# Exploring Space Syntax Integration at Public Transport Hubs and Public Squares Using Drone Footage
## 2 Materials and Methods
### Data Collection and Video Analysis  
The drone video recordings were made using a DJI mini 2 at a height of 120 m to capture a large area and to make sure that it is impossible to recognize individual  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **GPS Coordinates** & **No. of Flights** & **Date/Time of Flights** \\ \hline Folkets Park & 55.595651, 13.013346 & 1 & 26 April 2021/ca. 13:00 \\ \hline Gustav Adolfs Torg & 55.602512, 12.999201 & 1 & 12 November 2020/ca. 15:00 \\ \hline Mastorget & 55.611683, 12.980771 & 1 & 15 September 2021/ca. 15:00 \\ \hline Mollevängen & 55.591632, 13.007936 & 1 & 17 September 2021/ca. 09:00 \\ \hline Stortorget & 55.606145, 13.000500 & 2 & 15 September 2021/ca. 14:00 \\ \hline Triangleh South & 55.591960, 13.001452 & 1 & 17 September 2021/ca. 11:00 \\ \hline Triangleh & 55.594597, 13.000484 & 2 & 29 October 2020/ca. 16:00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The seven locations selected in Malmö and the number of flights made at each site.  
Figure 2: Image from drone footage at Folkets Park, Malmö.  
road users. The recording was made at non-peak hours to ensure that no flying was conducted over crowds. This approach ensured that both the Swedish regulation about video surveillance [22] and the regulation about drone flying [23; 24] were followed. At some of the locations, two consecutive flights were made to capture the entire area. Note that the most important factor when choosing the camera view was that all entrances and exits of the studied area could be seen in either camera view, ensuring that the analysis could be conducted on the entire area.  
The data processing and analysis were conducted following the framework described by [PERSON] et al. (2017) [25] involving Pre-Processing, Stabilization, Calibration and Object Detection. The calibration of the videos was carried out using T-Analyst software, following the process described by [PERSON] et al. (2018) [26] which in turn uses the calibration technique presented by [PERSON] (1987) [27]. If two flights were made to cover a single location, both videos were calibrated using the same map and linked to the same coordinate system. Finally, object detection was achieved using STRUDL software [28]. STRUDL provides a relatively simple framework for using a convolutional neural network to undertake automated video-based traffic analyses. To increase the performance of the object detection, about 200 images from the drone footage were manually annotated to provide additional training material to the already pre-trained network.  
The video was recorded with a resolution of 1920 \(\times\) 1080 at 25 frames per second. In the pre-processing step, the video editor software DaVinci Resolve was used to merge video files made during the same flight. At the same time, the resolution was downscaled to 1536 \(\times\) 864, and the video was stabilized. The downscaling was done to make sure that the neural network used in the object detection step could handle the video with an Nvidia GTX 1080 Ti graphics card.  
Note that tracking was not used in this paper. Tracking refers to the process of following a road user throughout the video [29]. Unfortunately, the tracking algorithm used in the STRUDL framework was unable to robustly track pedestrians throughout the study area. This paper is therefore limited to only using the pedestrian detections made in each frame; we were unable to follow a single pedestrian. However, the STRUDL framework gathers movement information about each road user and this information was used to identify and remove pedestrians that did not move. This removed many situations in which pedestrians stood still for a significant amount of time, which otherwise would have led to a very large amount of pedestrian activity being recorded in those areas. This process also helped remove mistakes made by the neural network, such as identifying dustbins and other objects as pedestrians.