## 2 Literature Review
### Data Streams and Feature Extraction in UCM  
Models are regarded as representations of reality [4]. In other words, models using cellular automata, agent-based systems, and shape grammar are popular and powerful in many applications: for instance, land use, mobility, spatial planning, and so on. They require a set of properties to define the inquiry objects. An example of replicating a specific urban form is the construction of a multi-agent system computing properties and their interrelationships that represent the village morphology, roadwork pattern, and building structure form [8]. Integer programming is used for generating optimized urban and building layouts that meet the sunlight requirement, where the buildings are encoded as grid-based infill modules, by translating the spatial planning problem under the limitation of sunlight requirements to a packing problem in a fixed area [9]. Stream-line-based shape grammar for site splitting is used for generating street and parcel layouts where the spatial planning problem is represented by a subdivision problem [10; 11]. With the rapidly growing accessibility of digital data, researchers in various fields widely use urban remote sensing and geospatial data processed by machine learning techniques. Cartographic data and street view imagery are ingrained as important urban data sources [3]. Analytical cartography is essential for urban planning and morphology to illustrate spatial patterns. The urban form visualizations are comprehensive information artifacts that reflect urban complexity [12]. Therefore, researchers have made efforts to build collaborative spatial information systems for exploring urban fabric patterns and spatial order to inform urban planning and urban design [13; 14]. Apart from geometry aspects, visual appearance is closely related to urban perception. It is relatively implicit knowledge but important for understanding the perception of spaces, such as greenery [15], understanding symmetries of urban blocks and so on [16]. SVI is used in a significant number of urban perception studies because it enables characterizing urban space from a human perspective. One of the main challenges in UCM with data streams lies in integrating heterogeneous data from multiple sources [17]. Therefore, in a technical view, the challenge is to find a generic feature extraction mechanism for data in different formats (geometric data, pictorial data, etc.). Feature engineering and clustering approaches have the potential for going over urban dimensionality to multi-source integration. Feature extraction is an essential step for extracting samples' features that are useful for clustering. Multiple quantitative approaches are implemented for extracting urban features. Researchers use pre-proposed indicators (e.g., semantic indicators, geometrical indicators) to index urban spatial characteristics for the follow-up studies [18]. The indicators are decided depending on the specific task and should be verified by experiments. So, it takes effort to decide on the indicators. Classic statistical indicators for urban morphology include the \"plot size\", the plot \"edge length\", \"Size/Edge2\", \"GSI\" and \"FSI\". Using compressed feature vectors from neural networks provides a flexible way to comprehensively represent urban fabrics that excludes the artificial selection of morphological indicators [19]. The visual field, isovist, refers to visibility that represents the size of a space that people visually perceive. Isovist is popular in analyzing visibility in space, taking human-centric perception to describe spatial characteristics [20; 21]. In a convolution neural network (CNN), the feature mapping of the images extracts the input's underlying features by convolution kernels, promoting deep learning algorithms in recent decades [22]. Neural networks are applied to solve problems such as the prediction of energy performance [23], the pattern recognition of 2D images [24], as well as typological form-finding on 3D models [25].