A method and system for analyzing user behavior activities and place significance characteristics based on NVivo

Abstract

A method and system for analyzing the characteristics of user behavior activities and place significance based on NVivo, involving the field of behavior activities and place significance characteristics analysis. The method solves the problem that existing methods are difficult to effectively extract the constituent elements of diverse behavior activities and place significance, identify the subtle hierarchical structure of behavior activities and place significance, and cannot accurately propose design countermeasures to improve the experience of urban public space and strengthen the emotional connection between people and land. The method includes: obtaining text information of behavior activities and place significance; constructing a text database for behavior activities and place significance; constructing a model of constituent elements of behavior activities and place significance; encoding and extracting the reliability and validity of the model of constituent elements of behavior activities and place significance; and using the verified model of constituent elements of behavior activities and place significance to perform characteristic analysis on the interactive relationship between behavior activities and place significance. It is applied to the field of user behavior activities and place significance measurement.

Description

Technical Field

[0001] The present invention relates to the field of analysis of behavioral activities and place significance characteristics, and in particular to a method for analyzing user behavioral activities and place significance characteristics based on NVivo. Background Art

[0002] Taking urban parks as an example, according to the China Statistical Yearbook - 2022 Annual Data, the total number of urban parks in my country was only 9,955 in 2010, and by 2021 the total number of urban parks in the country had grown to 22,062. This data shows that thousands of parks have been built in my country every year in the past decade. However, from the perspective of actual usage, the rapid construction process of urban public spaces in the past decade has failed to achieve the development goal of "quantity increase and quality improvement", which has led to huge differences in the degree of residents' love and recognition of different public spaces. Some public spaces have shown a relatively long-term attraction due to their high construction quality, while some public spaces have long-term problems of insufficient population vitality due to the difficulty of meeting the physical and mental experience needs of users due to their construction quality. The reason is that these public spaces with low population vitality are often dominated by supply in the entire process of design, construction and operation, and ignore the physical and mental experience needs of users' behavioral activities and emotional significance. As a result, although the number of public spaces has increased, it is still difficult to promote the establishment of deep emotional connections between people and public spaces.

[0003] From the perspective of user experience, we explore ways to actively promote the emotional connection between people and land, and then enhance residents' sense of belonging and identity, promote the transformation of urban public space from traditional "function providers" to advanced "emotional dependents", stimulate positive emotional connections between people and places, and help solve a series of human-land relationship problems in the process of urbanization. Therefore, accurately revealing the constituent elements and subtle structures of users' diverse behavioral activities and place meanings in urban public spaces, and analyzing the interactive relationship characteristics between behavioral activities and place meanings, so as to accurately identify behavioral activities and place meanings with different intensities of human-land contact, is crucial to understanding the reasons why users have better or worse experience feelings, and to improve and enhance the process of users' experience of urban public spaces in a targeted manner. It has become an issue that urgently needs widespread attention in the current field of humanized construction of urban public spaces.

[0004] At present, although many scholars have conducted extensive discussions on the investigation and measurement methods of place experience elements such as behavioral activities and place significance, the existing methods are mostly focused on the single dimension of behavioral activities or place significance, and pay less attention to the integrity and indivisibility of behavioral activities and place significance. At the same time, the existing methods are mainly based on observation, questionnaire and picture measurement methods, which are difficult to effectively extract the diverse behavioral activities and place significance elements that are constantly being constructed and reconstructed, and it is also difficult to identify the subtle hierarchical structure of the gradual evolution and coordinated development of behavioral activities and place significance, so as to accurately support the design countermeasures for improving the place experience of urban public spaces and strengthening the emotional connection between people and places. Therefore, the investigation and analysis of the behavioral activities and place significance of users of urban public spaces urgently needs the innovation of methods and tools. Summary of the invention

[0005] In view of the problem that existing methods are difficult to effectively extract the constituent elements of diverse behavioral activities and place meanings that are constantly being constructed and reconstructed, and to identify the subtle hierarchical structure of behavioral activities and place meanings that evolve layer by layer and develop in coordination, and thus cannot accurately propose design countermeasures to improve the place experience of urban public spaces and strengthen the emotional connection between people and places, the present invention proposes a method for analyzing the characteristics of user behavioral activities and place meanings based on NVivo, the method comprising:

[0006] S1: Obtain textual information about user behavior and place significance;

[0007] S2: Construct a text database for behavioral activities and place meanings based on text information;

[0008] S3: Randomly sample 2 / 3 of the data in the text database of behavior activities and place meanings to construct a model of the elements of behavior activities and place meanings;

[0009] S4: reliability and validity test of the coding and extraction results of the model of behavioral activities and place meaning components;

[0010] S5: Use the verified model of behavioral activities and place meaning components to conduct characteristic analysis on the interactive relationship between behavioral activities and place meaning.

