Data-driven environment protection with react native (iOS and Android) implementation

In modern era, urbanization is taking place at an unprecedented scale and the process is irreversible. It has imposed various adverse impacts on different social aspects, and one very important consequence of this process is environment pollution. While there are existing measures aiming at mi...

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Bibliographic Details
Main Author: Xiong, Hengjie
Other Authors: Smitha Kavallur Pisharath Gopi
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/76220
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Institution: Nanyang Technological University
Language: English
Description
Summary:In modern era, urbanization is taking place at an unprecedented scale and the process is irreversible. It has imposed various adverse impacts on different social aspects, and one very important consequence of this process is environment pollution. While there are existing measures aiming at mitigating the adverse impacts of pollution such as public education on media platforms and events organized by NGOs, most failed to address the lack of motivation form the general public. To address the above problem, this project aims to implement an environment protection related platform with react native on both iOS and Android platforms. Public datasets will be utilized to indicate the ‘trash points’ and users will be able to add their own trash points to the central database as well. Furthermore, users will be able to create and join environment groups and activities and share it on their social medias such as Facebook so that it would be easier to reach out to users’ network of friends and attract more people to participate in environment protection activities. While there many technical challenges faced such as the choice of back-end server, cleaning of public datasets, updating of database information form user input and propagation of changes etc., the initial objective of the project has been achieved. React-native applications were successfully developed with an optimized data pipeline and backend server deployed on Amazon’s AWS EC2 cloud instance.