## 4 Data and Resources
### Cellphone Location Data  
This research uses real-world fine-grain mobility data provided by a data-as-a-service company that specializes in collecting and providing anonymized population movement data collected through GPS signals of cellphones across the U.S. We are obliged not to name the data provider due to contractual restrictions. Global Positioning System (GPS) is a navigation system that uses a network of satellites to provide location and time information to GPS receivers on Earth. Each satellite broadcasts a signal that includes a timestamp and information about its location, which is used by GPS receivers to calculate their own position and velocity [40]. Data provider claims to provide precise polygon-based building footprints for over 6 million places in the U.S. from over 20 million devices daily, with an accuracy rate of up to 10 m [41].  
Our study utilizes data which implements robust privacy measures to ensure the protection of individual privacy through advanced anonymization techniques, data aggregation, and strict adherence to consent protocols. Data vendor provides geospatial location data by aggregating raw GPS signals from mobile devices, merged with precise polygon places, to identify visits to specific points of interest. These data are sourced from a network of third-party applications and software development kits (SDKs), with compliance to privacy laws through pseudonymization and aggregation to protect individual identities. Despite these robust measures and the high credibility of the data, there remains the potential for inaccuracies due to factors such as signal interference, atmospheric conditions, and the urban built environment, which can all affect the precision of GPS-based location data.  
The dataset used in this study consists of location signals from across the U.S. for the time period of 1 March 2019 to 31 December 2019 (Figure 2). This period was specifically chosen because it represents the most recent year of available data for us that is unaffected by the COVID-19 pandemic and the associated restrictions that significantly altered mobility patterns and economic activities in subsequent years. This allows for an accurate assessment of the MLB game days' economic impact under normal conditions, making the findingsmore relevant and generalizable. Each record in the dataset contains a unique 'caid', latitude, longitude, and timestamp, where 'caid' represents a unique identifier for each mobile device; latitude and longitude represent the geographical coordinates of the device at the time of the recording; and timestamp indicates the date and time of the record (Appendix A). The dataset provides detailed information on the movement patterns of individuals within the SLMA during the study period (Figure 2). By analyzing the data, we can identify changes in population movement and spending patterns that may be attributed to the games. These fine-grain mobility data can be used to gain insights into the behavior of individuals and businesses in the area and can assist in making data-driven decisions.  
We counted the number of visitors in our POI and a nearby ballpark village to track visitor foot traffic, as shown in Figure 2. Data from the Saint Louis Cardinals vs. Pittsburgh Pirates game on 12 May 2019, which had the highest recorded attendance of 48,555 at the Cardinals Stadium, were used for analysis [42]. The figure indicates that there were more visitors in our POI on 12 May compared to a non-game day on 19 May. The number of visitors remained stable during the game time and gradually decreased after the game ended.