ANIMA D3.7 - Dynamic Noise Maps and Social Media Analysis: novel technologies for noise impact assessment
Creators: name: Van Oosten, Nico
name: Ganić, Emir
name: Kotzinos, Dimitris
name: Gharbi, Asma
name: Lavandier, Catherine
Publisher: name: Zenodo
Year: 2022

description: Deliverable 3.7 is focusing on novel methods to account, measure and possibly understand and explain people’s annoyance from airport activities that produce noise. We focus on the ability to calculate two types of information that would help the assessment of the annoyance. We firstly work on the calculation of dynamic noise maps, acoustic maps that illustrate in real time the temporal change of noise levels. We introduce in these noise maps calculations that are based on the mobility patterns of the individuals. In this deliverable, different ways of collecting human mobility patterns will be explained in more details along with the methodology used to incorporate it into dynamic noise maps. Furthermore, a real case study conducted within the ANIMA project, based on the one-year air traffic data, sheds light on the benefits of using this new approach and demonstrate how daily movements influence the estimated population noise annoyance around an airport. The work uses daily mobility patterns to assess the impact on people and more specifically the number of people that have been annoyed /affected on a daily basis. Real world scenarios for Ljubljana and Heathrow airports are considered and discussed. The Heathrow airport case study described in Annex 1 is confidential and will be publicly available upon publication of these results. On the other hand, we try to measure and understand the annoyance as it is expressed by people who are living in the area on social media posts. We capture and analyse millions of such posts and classify them based on their polarity. It is by itself an interesting research problem to be able to define the notion of positive (no or minimal annoyance) and negative (high levels of annoyance) sentiments that people carry in those posts. We exploit in this study the ability to capture a significant amount of information, which has the potential to allow us to break free from the limitations of traditional surveying methods. On the other hand, further research is necessary into accounting for the biases already existing on the internet. Thus, in this part of the study, we restrict ourselves into conclusions that can be drawn by comparing the collected data after the classification process (aka among itself) and no generalization to the general public is attempted. The study aims to create a prototype and assess the feasibility of the task of understanding people’s annoyance based on social media posts and it does not try to provide final conclusions on this. We do this by relying to a set of Machine Learning (ML) and Artificial Intelligence (AI) tools, which we train and use to learn from the data. While the main goal of the current work is to explore the sentiment as positive or negative, the effort could be further expanded to extract additional semantics. Furthermore, the work described here is based on two independent methods to assess social media posts in both real-time and offline. We plan to release these tools as a comprehensive open-source toolkit to be usable by any researcher. The advances in computational technology and the advanced ability to capture data, being mobility habits or social media activity, provide us with a unique environment that allows to better understand the effect of airport related activities to the life of people in a non-intrusive and scientifically sound way. This deliverable captures two such efforts, supported by diverse case studies in different countries and demonstrates that additional insights are possible.
description: This document is the public version of deliverable D3.7 which contains an additional annex. Results introduced in this annex are not public up to their forthcoming publication in a peer-review journal.