# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Classification
#### 3.3.1 Classification Criteria  
The classification of query points involves four criteria: buffer creation, overlapping with mesh block, weight calculation, and using the mode function in QGIS for query point categorization based on the respective weights.  
Buffer Creation  
The first criterion is buffer creation, where spatial areas are generated around each point of interest to classify the query points. Buffers are generated based on specified distances. This study employed four different buffer distances: 1 km, 5 km, 10 km, and 15 km. These buffer distances were selected according to previous research on healthcare accessibility and surveys [35; 36; 37]. These studies provide insights into acceptable distances for accessing healthcare from households and examine the relationship between distance and utilization of health services.  
After creating the POI buffer with the specified distances, the criterion of overlapping with mesh block comes into play. Mesh blocks containing the query points are retrieved. POIs buffers are overlaid with mesh block shapes (Figure 9 with the mesh block in red shape inside the red square and POIs buffer in green and purple). This overlapping process determines the number of weight attributes used in calculating the density of POIs. The calculation involves counting the POIs buffer distances intersecting each mesh block, thereby contributing to the overall density calculation. The resulting count is stored in the database column (Figure 10).  
Figure 9: \(Cont.\)  
Figure 10: The table in a database which contains the number of POIs buffer that overlaid with mesh blocks.  
Figure 9: Mesh blocks with overlaid POI buffer. (**a**) Mesh block with \(1\) km POI buffer; (**b**) Mesh block with \(1\) km and \(5\) km POI buffers; (**c**) POI buffers (1 km, \(5\) km, \(10\) km, and \(15\) km).  
Weight Calculation and Query Point Classification Method  
The weight attributes, securely stored in the database, play a crucial role in QGIS. They are essential for calculating the density of POIs and creating a heatmap for each query point. Values from overlapping POI buffer zones at various distances are combined to ensure a comprehensive representation. This combined value is a crucial parameter in the QGIS value function, which is necessary for creating both the heatmap and graduated visualizations. The decision to combine all POI buffer weights is made to provide a clear and consistent representation of the overall POI density. This aggregated weight, derived from the intersect count, is also used with the data classification in QGIS, thus contributing to the systematic classification of query points based on their weights.  
In this research, quantile classification was employed to classify query points. This approach offers several advantages, including its capacity to distribute query points uniformly across different classes, thereby providing a balanced representation. Additionally, quantile classification is less sensitive to outliers, thereby ensuring that extreme values do not disproportionately influence the classification results. This robustness makes it particularly suitable for scenarios where the distribution of POI density exhibits variations, thereby allowing for a more reliable and resilient classification process. Considering future research directions, exploring alternative heatmap calculation methods, such as kernel density estimation (KDE), may be valuable. Experimenting with different techniques could offer insights into potential variations in heatmap results. Moreover, alternative data classification methods beyond quantile classification could be explored in future investigations, thereby enhancing the methodology's adaptability and addressing potential variations in data distribution. Discussing these possibilities in the future work section can highlight potential enhancements to the methods and acknowledge the continuous improvement and refinement of research techniques.