OBJECTIVE ENVIRONMENTAL CONDITIONS
Lighting objective analysis
In the introductory part relating to light, part A section 4, the concepts of luminance and illu-minance were explained in photometric terms, the aims of outdoor lighting project were intro-duced as well as the binding lighting norms and legislation in the areas of research. It was intro-duced the necessity to verification of some stan-dards parameters. In this chapter, instead, the methodology used to collect objectively measu-rable lighting data will be introduced with par-ticular attention to standard required values and luminance distribution of the observed scenes. The main purpose of the valuation was to document lighting settlement during the sub-mission of on-site survey, introduced in the pre-vious chapter and explained in the next section dedicated to the collection of observer-based data, in order to have the most objectively possi-ble environmental conditions to correlate with subjective perception values.
According with norms UNI 11248 and UNI EN 13201, each measure point was sectioned in three by three-meter horizontal grid betwe-en two consequbetwe-ent luminaires; vertices were numerated and on its data were collected. If pedestrian path surface had been less than three meters wide, the grid was reduced to a single
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same field of view, it was important identified the angle of the photos shooted on site. Accor-ding with the information found on the ma-nufacturer's website, the camera mounts 18.0 MP APS-C CMOS sensor; with 17-50 mm lens applied this sensor is equivalent to 1.6 times the focal length applied to full frame sensor. The region of space in the photographs are between 67° horizontal, 48° vertical and 78° diagonal of radial angle from the point in which is positio-ned the photographer. (Hollows, 2011) In the Loe’s article quoted above, the field of view of the luminance meter instrument was limited positioning physical barriers at 40° in altitude and 90° in azimuth. In the current experiment it was impossible to act on physical barriers because the gap in time from the taking of the photos and the analysis. It was considered ap-propriate reproduce it using virtual geometry construction in SketchUp software, the same shooting environment was recreated and po-sitioning virtual barriers at 40° in altitude and 90° in azimuth and after it sectioned them in the photos plan. The photo’s area into 40° band was identified and reported in the scheme visible in colored part of Fig. B1. The statistical values extracted by the software are: the average lumi-nance, dispersion in luminance value, maxi-mum luminance, minimaxi-mum luminance, ma-ximum to minimum ratio (when minimum value was not zero) and maximum to average ratio. Every luminance values are misused insi-de 40° band and expressed in caninsi-dela per square meter. One of the critic that can be moved to this method is the fact that the 40° band inclu-using auto bracketing features, in .cr2 extension.
Before starting the shooting, camera was pre-viously calibrated in specific laboratories using technique that associate for every pixel value a luminance value, turning the instrument into a photometer. It is possible using several techni-ques, one of those use MATLAB software and a proper testing room (Suway & Suway, 2017).
Analyzing a lit scene in luminance distribution terms means photographing it in photometric way. Finally, finished subjective data collection, the researchers filmed a sound-light walk pas-sing through every measuring point upas-sing Go Pro Silver model 4 in UHD standard video.
That further document the environmental con-dition during each specific night.
Three different method was used for collecting luminance value of each scene starting from the same photography. All the document passed by LMK lab soft 4, 17.10.10 version that, using the luminance calibration file mentioned befo-re, it was able to extrapolate luminance value and provide descriptive statistical informations.
