Analyzing the Acoustic Urban Environment

A Geofencing-centered Approach in the Curitiba Metropolitan Region, Brazil

adia P. Kozievitch

, Luiz C. Gomes-Jr

, Tatiana M. C. Gadda

, Keiko V. O. Fonseca

and Monika Akbar

Federal University of Technology, Curitiba, PR, Brazil

University of Texas at El Paso, El Paso, Texas, U.S.A.

Keywords:

Noise, Geofencing, GIS.

Abstract:

The industrial development and Brazilian economic context led to important structural changes, among others,

the increase of population migration (rural to urban spaces), number of private vehicles (due to tax reduction

and state subsidies for new cars and fuel), among others. Such changes impact not only the urban mobility at

big cities but also the urban life quality, which is directly affected by pollutant emissions and noise. In order

to limit emission impacts on sensitive population (children, elderly people, for example), city managers can

enforce bounds on emissions and noise pollution generated by the city trafﬁc in speciﬁc regions deﬁned by

geographical boundaries. This paper aims to contribute to the challenge of managing urban noise by exploring

and analyzing the data with a geofencing approach. In particular, we present a exploratory data analysis

toward a case study in Curitiba (1,800,000 inhabitants, a southern Brazilian city) aiming at analyzing possible

sources of noise based on a particular data set of noise measurements, geographical information data, trafﬁc,

transportation and city licensing data.

1 INTRODUCTION

Since the late nineteenth century, noise has been the

subject of complaints, regulation, and legislation. By

the early 1900s, till today, cities have fought noise

from factories, steam trains, automobiles, loud neigh-

borhood, among others (Bijsterveld, 2008). Noise

pollution in large urban areas can be directly linked to

the trafﬁc ﬂow on roads and the types of vehicles, as

well as several other sources (Rodrigues, 2010). The

data analysis related to trafﬁc ﬂow as well as inde-

pendent variables might impact urban management,

the adoption of modals based on electric vehicles, the

location of schools and hospitals, among others.

Controlling noise pollution can be achieved by

two strategies: (i) coercion: to enforce the installation

of noise control devices, in situations where compli-

ance with the law is not observed; and (ii) prevention:

to establish policies that disallow the concentration of

activities in already polluted areas.

Through the concept of exploratory data analysis

and geofencing, we can (i) explore patterns or clues

from the available data (without any pre-conceived

ideas), and (ii) restrict the analysis from a geograph-

ical perspective (such as the metropolitan region of

a city). The objective is to restrict location-relevant

information to a geographic “fence” or boundary

around the information’s demarcation area (Green-

wald et al., 2011). The understanding of the related

topics, however, is a non trivial task since it requires

skills and knowledge of various domains (computa-

tional, urban planning, architecture, networks, cities

domain, engineering, etc.), along with technologies

to support the analysis process.

In particular, a group of cities, the C40 cities

had set ambitious targets to improve urban life quality

and protect their environment. Curitiba has developed

and implemented mass transport corridors, densiﬁ-

cation of land-use along these corridors, and mobil-

ity solutions using Bus Rapid Transit (BRT) systems,

and became a model of sustainable city based on ur-

ban concepts that have shaped the city landscape. In

spite of its urban planning model, in Curitiba, the ge-

ofencing concept has not been explored to minimize

noise and particulate emissions coming from its Pub-

lic Transportation System (buses). The impacts of a

virtual perimeter is not fully understood, in particular,

due the complexity of noise measurements and anal-

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Kozievitch, N., Gomes-Jr, L., Gadda, T., Fonseca, K. and Akbar, M.

Analyzing the Acoustic Urban Environment - A Geofencing-centered Approach in the Curitiba Metropolitan Region, Brazil.

In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2016), pages 78-85

ISBN: 978-989-758-184-7

ysis (Zannin et al., 2003). A variety of factors, such

as emission sources, available data, architecture char-

acteristics,and climate conditions could directly affect

the analysis and possible simulation of noise impact.

