## 4 Results and Compilation
### Data Compilation  
Data compilation results from data acquisition using both methods are as follows. So far, 1,763,218 nongeotagged data have been obtained, compared to 81,941 geotagged data obtained from 2491 users (Figure 6). The database consisted of 2491 collections; these needed to have duplications of document in each collection filtered out, as well as out-of-range data in terms of area and time. PyCharm software Community Edition \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**\#** & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\ \hline
1 & \(-\)6.154017,106.138792 (3) & Cip1\_2204170820.csv & 10 & 5 & 3 & \(-\)6.134539, 106.167998 (3.88) \\
2 & \(-\)6.140438,106.147322 (3) & Cip2\_2205170820.csv & 13 & 10 & 3 & \(-\)6.173101, 106.071077 (9.18) \\
3 & \(-\)6.144682,106.158796 (3) & Cip3\_2206170820.csv & 13 & 10 & 3 & \(-\)6.173101, 106.071077 (10.20) \\
4 & \(-\)6.144942,106.161058 (3) & Cip4\_2207170820.csv & 15 & 8 & 2 & \(-\)6.173101, 106.071077 (10.43) \\
5 & \(-\)6.133726,106.173318 (3) & Cip5\_2208170820.csv & 15 & 7 & 4 & \(-\)6.173101,106.071077 (12.13) \\
6 & \(-\)6.162454,106.181465 (3) & Cip6\_2209170820.csv & 15 & 8 & 3 & \(-\)6.209480, 106.129581 (7.75) \\
7 & \(-\)6.137307,106.198948 (3) & Cip7\_2209170820.csv & 15 & 7 & 4 & \(-\)6.173101,106.071077 (14.70) \\
8 & \(-\)6.118432,106.198660 (3) & Cip8\_2210170820.csv & 15 & 7 & 3 & \(-\)6.173101,106.071077 (15.36) \\ \hline \multicolumn{5}{c}{Aggregation:} & 47 & 27 & 7 \\ \hline \hline \end{tabular}  
* Notes: (1) Reference Point and Capturing distance (km); (2) Source of file name; (3) Number of unique data; (4) Number of user(s); (5) Number of location(s); (6) Farthest point and distance (km).  
\end{table} 
Table 2: Twitter data retrieving results using multi-reference points (Example: Cipocokjaya zone). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Archive Data**} \\ \cline{2-5}  & **2018** & **2019** & **2020** & **2021 * \\ \hline Number of Users & 506 & 742 & 1632 & 1032 \\ Number of Geotagged Data & 232 & 476 & 1286 & 833 \\ Number of Nongeotagged Data & 61,825 & 129,300 & 259,253 & 69,894 \\ \hline \hline \end{tabular}  
* until 30 April 2021.  
\end{table} 
Table 3: Number of Retrieved Archive Data. version 2019.1.2 was used to edit every document in each collection in order for these to be readable by the Python-based program in the filtering process. Among the geotagged data, with respect to the source of data, there were 21,996 (40.6%) data from streaming and 32,152 (59.4%) from archiving. Among a collection of 1769 users that met the statistical requirements (geotagged data \(\geq\) 4), only 29% of the data collection from streaming data and 41% of data collection met the statistical rules; this was due to additional data coming from archive data. Thus, archive data can make a significant contribution to readiness data for trip production modeling. A large amount of data has been archived as these showed tweets with multipoints and not just one point as in streaming data. (See example below). Documents #1 (Table 4) and #2 (Table 5) provide the coordinates of  
A large amount of archive data were also obtained that showed tweets with a different date and month timestamp compared to the same tweet from streaming data. However, it was intuitively determined to be the same timestamp, as can be seen in the example below. Document #1 has archive data stamped 12 June, and document #2 has streaming data stamped 6 December having the same year, clock hour, and minute. Since the Twitter platform only gives access to streaming data from the previous seven days, it was impossible to pull data from as far back as 12 June 2020. For trip production modeling, textual data were collected from both streaming and archive data; some of these tweets were not from the user him/herself, but from others, as seen in the example below.