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Noise Distribution Visualization
A spatial data visualization project
Time Span: Sep 2014 - Jun 2015
Group Member: Yiwei Xu, Kiki Yang, Xingran Yuan, Yue Liu
My Role: Data analysis, data visualization
Data Collection
Noise data come from Prof. Tan Xiaodong in Wuhan University which collected by sound level meter in 2014 Cherry Blossom Festival.
Introduction
Every March and April, Wuhan university faces an influx of a large number of tourists in the so-called "Cherry Blossom Festival', which brings difficulties to university management and brings noise disturbance to student study environment. In 2014, funded by undergraduate Training Program for Innovation and Entrepreneurship, I had the chance to look into this topic through a project on tourists spatial behavior, which provided a great reference to university management through noise distribution analysis. The noise data was visualized through ArcGIS. By uploading the geometry of Wuhan University, we created a noise distribution map to give guidance on tourists spatial behavior.
Project Overview
Data Preprocessing
Digitalize map of Wuhan university by ArcMap, different color symbolize different function area.
Spatial Interpolation
"Assume we are dealing with a variable which has significant values at every point within a region (e.g., temperature, elevation, the concentration of some mineral. Then, given the benefits of that variable at a set of sample points, we can use an interpolation method to predict values of this variable at every point" ––––– Eugene Brusilovskiy.
Using Geostatistical Analyst Tool in ArcGIS, checking whether noise data follow normal distribution and autocorrelation to choose the interpolation method.
According to the result, we should employ Inverse Distance Weighting to visualize noise distribution.
After taking interpolation to 5 different time (8:00-10:00, 10:00-12:00, 12:00-14:00, 14:00-16:00.16:00-18:00) in turn, we can get 5 IDW result. The red color shows the most serious noise disturbance.
8:00-10:00 IDW
Noise changes in different spots and time
According to noise data variation chart, we can see that noise in 7 positions are exceeding standard(60 dB) in 22nd Mar. Among these, the Cherry Avenue(purple line), Center Lake(Deep green line), Kunpeng Square(Light green line) and Old library(Blue line) are most serious.
Data Analysis and Visualization
Export layers after interpolation and then overlay it and Wuhan University digital map.
According to the city noise standard, noise over 60db is substandard, which means when classes equal or over 2, noise is substandard. Let's look at time-phased noise distribution.
8:00 - 10:00, most of the function area are exceeding.
10:00 - 12:00, students and teacher gathered in learning area and offices, in order to show the influence more directly, we hide other area, we can clearly see all of the learning area are exceeding the rate.
12:00 - 14:00 is noon breaks, so students and teachers have activities around the dorm, when hiding another function area, we can see dorm in the south suffer serious disturbance.
14:00 - 16:00, students and teachers concentrated in office and learning area, we can see most of those areas are exceeding the standard.
Conclusions
According to the noise distribution in different places like Sakura Avenue and Old library in Wuhan University, we could predict tourists behavior in different time. For instance, tourists prefer to go to visit on 10 - 12:00 am when students are taking classes and study. When in 12 - 14:00 tourists are near dorm 1,2,3, which would definitely affect students rest.
The visualization project enables me to realize the power of data; data always bring more to us than what we thought. The project is my idea origin of Whulib; I wanted to make use of the project to build a platform for students to find a better study environment. Though Whulib focuses on another factor, this project still provided me with many insights about the significance of data.
© 2018 by Yiwei Xu. Thanks for your coming.
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