This paper presents the concepts, applications,

and challenges towards a exploratory data analysis of

noise through the concept of geofencing. The objec-

tive here is not to understand speciﬁc details (such

as mathematical models), or integrate environment-

related parameters (such as temperature or wind), but

to understand sources and obstacles which create im-

pact from a general overview. The main goal is

to discover what the data can tell us about the ob-

served noise with the help of visualization and dis-

play techniques of GIS perspective. The rest of this

document is organized as follows: Section 2 con-

tains an overview of a motivating example, Section

3 presents the related work. The methodology is pre-

sented within Section 4. Conclusions and future work

are presented in Section 5.

2 A MOTIVATING EXAMPLE

Noise pollution is a serious environmental problem

faced by several cities in Brazil (Zannin et al., 2003;

Oliveira et al., 1999; Arndt et al., 2010; Rodrigues,

2010).

Curitiba has 1.8 million people inside a total area

of 430,9 km

, according to the Brazilian Institute of

Geography and Statistics (IBGE)

. This area encom-

passes 75 neighborhood districts. Although noise in

Curitiba has been studied under distinct perspectives

(Zannin et al., 2002; Calixto et al., 2003; Zannin et al.,

2003), we believe that new clues about possible noise

sources can be derived from the analysis of new do-

mains.

A formerly traditional residential area of Curitiba,

with about 12,000 inhabitants, the Batel district is cur-

rently full of restaurants and options for nightlife. In

the 18th century, the current Batel Avenue was one

of the ways used by drivers. In the early 20th cen-

tury, the region already had two breweries, two yerba

mate processing plants, soap factories, and a small

trade. One of the main mass transportation axis (ex-

press bus lanes) crosses the region, establishing an ur-

ban canyon of skyscrapers with the express bus lanes

inside the deep valley. Curitiba was already stated as

a city which has problem with noise pollution (Arndt

et al., 2010), and according to (LNZ Soluc¸

oes em

Vibrac¸

oes e Ac

ustica, 2011), Batel is one of the most

noisy region compared to all other regions in Curitiba.

http://www.ibge.gov.br Last visited on 14/05/2015.

In order to investigate the reason for this, sev-

eral data sources (e.g., health facilities, education fa-

cilities, streets, city hall noise measures, particular

noise measures, bus lines), within different time range

(from 1890 till 2014), were integrated and analyzed.

The noise measure report compared the data

against the Municipal Law Number 10.625, published

in 19/12/2002, which establishes noise limits

. The

limits are ﬁxed as follows: (i) daytime: from 07h:01

to 19h; (ii) evening: from 19h01 to 22h; (iii) noctur-

nal: from 22h01 to 07h.

Considering the information listed above, sev-

eral questions might arise, such as why some loca-

tions produce more noise during the day compared

to night and vice versa, or why speciﬁc locations

produce more noise compared to others. The ques-

tions can be explored under different domains, such as

GIS (through spatial locations), architecture (impact

of noise on different building types) and mathmat-

ics (through statistical models (Calixto et al., 2003)).

Different domains might present different background

concepts (theory, software, practice), along with do-

main limitations, which, if integrated, might provide

better answers.

Brieﬂy, the efforts of integrating different domains

may vary in several directions: (i) from the GIS per-

spective, the challenge is how to explore data with

efﬁciency (and to enable any future integration with

other systems); (ii) from the integration perspective,

the challenge is not to have a bottleneck; (iii) from

the hardware perspective (for receiving noise data,

for example), the challenge is how to detect limita-

tions and standardize calibrations; (v) from the ped-

agogical perspective, the challenge is how to explore

all this structure and have a interdisciplinary learning

environment; (vi) from the software perspective, the

challenge is how to adapt an interface which explores

different points of view of analyzing the information.

3 RELATED WORK

3.1 Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a philosophi-

cal approach to data analysis (NIST/SEMATECH,

2012). The posed question is: what data can tell us

about certain relationships, properties or structures.

There are no imposed techniques to apply to the data

set but graphical visualization plays an important role

in this approach (Hartwig and Dearing, 1979). The

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Analyzing the Acoustic Urban Environment - A Geofencing-centered Approach in the Curitiba Metropolitan Region, Brazil

non-inferential approach in data analysis encourages

the openness perspective required to integrate new

domains. The availability of processing power and

data storage provide new tools for handling massive

amout of data processing allowing ﬂexible search of

evidences in the available data through designed ex-

periments (Martinez et al., 2010).

