Using 3D city model platform for the analysis of Andorra’s Soundscape
Rosa Ma Alsina-Pagès(1) , Marc Vilella(2) , Marc Pons(2,3) and Robert Garcia Almazan(1)(1) Grup de recerca en Tecnologies Mèdia (GTM), La Salle - Universitat Ramon Llull, C/Quatre Camins, 30, 08022 Barcelona(Spain) , rosamaria.alsina@salle.url.edu(2) Observatori de la Sostenibilitat d’Andorra (OBSA) , AD600, Sant Julià de Lòria, Andorra, mvilella@obsa.ad(3) Andorra Innovation Hub, Actua Innovació„ AD700, Escaldes-Engordany, Andorra , marc.pons@actuatech.adAbstract
In a SmartCities world, with the citizen in the center of all the decisions, the measurement of environmental
noise cannot be limited to provide an equivalent level captured over a certain time in a place of interest. Bymeans of WHO reports and other studies, we know that the type of noise (traffic, anthropomorphic, industrial,etc.) has a different impact on people and that it is important to identify the source of the measured noise levels.
In order to facilitate this work for the competent authorities, in Andorra a 3D City model platform is used tovisualize acoustic measurements carried out at three strategic points in the country. The platform represents themeasured noise differentiating the type of noise, the equivalent acoustic level it is providing and the salience ofthe event with respect to background noise by means of the calculation of the Signal-to-Noise Ratio, providing
a wider and more complete amount of information to allow to decide policies to minimize the impact on people.
Keywords: noise monitoring; anomalous noise event; 3D model; recording campaign; urban soundscape

1INTRODUCTIONAcquisition, processing and interpretation of sensed data is a recurrent task among scientists and analysts. The
whole process usually involves a set of theoretical knowledge, complex math algorithms, system patterns anddata transformation that lead to the final results and conclusions, generally addressed to other professionals on
the same fields of knowledge. There are some other scenarios, however, where the final target of the research
may not have a scientific or technical background, and results are intended to be used as support for policy
making, awareness raising campaigns or community engagement. In this context, results showcase must be
designed to provide more understandable information [8] other than just tables and complex charts, and experiment with alternative visual and dynamic representations, such as animations or time-lapses, moreover if thisvisualizations are updated in real-time whenever an interaction triggers any parameter change.
The CityScope Andorra [4] is a framework developed by the MIT City Science group in collaboration withAndorra stakeholders, such as the Sustainability Observatory of Andorra (OBSA), intended to help non-experts
engage in conversations through visualization of mobility patterns, based in projections on a 1 x 3.15 m 3D
topographical model of the country’s capital. The same concept has already been exploited to project an interactive overview of the rooftop photo-voltaic potential in Andorra buildings [7] and generate a discussion on theviability of public subsidies policies or organize workshops with local schools1 . In Figure 1 we can observe theinteractive projection in a 3D surface of the rooftop photovoltaic potential in Andorra buildings.
One of the goals of the Andorra Government and the municipality of Escaldes-Engordany is to create a systemof real-time monitoring and data integration of several sources of information to inform about the urban dynamics such as traffic and pedestrian behavioural patterns, energy consumption and environmental pollution, among
others. The recording campaign and analysis presented in this study permitted us to explore the potential tointegrate and cross noise information of different locations of the city in order to better understand what are the1 https://youtu.be/FdUKJtJd9ts

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Figure 1. Interactive projection in a 3D surface of the rooftop photovoltaic potential in Andorra buildings.
different sources that are impacting the most the citizens and tourists, and what are their spatial and temporal
dynamics. First, the inclusion of this kind of information in the integral “single-view” system of the city canhelp us to better understand the most important drivers and their dynamics that led to an increase of the noise
pollution. Second, this data analyzed together with the different sources of information can provide relevant
results to design more suitable policies, measures and urban interventions to reduce noise pollution in the areas
where a higher number of citizens and tourist are exposed to, and help in the objective from both the Government and the municipality to increase the quality of the environment and the welfare of these groups [5, 3]
Further work will be devoted to develop a longer measuring campaign deploying in parallel other devices suchas cameras to apply computer vision to continuously record metrics about the events that occurred during themeasurements periods and spanning a greater period to capture other dynamics such as day-night and inter-dailyvariability.
This paper is structured as follows. Section 2 gives details of the acoustic data collection and processing, andin Section 3 works on the visualization of the measurements conducted. Section 4 states the conclusions andthe future work likes of this paper.

