1. Backend: Source code for the application backend (harvesters, analytics, YAML specifications, etc.)
2. Frontend: Source code for the client part of the application (Jupyter notebooks) including individual development and final combined version.
3. Test: Source code for backend automated tests
4. Data: Examples of SUDO data and pedestrian counting system sensor locations.
5. Docs: Final report for this project.
6. Database: Elasticsearch type mappings and queries.
## Data Sources
1. Spatial Urban Data Observatory (SUDO): https://sudo.eresearch.unimelb.edu.au/
2. Mastodon: https://mastodon.au and https://aus.social
3. Melbourne Data - the City of Melbourne’s Open Data Platform: https://www.data.vic.gov.au/train-service-passenger-counts
4. Train Service Passenger Counts Dataset: https://data.melbourne.vic.gov.au/explore/dataset/pedestrian-counting-system-monthly-counts-per-hour
3. Melbourne Data - the City of Melbourne’s Open Data Platform: https://data.melbourne.vic.gov.au/explore/dataset/pedestrian-counting-system-monthly-counts-per-hour
4. Train Service Passenger Counts Dataset: https://www.data.vic.gov.au/train-service-passenger-counts
@@ -27,10 +35,5 @@ The project focuses on analyzing five scenarios:
4. Train Service Passenger Counts: Trends and volume of passenger flow.
5. Real-time Congestion: Visualization of traffic congestion data.
## Table of Contents for Project Repository
1. Backend: Source code for the application backend (harvesters, analytics, etc.)
2.
3.
4.
5.
6.
## Other Branches
Other branches are for each team member to store their codes and results from the initial design to local, testing on local machines with ElasticSearch, straightforward deployements on K8s, and final YAML spec deployment on Fission.