# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 5 Discussion
This significant finding has the potential to contribute to future research endeavors substantially focused on analyzing the conditions under which the Euclidean distance can be employed as a viable trade-off between accuracy and computational time, particularly when considering the classification of query points based on POI density. By highlighting the circumstances in which this alternative metric performs acceptably across different density classes, our study provides valuable insights for practical implementations. This understanding becomes particularly crucial for scenarios where the computational efficiency gained from using Euclidean distance outweighs the marginal decrease in accuracy, thereby offering a practical solution for certain types of queries in real-world applications. Previous studies have extensively investigated the Euclidean distance method's accuracy and used the road network distance method as a benchmark. Notably, [PERSON], [PERSON], and [PERSON] (2018) [29] employed Euclidean distance as a computationally efficient alternative for estimating road network distance, specifically in group nearest neighbor (GNN) queries. The experiments compared the Euclidean decider, which utilizes Euclidean distance, with road network distance as a benchmark. Real road network graphs from various cities created scenarios with different query points and POI distributions. The study varied parameters such as the number of query points, POI density, value of k, and road network structure. The results indicate that the Euclidean metric achieved high accuracy levels, exceeding 74.7% for kNN queries and ranging from 86% to 95% for general GNN queries, thereby showcasing its effectiveness in achieving a balance between accuracy and computational efficiency in transportation services. In comparison, our studies demonstrated high accuracy at certain values of k when using kNN queries and achieved an accuracy rate above 50% for range queries. The results from their approach for kNN queries showed significantly higher accuracy. This difference may arise due to the influence of various parameters or variables affecting accuracy. The substantial contrast in outcomes could be attributed to specific conditions, configurations, or factors inherent in their approach compared to ours. Further investigation into the particular variables or parameters contributing to this marked variation in accuracy would be essential for a comprehensive understanding of the results. Our study categorized the query points by utilizing the attribute weight derived from counting the number of POI buffers overlaid with the mesh block containing the query points. This approach enabled us to use the weight attribute to calculate the density using a heatmap. However, it is important to acknowledge several limitations that may impact the accuracy of the result. Firstly, the query point classification criteria, including the weight attribute selection, the method employed for heatmap calculation, and genotypes, can introduce variability into the results. Additionally, the road structure for each city potentially affects the result of the POIs for the road network distance method, which might impact the accuracy of the Euclidean distance. Furthermore, we recognize that there may be an imbalance in the number of POIs across different cities. This could potentially introduce bias in the analysis, as certain cities may have a higher concentration of POIsthan others. This imbalance could influence the results, particularly when comparing the accuracy of the Euclidean distance method across different cities. By acknowledging these limitations, we demonstrate a critical awareness of the potential factors that could influence the accuracy and reliability of our study's results.