# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Eye Movement Data Preprocessing  
Following the experiment, raw eye-tracking data were processed using Pupil Player (Braincraft, Beijing, China) and eyeseso Studio 6.23 (Braincraft, Beijing, China) to ensure data integrity and consistency. Data segmentation, primarily conducted in Pupil Player, involved multiple steps, including blink removal, target area identification, and video cropping. The segmented data were then imported into eyeseso Studio for further analysis. Subsequent procedures included gaze statistics, definition and editing of Areas of Interest (AOIs), generation of attention heatmaps (as illustrated in Figure 7), and visualization of eye movement trajectories. The software extracted 31 specified signal parameters and exported them as CSV files. The hotspot map analysis algorithm calculates the aggregated Gaussian distribution of each fixation point on a given visual stimulus. Assuming a mean \(\mu=0\), the standard deviation \(\sigma\) and the co-directional distribution are computed using the following Formulas (1):  
\[f(x,y)=\frac{e^{\frac{x^{2}+y^{2}}{2\sigma^{2}}}}{2\pi\sigma^{2}},x,y\in[-s,s] \tag{1}\]  
The standard deviation \(\sigma\) is internally defined as \(\sigma=\text{s}/5\). For each fixation, every element of the kernel template is scaled by a weight proportional to that fixation's duration. These weighted kernels--anchored at their respective fixation coordinates--are then summed into an array representing stimulus intensity. Finally, the array is normalized, yielding stimulus magnitude and height maps (topographic maps).  
Attention heatmaps employ a two-dimensional Gaussian kernel convolution to visually depict subjects' attention distribution across the stimulus. Red denotes areas of concentrated focus, while yellow and green indicate lower attentiveness. From these maps, we observe the following:  
(1) Architectural landscapes: All three villages exhibit focal fixation on discrete features--wall details in Xutun Village, landmark edifices in Yangquan Circle Village, and dispersed points of interest in Shabao Village--underscoring the visual salience of unique or intricate architectural elements.  
(2) Water landscapes: Hotspots arise at feature boundaries or protrusions--the stream banks in Xutun Village, cliff faces along Yangquan Circle Village's shore, and open water in  
Figure 7: Eye movement hot spot map. The concentration of gaze decreases from red and yellow to green.  
Shabao Village--suggesting that contrasts between dynamic/static and smooth/abrupt forms guide gaze behavior.  
(3) Vegetation landscapes: Attention clusters correspond to variations in color, stratification, and morphology--layered vegetation in Xutun Village, distinctive species in Yangquan Circle Village, and density fluctuations in Shabao Village--demonstrating that biodiversity and structural diversity capture visual interest.  
(4) Road landscapes: Both linear pathways and discrete roadside elements shape eye trajectories--unique road alignments in Xutun Village, signage in Yangquan Circle Village, and surrounding objects in Shabao Village--highlighting the combined influence of spatial guidance and salient features on gaze patterns.