3.2 Geofencing

Geofencing is a virtual perimeter for a real-world ge-

ographic area (Ravada et al., 2013). A geofence could

be a radius around a store or point location, or a pre-

deﬁned set of boundaries, like zip code boundaries.

Geofencing is still on-going research (Ryoo et al.,

2012; Sheth et al., 2009), but basically it explores the

containment of data “within” or “inside” an area. A

lot of relevant techniques in GIS (such as geometry

location, spatial indexing, and spatial query process-

ing) can be explored from a database perspective.

From the GIS perspective, geofencing is explored

through the deﬁnition of points (latitude/longitude),

along with lines, polygons, and geometries deﬁned

across the data. But indeed, the same information

can be visualized within different categories (Ro-

driguez Garzon and Deva, 2014) (as shown in Ta-

ble 1).

Table 1: The different geofencing categories (Ro-

driguez Garzon and Deva, 2014).

Category Adressing Instances Example

Scheme

Spatial Geometric Circles, Polygons, ”Region of Berlin”

Geofence Polylines

Hierarchy- Symbolic Country, City, ”Germany/Berlin/

based Street, Ernst-Reuter-Platz”

Geofences Building...

Network- Symbolic Cell-Ids, ”BSSID of WLAN ”

based WLAN-(B) in a McDonalds

Geofences SSID,... restaurant”

Semantic Geometric Combination of ”Close to a

Geofences and Symbolic the above McDonalds

restaurant”

Theoretically, geofencing can be formalized as a

combination of states and transition-based geofence

models, created by system notiﬁcations (such as in-

coming calls, emails, breaking news or software

update notiﬁcations) (Rodriguez Garzon and Deva,

2014). In particular, we adopted the geofencing ap-

proach in order to take advantage of physical limits

within districts and cities.

3.3 The Acoustic Model

Sound is produced by sources (the noise emitting el-

ements, having a geometry and properties which vary

for each case), modiﬁed by obstacles (barriers to the

propagation of sound, absorbing it or reﬂecting it in

varying percentages, depending on their nature and

their position in the vicinity of sources), and per-

ceived by receivers (the elements which are disturbed

by the noise, that is, buildings, installations, and peo-

ple, or a combination of them). Sound (along with

other parameters such as weather condition, level of

air pollution, noise pollution, trafﬁc condition) are

widely monitored by sensors. One of the caveat of

sensor data is that the data captured by sensors do

not include information on the cause of the data.

Thus, sensor data alone is not sufﬁcient for draw-

ing meaningful conclusion. In recent years, the idea

of using citizens as sensors are gaining momentum

(Mostashari et al., 2011). From the academic per-

spective, researchers are also proposing frameworks

to effectively combine geofencing with mobile tech-

nologies to infer state of the user (Chen et al., 2014).

From the urban noise modeler’s point of view, the

mentioned parameters are impacted by the following

acoustic modeling aspects: (i) the propagation and

(ii) the combination of effects from sources. These

two aspects are useful to other environmental prob-

lems (Oliveira et al., 1999). Further details about

mathematical formulations to understand the impact

of noise decay can be checked at (Oliveira et al.,

1999; Rodrigues, 2010; Arndt et al., 2010) (Calixto

et al., 2003). But indeed, the analysis is performed

on a delimited area, combining the propagation and

combination to a buffer polygon (through GIS), traced

around the original interest area at a deﬁned distance.

The noise impact inside a region under consider-

ation can be estimated according to (Oliveira et al.,

1999): (i) the noise considered by each economic ac-

tivity; (ii) the trafﬁc noise; (iii) the sound attenuation;

(iv) the combination of the previous steps in order to

obtain the noise generated by all sources.

Trafﬁc noise poses also as one of the main prob-

lems for city managers to solve. Suitable measures

for noise reduction includes asphalt type, grass cov-

ered tramways, noise barriers, soundproof windows,

night driving bans for trucks (Louen et al., 2014),

as well as, speed reduction zones (Madireddy et al.,

2011) and electro-mobility solutions (Verheijen and

Jabben, 2010).