[0011] Furthermore, a preferred method is proposed, wherein step S3 comprises:

[0012] Encode the randomly sampled 2 / 3 user behavior activities and place meaning original text data to obtain the summary of the behavior activities and place meaning keywords, phrases or short sentences to form the third-level nodes;

[0013] According to the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying, the third-level nodes with similar meanings and repeated occurrences are classified and integrated to form the second-level nodes;

[0014] A comparative analysis is conducted on the third-level nodes, second-level nodes and original text materials. The second-level nodes are classified according to the principle of the same field and integrated into the corresponding first-level nodes to complete the construction of the model of the elements that constitute behavioral activities and place meanings.

[0015] Furthermore, a preferred method is proposed. In the model of behavioral activities and place significance components, behavioral activities include three main categories: participatory observation behavior, place attention behavior, and high-intensity connection behavior with the place, as well as nine sub-dimensions: walking or running, facility activities, site activities, free activities, appreciating nature, relaxation and static rest, social interaction, regional characteristic activities, and cultural experience activities; place significance includes three main categories: sensory enjoyment significance, emotional connection significance, and self-exploration significance, as well as seven sub-dimensions: place adaptation, place characteristics, personal emotions, memory and nostalgia, cultural connection, strengthening of external social connections, and exploration of internal self-worth.

[0016] Furthermore, a preferred method is proposed, wherein step S4 comprises:

[0017] According to the theoretical saturation test method, the reserved 1 / 3 user behavior activities and place meaning original text data were coded and analyzed separately to analyze whether new behavior activities and place meaning concepts and categories appeared, and to test whether the coding results of the components of behavior activities and place meaning reached theoretical saturation;

[0018] The model of behavioral activities and place significance components was tested based on the Holsti inter-group reliability test.

[0019] Furthermore, a preferred method is proposed, wherein the model of the elements of behavioral activities and place significance is tested according to the Holsti inter-group reliability test, including:

[0020]

[0021] Among them, M is the number of codes that the two groups of coders maintain consistent during the coding process; N1 is the number of codes of the first group of coders; N2 is the number of codes of the second group of coders.

[0022] Furthermore, a preferred embodiment is proposed, wherein step S5 comprises:

[0023] The 9 types of behavior activities in the 3 main categories of behavior activities were created as first-level nodes, and under each first-level node of behavior activity mode, the 7 types of place meanings in the 3 main categories of place meanings were created as second-level nodes, forming a cross-coding matrix of behavior activity mode × place meaning category;

[0024] According to the cross-coding matrix, the original text data of each user's behavior and place significance is analyzed sentence by sentence, and a one-to-one correspondence is established between the behavior and place significance categories mentioned by each user, completing the cross-coding between behavior and place significance;

[0025] The number of cross-occurrences of different behavioral activities and place meaning categories within each main category is classified and counted to obtain the number of different types of place meanings carried by different behavioral activities.

[0026] Based on the same inventive concept, the present invention also proposes a user behavior activity and place significance feature analysis system based on NVivo, the system comprising:

[0027] A collection unit, used to collect text information about user behavior and the significance of the place;

[0028] A database construction unit, used for constructing a text database for behavior activities and place significance according to text information;

[0029] The model construction unit of the elements of behavior activities and place meaning is used to construct the model of the elements of behavior activities and place meaning from the data of 2 / 3 behavior activities and place meaning text database extracted by random sampling;

[0030] Verification unit, used to verify the reliability and validity of the coding and extraction results of the model of behavioral activities and place meaning components;

[0031] The feature analysis unit is used to perform feature analysis on the interactive relationship between the behavior activities and the place significance by using the verified model of the constituent elements of the behavior activities and the place significance.

[0032] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a method for analyzing user behavior activities and place significance characteristics based on NVivo as described in any one of the above items.

[0033] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program executes any of the above-mentioned methods for analyzing user behavior activities and place significance characteristics based on NVivo.

[0034] The present invention is beneficial in that:

[0035] Traditional methods are difficult to effectively extract diverse behavioral activities and place meaning components because they may exist in various forms in different types of text information. The present invention proposes a user behavioral activity and place meaning feature analysis method based on NVivo. By constructing a behavioral activity and place meaning component model through NVivo technology, these elements can be more effectively extracted from text information from different sources, because NVivo has powerful text analysis functions and can process text information in various forms. Traditional methods cannot accurately capture the evolutionary relationship and subtle hierarchical structure between behavioral activities and place meaning. The present invention proposes a user behavioral activity and place meaning feature analysis method based on NVivo. By constructing a behavioral activity and place meaning component model and conducting reliability and validity tests on it, the relationship between them can be more accurately revealed, thereby overcoming this problem. Since traditional methods cannot provide accurate feature analysis of behavioral activities and place meaning, they cannot support the formulation of design countermeasures. This method can provide more accurate support by using a verified behavioral activity and place meaning component model for feature analysis, thereby solving this problem.