2REAL-OPERATION ANDORRA ACOUSTIC DATAIn this section we describe the areas of the three measuring locations in Andorra la Vella and EscaldesEngordany, together with the reasons that guided the choices, mainly related to the results of preliminary analysis with automatic level meters deployed in several critical locations in the country.
2.1 Locations
The selected measuring locations correspond to three important nodes in terms of volume, both for pedestrians
or for vehicles. Location (a) (see Figure 2a) is the connection between the 3 main country’s roads in the capital[5, 3], with the highest traffic congestion experienced at rush hours. In this area the main sources of noise aremainly expected to be cars. This is expected to have constantly high levels of noise and a lower variability interms of contributing sources compared to other areas. Location (b) (see Figure 2b) is the intersection between
CG-3, the road leading traffic from the capital to the north valley, with the main pedestrian and commercial
axis, one of the most frequented points by tourists. In this area the mix of potential sources of noise should behigher, as should the variability of contributing sources of noise in different days or during the day, increasing
its interest to be mapped. This location is of additional interest due to its recent transformation into the biggest
pedestrian zone in the country. Location (c) (see Figure 2c) is also the intersection of an important road and6701

the pedestrian axis, sharing the same interest for the study as (b). Compared to area (b), in this case the axis is
not fully pedestrianised but with an intervention of reducing and slowing the traffic. In this case, the variabilityof the sources should also be higher during the day. In this case, the impact of two different types of urban
intervention is expected to produce different noise dynamics in the area. Thus, these 3 locations were chosen
to analyse noise source identification. Furthermore, these will be most probably the places to prioritize futureinterventions and mitigation measures.

(a) Ctra. de l’Obac / Carrer de la Unió(b) Av. Carlemany / Carrer de la Unió(c) Av. Meritxell no 73

Figure 2. Images above show the three places where the recordings took place from a satellite perspective fromGoogle maps (Map data: Google 2019). The red dot in each picture indicates the place where the recordings
were conducted.

2.2 Types of Events
The types of noise of each area have been analyzed to evaluate common events and noise dynamics. In orderto classify them, four categories were decided by the OBSA and Actua. Traffic played an important part in
the decision, so a Vehicles category was required and it was the one with most of the labels. Besides Vehicles,
already expected to be one of the main sources of noise in all three pilot areas, three other classes wereconsidered in the analysis. People, as any noise made by persons, because two of the three pilot areas arehighly frequented places for tourists, with crossing points for pedestrians (b) or half-pedestrian (c) areas. In thisline, City Life class, as any noise taking place in an urban area but omitting traffic noise, has been also used
due to expected noises coming from urban elements like traffic light emitters or street music. Finally, someconstruction works were carried out in places near to the measuring pilot areas, so the Works category was also
considered.
• Vehicles: It is understood as vehicle all noises coming from any mean of transport, not necessarily having
an engine.
• Works: All noises related to construction jobs.

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• City Life: Any noise taking place in an urban area excluding vehicles.
• People: Any noise made by a person.
The authors refer the reader to [2] for the details of the events analysis, and to find the list of all the labelleditems included in each category.
2.3 Analysis of the Real-Operation Audio Collected
In this section, we describe briefly the measurements conducted over the raw acoustic data to be visualized. Theduration, the equivalent level LAeq , the time stamp and the Signal-to-Noise Ratio (SNR) are the four evaluations
needed to proceed with the conversion of the information to images.
• Duration: It is evaluated in fractions of second, and corresponds to the time that the visualization will
spend to show its values.
• LAeq : It is the equivalent continuous sound level in decibels, equivalent to the total sound energy measured
over a stated period of time and is also known as the time-average sound level [1].
• SNR: It is defined as the relation between the power of the noise event being evaluated and the previousand posterior RTN power of that given event. The following equation explains how it is calculated [6].
N Px = ∑t=1x(t)2N,(1)where N is the number of samples and x(t) is the event we want to calculate its power. Once this is done,
SNR is computed as stated in its definition.
SNR = 10log10PNEPRT N
,(2)where PNE matches the noise event power and PRT N stands for the previous and posterior RTN to that event. It
is to take into account that when calculating both powers, it is not strictly necessary for N to be the same size,
since it is a summation that will be divided by N in the end. Furthermore, it can observed that some events
will have a negative SNR. This can happen due to high traffic noise that makes an event irrelevant despite being
clearly audible.
There is a large amount of events with a negative SNR value which can either mean that the surrounding vehicle
noise was louder than the labelled noise or that the event itself was very weak, despite they were labelled asheard. Whatever the reason, these events are not relevant in the specific moment they took place as they do notrepresent an impact on humans.

