Capital bikeshare dataset 2020/05~2024/08

# Capital bikeshare dataset

## 1. File information: 4 files /duration 2020/05~2024/08

#### - Daily rent data
- `ride_id`: ride id
- `rideable_type`: ride type. I.e. docked_bike, electric_bike, classic_bike
- `started_at`: start date and time
- `ended_at`: end date and time
- `start_station_name`: starting station name
- `start_station_id`: starting station id
- `end_station_name`: ending station name
- `end_station_id`: ending station id
- `start_lat`: start latitude
- `start_lng`: start longitude
- `end_lat`: end latitude
- `end_lng`: end longitude
- `member_casual`: Indicates whether user was a "registered" member (Annual Member, 30-Day Member or Day Key Member) or a "casual" rider (Single Trip, 24-Hour Pass, 3-Day Pass or 5-Day Pass). I.e. casual, member

**Data source**: https://capitalbikeshare.com/system-data

#### - Station list
- `station_id`: station id
- `station_name`: station name

**Data source**: organized from Daily rent data

#### - Usage frequency
- `date`: date
- `station_name`: station name
- `pickup_counts`: daily pickup of the station
- `dropoff_counts`: daily dropoff of the station

**Data source**: organized from Daily rent data

#### - Weather
- `name`: location
- `datetime`: date
- `tempmax`: maximum temperature at the location.
- `tempmin`: minimum temperature at the location.
- `temp`: temperature at the location. Daily values are average values (mean) for the day.
- `feelslikemax`: maximum feels like temperature at the location.
- `feelslikemin`: minimum feels like temperature at the location.
- `feelslike`: what the temperature feels like accounting for heat index or wind chill. Daily values are average values (mean) for the day.
- `dew`: dew point temperature
- `humidity`: relative humidity in %
- `precip`: the amount of liquid precipitation that fell or is predicted to fall in the period.
- `precipprob`: the likelihood of measurable precipitation ranging from 0% to 100%
- `precipcover`: the proportion of hours where there was non-zero precipitation
- `preciptype`: an array indicating the type(s) of precipitation expected or that occurred.
- `snow`: the amount of snow that fell or is predicted to fall
- `snowdepth`: the depth of snow on the ground
- `windgust`: instantaneous wind speed at a location
- `windspeed`: the sustained wind speed measured as the average windspeed that occurs during the preceding one to two minutes. Daily values are the maximum hourly value for the day.
- `winddir`: direction from which the wind is blowing
- `sealevelpressure`: the sea level atmospheric or barometric pressure in millibars
- `cloudcover`: the sea level atmospheric or barometric pressure in millibars
- `visibility`: distance at which distant objects are visible
- `solarradiation`: (W/m2) the solar radiation power at the instantaneous moment of the observation (or forecast prediction)
- `solarenergy`: (MJ /m2 ) indicates the total energy from the sun that builds up over a day.
- `uvindex`: a value between 0 and 10 indicating the level of ultra violet (UV) exposure for that day.
- `severerisk`: a value between 0 and 100 representing the risk of convective storms
- `sunrise`: the formatted time of the sunrise
- `sunset`: the formatted time of the sunset
- `moonphase`: represents the fractional portion through the current moon lunation cycle ranging from 0 (the new moon) to 0.5 (the full moon) and back to 1 (the next new moon)
- `conditions`: textual representation of the weather conditions.
- `description`: longer text descriptions suitable for displaying in weather displays
- `icon`: a fixed, machine readable summary that can be used to display an icon
- `stations`: the weather stations used when collecting a historical observation record
Parameters information: https://www.visualcrossing.com/resources/documentation/weather-api/timeline-weather-api/

**Data source** : https://www.visualcrossing.com/

## 2. Recommended analysis
#### - EDA / Visualize the rent information
#### - Predict demand from the weather
Regression technique may apply
X: weather data (selected wisely; PCA might help); y: daily pickup/ dropoff of the station
#### - Reschedule the bike-sharing
Clustering technique may apply

## Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

### 数据集描述：首都自行车共享数据集

## 文件信息：4个文件 / 时间范围 2020年05月至2024年08月

#### - 每日租赁数据
- `ride_id`：骑行ID
- `rideable_type`：骑行类型。例如，停泊式自行车、电动自行车、经典自行车
- `started_at`：起始日期和时间
- `ended_at`：结束日期和时间
- `start_station_name`：起始站名称
- `start_station_id`：起始站ID
- `end_station_name`：结束站名称
- `end_station_id`：结束站ID
- `start_lat`：起始纬度
- `start_lng`：起始经度
- `end_lat`：结束纬度
- `end_lng`：结束经度
- `member_casual`：指示用户是否为“注册”会员（年度会员、30天会员或日钥匙会员）或“非正式”骑行者（单程、24小时通行证、3天通行证或5天通行证）。例如，非正式，会员

**数据来源**：https://capitalbikeshare.com/system-data

#### - 站点列表
- `station_id`：站点ID
- `station_name`：站点名称

**数据来源**：由每日租赁数据整理而来

#### - 使用频率
- `date`：日期
- `station_name`：站点名称
- `pickup_counts`：站点的每日取车次数
- `dropoff_counts`：站点的每日还车次数

**数据来源**：由每日租赁数据整理而来

#### - 天气
- `name`：位置
- `datetime`：日期
- `tempmax`：位置的最高温度
- `tempmin`：位置的最低温度
- `temp`：位置的温度。每日值为该日的平均值（均值）
- `feelslikemax`：位置的体感最高温度
- `feelslikemin`：位置的体感最低温度
- `feelslike`：考虑热指数或风寒因素后的温度感觉。每日值为该日的平均值（均值）
- `dew`：露点温度
- `humidity`：相对湿度百分比
- `precip`：在指定时间段内预计或已降水的液态降水量
- `precipprob`：可测量降水发生的可能性，范围从0%到100%
- `precipcover`：存在非零降水的比例（以小时计）
- `preciptype`：表示预期或已发生降水类型的数组
- `snow`：预计或已降水的雪量
- `snowdepth`：地面上的积雪深度
- `windgust`：位置的瞬时风速
- `windspeed`：在先前一至两分钟内测量的持续风速。每日值为该日的每小时最大值
- `winddir`：风向
- `sealevelpressure`：海平面大气或气压（以毫巴计）
- `cloudcover`：云量
- `visibility`：远处物体可见的距离
- `solarradiation`：（W/m2）观测（或预测预测）瞬时的太阳辐射功率
- `solarenergy`：（MJ /m2）表示一天内太阳总能量的累积
- `uvindex`：介于0到10之间的值，表示该天的紫外线（UV）暴露水平
- `severerisk`：介于0到100之间的值，表示对流风暴的风险
- `sunrise`：日出时间的格式化时间
- `sunset`：日落时间的格式化时间
- `moonphase`：表示当前月相周期的分数部分，范围从0（新月）到0.5（满月）再回到1（下一个新月）
- `conditions`：天气条件的文本表示
- `description`：适合在天气显示中显示的较长文本描述
- `icon`：用于显示图标的固定、机器可读的摘要
- `stations`：收集历史观测记录时使用的天气站

**数据来源**：https://www.visualcrossing.com/

## 2. 推荐分析
#### - 探索性数据分析（EDA）/可视化租赁信息
#### - 从天气预测需求
可以使用回归技术
X：精心选择的天气数据（PCA可能会有帮助）；y：站点的每日取车/还车次数
#### - 重新安排自行车共享
可以使用聚类技术

请随时在讨论区留言。如果您认为我的数据集有用，我将非常感激您的点赞！😀