[0036] The present invention proposes a method for analyzing the characteristics of user behavior activities and place significance based on NVivo. By constructing a text database of behavior activities and place significance, a large amount of text information can be effectively integrated and managed, providing a reliable data basis for subsequent analysis. By randomly sampling the data in 2 / 3 of the text database of behavior activities and place significance, and conducting reliability and validity tests on the constructed model of the components of behavior activities and place significance, the credibility and reliability of the research results can be improved. Using the verified model of the components of behavior activities and place significance to perform characteristic analysis on the interactive relationship between behavior activities and place significance can more accurately reveal the relationship and evolution law between them, and provide more precise support for the formulation of design countermeasures.

[0037] The present invention proposes a method for analyzing the characteristics of user behavior activities and place significance based on NVivo, and establishes a method for extracting and analyzing the interactive relationships of user behavior activities and place significance elements in urban public spaces based on the NVivo qualitative analysis tool. The method can effectively extract the constituent elements of diverse behavior activities and place significance that are constantly being constructed and reconstructed, and accurately identify the subtle hierarchical structure of the layer-by-layer evolution and coordinated development of behavior activities and place significance. The method has significant advantages in quantitatively analyzing the constituent elements of behavior activities and place significance and their interactive relationship characteristics, and can support the design strategies for improving the place experience of urban public spaces and strengthening the emotional connection between people and the land, so as to achieve the goal of humanized construction of urban public spaces.

[0038] The present invention is applied to the field of user behavior activities and place significance measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a method for analyzing user behavior activities and place significance characteristics based on NVivo as described in implementation mode 1. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0041] Implementation method 1, see Figure 1 This embodiment describes a method for analyzing user behavior activities and place significance characteristics based on NVivo, the method comprising:

[0042] S1: Obtain textual information about user behavior and place significance;

[0043] S2: Construct a text database for behavioral activities and place meanings based on text information;

[0044] S3: Randomly sample 2 / 3 of the data in the text database of behavior activities and place meanings to construct a model of the elements of behavior activities and place meanings;

[0045] S4: reliability and validity test of the coding and extraction results of the model of behavioral activities and place meaning components;

[0046] S5: Use the verified model of behavioral activities and place meaning components to conduct characteristic analysis on the interactive relationship between behavioral activities and place meaning.

[0047] Traditional methods have difficulty in effectively extracting diverse behavioral activities and elements that constitute the meaning of places, because they may exist in various forms in different types of text information. The method proposed in this embodiment uses NVivo technology to construct a model of behavioral activities and elements that constitute the meaning of places, and can more effectively extract these elements from text information from different sources, because NVivo has powerful text analysis capabilities and can process text information in various forms. Traditional methods cannot accurately capture the evolutionary relationship and subtle hierarchical structure between behavioral activities and place meanings. The method proposed in this embodiment overcomes this problem by constructing a model of behavioral activities and elements that constitute the meaning of places, and testing its reliability and validity, which can more accurately reveal the relationship between them. Since traditional methods cannot provide accurate feature analysis of behavioral activities and place meanings, they cannot support the formulation of design countermeasures. This method solves this problem by using a verified model of behavioral activities and elements that constitute the meaning of places to perform feature analysis, which can provide more precise support.

[0048] By constructing a text database of behavioral activities and place meanings, a large amount of text information can be effectively integrated and managed, providing a reliable data basis for subsequent analysis. By randomly sampling the data in 2 / 3 of the behavioral activities and place meanings text database and testing the reliability and validity of the constructed behavioral activities and place meanings component model, the credibility and reliability of the research results can be improved. Using the verified behavioral activities and place meanings component model to perform feature analysis on the interactive relationship between behavioral activities and place meanings can more accurately reveal the relationship and evolution law between them, and provide more precise support for the formulation of design countermeasures.

[0049] Implementation method 2: This implementation method further limits the method for analyzing user behavior activities and place significance characteristics based on NVivo described in implementation method 1. Step S3 includes:

[0050] Encode the randomly sampled 2 / 3 user behavior activities and place meaning original text data to obtain the summary of the behavior activities and place meaning keywords, phrases or short sentences to form the third-level nodes;

[0051] According to the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying, the third-level nodes with similar meanings and repeated occurrences are classified and integrated to form the second-level nodes;

[0052] A comparative analysis is conducted on the third-level nodes, second-level nodes and original text materials. The second-level nodes are classified according to the principle of the same field and integrated into the corresponding first-level nodes to complete the construction of the model of the elements that constitute behavioral activities and place meanings.

