# Layout Model of Bike-Sharing Facilities in the Transfer-Influenced Area of a Subway Station
## 2 Methodology
### Data Collection
#### 2.1.2 Data Cleaning  
Data cleaning is a necessary preparation for data analysis, which directly affects the quality of data. The communication positioning between the shared bike and the server may cause abnormalities due to signal shielding, operation errors, positioning failures and other reasons. This affects the accuracy of the background cycling data statistics. Therefore, in this study, it was necessary to preprocess the data to reduce the impact of errors. The data cleaning in this paper mainly included the following points:  
1. Deletion of null data. The original data contained abnormal data for system reasons, such as GPS equipment and data transmission. For example, there were null values in the riding time and geographic location information. These null data were deleted.
2. Deletion of duplicate field data. The order number must be a unique value. Abnormal data with duplicate fields in the order data were deleted.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Order ID** & **Bike ID** & \begin{tabular}{c} **Borrowing** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Origin** \\ **Longitude** \\ \end{tabular} & \begin{tabular}{c} **Origin** \\ **Latitude** \\ \end{tabular} & \begin{tabular}{c} **Returning** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Destination** \\ **Longitude** \\ \end{tabular} &
\begin{tabular}{c} **Destination** \\ **Latitude** \\ \end{tabular} \\ \hline MBK0200021 & 200021552 & 07:28:35 & 2017/09/18 & 118.867007 & 31.9525406 & 07:36:55 & 118.871392 & 31.9421487 \\ MBK0200021 & 200021552 & 19:15:11 & 118.871392 & 31.9421487 & 19:18:00 & 118.870768 & 31.9429941 \\
*0573239852 & 200021552 & 2017/09/18 & 118.870791 & 118.870791 & 118.870768 & 31.9429941 \\ MBK0200037 & 200037808 & 15:02:34 & 118.707891 & 31.7227853 & 15:06:30 & 118.709543 & 31.7261972 \\
*0571814972 & 20017/09/18 & 2017/09/18 & & 2017/09/18 & & 2017/09/18 & \\ MBK0200037 & 200037808 & 17:02:20 & & 17:58:14 & & 17:58:14 & \\
*05725336101 & 20037/09/18 & 2017/09/18 & 18.709543 & 31.7261972 & 2017/09/18 & 118.709840 & 31.7261814 \\ \hline \hline \end{tabular}  
Please note that \(\widehat{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\textDeletion of cross-border data. The coordinates of the city area of Nanjing are from 31\({}^{\circ}\)14\" N to 32\({}^{\circ}\)37\" N, and from 118\({}^{\circ}\)22\" E to 119\({}^{\circ}\)14\" E, so it was necessary to delete the data located outside the coordinate range.
4. Deletion of abnormal usage time data. It was necessary to delete the records in which the rental date and return date were not the same day.
5. Deletion of abnormal data of riding time. The 98% quantile of the original riding data was 40 min, so 30 min was used as the cleaning standard for the time dimension, and data with a riding time exceeding 40 min were deleted. Taking into account the operation time for the user to pick up and return the bicycle, records with less than 60 s were deleted.
6. Deletion of abnormal data of riding distance. Due to the bike-sharing company's policy of rewarding users for using bike-sharing, some users have generated many abnormal orders, so the records of travel distances less than 150 m were excluded in this research. In this study, the main role of bike-sharing was to transfer to rail transit, so the data concerning long-distance travel were excluded. The upper distance limit was 5 km.  
After cleaning the original data based on the above rules, there were 3.32 million order data remaining, accounting for 91.7% of the original bike-sharing data sample.