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.
|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.|
|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||IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021|
|Period||5/08/21 → 7/08/21|
Bibliographical notePublisher Copyright:
© 2021 IEEE.
- machine learning
- open data
- Public service