[0053] By encoding the original text data into summaries of keywords, phrases or short sentences of behavioral activities and place meanings, and forming third-level nodes, the construction of nodes is made more refined. Such refined nodes can better reflect the subtle differences and characteristics in the text information, which is helpful for subsequent analysis and integration. According to the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying, the third-level nodes with similar meanings and repeated occurrences are classified and integrated to form the second-level nodes. Such integration and classification process is more efficient, which can effectively reduce redundant information and extract more representative and key nodes. The third-level nodes, second-level nodes and original text data are compared and analyzed to further understand the inherent connections and characteristics in the text information. This comparative analysis helps to identify the relationship and mutual influence between nodes, and provides strong support for the construction of a more complete model of the elements of behavioral activities and place meanings. The construction of the model of the elements of behavioral activities and place meanings is completed by classifying the second-level nodes and integrating them into the corresponding first-level nodes. This step-by-step construction process can ensure the integrity and comprehensiveness of the model and ensure a comprehensive grasp and understanding of the behavioral activities and place meanings.

[0054] Implementation method three. This implementation method is a further limitation of the user behavior activity and place significance feature analysis method based on NVivo described in implementation method two. In the model of the elements of behavior activity and place significance, behavior activities include three main categories of participatory observation behavior, place attention behavior, and high-intensity connection behavior with the place, as well as nine sub-dimensions of walking or running, facility activities, site activities, free activities, appreciating nature, relaxation and static rest, social interaction, regional characteristic activities, and cultural experience activities; place significance includes three main categories of sensory enjoyment significance, emotional connection significance, and self-exploration significance, as well as seven sub-dimensions of place adaptation, place characteristics, personal emotions, memory and nostalgia, cultural connection, strengthening of external social connection, and exploration of internal self-value.

[0055] Specifically, this embodiment is explained in conjunction with the second embodiment. In NVivo 12 software, 2 / 3 of the original text materials of user behavior activities and place significance extracted by random sampling are analyzed piece by piece and sentence by sentence, so as to realize the summary and extraction process of behavior activities and place significance keywords, phrases or short sentences. If the same interviewee mentions a word, phrase or short sentence that is exactly the same or very similar multiple times, then this word, phrase or short sentence will only be coded once. All these extracted original information points constitute the third-level nodes of the behavior activities and place significance coding.

[0056] By repeatedly comparing the content of the third-level nodes and their connections, and taking into full account the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying them, the third-level nodes with similar meanings and repeated occurrences are classified and integrated to form the second-level nodes. Among them, the second-level nodes of the behavior activities of urban public space users are developed around 9 sub-dimensions: walking or running, facility activities, site activities, free activities, appreciating nature, relaxation and static rest, social interaction, regional characteristic activities, and cultural experience activities. The second-level nodes of the meaning of urban public space users' places are developed around 7 sub-dimensions: place adaptation, place characteristics, personal emotions, memory and nostalgia, cultural connections, strengthening external social connections, and internal self-value exploration.

[0057] After obtaining the coding results of the second-level and third-level nodes, it is necessary to repeatedly compare and analyze the extracted second-level and third-level nodes and the original materials, so as to classify each second-level node according to whether it belongs to the same field and integrate it into the corresponding first-level nodes, so as to establish the final three-level node coding system, that is, the constituent element model of behavioral activities and place meaning. Among them, the first-level nodes of the behavioral activities of urban public space users are developed around the three main category levels of participatory observation behavior, place attention behavior, and high-intensity connection behavior with the place, and the first-level nodes of the place meaning of urban public space users are developed around the three main category levels of sensory enjoyment meaning, emotional connection meaning, and self-exploration meaning.

[0058] Based on the three-level node coding results of behavioral activities and place significance, we finally established a behavioral activity component model with 3 main categories and 9 sub-dimensions, as well as a place significance component model with 3 main categories and 7 sub-dimensions, which objectively present the basic structure and process evolution level of the behavioral activities of urban public space users and the significance of place.