Road trafﬁc-induced air pollution is also one of

the main sources of air pollution affecting environ-

mental living quality in urban areas (Duclaux et al.,

2002). However, the phenomenon of road trafﬁc air

pollution shows considerable variation within a street

canyon, or horizontal and vertical obstacles.

Linking up dispersion models with a GIS environ-

ment is a mean to resolve this shortcoming. Providing

information about trafﬁc pollution or noise, and ﬁnd-

ing out its distribution is therefore a crucial starting

SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems

point for planning effective measures to improve air

quality or trafﬁc noise.

4 OVERVIEW OF THE

METHODOLOGY

The methodology used for the exploratory data anal-

ysis comprised the following steps: (i) the acqui-

sition/characterization of the data sources; (ii) the

data integration and processing; (iii) the data analy-

sis (along with hypothesis and limitations); and (iv)

the deﬁnition of parameters/challenges which impact

the proposed problem solution.

4.1 Data Sources

The dataset used is based on Instituto de Planeja-

mento de Curitiba (IPPUC), the city mall of Curitiba

along with data from Open Street Map

. Figure 1-top

shows the input sources, with data ranged from 1890

Inputs Intermediary Results Outputs

Education

Facilities

Health

Facilities

Bus Lines

Shoppings

Noise Measures

Exposure

Area

Bus Stops

Bus Lines /

Noise Measure

Locations

Schools /

Noise Measure

Locations

Open Street

Maps

Google

Maps

Shoppings /

Noise Measure

Locations

Bus Lines /

Noise Measure

Locations

Complete List

Of Impacted

Facilities

Hospitals /

Noise Measure

Locations

Noise Measure

Locations/

Time Impact

Complete List

Of Impacted

Facilities

Challenges

To be Addressed

Streets

Churches

...

Figure 1: Schematic of the data model (top); and (B) Time

Range of the input data (bottom).

http://www.curitiba.pr.gov.br/DADOSABERTOS/ Vis-

itado em 15/05/2015.

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14/05/2015.

till 2014 (as shown in Figure 1-bottom). Details are

listed below.

Education Facilities - The region has 15 education

facilities (such as Col

egio Nossa Senhora de Sion -

Sede, Col

egio Estadual J

ulia Wanderley, Escola Osny

Macedo Saldanha, Campus de Artes e M

usica da

UFPR, Faculdade Fapar, Centro de Educac¸

ao Infan-

til Engenheiros do Saber, among others). The data

creation ranges from 1938 (Col

egio Nossa Senhora

de Sion - Sede) till the last update in 2013, having as

source the IPPUC

. Education facilities present some

categories, such as CEI (portuguese acronym for Mu-

nicipal center of Early Childhood Education, divided

between private and the ones who has a contract

with the state government), UEI (portuguese acronym

for Integrated Education Unit), State Schools, Private

Schools, and Higher Education Institutions.

Health Facilities - The region has exactly eight

health facilities (namely Hospital da Cruz Vermelha

Brasileira, Hospital Geral de Curitiba, Clinica Cen-

tral de Oftalmologia, Hospital de Olhos do Paran

Hospital Santa Cruz and Hospital Vita Batel). The

data creation ranges from 1890 till the last update in

2013, having as source the Instituto de Planejamento

de Curitiba (IPPUC). Note that the older health facil-

ity - Hospital Geral de Curitiba, was initially created

at 1890, and moved to the actual location in 1920.

Bus Lines - The region has 21 bus lines (such

as InterHospitais, Circular Centro (Anti-Hor

ario),

Port

ao-Cabral, Interbairros I (hor

ario), Interbairros I

(anti-hor

ario), Cic-Tiradentes (Manh

a), Ctba-Campo

Largo, among others). The 21 lines are divided within

8 bus categories (“Circular Centro” (1 type), “Con-

vencional” (8 types), “Expresso” (1 type), “Interbair-

ros” (2 types), “Interhospitais” (1 type), “Linha Di-

reta” (2 types), “Metropolitano” (5 types) and “Tron-

cal” with one type).