3METHODOLOGY AND VISUALIZATIONIn this section, we detail the transformation of the collected data into its proposed visualization. The proposed
visualization represents all captured sounds as wavy circles. Each circle is drawn with multiple lines producing
displaced waves and giving a more dynamic feeling. All parameters of a each circle depend on a different
sound attribute, where the color represents the sound’s category, the radius represents the equivalent backgroundnoise level and the ripple amplitude represents the SNR. Two different approaches have been considered (Figure3); in the first case the wave’s origin is centered in the circle’s radius distance, while in the second case theripple is expanded and the minimum crest matches the circle’s radius distance. Despite the first approach looks

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more balanced, second approach produces a less chaotic and clumsy scene when combining multiple circles torepresent different overlapping sounds.

Figure 3. Sound representations with wavy circles
Any SNR bigger than 3 dB would be represented by a wave with an amplitude more than twice the radius of
the circle. Being sound events susceptible to reach more than 20 dB, visualization would get out of 3D surface
bounds and become totally useless in terms of interpretation. To overcome this limitation, the SNR representation is downscaled to a limited range of amplitudes, to the detriment of the real ratio between noise and soundlevels.
Another limitation comes with negative SNR. A proposed solution, shown in Figure 4, could be to represent
positive SNR as outside ripple and negative SNR as inside ripple, being the ripple’s amplitude bigger as bigger(negative) is the SNR. While this solution represents all recorded sounds, it may introduce a cognitive bias andcreate confusion in order to understand the impact of very low level sounds.
The visualization is developed in Processing2 , a visualization playground that uses an extended subset of Java
language and is focused to visual arts and multimedia. Processing allows full control on graphics usage and hasdozens of libraries implemented to work with IO components, computer vision, timing control or multiple filetypes. The input data for the simulation is a CSV file with a record for each one of the captured sound events,
ordered chronologically. Every event is defined by 4 fields, which are common with the list section 2.3:
• ANE: Categorization label for every sound event, corresponding to one of the 4 groups. Labels are
2 www.processing.org

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Figure 4. Proposal of representation for negative SNR representations.
grouped in a more generic category to avoid an oversaturated representation.
• SNR: Signal-to-Noise relation in dB of the event over the road traffic noise (RTN).
• LAeq : Equivalent noise level.
• TS: Timestamp of the event capture in the format hh:mm:ss,d. Even if the resolution could be higher, adecisecond was chosen as it was considered enough for representational goals.
Table 1. Sample of the input data
ANE
TRCK
RTNBRAK
CMPLX

SNR-8,3590-9,655-6,529

LAeq
66,3835322
64,8430047
65,694417
67,479711

TS
00:07:45,3
00:07:46,1
00:07:46,6
00:07:46,9

The input data is loaded during the visualization initialization stage. While a stream loaded by chunks would
make more sense in a real-time animation, the input data is actually a preprocessed file with grouping and corrections applied beforehand. Loading the whole file at once is an easier approach in terms of data architecture,that implies few extra benefits such as global statistics and the possibility to control the timeline.
Figure 5 shows the final visualization, a projection of different animated circles located on the 3D surface atthe spot where they were measured, and varying their diameter and ripple’s amplitude depending on the soundmeasurements at each instant. No controls are provided at the moment other than stopping and resuming theanimation, but simple timeline control options are on the roadmap in the short term.

4CONCLUSIONSIn a noise measurement system with source identification, it is crucial to be able to clearly and accurately distinguish the origin of sounds. But it is equally important how you show your results to the public, since themeasurements you take are often not easily understood by the general public.
Scientists and technicians may be often worried about not displaying the whole set of numbers and correlations,but to develop a simple visualization is a key aspect when spreading research results to non-specialist public.

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Figure 5. Sound circles on the 3D model.
Proposed visualization does not provide a deep understanding on noise effects to the population or the environment, but gives a big picture on sound events through colors, shapes and sizes. This information can be sharedeasily with kids, politicians and non-technical audience and allow them to compare where, when and what ishappening in their city.
Having this kind of easy-to-understand visualizations on a 3D model of the city, where they can be spatiallyand temporally crossed with other sources and layers of information, has a great potential to foster interpretation and discussion about the impact of noise pollution events and sound landscapes on the city. This approach
could help to collaborativelly understand and raise awareness in differnt types of stakholders as well as help
to explore and discuss potential measures and interventions and support decision-making processes or promotesocial and behavioral changes of citizens.
Future work of this project focus on the implementation of the automatic class detection system, which isalready working in other environments with two classes. The second of the future lines is the proposal for
real-time operation with a network of sensors deployed in Andorra and Escaldes Engordany.

ACKNOWLEDGEMENTS
The research presented in this work has been partially supported by the LIFE DYNAMAP project (LIFE13ENV/IT/001254). Rosa Ma Alsina-Pagès thanks the Obra Social La Caixa for grant ref. 2018-URL-IR2nQ-038.

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