[0059] By breaking down behaviors and place meanings into main categories and sub-dimensions, this approach builds a more detailed and comprehensive model. The advantage of this classification is that it can more accurately capture and describe the characteristics of different behaviors and place meanings, making the analysis more refined and in-depth. According to this model, researchers can purposefully collect and analyze data related to each main category and sub-dimension. This targeted data collection and analysis helps to gain a deeper understanding of users' behaviors and perceptions in different places, so as to better grasp users' needs and preferences. With this detailed classification and dimensional division, the analysis results will be richer and more diverse. Researchers can better present and explain the relationship between different behaviors and place meanings, further explore the deep meaning behind the data, and provide a more comprehensive perspective for research. Understanding users' behaviors and perceptions in different places is essential for designing, planning, and managing environments with relevant places. This detailed model can provide more targeted advice and support for decision makers, helping them to better optimize place design, improve user experience, and formulate relevant policies and measures.

[0060] Embodiment 4: This embodiment further limits the method for analyzing user behavior activities and place significance characteristics based on NVivo described in Embodiment 1. Step S4 includes:

[0061] According to the theoretical saturation test method, the reserved 1 / 3 user behavior activities and place meaning original text data were coded and analyzed separately to analyze whether new behavior activities and place meaning concepts and categories appeared, and to test whether the coding results of the components of behavior activities and place meaning reached theoretical saturation;

[0062] The model of behavioral activities and place significance components was tested based on the Holsti inter-group reliability test.

[0063] In this embodiment, by adopting the theoretical saturation test method and conducting separate coding analysis on the reserved original text materials, it can better ensure that the constructed behavioral activities and place meaning model has sufficient theoretical saturation. This helps to verify the integrity and accuracy of the model, ensuring that all possible behavioral activities and place meaning concepts and categories are covered, thereby improving the credibility and applicability of the model. By testing the behavioral activities and place meaning component model through the Holsti inter-group reliability test, the coding consistency between different researchers can be evaluated. This helps to determine possible deviations or errors in the coding process and take appropriate measures to correct them, thereby improving the reliability and consistency of the data.

[0064] Implementation mode 5: This implementation mode further limits the method for analyzing the characteristics of user behavior activities and place significance based on NVivo described in Implementation mode 4. The method tests the model of the constituent elements of behavior activities and place significance based on the Holsti inter-group reliability test, including:

[0065]

[0066] Among them, M is the number of codes that the two groups of coders maintain consistent during the coding process; N1 is the number of codes of the first group of coders; N2 is the number of codes of the second group of coders.

[0067] Specifically, first, a detailed node coding guidance document needs to be developed to explain in detail the code, meaning and examples of each level of nodes, and to introduce in detail the specific operational procedures for coding each level of nodes. After that, another group of researchers needs to be invited to independently code all behavioral activities and place significance texts. After obtaining the coding results of behavioral activities and place significance of two different groups of people, the reliability of the coding results can be calculated. When the reliability of the coding results is higher than 90%, it is considered that the consistency level between the coding results of the two groups of coders for the behavioral activities and place significance categories is high, which also proves that the final constructed behavioral activities and place significance constituent element model has a high reliability. It should be further explained that when there are deviation items in the coding results of the two groups of coders for the behavioral activities and place significance categories, the two groups of members need to discuss and negotiate together to unify the deviation items in the place significance category. The reliability calculation formula is:

[0068]

[0069] Where: M is the number of codes that the two groups of coders maintain consistent during the coding process; N1 is the number of codes of the first group of coders; N2 is the number of codes of the second group of coders.

[0070] Embodiment 6: This embodiment further limits the method for analyzing user behavior activities and place significance characteristics based on NVivo described in Embodiment 3. Step S5 includes:

[0071] The 9 types of behavior activities in the 3 main categories of behavior activities were created as first-level nodes, and under each first-level node of behavior activity mode, the 7 types of place meanings in the 3 main categories of place meanings were created as second-level nodes, forming a cross-coding matrix of behavior activity mode × place meaning category;

[0072] According to the cross-coding matrix, the original text data of each user's behavior and place significance is analyzed sentence by sentence, and a one-to-one correspondence is established between the behavior and place significance categories mentioned by each user, completing the cross-coding between behavior and place significance;

[0073] The number of cross-occurrences of different behavioral activities and place meaning categories within each main category is classified and counted to obtain the number of different types of place meanings carried by different behavioral activities.

[0074] This implementation method supports the quantitative analysis of the interactive relationship characteristics between behavioral activities and place significance, revealing the subtle structure of the behavioral activities of urban public space users and the meaning of place. At the same time, by analyzing the main types of place significance carried by different behavioral activities and their hierarchical depth, the behavioral activities and place significance elements contained in different levels of human-land contact intensity are identified.