Street Data - The street data was explored both with

the data obtain from IPPUC and Open Street Map.

Noise Measures - The ﬁrst noise source explored data

between 2010 and 2011 (LNZ Soluc¸

oes em Vibrac¸

oes

e Ac

ustica, 2011), having Batel as the district with

higher noise measures within the city. Eight locations

are explored for measuring noise: Sensor 10 (Rua

Gonc¸alves Dias, 406), Sensor 11 (Rua Bispo Dom

Jose, 2365), Sensor 12 (Rua Hermes Fontes, 506),

Sensor 13 (Av. do Batel, 1750), Sensor 14 (Rua Fran-

cisco Rocha, 510), Sensor 15 (Av. do Batel, 1230),

Sensor 16 (Rua Benjamin Lins, 555), and Sensor 17

(Rua Pasteur, 260).

The eight locations produced the noise measures

listed in Table 2. The last two columns from the ta-

ble list the noise law limit for that region, and how

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Analyzing the Acoustic Urban Environment - A Geofencing-centered Approach in the Curitiba Metropolitan Region, Brazil

the noise measure percentage is above law. Note that

(i) locations 13 and 14 produce more noise during the

day and night and (ii) locations 11,13, and 14 pro-

duce more noise compared to the others. For the

comparison, only the Equivalent Continuous Sound

Level (Leq) was considered, under SAD69 database

and UTM coordinates. The equipment used was Icel

DL4200 type 2 and 01 dB Solo type 1. The report al-

ready stated that the majority of noise is related to the

trafﬁc within the region.

Table 2: Noise Measures within the Batel neighborhood.

Sensor Period of Date Hour Noise Law

the day Limit

13 Daytime 2010-12-22 09:27 76.7 55

14 Daytime 2010-12-22 09:00 74.4 55

11 Daytime 2010-12-21 09:20 74.3 55

12 Daytime 2010-12-21 09:00 63.2 55

16 Daytime 2010-12-22 10:51 70.8 65

15 Daytime 2010-12-21 09:43 67.3 65

10 Daytime 2010-12-21 09:43 67.3 65

17 Daytime 2010-12-22 10:35 62.8 65

13 Evening 2011-04-14 17:34 73.2 55

14 Evening 2011-04-14 17:05 70.2 55

11 Evening 2011-04-14 18:21 67.8 55

12 Evening 2011-04-14 17:26 62.5 55

10 Evening 2011-04-14 18:41 69.3 65

15 Evening 2011-04-14 18:41 69.3 65

16 Evening 2011-04-11 18:31 68.2 65

17 Evening 2011-04-11 18:51 65.5 65

11 Nocturnal 2010-12-20 23:25 74 45

14 Nocturnal 2010-12-21 23:39 71.4 45

13 Nocturnal 2010-12-21 23:20 70.9 45

12 Nocturnal 2010-12-20 23:00 57.2 45

16 Nocturnal 2010-12-22 23:05 68.9 55

10 Nocturnal 2010-12-20 23:47 67.6 55

15 Nocturnal 2010-12-20 23:47 67.6 55

17 Nocturnal 2010-12-22 23:02 59.1 55

Table 3: Number of noise complaints within the Batel

neighborhood, within years 2010 and 2014.

Year Daytime Evening Nocturnal

2010 69 24 50

2011 84 18 59

2012 78 29 42

2013 58 28 31

2014 48 15 64

The drawback of having just one sample over time

divided between two years sent us to the second noise

dataset. The second source registers citizen com-

plaints against noise within years 2010 till 2014 (ob-

tained from the city hall of curitiba - Table 3). The

average is 58 complaints by month, having February

as the worst month (average of 75 complaints, gen-

erally the month of the Brazilian Carnival). In Cu-

ritiba, 50% of the citizen complaints are related to

noise. The majority are related to bars and cars with

high volume music. Within this source, Batel is the

5th district with more noise complaints (with 58% of

complaints during the day) and is located next to the

most noisy district (Downtown).

