# GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
## 2 Methodology
### Data Collection and Pre-Processing Approaches  
The collection of the data included all the GIS data about the city, including the transportation network, to evaluate the impact of optimized event routes on the city. We obtained the data from the National Geographic Information Institute (NGII), which manages spatial information under the leadership of the South Korean government. The NGII provides city data in the following categories: traffic, buildings, facilities, vegetation, water systems, topography, boundaries, and cycles. Table 2 shows the built data categories and the detailed data composition. We required GIS data for four different elements: (a) transportation networks; (b) buildings; (c) facilities; and (d) topography. (a) We generated transportation-network data centering on road routes composed of lines and points where road routes intersected. Each route contained attribute information for effective road management (e.g., road ID, road width, traffic type, the number of lanes, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Dataset** & **Data Type** \\ \hline \multirow{3}{*}{Transportation networks} & Roads & Line shapefile \\ \cline{2-3}  & Pedestrian roads & Line shapefile \\ \cline{2-3}  & Attribute information & Road ID, road width, traffic type, number of lanes, existence of a median strip, materials of road pavement, classification of road \\ \hline \multirow{3}{*}{Buildings and facilities} & Educational institutions & Polygon shapefile \\ \cline{2-3}  & Child- and & Polygon shapefile \\ \cline{2-3}  & geriatric-welfare institutions & Polygon shapefile \\ \cline{2-3}  & Public institutions & Polygon shapefile \\ \cline{2-3}  & Medical facilities & Polygon shapefile \\ \cline{2-3}  & Transportation facilities & Polygon shapefile \\ \cline{2-3}  & Attribute information & Building (or facility) name, usage, floors, height \\ \hline Topography & Contour line & Line shapefile \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data categories and composition. Figure 2: Framework of the proposed routing model. and the existence of a median trip); this can usually be interpreted as the physical structure of the road, and the physical relationship between lines can be extracted (e.g., the length of lines and the angles between them). We used traffic data to extract roads that can be used as tracks and as input parameters for routing algorithm. (b) We used building and (c) facility data to configure the environmental variables. The shape can be checked through the outlines of the elements in the GIS data, which can be visualized through the number of floors and height information. Furthermore, road routes to be avoided (e.g., educational and medical facilities) can be identified based on the usage information of the building. (d) Topographic data are usually composed of contour lines, which constrain the permitted slope of the route based on the terrain-slope information; they also support stakeholder decision making by visualizing the topographic data. We set the range of data collection to a radius of 2 km from the main point of the event, since the Formula E racetrack is a closed route and the maximum length of one lap of the competition is 3.4 km. This is the scope for reducing the amount of data and limiting computer operations. The user selects the central location of the event. In event planning, sometimes, there is a need to designate an iconic building or space within a city as the main focal point for dramatic production. In addition, temporary facilities, such as TV compounds, pit garages, and medical centers should be located at the base of the event. Hence, we randomly selected the base of the event according to the user's needs and collected data rooted in the base. We integrated the data into a network dataset using the open-source software program, QGIS. Subsequently, depending on how the data were to be employed, they were classified for use in optimal routing or for evaluation and visualization. We extracted the classified data as CAD and Excel files.