[0075] Embodiment 7: This embodiment describes a user behavior activity and place significance feature analysis system based on NVivo, the system comprising:

[0076] A collection unit, used to collect text information about user behavior and the significance of the place;

[0077] A database construction unit, used for constructing a text database for behavior activities and place significance according to text information;

[0078] The model construction unit of the elements of behavior activities and place meaning is used to construct the model of the elements of behavior activities and place meaning from the data of 2 / 3 behavior activities and place meaning text database extracted by random sampling;

[0079] Verification unit, used to verify the reliability and validity of the coding and extraction results of the model of behavioral activities and place meaning components;

[0080] The feature analysis unit is used to perform feature analysis on the interactive relationship between the behavior activities and the place significance by using the verified model of the constituent elements of the behavior activities and the place significance.

[0081] Embodiment 8. A computer device described in this embodiment includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a method for analyzing user behavior activities and place significance characteristics based on NVivo as described in any one of Embodiments 1 to 6.

[0082] Implementation method nine: A computer-readable storage medium described in this implementation method is characterized in that the computer-readable storage medium is used to store a computer program, and the computer program executes a user behavior activity and place significance feature analysis method based on NVivo as described in any one of implementation methods one to six.

[0083] Implementation 10: This implementation provides a specific example of the method for analyzing user behavior activities and place significance characteristics based on NVivo described in Implementation 1, and is also used to explain Implementation 2 to Implementation 7. Specifically:

[0084] Step 1: Collect information about user behavior and place significance:

[0085] (1) Develop an interview outline based on the survey needs of the behavioral activities of urban public space users and the significance of places. All questions in the interview outline are open-ended questions. Interview questions on behavioral activities include "What type of behavioral activities do you most often engage in in this place?", "What kind of participatory observation behaviors do you engage in in this place?", "What kind of place attention behaviors do you engage in in this place?", "What kind of behaviors with high intensity of place connection do you engage in in this place?", so as to gradually collect in-depth text data on the interviewees' descriptions of behavioral activities. Interview questions on the significance of places include "What is the special meaning of this place to you?", "What kind of sensory enjoyment do you have for this place?", "What kind of emotional connection do you have for this place?", "What kind of self-exploration do you have for this place?", so as to gradually collect in-depth text data on the interviewees' descriptions of the significance of places.

[0086] Table 1 Interview outline

[0087]

[0088] (2) Based on the interview outline, semi-structured interviews were conducted in a one-to-one manner. The survey process of the five urban public spaces lasted a total of 16 weeks, with two working days and two rest days selected each week for investigation. At the same time, the survey time of the day was divided into three time periods: 7:00-11:00, 13:00-17:00, and 18:00-20:00, with a total of 10 hours of investigation per day. This allows the survey process to cover as many people as possible who come to public spaces for activities at different times. In the end, a total of 787 sample data were collected from the five public spaces, and each interviewee was interviewed for 30 to 45 minutes. In this way, in-depth interviews were conducted to explore the actual behavioral activities and place significance of the respondents in their daily use of public spaces.

[0089] (3) After the investigation was completed, the recordings and recorded statements of all interviewees were transcribed and organized to prepare data for the next step of specific analysis. For the interview process on behavioral activities, a total of about 11,800 words of interview text were collected; for the interview process on the meaning of place, a total of about 80,000 words of interview text were collected.

[0090] Step 2: Build text data on behavioral activities and place meanings for qualitative analysis in NVivo:

[0091] (1) A total of 787 pieces of behavioral activity text data (about 11,800 words) were collected from the sample public spaces using semi-structured interviews. Two-thirds of the behavioral activity text data (a total of 525 pieces, about 7,900 words) were randomly sampled for coding analysis and model construction, and the remaining 1 / 3 of the behavioral activity text data (a total of 262 pieces, about 3,900 words) was used for theoretical saturation testing.

[0092] (2) A total of 787 pieces of text data (approximately 80,000 words) on the meaning of place were collected from sample public spaces using semi-structured interviews. Two-thirds of the text data (525 pieces in total, approximately 53,000 words) were randomly sampled for coding analysis and model construction, and the remaining one-third of the text data (262 pieces in total, approximately 27,000 words) were used for theoretical saturation testing.

[0093] Step 3: Encoding and extracting the elements of behavioral activities and place meaning:

[0094] (1) In NVivo 12 software, by coding 2 / 3 of the randomly sampled behavioral activity text materials (a total of 525 pieces), more than 100 original information points about behavioral activities can be extracted to form the third-level nodes of behavioral activity coding; by repeatedly comparing the content of the third-level nodes and their connections, and taking into full consideration the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying them, the third-level nodes can be summarized into 9 sub-dimensions, namely walking or running, facility activities, free activities, relaxation and static rest, appreciation of nature, regional characteristic activities, cultural experience activities, venue activities, and social interaction, to form the second-level nodes of behavioral activity coding; then, the extracted second-level and third-level nodes and the original materials are repeatedly compared and analyzed, so that each second-level node is classified and integrated into three main category levels according to the principle of whether it belongs to the same field, namely participatory observation behavior, place attention behavior, and high-intensity contact behavior with the place, to form the first-level nodes of behavioral activity coding. The final three-level node coding system is the constituent element model of urban public space user behavior activities.