4.2 Data Integration and Processing

The complete IPPUC dataset along with the noise

source 1 (extracted from (LNZ Soluc¸

oes em

Vibrac¸

oes e Ac

ustica, 2011)) and noise source 2

(within data from the city mall) were inserted in a

PostGIS

database. Different sources were created

as different tables in the database. Later, speciﬁc ta-

blespaces and indexes were created in order to opti-

mize the access. Since semantic errors were present

(different sources presented different street names for

the same location, for example (Barczyszyn, 2015)),

geolocation and speciﬁed time range were used to cor-

relate the data.

4.3 Data Analysis

The objective was apply a geofencing around the dis-

trict of Batel, and using the available datasets, locate

the less impacted area and the most impacted area

within the district. The data analysis process used

the QGIS visualization tool

. The integration of the

8 intermediary input GIS layers (illustrated in Fig-

ure 1-A) produced intermediary results, such as the

relation of bus lines × noise measure locations, the

impacted schools and hospitals in the region, the im-

pact of shopping centers within the region, along with

the how the time distribution of each facility impacted

the ﬁnal analysis. Note that we decided to combine

different source of data, in order to explore different

domains of information.

As ﬁnal outputs, the exploratory analysis identi-

ﬁed the exposure area and the complete list of im-

pacted facilities. Figure 2 lists the main schools,

health facilities, bus stations and the main streets.

The exposure area was limited to Batel neighborhood

(centralized in the ﬁgure). The complete list of im-

pacted facilities can be visualized at the same ﬁgure.

If we consider a 300 meters radius from the point

of measurements of noise, for example, four colleges,

three private CEIs, three state schools, ﬁve hospitals,

and two churches are impacted, as shown in Figure 2

(right). Within this approach, note that the upper part

of the district (next to sensor 12) is still the less noisy

region, comprised within a residential area.

If we increase the radius to 600 meters, all

schools, hospitals and churches are impacted within

the district, through the day, afternoon, and night.

This approach suggests that all the district is affected

by the noise.

The integration of the second noise source (Fig-

ure 3, left) stresses that complaint regions remain the

same along the years, with an average of 121 registers

per year. This source reinforces that the region around

sensor 12 is less noisy, and the top right region is the

region with the majority of citizen complaints (even

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SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems

Figure 2: The eight noise measure locations (left) and the impacted schools in 300 meters radius (right).

Figure 3: Additional noise source data from all years (left) and the same neighborhood location provided by Google Earth

(right).

the concentration of black triangles are superimposed

within the Figures). Note that this is a residential area

with a lot of bars, and has a physical perimeter with

the downtown district, which is the top one on the list

of the noise complaints. In summary, Batel geofence

from is impacted by a “noisy” district, the downtown

district.

Along with the impacted region, authors also

wanted to explore other factors, such as bus lines. The

initial hypothesis considered was that the region was

noisy due to a high number of bus lines. Nevertheless,

the data indicates toward other direction: the locations

with higher noise measures did not present an expres-

sive number of line buses within their range. Mea-

sure point 10, for example, has 5 lines within the same

area (V.Sandra, Tramontina, Jd.Social-Batel, R. XV-

Barigui, Camp. Siqueira-Batel); measure point 11 has

exactly the 5 lines as measure point 10, but some have

duplication (due to the two-way street); measure point

13 has the same 5 lines of point 10; measure point 14

is impacted by only one bus line (Interbairros I anti-

hor

ario); measure point 15 is impacted by the union

of bus lines from point 10 and 14; and point measure

16 is impacted also by the same bus lines presented

in point 10. Brieﬂy, if we remove the point measure

14, all the locations are impacted by almost the same

bus lines. Note that the bus categories already listed

in phase one stated that this region is not impacted by

the biggest type of bus available in curitiba (the “ex-

presso”, a biarticulated bus with 28 meters).

Figure 3 (right) explores the same region under

Google Satellite. Note that Figure 3 provides a new

visual information: when the region was analyzed

by Google, the new shopping P

atio Batel (within the

black square) was still under construction. The build-

ing construction started back in 2008 and opened only

in 09/09/2013 - exactly at the same time range in

which the the noise data samples were collected. If

Google had already updated the satellite information

till the end of this paper publication, it would be rather

difﬁcult to navigate historically through time to un-

derstand the same point of view.

