Mapping the Sound Environment of Andorra and Escaldes-Engordany by Means of a 3D City Model Platform
2. Real Operation Andorra Acoustic Data
2.1. Measurement Equipment Requirements
2.2. Recording Locations in Andorra and Escaldes-Engordany
- Location (a), found at 3024.0 N 3202.5 E (see Figure 2a), is a key point connecting the three main roads of Andorra in the capital [35,36]; it has the heaviest traffic during rush time, especially during weekdays. The principal source of noise was expected to be traffic, so it should have constant high equivalent levels of noise and a lower variation of noise sources compared to other areas during the mentioned rush hours and will only decrease as the day goes by.
- Location (b), found at 3030.8 N 3202.8 E (see Figure 2b), is the intersection between a main road named CG-3, which leads the traffic from Andorra La Vella and Escaldes-Engordany to the northern part of the country, with a wide pedestrian and commercial axis. This is not only a traffic area like Location (a), but one of the most common promenade points for Andorrans and also for tourists. In this area, the number of potential noise sources is wider also during the entire day, which consequently increased our interest in obtaining effective mapping results. Location (b) is of great interest since it recently became the biggest pedestrian area in the country, making it a key point to study the noises that occur in and around it.
- Location (c), found at 3032.3 N 3143.9 E (see Figure 2c), is also the crossing of a main road and the final part of a pedestrian and commercial street, similar to Location (b). The difference with Location (b) that this street is not totally pedestrianized, since it depends on traffic lights for people to cross the road. Therefore, we expected a high variability of noise sources during the day as reported in Location (b), but with the additional street works detected during both days of the recording campaign due to different circumstances, creating a totally different noise dynamics for further analysis.
3. Data Description and Categorization
3.1. Exhaustive Labeling of Events
3.2. Categorization of Street Noise Events
- Vehicles: Noise sources coming from any kind of vehicle, albeit without an engine. This category was chosen as all of the measurement points have a road nearby where a significant density of vehicles circulate every day, so traffic plays an important role in acoustic pollution.
- Works: Noise sources related to street or construction jobs. This category is defined to consider construction works located near the recording points and helps to characterize the contribution of construction to the impact of sound on the population.
- City life: Noise sources from typical events occurring in an urban area (other than vehicles) that helps to provide a wider context to the measurements taken and the local surroundings. This category comprises noise events coming from traffic light emitters, sirens, or music coming from commerce or cars.
- People: Human noise sources. This category is important as some of the measurement areas have pedestrian or half-pedestrian zones highly frequented by both residents and tourists, so it is expected that in this category, human noise sources will be one of the most significant or frequent labels identified during the measurement periods.
4. Analysis of the Real Operation Audio Collected
- Duration: This was evaluated in fractions of a second and corresponds to the time that the 3D visualization platform would spend to show its values.
- : This is also known as the time-average or equivalent sound level , and it stands for the equivalent of the total sound energy measured over a limited period of time.
- SNR: The Signal-to-Noise Ratio is the relation between any noise event’s evaluated power and the power of the previous and the posterior Road-Traffic Noise (RTN) surrounding the event under study. For more details about the calculus of the SNR, the reader is referred to .
5. Methodology and Visualization
5.1. First Visualization Approach
5.2. Visualization Approach Including
- When a sound event occurred, the circle rippled according to the event’s SNR. The visual effect looked harmonic in a single circle scenario, but it became cluttered when multiple circles were overlapped.
- Any SNR bigger than 3 dB produced a ripple amplitude more than twice the radius of the circle. If the sound events were susceptible to reaching more than 20 dB, the circle would overpass 3D surface bounds and become totally useless in terms of the interpretation.
- Negative SNR values cannot be represented in terms of ripple amplitude.
5.3. Implementation of the Visualization over the 3D City Model
- ANE (Anomalous Noise Event): categorization label for every sound event. The label was indexed and grouped by the 4 categories described in Section 3.2.
- SNR: Signal-to-Noise relation in dB of the event over the RTN.
- : equivalent noise level.
- TS: Timestamp of the event captured in the format hh:mm:ss,d. Even if the resolution could be higher, a decisecond was chosen, as it was considered enough for representational goals.
Conflicts of Interest
|ANE||Anomalous Noise Event|
|END||Environmental Noise Directive|
|Equivalent Level A-weighted filter|
|OBSA||Observatori de la Sostenibilitat d’Andorra|
|RTN||Road Traffic Noise|
|SPL||Sound Pressure Level|
|WASN||Wireless Acoustic Sensor Network|
|WSN||Wireless Sensor Network|
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|BARK (dog barking)||6.745||62.6||00:12:23.4|
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Alsina-Pagès, R.M.; Vilella, M.; Pons, M.; Garcia Almazan, R. Mapping the Sound Environment of Andorra and Escaldes-Engordany by Means of a 3D City Model Platform. Urban Sci. 2019, 3, 89. https://doi.org/10.3390/urbansci3030089
Alsina-Pagès RM, Vilella M, Pons M, Garcia Almazan R. Mapping the Sound Environment of Andorra and Escaldes-Engordany by Means of a 3D City Model Platform. Urban Science. 2019; 3(3):89. https://doi.org/10.3390/urbansci3030089Chicago/Turabian Style
Alsina-Pagès, Rosa Ma, Marc Vilella, Marc Pons, and Robert Garcia Almazan. 2019. "Mapping the Sound Environment of Andorra and Escaldes-Engordany by Means of a 3D City Model Platform" Urban Science 3, no. 3: 89. https://doi.org/10.3390/urbansci3030089