# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 3 Results
### Experimental Results  
In this section, we unveil our findings and provide the evaluation results concerning different aspects, such as Local Moran's I and the POI intensity per fishnet. Additionally, we present and compare POI-based LUM identification outcomes from these two distinct urban settings. Using the point-based data from Google Maps API and ad listings data to predict LUM, we present the LUM results on a map to further understand their distribution across Ankara city. Figure 8 shows the areas in Ankara according to their LUM values, in which gray areas show not applicable results. The colored squares indicate the LUM values of the associated places. According to the map, as the color becomes dark, the LUM values increase. In general, areas that are far from the city center usually have lower LUM values. On the other hand, the areas with higher LUM values are places that have both residential and non-residential activities in place. Gray areas show the fishnets with not enough POI data, and the darker the color, the higher the LUM. Figure 7: Regularity percentage vs grid size. To evaluate our LUM findings, we first observed the overall results with respect to the ground truth. In general, we wanted to identify whether an area is mixed-use or not. On top of the entropy-based mix estimation, it was essential to label areas as mixed and non -mixed with a confidential threshold. We examined the classification accuracy, precision, recall, and F1-score. In order to classify areas whether they have mixed land use or not, we first defined a cutoff point. For this purpose, we first set 20% of the regions we managed to identify LUM values aside based on a stratified sampling where we obtain samples from places having [0-0.1), [0.1-0.2), [0.2-0.3),..., [0.9-1] LUM values. We then tested different thresholds on that portion via drawing ROCs for each of the cutoff points, which are depicted in Figure 9.  
As a result, we selected the one that has the largest AUC, which is cutoff = 0.5 with AUC = 0.57. After setting the cutoff point to 0.5, we assumed that places having equal or lower than 0.5 LUM values have no mixed land use and others have mixed land use. Additionally, we also set two baselines to compare the results--one for all the fishnets with no mix representing homogeneous single land use and one for all the fishnets having mixed land use. The overall results are presented in Table 2.  
Overall, our only POI-based model's accuracy is 51%, its precision is 48%, its recall is 69%, and its F1-score is 57%. To further understand the results, we scrutinized the areas with respect to neighborhoods and POI density. In addition to directly comparing the predicted and ground truth LUM values, we also examined the local spatial autocorrelation. Local spatial autocorrelation measures landscape fragmentation in comparison to landscape metrics [32]. It finds spatial dependency of features in space to imply landscape heterogeneity. In this study, we used one of the  
Figure 8: Predicted LUM values are presented on a map. Darker colors indicate higher LUM values. local spatial autocorrelation methods, Local Moran's I, [33] to identify the spatial pattern of different fishnet LUM values. Local Moran's I fundamentally has two interpretations. On the one hand, it lets you find hot spots. On the other hand, it identifies outliers. Thanks to Local Moran's I (LMI), we detected local clusters and local spatial outliers to further analyze our LUM classification findings depending on the Local Moran's I index of each fishnet's LUM value. As [PERSON]'s first law of geography states, spatial distance affects the relationship among things. Hence, ignoring spatial distance among fishnets will make the results biased. Therefore, we calculate the Local Moran's I for each fishnet based on the assigned LUM values. In Table 3, we presented the classification accuracy, precision, recall, and F1-score for different significance ranges of the Local Moran's I analysis.