## 5 Discussion and Further Research  
We have presented a framework for the collection, processing, storage, clustering, and interconnecting of spatial and visual characteristics to enable probabilistic navigation in the space to support decision making using urban cases in Nanjing. This section further justifies the system, starting with a discussion of the objectives mentioned in the introduction. This is followed by discussing the framework as a basis for future research in navigation among the explorer space for urban design.  
The IGIF provides an efficient and comprehensive encoding and feature extraction approach for urban plots. Compared with pixel-based feature extraction where the samples are  
Figure 11: Query for locations and SVIs that have similar spatial characteristics with the input marked with a red box (step 1) and query for case packages according to the selected SVIs (marked with a red box) based on the step 1 output (step 2).  
Figure 10: Locations in similar spatial traits are retrieved according to geometric features.  
represented by 2048-dimensional features [40], IGIF has more condensed 51-dimensional features. Therefore, the following data clustering process has higher efficiency. Although the IGIF has fewer feature dimensions, it is still informative. It takes into consideration master planning and urban perception to bring implicit knowledge into urban computational modeling. It performs well in finding observing points with similar spatial characteristics by geometric vectors extracted by the obstacle-detecting method and in finding points with a similar visual appearance by imagery vectors that are composed of semantic segments percentages.  
The data clustering by SOM provides spectra of the urban plots corresponding to the different aspects, which further provides a platform for urban designers to navigate based on probabilistic. An SOM is spreading the network in the data space to reach the data as much as possible, forming a spectrum for case retrieval. Data with similar spatial or visual characteristics are put in the same or close clusters. Then, urban designers can perform query and feedback loops instantly because of the efficiency to navigate among the cases in the built environment and learn from the existing cases. In addition, the interplay between the SOMs connects data from different sources. Therefore, in our framework, SOM does not only cluster but also generates, predicts, and visualizes from one dimensionality to another according to the probabilities.  
From the urban design perspective, the framework provides flexible computational modeling by encoding implicit knowledge with the generic mechanism, interconnecting heterogeneous data, and creating an explorer space to bring together machine and human intelligence. It supports a mindset shift from generating to retrieving. Moreover, it proposes that the machine should not only be used for optimization but rather as an instrument that leverages the active role of the designer.  
The framework opens possibilities for UCM in the data stream context. In fact, a methodology similar to that used on developing CFD emulation would be a valuable extension [35]. In other words, the framework is flexible to be further extended to implement urban performance, urban perception, urban fabric aspects, and so on. A demonstration of the conceptual design process based on a specific task and personal preference using the program would be also beneficial. There are also possible applications in practical studies. For instance, the core program could serve as a back end for a web-based city search engine for place finding such as real estate or urban renovation, with flexible selection criteria depending on specific tasks. An immersive experience for place selection, on-site visiting, and decision making can also be applied by integrating extended reality techniques.