# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 2 Framework
### Feature Extraction  
#### 2.3.1 Street Visual Features  
Excepting these _hard_ factors, the _soft_ factors, such as visual characteristics of a community, which reflect the safety, lively, depressing, or beauty of the neighborhood environment, also have an impact on the house price [24; 25; 26]. The DeepLab V3+ [27; 28] deep learning framework was adopted for the semantic segmentation and extracting of visual features from BSV images. Ten categories of visual elements, _road_, _sidewalk_, _sky_, _building_, _vegetation_, _vehicle_, _person_, _traffic_sign_, _pole_, and _fence_, were extracted from BSV images, as shown in Figure 2.  
\begin{table} \begin{tabular}{c c c} \hline \hline
**Datasets** & **Description** & **Data Source** \\ \hline House prices & Including area, date, total prices & Lianjia \\ POIs & Including name, type, location & Amap \\ Road networks & Road, community boundaries & Open Street Map \\ BSV images & Baidu Street View images & Baidu Maps \\ \hline \hline \end{tabular}   
* **House price data**. House price data comes from a popular online real estate website—Homelink—which mainly describes the structural attributes of houses. Each record of house prices includes the location, area, unit price, etc. A total of 20,785 records in Beijing were obtained after processing and 20,098 records in Chongqing were obtained after processing. * **POI data**. Typically, locational amenities refer to the facilities near the house. Here, we use the POI information to show the location characteristics of a nearby house. POI data were collected from Amap, which consists of nine subcategories: primary schools, parks, subway stations, etc. Finally, 13,532 POI records were obtained in Beijing and 8918 POI records in Chongqing. * **Road network data**. The road network data was downloaded from the Open Street Map (OSM). After extracting five types of roads, including highways, primary, secondary roads, tertiary roads, and residential roads, 4412 roads within the fifth ring road of Beijing and 2137 roads in the main urban area of Chongqing were obtained. Combined with the distribution of the neighborhood and the connectivity of the road network, the study area was divided into 1640 research units and 747 research units, respectively. * **BSV images data**. BSV images are downloaded by utilizing the Baidu street view API. BSV images have been widely used to describe the physical settings of urban environments and neighborhoods. A set of geo-referenced sampling points are generated along the roads with a fixed distance interval of 50 m. Finally, 64,569 panorama images in Beijing were collected after removing pictures of indoor or abnormal through visual inspection, and 38,987 images were obtained in Chongqing. The time and volume at which the data sources were collected have been listed in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Collecting Time**} & \multicolumn{2}{c}{**Volume**} \\ \cline{3-4}  & & **Beijing** & **Chongqing** \\ \hline House prices & May 2021 & 20,785 records & 20,098 records \\ POIs & July 2021 & 13,532 records & 8918 records \\ Road networks & July 2021 & 4412 edges & 2137 edges \\ BSV images & September 2019 & 64,569 images & 38,987 images \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time and volume of the data sources. Based on the existing studies and related theories of house prices [29], this paper proposed six visual indices using the information extracted from the BSV images. The detailed computation method for each index is as follows. * _Green View Index (GVI), Building View Index (BVI), Sky View Index (SVI)._ Existing researches have proven that using street view images can explore the relationships between a set of visual elements and its social-economic attributes, such as the crime rate, the property value, and the population density, well [30; 31]. Referring to the GVI calculation formula proposed by [32; 33; 34], the calculation formulas of BVI and SVI are proposed, respectively.