All of these consist in identification of regions of the image in which the values were extrapo-lated. They are:
• Luminance of horizontal 40° band
• Luminance of physical elements
• Luminance of fixed image regions
The first method is based on the experiments conducted by Loe, Mansfield and Rowlands explained in part A section 4. (Loe, Mansfield,
& Rowlands, 2001) In order to reproduce the
objective environmental conditions Fig. B1a. horizontal 40°
band construction
Fig. B1b. physical ele-ments identification
Fig. B1c fixed image re-gions identification
Fig. B1d. horizontal 40°
band construction
Fig. B1e. physical ele-ments identification
Fig. B1f. fixed image re-gions identification
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of the operator that performing the records, in order to simulate how the participant perceived the surrounding sound environment; in fact a five minutes audio file was created to analyze the objective physics levels that describe the sound-scape of each station and each area, as defined in part C, sections 13, 14, 15, 16, 17. Every night, before starting the registration, microphone calibrations were performed via tablet app Sie-mens Testlab Scope 7. All the audio files were directly saved in a memory card in the SCADAS XS itself and in a micro SD memory card in the Huawei tablet. The measurements were then moved into a personal computer protected by a password where the data are analysed using the Siemens software. The audio files collected are analyzed using the Simcenter LMS Testlab (Siemens PLM Software, v.17). The values com-puter from the acoustic recording were those explained in part A section 5, or the following:
A-weighted equivalent sound level LAeq and Psychoacoustic Metrics (Articulation Index, Fluctuation Strength, Loudness ISO532A VI, Loudness Stevens VII, Noise Criterion, Noise Rating, Roughness, Sharpness Aures, Sharp-ness DIN45692, ANSI Speech interference le-vel, Speech interference level SIL3, Specific Lou-dness ISO532B FF and Tonality). All measured data are collected as raw data in the Appendix reported and it is possible to vision all collected values in a web file that it possible to download.
Subsequently, in order to analyze the data col-lected, statistical analysis were used, as defined in part B section 11 and the results of these analyzes are reported in part E section.
to correlate with subjective perception values.
The analysis methodology of the sound envi-ronment derives from previous studies analyzed in part A section 5. This methodology consists of an objective and a subjective part, the objecti-ve part is the in field acoustic measurements.
Measurements took place on five weeks from November 2018 to December 2018, a week for each examined site, for three days a week (Mon-day, Thurs(Mon-day, Saturday) in the evening betwe-en 08:00 pm to 00:00pm. The acoustic mea-sures were carried out through a sound-light walk, as defined in part A section 5, with prede-fined measurement points, as deprede-fined in part C, sections 13, 14, 15, 16, 17: a measurement was made from station number one at station num-ber five without any stops, while the other mea-sures, those used mainly for analysis, were made for each station with a duration of 5 minutes each. Therefore for each station a 30-minute stop for questionnaires and a 5-minute stop for soundscape measurements was provided.
Acoustic measurement were performed using a binaural recording system kindly offered by Applied Acoustics Laboratory LAA of the department of Energy DENERG "Galileo Fer-raris", to which a heartfelt thanks go. It is com-posed of a portable audio recorder SCASAS XS connected to earphones with a pair of omnidi-rectional microphones; this system is controlled via android app Siemens Testlab Scope 7 and Simcenter Testlab Apps for Scadas XS.
The microphones were positioned at the level of the ear as they were included in the earpho-nes, one on the left and the other on the right
objective environmental conditions
Acoustic objective analysis ded various elements with very different
lumi-nance characteristics such as road pavement and the sky, or light vertical partitions and the vege-table cover; in very different way compared to indoor experiment in which the area included front wall and small part of ceiling and floor.
To solve this question, the second method invol-ves measuring started from the environmental elements (like path, near buildings, far vertical element etc..) visible in the frame. As you can see in FigB1b., from them it was identified areas, changing photos by photos, and consequently regions to measuring. The lower region inclu-ded pedestrian path, or the element in which the individual walks. Near vertical ones is com-posed by walls, fences or opaque glass façades in proximity of the observer, and vertical far region contain element far from the observers, typical-ly buildings or trees. For each one the statistical values extrapolated by the software are the ave-rage, maximum, minimum luminance and the dispersion. A limit of this second strategy was the sky. If the photo is mainly composed by the sky or no element is present directly in front of the observers’ path, there is no element to consider, and consequently there is no overall luminance value that could give a general infor-mation of sight natural calibration that human eyes automatically operate based on luminance of lit scene.
