The core crux of the visualizations created for the Project 2: Bicycle Race was the data provided to the team. The data for visualization was at Bike Sharing Data Website . The website also had cleaned up data divided into multiple forms which helped the team to get all the data they needed. The SQL data for the visualizations was pulled from Steve Vance's Github This helped the team to get the huge dataset onto a MYSQL server which helped the team to transform data according to each team member's needs and also the whole project's need. The Time Interval to be explored for the project was between 28th June and 31st December 2013. The Team also had to show weather during calendar playback mode so as the user can get a sense of the usage patterns during major weather events and daily events like sunrise and sunset.
Weather DataThe team was dependent on WunderGround for historial weather data. The team procured a dev key and wrote a python script to get data into csv files split by each day of the dataset to be explored. This data was exact to the hour of each day which helped us get a sense of how the weather changed during the team interval the team was exploring. This plays a huge role when you take into account how unpredictable seasonal changes can be in the City of Chicago. This was one of the most important component of the application which helped the team to explore the patterns across various seasons in Chicago.
Mapping Stations with Communities:
The data we received is not completely cleaned. As an example we have the stations_data.csv file which have all the stations related information including latitude and longitude. But this data did not have communities. We manually mapped the all the 300 stations to one of the 77 communities. This ensured to investigate and produce visualizations even based on the communities.Historical Weather Data The data has been stored in JSONs and can be found here.. This includes historical data for each day and subdivided by the hour. Java Code used for Data Cleaning
The sample scripts that are used for data cleaning are available in this public repository. These script files generated JSON files which have all the required data specific to the particular visualization. All the JSON files we generated are stored here.
Data was primarily needed for the following tasks during the project. Historical Weather Data The data has been stored in JSONs and can be found on git in the /App/Json/Map/Weather path. This includes historical data for each day and subdivided by the hour. The data is updated to an hour interval. D3 Visualization Leaflet/Map Visualization Scripts for Data Cleaning The sample scripts that are used for data cleaning are available in this public repository.