Abstract
In recent years, accessibility has been widely studied in different domains, like transportation, health, governmental services, parks, and jobs. However, these studies present limitations regarding the number of analyzed services and the limited data access. Therefore, we propose a new accessibility metric to solve both problems, considering six different public services using open data from Open Street Maps. The idea behind this approach is to group deprived zones from public services access. We perform our study in Lima city, which is one of the most populated cities in South America.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781665412216 |
| DOIs | |
| State | Published - 5 Aug 2021 |
| Event | IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021 - Virtual, Lima, Peru Duration: 5 Aug 2021 → 7 Aug 2021 Conference number: 28th |
Conference
| Conference | IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021 |
|---|---|
| Country/Territory | Peru |
| City | Virtual, Lima |
| Period | 5/08/21 → 7/08/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 11 Sustainable Cities and Communities
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SDG 16 Peace, Justice and Strong Institutions
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SDG 17 Partnerships for the Goals
Keywords
- accessibility
- machine learning
- open data
- Public service
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