## 1 Introduction  
Computational modeling is necessary for scientific investigation because it impacts how we translate real-world phenomena to a digital representation. The foundation of computational modeling technologies changes with growing diversity. Various modeling methods increase the capacity for applications in the urban study and urban design domain. Urban computational modeling (UCM) has evolved from analytical to computational network methods, which is followed by methods using data streams [1]. Cities are considered complex and open environments with multidimensional aspects including urban forms, urban imagery, and urban energy performance [2]. So, a platform that supports the dialogue between the user and the machine is crucial in urban modeling investigation compared to executing analytical urban programs to calculate answers. In previous studies, researchers applied computational modeling methods to interactively generate 3D city models and used street view images (SVI) for user behavior studies and urban analytics [3]. However, more studies that include those two aspects are still required to investigate generic urban modeling techniques for encoding complex phenomena and multidimensional urban study.  
Existing urban computational studies do not account for the integrated modeling of explicit knowledge and implicit knowledge, and there is a lack of efficient modeling approaches to include the geometrical and imagery dataset. A comprehensive urban computational modeling approach should allow a high efficiency, flexible interface with different input, process with heterogeneous data, and the provision of an instrument for navigation among the explorer spaces formed by existing projects. There is a lack of applications with data streams in urban design for  
* Integrating geometric and imagery aspects of computational modeling for urban design;
* Extracting condensed but informative feature vectors of urban cases for efficient machine learning implementation;
* Integration and interrelation of multi-source data for case retrieval from the built environment;
* Facilitating personal preference and intuition in case retrieval to bring humans in the loop.  
In this paper, we propose a method to navigate within cities using integrated geometric and imagery features (IGIF) and machine learning. We use the map data from the city of Nanjing, China as a dataset. We encode the geometrical-based space features and imagery features from a collection of observers' viewpoints, using Isovist and SVIs, respectively. The self-organizing map (SOM) and k-nearest neighbor (KNN) technology communicate the geometrical and imagery aspects. Our work demonstrates that IGIF helps probabilistic navigation among complex city phenomena. It supports urban design decision making and opens a novel perspective for a generic urban modeling method. The novel contributions of this paper are as follows:  
1. A feature extraction method for geometric and imagery aspects of urban space using IGIF which is condensed but informative, supporting efficient clustering;
2. Implementing a program that allows navigation among the cases in the built environment, creating an explorer space using SOM which can help design decision making;
3. A framework designed to facilitate new modes of UCM with data streams, allowing integration and interplay among heterogeneous data.