# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 6 Discussion  
In Figure 10 of the previous section, the estimation errors for the three LPWAN datasets are shown. Table 5, lists the optimal value for \(k\) as well as the corresponding mean and median location estimation error. When observing Figure 10, it becomes clear that the fingerprinting method has the best results on the rural Sigfox dataset. This is mainly due to the spatial spread of this dataset, which can be seen in Figure 1. Measurements were conducted by people who commute by car in the rural area between Antwerp and Ghent, but a major part of these measurements were taken while the cars were parked, usually even on the same parking spot. Therefore, the rural Sigfox dataset mainly consists of dense message clusters on several small parking lots which are far apart from each other. This explains why we obtain the best results when we estimate a location based on the single nearest neighbor: there is a high probability that the nearest neighbor of a test sample is located in the same message cluster. If a test sample is not located in such a cluster, the location estimation based on a single nearest neighbor can have an error of almost 25 km, as shown in the outliers of the box plot. Concisely, the fingerprinting results for the rural Sigfox dataset are only highly accurate if a test sample is located in one of the few message clusters.  
The second box plot depicts the estimation error of the urban Sigfox dataset. As shown in Figure 4, this dataset has a more equal spatial density compared to the rural Sigfox dataset. Consequently, location estimations will be based on a higher number of nearest neighbors: we evaluated that the optimal \(k\) for this dataset equals 10. Compared to the rural Sigfox dataset, the urban Sigfox dataset has a larger mean estimation error. However, the urban dataset has a more equally divided spatial spread, which causes the estimation error outliers to be significantly smaller compared to the outliers of the rural dataset.  
Lastly, the estimation error of the urban LoRaWAN dataset can be seen in the third box plot of Figure 10. For this set, we found that location estimates based on 11 nearest neighbors resulted in the smallest estimation error. The mean and median estimation error of the LoRaWAN dataset are significantly lower than those of the urban Sigfox dataset, which is mainly a consequence of the large size of the LoRaWAN dataset (123,529 messages). In the LoRaWAN set, it is more likely that the 11 nearest neighbors of a test sample are close to the actual location of the test sample, as the dataset holds more messages in the same urban area. This can also be empirically evaluated by comparing Figures 4 and 7, which display the spatial spread of the urban Sigfox dataset and the LoRaWAN dataset respectively. Hence, we expect to decrease the location estimation error of the urban Sigfox dataset by adding more messages to this set.  
After comparing the fingerprinting results of each dataset, we can conclude that the spatial spread of a dataset is an important factor to determine the value of \(k\). For datasets with an equal spatial spread, such as the urban Sigfox and LoRaWAN datasets, the best value for \(k\) equals 10 or 11. The rural Sigfox dataset consists of dense message clusters that are located far from each other. In this case, \(k\) should equal 1 to achieve the smallest location estimation error.