MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
MiBici (translated as MyBike in english) is a public bike service used in my home city of Guadalajara (GDL), Jalisco, Mexico. This service is used in Guadalajara's Metropolitan Area, which has a population of 5,268,642 (as of 2020) distributed in eight main municipalities and a total area of 2,543.13 squared km (981.91 squared mi).
This service has established stations where users can take and return a public bike, all they need is to sign up through MiBici's platform and pay a charge for 1, 3, 7 days, or annual use. After signing up, users get a transport card that can be used at any station in Guadalajara's Metropolitan Area.
MiBici makes their data public and has published it every month since December 2014.
A GitHub repository is also available with the R scripts used to merge, transform, and clean data from 4,496,890 bike trips registered in 2024 and hourly weather data obtained through the Open-meteo API, as well as the combined bikeshare + weather data and standalone hourly weather data available as csv files.
Data was obtained directly from MiBici's public data website. In this site, data is published in CSV files corresponding to individuals month from December 2014 to Febuary 2025. Data from 2024 was downloaded, cleaned, and transformed into a hourly format, as well as merged with hourly weather data.
variable | description | units |
---|---|---|
date | date in yyyy-mm-dd format (i.e. '2024-01-01') | date |
month | month of year (1 = jan, 2 = feb, ... , 12 = dec) | month |
day | day of month | day |
hour | hour of the day in 24 h format starting at 0 | hour |
trip_count | count of hourly bike trips | count |
is_weekend | is the day a weekend? i.e. saturday/sunday (1 = yes, 0 = no) | binary |
is_holiday | is the day a federal holiday in Mexico? (1 = yes, 0 = no) | binary |
apparent_temperature | perceived temperature combining wind chill factor, relative humidity and solar radiation | °C |
wind_speed | wind speed at 10 meters above ground | km/h |
is_day | 1 if the current time has daylight, 0 at night | binary |
temperature | air temperature at 2 meters above ground | °C |
relative_humidity | relative humidity at 2 meters above ground | % |
precipitation | total precipitation (rain, showers, snow) sum of the preceding hour | mm |
weather_code | weather condition as a numeric code. Follow WMO weather interpretation codes (see below) | WMO code |
season | season of the year (winter, spring, summer, fall) | category |
code | description |
---|---|
0 | clear sky |
1 | mainly clear |
2 | partly cloudy |
3 | overcast |
45 | fog |
61 | rain: slight |
63 | rain: moderate |
80 | rain showers: slight |
81 | rain showers: moderate |
95 | thunderstorm |
96 | thunderstorm with slight hail |
I have also cleaned, transformed, and combined all bikeshare data from Dec 2014 to Mar 2024 and published the final dataset (2.51 GB) in Kaggle.
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MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
MiBici (translated as MyBike in english) is a public bike service used in my home city of Guadalajara (GDL), Jalisco, Mexico. This service is used in Guadalajara's Metropolitan Area, which has a population of 5,268,642 (as of 2020) distributed in eight main municipalities and a total area of 2,543.13 squared km (981.91 squared mi).
This service has established stations where users can take and return a public bike, all they need is to sign up through MiBici's platform and pay a charge for 1, 3, 7 days, or annual use. After signing up, users get a transport card that can be used at any station in Guadalajara's Metropolitan Area.
MiBici makes their data public and has published it every month since December 2014.
A GitHub repository is also available with the R scripts used to merge, transform, and clean data from 4,496,890 bike trips registered in 2024 and hourly weather data obtained through the Open-meteo API, as well as the combined bikeshare + weather data and standalone hourly weather data available as csv files.
Data was obtained directly from MiBici's public data website. In this site, data is published in CSV files corresponding to individuals month from December 2014 to Febuary 2025. Data from 2024 was downloaded, cleaned, and transformed into a hourly format, as well as merged with hourly weather data.
variable | description | units |
---|---|---|
date | date in yyyy-mm-dd format (i.e. '2024-01-01') | date |
month | month of year (1 = jan, 2 = feb, ... , 12 = dec) | month |
day | day of month | day |
hour | hour of the day in 24 h format starting at 0 | hour |
trip_count | count of hourly bike trips | count |
is_weekend | is the day a weekend? i.e. saturday/sunday (1 = yes, 0 = no) | binary |
is_holiday | is the day a federal holiday in Mexico? (1 = yes, 0 = no) | binary |
apparent_temperature | perceived temperature combining wind chill factor, relative humidity and solar radiation | °C |
wind_speed | wind speed at 10 meters above ground | km/h |
is_day | 1 if the current time has daylight, 0 at night | binary |
temperature | air temperature at 2 meters above ground | °C |
relative_humidity | relative humidity at 2 meters above ground | % |
precipitation | total precipitation (rain, showers, snow) sum of the preceding hour | mm |
weather_code | weather condition as a numeric code. Follow WMO weather interpretation codes (see below) | WMO code |
season | season of the year (winter, spring, summer, fall) | category |
code | description |
---|---|
0 | clear sky |
1 | mainly clear |
2 | partly cloudy |
3 | overcast |
45 | fog |
61 | rain: slight |
63 | rain: moderate |
80 | rain showers: slight |
81 | rain showers: moderate |
95 | thunderstorm |
96 | thunderstorm with slight hail |
I have also cleaned, transformed, and combined all bikeshare data from Dec 2014 to Mar 2024 and published the final dataset (2.51 GB) in Kaggle.