## 4 Case Studies of the Framework  
In Figure 8, clusters of narrow space, open space, semi-open space, cross space, T-shape space, and so on can be intuitively seen in the visualization of the ray SOM. The mask SOM shows an obvious gradient of SVI patterns. For instance, from right up to left bottom, it shows the gradient from mainly building-covered images to wide open space mainly with the sky. The right bottom part shows SVIs with a large proportion of vegetation. With the help of the SOMs, one can search for similar urban plots by spatial radar or SVI input. We show some case studies of queries for the SOMs.  
We test the SOMs in two different ways, including querying a single SOM for similar patterns, and an interplayed query of two SOMs for cases similar in both features. As  
Figure 8: Results of ray SOM (**left**) and mask SOM (**right**) with a demonstration sample for each BMU.  
Figure 7: The scatter plots showing quantization error and topographic error of ray SOM models (**left**) and mask SOM models (**right**).  
shown in Figure 9, the user interface is capable of two kinds of input, including location and SVI, regarding the geometry feature and imagery feature, respectively. The retrieval loop can go from A to B to A, which means retrieving locations with similar spatial traits by inputing a location or the ray distances. It can also go from A to B to C, which means retrieving street view images of the locations with similar spatial traits. Similarly, if the retrieval loop goes from C to D to C, it means finding similar street view images. If it goes from C to D to C, it means finding locations that are similar in visual elements. Moreover, the framework supports the full loop retrieval from A to B to C to D to A. Users can find locations that are similar in both spatial traits and visual appearance by inputing geometric features and subsequently selecting locations that have close imagery features from the results or vice versa. We show some case retrievals in the following paragraphs.  
Firstly, we query for locations in Nanjing that have similar spatial traits as the input. Three examples are set as input, including narrow space, half-open space, quarter-open space, and so on. Eight retrieved locations of each are shown in Figure 10. From the results, the plots that have the spatial characteristics are successfully retrieved by RD, and the locations of the observer are displayed.  
Apart from retrieval according to one SOM, a recursive search is essential, especially for design inspiration. Therefore, the interplay between two SOMs enables users to search among the possibilities by sharpening the target in the input-feedback-input loop. We show the case studies of retrieving across the SOMs. Firstly, we search for locations and street view images with a viewpoint similar with the input radar or location. Then, we take one point with the SVIs that we are interested in according to the retrieved locations. Furthermore, we query again for the cases that have similar visual appearance from the previous output. Therefore, we query for case packages with master plans, street view images and satellite images according to the spatial ray and user's feedback. Figure 11 shows two case studies in this query way.  
Figure 9: The retrieval loop with 4 ends including 2 user interface ends and 2 database ends.