The building models we collected needed to be converted into point cloud data before they could be presented as particles in UE5. To save modeling time, we first downloaded ready-made architectural models from Sketchfab that matched the style of London neighborhoods, and then used point cloud processing software such as CloudCompare to export them as .ply or .xyz files. During the conversion process, we encountered some difficulties: the high texture repetition on some models confused the point cloud generation algorithm, leading to a large number of overlapping noise points. We had to manually inspect the point clouds frame by frame, selecting and deleting outliers that deviated from the main structures—a time-consuming step. After importing the processed point clouds into UE5, we assigned different point density and color settings to the rich and poor areas—the rich area had denser points (symbolizing concentrated resources) with warmer tones (gold, beige), while the poor area had sparser points with cooler tones (grey, blue). This visual distinction helps viewers perceive the economic differences between the two areas at a glance.
