# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 2 Related Work  
Many infectious diseases are highly contagious and seriously affect human health, economic activities, education, sports, and leisure [1]. Restricting the movement of people (e.g., with a quarantine) is an effective infection control measure when an epidemic spreads [2; 3]. GNSSs, such as the US Global Positioning System (GPS) [27] and the Russian Global Navigation Satellite System (GLONASS) [28], can provide autonomous geospatial positioning. They are widely used in various applications [6; 7; 8; 9; 10; 11; 12], such as prevention and control. In [6], some methods were proposed for the regional and global surveillance of infectious diseases; for example, they used GPS data to track the detailed movement patterns of individuals. The positions obtained from GPS can also be entered directly into a geographic information system (GIS). A GIS can locate highly endemic areas and high-risk groups and can help make resource allocation decisions [7]. In [9], the aim was to emphasize the use of GNSS satellites in environmental monitoring. In [10], smartphones were used to track COVID-19 patients and diagnosed people. In [11], mapping inundation dynamics and the extent of flooding were found to be essential in various applications, such as the prediction of the spread of infectious diseases, and a GNSS can help with this application. Finally, in [12], an Indian COVID-19 tracker was analyzed. The confirmation of whether a target is inside or outside of a targeted area can be mapped as a PtInPy problem. Many works in the literature discussed related issues in the past [13; 14; 15; 16; 17; 18]. In [13], a detailed discussion of the PtInPy problem for arbitrary polygons was presented. No single algorithm is the best in all categories, so this study compared the capabilities of the better algorithms. The variables examined, including different types of polygons, the amount of memory used, and the preprocessing costs, were discussed in [14]. In [15], the authors outlined a fast and efficient method for determining if a coordinate point lies within a closed region or polygon that is defined by any number of coordinate points. In [16], an efficient polygon clipping algorithm, as opposed to a rectangularly clipped window, was proposed. It used a parametric representation of polygon edges. By using the concept of point clipping, it found the required intersection points of the edges of a polygon with clip window boundaries. In [17], an extension of a winding number and PtInPy algorithm was presented. In [18], a provably correct implementation of a PtInPy method that was based on the computation of the winding number was presented. Simply, a PtInPy function can also be used to represent a function whose inputs are a geographical point and a polygon. Applying a PtInPy function will assert that the geographical point is an inner point or an outer point of the polygon. In addition, O_PtInPy (Only PtInPy) can also be used; it is a type of PtInPy method in which each target has to be confirmed by applying a PtInPy function to obtain a result. The methods used in [13; 14; 15; 16; 17; 18] were of the O_PtInPy type, and they could not handle many targets. In [20; 21], methods that did not require every target to be confirmed by applying a PtInPy function were proposed. In [20], the authors planned a large rectangle that could cover the entire target area, and the targets outside the rectangle did not need to be confirmed by a PtInPy function. In short, L_Rect (large rectangle) was used to represent this method. However, the range of the rectangle may be much larger than the range of the specific area, and the number of targets confirmed by the PtInPy function will still be very high. When the number of targets is very high, it is unreasonable to load the data of all of the targets into the program. In addition, the computer's computational capabilities also need to be considered.