• Following ISO 1996 for acoustic reports
• Low spatiotemporal resolution
• High cost for small entities or communities of users
Presenter
Presentation Notes
So, first, let me introduce a bit about the background. Every four years, university commissions a noise pollution study to an engineering company in order to see if noise levels on Campus are between the law established limits. This company follows a specific protocol for acoustic reports that is ISO 1996 certified. Despite results are very accurate, the study in general has an elevated cost (up to several thousands of euros) for the university and a low spatiotemporal data resolution. Just to let you know, in 2012, the study was carried out for the third time.
Background• 2011 – 2012: Noise Battle development
• Gamified mobile application for crowdsourced noise collection
Presenter
Presentation Notes
During 2011 – 2012, GEOTEC research group developed a gamified application for the crowdsourced noise collection that was presented in AGILEs last year edition. SO you can see a couple of pictures of the application.
Motivation
How good is VGI noise data comparedto professional sampling?
Presenter
Presentation Notes
So the main question here was: OK, it is technically possible to acquire noise samples with mobile devices AT A VERY LOW COST, but how good is VGI noise data compared to professional samples?
Professional noise data• Collected in 66
locations• ISO certified sound
level meter• Noise exposure: 5
minutes• Environmental
conditons:• Noise weakening• Wind speed• Distance to buildings
Presenter
Presentation Notes
So first we contacted the University Environmental office to request the data they received from the company and have a small sample of datasets. The company took noise samples in the entire Campus. The collection process considers environmental conditions, such as noise weakening, screening effects, wind speed or distance to buildings. The important thing here is the length of the noise exposure. The sound level meters were exposed to noise in five minutes intervals in order to attenuate possible sudden peaks.
Mapping partyScenario: UJI CampusUsers: 12 usersDate: 19 – 10 -2013Size: 585 x 487 metersArea: 0.285 sqkm
• Reduce study area• Users to get one obs
per node• Comparable sets
Presenter
Presentation Notes
Secondly, we needed to acquire our own volunteer noise data. For this purpose, what we did is helding a GEOTEC mapping party. We defined the study area as you can see in the slide inside our campus. Due to time restrictions, we could not repeat the experiment as the private company, so what we did was focusing our study area in the central part of the campus, comprising the three faculties, the central garden and the main access road. Reason for this is that the left inner side of the campus is mainly not occupied and the noise levels are very low and steady through time. So the study area is 585 meters long and 487 meters wide, with a total area of 0.285 sqkm, that might be considered a real case small-sized scenario. The important thing here is the following: in order to compare two datasets we needed to take noise samples in the same positions as the company did. For this purpose we modyfied the original NoiseBattle application to include this layer with red dots and included the boundaries where the users had to collect samples. Users participating in the experiment were encouraged to take at least one noise observation near the red nodes inside the square polygon.
Mapping Party
Device modelNum. of devices
Num. of samples taken
LG Nexus 4 4 282HTC One 1 35
HTC Wildfire S 1 67Samsung Galaxy S4 1 31Samsung Galaxy S3 1 112Samsung Galaxy S2 1 1*
Samsung Galaxy Ace 2 1 29Sony Xperia S 1 21
Celkon A27 1 3*
581 samples in 2 hours
Presenter
Presentation Notes
Twelve users participated in the experiment, altough two of them had problems with their mobile devices and could not provide a significant number of samples. However, in 2 hours, 581 observations were collected in the surroundings of each node.
Mapping Party
Presenter
Presentation Notes
A couple of captions of the mapping party
Professional noise data
Presenter
Presentation Notes
So what we did was representing in a GIS software the professional dataset. As noise is a continuous phenomenon we calculated an interpolation surface to see the levels of noise in Campus. As seen, the range of noise goes from 50 to 63 decibels, with the left inner part with the lowest noise levels and finding the highest ones near the main access gate. In general, there is a moderate noise level around the three faculties and the central garden. It is important to highlight the gridded pattern data are following. As seen the increase from low to high is smooth and gradual, due to the equal dispersion of nodes.
VGI noise data
Presenter
Presentation Notes
For our volunteer noise data collection we repeated the experiment. So, this is the output of representing 581 observations taken with 12 mobile devices. As you can see, there is a certain clustering around where grid node is supposed to be. Of course the accuracy of the location depends on the mobile device accuracy and the patience of the user to approach exactly to the marked point. The unequal distribution of responses causes the interpolation surface to be overfitting the samples and therefore, the interpolation surface does not present a smooth appearance. What we did was averaging the samples taken around a node to obtain a single volunteer value presented to a point. However this volunteer surface detects a similar noise pattern from left to right, and if we take a look to the legend, is measuring from 52 to 65 decibels, something that is close to the professional layer.
Difference between layers
Presenter
Presentation Notes
This layer represents the raster subtraction of the previous two images. The whiter the image the less difference between professional and volunteer observations. The lowest differences have been produced around the Sciences faculty, while the volunteer observations have measured between 2 and 4 decibels mores aroung Laws faculty (this one) and the library.
Particular case: Nexus 4
Presenter
Presentation Notes
So we wanted to extend the experiment a bit further. If you remember from the table presented before, there were 4 Nexus 4 participating in the study. We analysed those data separately, because they collected about one third of the samples. If you check the legend, Nexus 4 measured from 57 to 67 levels, which is a significant higher level of noise respect to professional data. However, the data is still valid to capture the noise increasing patterns when approaching to the road.
Particular case: Nexus 4
Presenter
Presentation Notes
This is the difference between layers between the professional and the Nexus 4 layer. As you can see, Nexus 4 might have a difference up to 9 decibels. Those differences are decreased when clustered samples are averaged.
Particular case: Samsung S4
Presenter
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Now we are considering the map generated by a Samsung Galaxy S4. We chose this device because its high-end capabilities at present. As you can see the color curves generated by the software are smooth and the result of the equally dispersed data. Samsung Galaxy measures from 50 to 74 decibels, which is a quite a wide range of noise.
Particular case: Samsung S4
Presenter
Presentation Notes
And here is the differences layer, at some points the device is capturing data with a difference of 13 decibels. So, it seems that when analyzing the samples individually for a particular mobile device, the results are not much accurate. However, when samples are averaged with others, results are more similar to the professional dataset.
Summarizing
Presenter
Presentation Notes
So, to summarize, this is the general depiction on how each device is capturing noise levels. First column represents the professional dataset, second one the averaged volunteer noise samples. It is important to highlight the low cost of data collection.
Conclusions• At a sufficient number of observations and mobile devices,
results tend to be accurate.
• Mean error of 3 – 5 dB, in line with literature.
• Noise patterns detected, even with a single device
• Suitable to detect potential noise city issues in places there is no official data
• If high accuracy is required, sound level meters are still better
Presenter
Presentation Notes
Conclusions are mainly two: At a sufficient number of observations and mobile devices, the prediction tends to be similar to professional noise data. This is in line with several articles found in literature. But, if absolute accuracy is required, a study with sound level meters is more suitable.
Thanks for yourattention!
Questions?
http://www.geotec.uji.es/
