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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Format: | Final Year Project |
Language: | English |
Published: |
2018
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Online Access: | http://hdl.handle.net/10356/76220 |
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Institution: | Nanyang Technological University |
Language: | English |
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. |
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