## 4 Research Process
### Data Extraction by Classification
#### 4.2.1 Citizen Sensing by User Check-In  
The extraction system used for citizen feelings was a website designed via a responsive web design (RWD) that could operate on a desktop, laptop, or mobile device. To indicate how the citizens felt about the quality of their location in the city, they only had to check-in and answer a questionnaire on the system. The system collected the coordinates and evaluations obtained from the users' check-in marks. The questionnaire was designed to describe the urban planning of a specific zone and to quantify it with countable levels. There were five levels to score how citizens felt regarding the quality of the environment (with zero being the lowest level, and four being the highest level: excellent). When describing a city's physical environment, the more infrastructure there is, the higher the score for the check-in is. In this way, we can acquire the scores of citizen feelings toward the environment as well as their location coordinates, at the same point in the raw data. In the next step, the hypothesis that traffic accessibility is related to the urbanization was verified,  
Figure 4: Prototype display of the citizen-sensing system: (**a**) checking-in on the social network and obtaining current location information on a mobile device; (**b**) collecting citizen feelings through questionnaire after marking location coordinates. and users were asked about their feelings for both of these factors in the questionnaire through the algorithm. 2.2 Quantifying Urban Spatial Data: Turning Citizens' Feelings into Values through the Questionnaire  
According to _The Image of the City_([PERSON], 1960) [3], the elements of the urban spatial structure are classified as paths, edges, districts, nodes, and landmarks. We referenced this rule to classify the elements of the urban spatial structure, which enabled us to design the questionnaire and to sort the information obtained from the user check-ins. The raw data from the completed questionnaires was analyzed to build the urban appearance through citizens' feelings, according to the description of their locations. The questionnaire quizzes included several levels to rate the environment: traffic, buildings, and general feelings. For effective data analysis, the questionnaire was designed to quantify the descriptions of feelings about the environment. Several examples of questions taken from the questionnaire are shown in Table 2.  
Traffic accessibility is an index of the urban spatial elements and is connected to the urban physical environment. In the experiment, we supposed that citizens' feelings about the urban environment change with the spatial construction and traffic. Accordingly, the questionnaire requested the bulk and traffic infrastructure details of the users' locations. We took the information from the questionnaire and compared it to the evaluation between the urban spatial construction and the citizens' feelings. Therefore, according to the quantification rules, the higher the traffic accessibility of a facility was, the higher the score it received. For example, for the transportation choices, bicycles received one point, scooters received two points, and public transport and motorized vehicles received three points. The users gave a score according to their feelings. Figure 5: Check-in marks on the map on the website. The spots show how the users feel regarding the environment. Table 3 presents the functional server-side definitions for the seven questions given to users. Q1 refers to the traffic level selected by the user when checking in. Q2 to Q4 are descriptions of traffic accessibility, Q5 and Q6 are descriptions of urbanization, and Q7 examines the subjective feelings of the user. The system recorded the user number (ID), check-in time (Time), IP address (IP), and the position of the mark (Coordinate) when the users checked-in. Each mark annotated the location, time, identity, and value each user provided. We then analyzed the data of citizen feelings.