[0095] Table 2 The three-level node coding system for user behavior activities

[0096]

[0097]

[0098] (2) In NVivo 12 software, by coding 2 / 3 of the randomly sampled place meaning text materials (a total of 525 pieces), more than 80 original information points about place meaning can be extracted, which constitute the third-level nodes of place meaning coding; by repeatedly comparing the content of the third-level nodes and the connections between them, and taking into full consideration the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying them, the third-level nodes can be summarized into 7 sub-dimensions, namely place adaptation, place characteristics, personal emotions, memory and nostalgia, cultural connection, strengthening of external social connections, and internal self-value exploration, which constitute the second-level nodes of place meaning coding; then, the extracted second-level and third-level nodes and the original materials are repeatedly compared and analyzed, so that each second-level node is classified and integrated into three main category levels according to the principle of whether it belongs to the same field, namely sensory enjoyment meaning, emotional connection meaning, and self-exploration meaning, which constitute the first-level nodes of place meaning coding. The final three-level node coding system is the constituent element model of place meaning for urban public space users.

[0099] Table 3 Three-level node coding system for user location meaning

[0100]

[0101] Step 4: Reliability and validity test of coding and extraction results:

[0102] (1) Based on the theoretical saturation test method, in NVivo 12 software, after separate coding and analysis of the reserved 1 / 3 original text data of behavioral activities and place significance (a total of 262 copies), it was found that no new concepts and categories of behavioral activities and place significance appeared, indicating that the construction of the behavioral activities and place significance categories of urban public space users in step 3 has reached theoretical saturation. The three-level node system shown in Tables 2 and 3 can better cover and characterize user behavioral activities and place significance.

[0103] (2) Prepare a detailed coding guide document, including the code, description and examples of each node level, invite another group of researchers to independently code all 787 interview texts, and conduct Holsti inter-rater reliability test on the coding results. The reliability calculation formula is:

[0104]

[0105] Where: M is the number of codes that the two groups of coders maintain consistent during the coding process; N1 is the number of codes of the first group of coders; N2 is the number of codes of the second group of coders.

[0106] After independent coding by two groups of people, the coding reliability was calculated as:

[0107]

[0108] The coding reliability was 94%, indicating that the coding results of the two groups of coders were highly consistent and reached an acceptable level. It also proved that the model of behavioral activities and place meaning components was reasonable. The deviation items in the coding were unified by the two groups through consultation.

[0109] Step 5: Analysis of the interactive relationship characteristics between behavioral activities and place significance:

[0110] (1) In NVivo 12 software, the nine behavioral activity modes within the three main categories of behavioral activities were created as first-level nodes, and under each first-level node of behavioral activity mode, the seven place meanings within the three main categories of place meaning were created as second-level nodes. Finally, a cross-coding matrix of "behavioral activity mode (9 columns) × place meaning category (7 rows)" was formed.

[0111] (2) Based on the cross-coding matrix, by analyzing the original text data of each user's behavior and place significance sentence by sentence, a one-to-one correspondence is established between the behavior and place significance categories mentioned by each user, so as to achieve cross-coding between behavior and place significance. The results of the cross-coding analysis of the behavior and place significance of 787 respondents in the five sample public spaces are shown in Table 4.

[0112] Table 4 Results of cross-coding analysis of behavioral activities and place significance

[0113]

[0114]

[0115] (3) After completing the cross-coding between behavioral activities and place meanings, the number of cross-occurrences between different behavioral activities and place meaning categories within each main category is classified and counted to obtain the number of different types of place meanings carried by different behavioral activities.

[0116] From the results, for the behavioral activity elements of "walking or running", "facility activities", "free activities", and "relaxation and static rest", the place meanings they carry are mainly "sensory enjoyment" and "emotional connection", accounting for about 80% each. Among them, the number of sensory enjoyment meanings carried by "walking or running", "free activities", and "relaxation and static rest" is the highest among all behavioral activity elements, accounting for more than 40%.

[0117] For the behavioral activity elements of "appreciating nature", "winter ice and snow activities" and "cultural experience activities", the place meanings they carry are also mainly "sensory enjoyment" and "emotional connection", accounting for about 80% each. However, the difference is that the number of emotional connection meanings carried by these three types of behavioral activity elements is the highest among all behavioral activity elements, accounting for more than 40%.