In summary, the integration of several sources,

along with a summarized visual analysis stated that

district is impacted by noise, but there are less noisy

regions (around sensor 12) and worst noisy regions

Analyzing the Acoustic Urban Environment - A Geofencing-centered Approach in the Curitiba Metropolitan Region, Brazil

(top right area). Note that all surrounded facilities

(hospital, schools, among others) are impacted day

and night by noise created by constructions, trafﬁc,

and bars. Once the impacted areas were identiﬁed,

and the period of the day which they are impacted,

short term solutions could include measures toward

citizen life quality, such as noise barriers and sound-

proof windows. In particular, from the computer sci-

ence perspective, remains some challenges of inte-

grating sources (such as Google Earth), applications

and methodologies which contribute to the analysis.

4.4 Important Parameters to Consider

for Solving the Geofencing Problem

The challenges and difﬁculties of exploring noise im-

pacts through a GIS perspective are as follows:

• Integration with Different Sources: (i) within

the noise measures data sample, several chal-

lenges arise, such as the non-correlation of ad-

dresses and coordinates, the use of different

equipments for the samples and a non-regular

data sampling over time; (ii) the complete data

characterization itself is a challenge, due to is-

sues such as the non matching of locations and

the comparison of latitude/longitude, UTM co-

ordinates and addresses; (iii) the standardization

of the data and schemes; (iv) and the integra-

tion with different data sources, such as Google

Satellite, and images might help to understand the

historical spatio-temporal gaps which alphanu-

meric data sources might not answer (such as the

construction of a shopping that was accidentally

captured by Figure 3), among others. Solutions

might include manual intervention, frameworks,

linked data (Moura and Davis Jr., 2013), and

data sources quality ranking (with metrics such as

PageRank (Brin and Page, 1998)).

• Updated Data: Different data sources might have

different time ranges for updating the data. Their

maintenance over time might be costly, and even-

tually manual intervention is necessary. Some so-

lutions include the use of linked data (Moura and

Davis Jr., 2013) and Volunteered Geographic In-

formation (VGI) (Mcdougall, 2011).

• Methodology Issues: evaluate noise measure

values with a consistent period of time (consid-

ering different periods of day, but maintaining the

pattern of noise/period samples and equipments);

specify a criteria for choosing the noise mea-

sure locations; ﬁnd a standard schema to integrate

the data; consider different strategies for evalu-

ating the environmental noise in a city (Brown

and Lam, 1987) including those based on crowd-

sourcing (Schweizer et al., 2011);

• Hardware Issues: different hardware calibra-

tions, along with the information that hard-

ware without maintenance might impact ﬁnal re-

sults (Schweizer et al., 2011);

• Spatio-temporal Data Issues: data might not

comprise the complete spatio-temporal windows

(as shown in Figure 1), and the relationship se-

mantics and spatial location might be an issue

over time (such as perimeters which impact other

regions). Solutions include the adoption of mov-

ing objects databases (Erwig et al., 1999), and

RDF triples as implementation technique (Moura

and Davis Jr., 2013).

• Domain Knowledge: depending on the analyzed

problem (along with the data limitations), the ex-

ploratory data analysis is not enough to solve a

question: additional domain knowledge is neces-

sary (such as the construction year of the biggest

buildings in the area).

5 CONCLUSION

Research in urban noise pollution is not recent, but

the exploration through different domains is still an

ongoing effort. The possibility of implementing mod-

els within GIS and integrating them with different

sources provides planners with a powerful and ﬂex-

ible tool for analyzing the land use, and deciding on

new business permits and living quality. This paper

presents the concepts, applications, and challenges of

exploring noise through the concept of geofencing.

Later these deﬁnitions are explored in a practical case

study, within the Curitiba metropolitan region, Brazil,

with data analysis aimed at supporting noise and pol-

lution control. Future work includes the study of the

noise impact decay, the integration of additional data,

and the use of personalization and recommendation

techniques in order to explore the data.

ACKNOWLEDGEMENTS

We would like to thank the Curitiba City Municipal-

ity, IPPUC, and RNP.

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