# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 6 Conclusions  
In conclusion, our study aimed to evaluate the accuracy of the Euclidean distance method compared to the road network distance method using kNN queries and range queries, thus considering the classification of query points. The investigation brought several significant insights.  
Our findings unveiled several critical insights. Specifically, in kNN queries, peak accuracy was achieved when k was set to one, thereby exceeding 85% across all classes. However, with an increase in k, a consistent decrease in accuracy was observed, thereby highlighting the sensitivity of results to the choice of k. Turning to range queries, a uniform accuracy pattern emerged across classes, with the turning point influenced by the varying range values. Notably, a higher density of POIs correlated with an earlier increase in accuracy.  
Furthermore, the comparative analysis between ambulance and hospital datasets questioned the assumption that more POIs ensure superior accuracy. Despite containing fewer POIs, the hospital dataset demonstrated higher accuracy, thus emphasizing the complex interplay between dataset characteristics and method performance. Our study contributes substantially by illuminating scenarios in which the Euclidean distance method is a feasible compromise between accuracy and computational efficiency, particularly in classifying query points based on POI density.  
The discussion provides a detailed understanding of the relationship between accuracy and the number of k in kNN queries. As k increased, the diversity in road structures and terrain introduced variations, thus impacting accuracy. Similarly, the observed increase in accuracy in range queries suggests that the distance range adequately covered surrounding POIs. However, the study cautions about potential interactions between road structure, POI density, and range distance influencing accuracy trends.  
While our approach adds valuable insights to the existing body of research, we acknowledge certain limitations. The query point classification criteria, the weight attribute selection, the heatmap calculation method, and the consideration of different geotypes introduce variability into our results. The impact of road structure on the accuracy of POIs in the road network distance method and potential bias due to an imbalance in the number of POIs across cities could influence the accuracy comparison of the Euclidean distance method.  
In future work, accounting for road structure in query point classification and addressing POI imbalances across cities will refine our understanding of the Euclidean distance method's performance. Additionally, exploring the impact of criteria for classification, the number of samples, the road network algorithm for extracting the shortest route, the weight attribute for heatmap calculation, and different geotypes (such as dense urban areas and sparse rural areas) will provide a more comprehensive assessment. By doing so, we aim to enhance the applicability of our findings to real-world scenarios while mitigating potential biases and improving the robustness of our methodology.  
Conceptualization, K.A. and P.T.; methodology, D.T., K.A. and PT.; software, D.T. and K.A.; validation, D.T., K.A. and P.T.; formal analysis, P.T.; investigation, P.T.; resources, K.A.; data curation, K.A.; writing--original draft preparation, P.T.; writing--review and editing, D.T., K.A. and P.T.; visualization, P.T.; supervision, D.T. and K.A.; project administration, D.T. and K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.