# Exploring Space Syntax Integration at Public Transport Hubs and Public Squares Using Drone Footage
## 4 Discussion  
Space Syntax theory is a method for understanding how the movement of people depends on the spatial environment. The results of this study further confirm the findings of many other studies, that is, that Space Syntax integration shows a significant correlation with pedestrian movement. In addition, this paper shows that this holds true even when considering roads as obstacles and when using a generalized way of defining integration levels. This might provide important insights into how micromobility and public transport can be better integrated on both a larger scale using a traditional approach and on a smaller scale as explored in this study. For example, there are e-scooter parking areas at both Folkets Park and Triangeln. At Folkets Park, the parking area is located on level 2 (the green area) while the parking area is placed on the edge between levels 3 and 4 (light and dark blue). It would be interesting to study whether this difference in integration also affects the usage of e-scooters at these locations. However, it must also be noted that Space Syntax is only one method among several potential ways of understanding movement. [PERSON] et al. (2019) [PERSON] et al. (2019) present a review of state-of-the-art methods for studying the integrated design of transport infrastructure and public spaces which includes Space Syntax but also several other approaches. Furthermore, it is important to keep in mind that the spatial design of an area does not offer a complete answer to the question of how and why people move in a certain way. For example, the study by [PERSON] et al. (2006) [PERSON] et al. (2006) identifies the presence of a diversity of destinations, residential density, walking infrastructure, aesthetics, traffic safety, and crime as factors relevant to walking. Using such metrics in combination with Space Syntax integration might improve the predictive power of the analysis and provide a more thorough understanding of the movement of people. There are also several technical caveats and distinctions which should be considered when discussing the results of this study. Firstly, when creating the polygon map it is not always entirely clear what to define as \"unavailable areas\" for pedestrians. Besides the obvious choices, such as buildings and roads, the analysis performed in this study also included areas with grass and trees, car parking areas, bicycle racks, fences, large flowerpots, and monuments. Objects and areas were considered \"unavailable\" if a pedestrian was unable, restricted or \"not supposed\" to walk there when passing through the area. For example, large areas with grass and trees were removed at Triangeln since they are typically not walked on by people moving through the area even though they are potentially permitted to walk there. Secondly, the computer vision approach used in this study relies on an automated neural network-based approach to make pedestrian detections. This approach provides a practical way to count pedestrians moving in the relatively large areas which otherwise would have been very time-consuming. However, the automated approach also comes with some drawbacks, starting with the difference between pedestrian detections and more traditional pedestrian counts. Since pedestrian detections are made in every frame of the video, it is possible to identify the same pedestrians upwards of 25 times per second as they move throughout the camera view. This also means that if a pedestrian moves fast or slow, this has an impact on the number of detections made in an area: slower walking results in more pedestrian detections than quicker walking. This can be seen in Figure 5, where there were fewer detections made on the pedestrian crossing on the road compared to its start and end where pedestrians stand and wait for the traffic light to change.