To compensate for this lack, the last method was developed based of central circular region and in general on the analysis of fixed region of the photo, FigB1c. The frame considers a centered circular region of 20 radial degree (C20); 120
In the introductory part relating to sound, part A section 5, the concepts of soundscape was explained and the indicators and factors of this were introduced as well as the sound nor-ms and legislation in the areas of research. It was introduced the necessity to verification of some standards parameters, explained in detail in the indicated part. In this chapter, instead, the methodology used to collect objectively measurable sound data will be introduced. The main purpose of the valuation was to document soundscape settlement during the submission of on-site survey, introduced in the previous chapter and explained in the next section, part B sections 9 and 10, in order to have the most objectively possible environmental conditions and 240 degree from vertical line are the limit for lateral regions left and right, and the walk area is considered in the bottom one. Also in this case, for each regions the statistical values extrapola-ted by the software are the average, maximum, minimum luminance and the dispersion.
Pros and cons can be considered for each strate-gy, this means the impossibility to identify a pri-ori the better strategy of image analysis. The re-search continues by pursuing all three methods for estimate subjective perception phenomena, the one that will explain it in the most accurate way it will be taken into consideration (compa-ring variance R and R2). It is possible to vision all collected values in a web file that it possible to download.
105 104 B.methodology
For all these areas the anonymous data of the SIM were extracted and these latter were used later for the statistical analyzes as defined in part B section 11. For this research it is impor-tant to know these data they are necessary to understand that there are different aspects to the perception of unsafety, as in this case the objective ones. This thesis aims to integrate all the various objective aspects of the environment into a single research and to compare them with subjective perception, explained in the fol-lowing section, part B section 11.
to Public Administrations (City Forecast pro-file) and to companies (Data Retail Analysis).
Indeed through the Tim Big Data solution, Public Administrations will be able to develop a series of initiatives for the transformation of cities into smart cities, where at the center of innovation there is man understood as a citizen with the right to live in an environment. (Oli-vetti, 2019) Tim Big Data is a service developed with Big Data technology that, through a web interface, represents in the form of dashboard and heat-map the concentration of the crowd present on the national territory according to the use of the TIM network. So through this service it is possible to know a whole range of information over time (15 minutes, hour, day, week and month) with a historical depth of one year. It is thus possible to know the characteri-stics of the interviewee such as sex, age group, nationality and type of sim client. It is also possible to know which areas are most crowded (presences estimate) and which are the areas of greatest origin or destination in a given place or event. (Bonato et al, 2018)
In the case study all this information was ex-tracted for each area analyzed; the selected areas were:
• 0.29 km2 for the Campus Einaudi with six FSAs used;
• 0.15 km2 for the Vitali area in Parco Dora with two FSAs used;
• 0.21 km2 for the Passerella Olimpica with three FSAs used;
• 0.11 km2 the San Salvario district with two FSAs used.
The analysis of the average hourly presence of people in the different places was carried out through the TIM Big Data Value platform in collaboration with the Technological Investi-gation Department of the Municipal Police Corps of the City of Turin. TIM Big Data is the Olivetti solution that offers advanced tools for qualitative and quantitative territorial analysis in the cloud through the valorization of data collected by the TIM network using SIM card. A SIM (Subscriber Identity Module or Subscriber Identification Module) card is a subscriber identification card for a mobile con-nectivity service (GSM or UMTS) and it has a microprocessor in which there are the essential data about the holder and the type of operator contract. (Singh et all, 2015) The potential of big data impact is used in a lot of sectors such as healthcare, media, energy or retail; an exam-ple of these is the media one which obviously includes the data obtained from the SIM cards.
Big data analysis of telecom, media and enter-tainment can enable the discovery and delivery of media content through dynamic interact and across multiple platforms. (Cavanillas et al, 2016) In a reality in which big data are increa-singly important, it is also important to be able to use them correctly, even if some technologies are already available in ever wider fields. In fact, for instance, the offer of TIM is only for the Pu-blic Administration and Tourism services, and in full respect of privacy, the service is designed to provide presence and mobility information Average people density analysis
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objective environmental conditions
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essential to define and understand their percep-tions and expectapercep-tions about what surrounds them. Starting from this last it is important to investigate the social and cultural background of the interviewed, it is also important to un-derstand how their background characteristic influence their perception; in order to do this we use the methodology of the questionnaires based on the researches studied, as explained in part A sections 4 and 5.