[0118] For the "site activities" and "social interaction" behavioral activity elements, the three types of place meanings they carry are relatively balanced. Among them, the "self-exploration" meaning carried by these two types of behavioral activities is the highest among all behavioral activity elements, accounting for 32.2% and 34.1% respectively.

[0119] Judging from the overall results of the cross-analysis of behavioral activities and place significance, each type of behavioral activity carries all types of place significance, which shows that the place significance carried by similar behavioral activities is extremely complex. Even if the behavioral activities are exactly the same, the intensity of human-land contact they reflect may be significantly different.

[0120] Further analysis of the mean number of place meanings carried by various types of behavioral activities revealed that as the level of behavioral activities deepened, the place meanings generated by users became richer and richer. For example, facility activities carried an average of 3.47 secondary categories of place meanings, while social interactions carried an average of 4.29 secondary categories of place meanings.

[0121] At the same time, although each type of behavior activity carries all types of place meanings, the main types of place meanings carried by different levels of behavior activities (i.e., the accompanying place meaning level depth) are obviously different. Overall, with the continuous improvement of the level of behavior activities, the proportion of sensory enjoyment meanings carried by various behavior activity elements gradually decreases, the proportion of emotional connection meanings first increases and then decreases, and the self-exploration meanings carried by various behavior activity elements continue to increase.

[0122] Although preferred embodiments of the present disclosure have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present disclosure. Obviously, those skilled in the art may make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

[0123] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A method for analyzing user behavior activities and place significance characteristics based on NVivo, characterized by: The method comprises: S1: Obtain textual information about user behavior and place significance; S2: Construct a text database for behavioral activities and place meanings based on text information; S3: Randomly sample 2 / 3 of the data in the text database of behavior activities and place meanings to construct a model of the elements of behavior activities and place meanings; S4: reliability and validity test of the coding and extraction results of the model of behavioral activities and place meaning components; S5: Use the verified model of behavioral activities and place meaning components to conduct characteristic analysis on the interactive relationship between behavioral activities and place meaning; Step S3 includes: Encode the randomly sampled 2 / 3 user behavior activities and place meaning original text data to obtain the summary of the behavior activities and place meaning keywords, phrases or short sentences to form the third-level nodes; According to the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying, the third-level nodes with similar meanings and repeated occurrences are classified and integrated to form the second-level nodes; Compare and analyze the third-level nodes, second-level nodes and original text data, classify the second-level nodes according to the principle of the same field, and integrate them into the corresponding first-level nodes to complete the construction of the model of the elements of behavioral activities and place meaning; In the model of behavioral activities and place significance components, behavioral activities include three main categories: participatory observation behavior, place attention behavior, and high-intensity connection behavior with the place, as well as nine sub-dimensions: walking or running, facility activities, site activities, free activities, appreciating nature, relaxation and static rest, social interaction, regional characteristic activities, and cultural experience activities; place significance includes three main categories: sensory enjoyment significance, emotional connection significance, and self-exploration significance, as well as seven sub-dimensions: place adaptation, place characteristics, personal emotions, memory and nostalgia, cultural connection, strengthening of external social connections, and internal self-value exploration.

2. The method for analyzing user behavior and place significance characteristics based on NVivo according to claim 1, characterized in that: Step S4 includes: According to the theoretical saturation test method, the reserved 1 / 3 user behavior activities and place meaning original text data were coded and analyzed separately to analyze whether new behavior activities and place meaning concepts and categories appeared, and to test whether the coding results of the components of behavior activities and place meaning reached theoretical saturation; The model of behavioral activities and place significance components was tested based on the Holsti inter-group reliability test.

3. The method for analyzing user behavior and place significance characteristics based on NVivo according to claim 2, characterized in that: The model of behavioral activities and place meaning components was tested according to the Holsti inter-group reliability test, including: ×100% Among them, M is the number of codes that the two groups of coders maintain consistent during the coding process; N1 is the number of codes of the first group of coders; N2 is the number of codes of the second group of coders.

4. The method for analyzing user behavior activities and place significance characteristics based on NVivo according to claim 1, characterized in that: Step S5 includes: The 9 types of behavior activities in the 3 main categories of behavior activities were created as first-level nodes, and under each first-level node of behavior activity mode, the 7 types of place meanings in the 3 main categories of place meanings were created as second-level nodes, forming a cross-coding matrix of behavior activity mode × place meaning category; According to the cross-coding matrix, the original text data of each user's behavior and place significance is analyzed sentence by sentence, and a one-to-one correspondence is established between the behavior and place significance categories mentioned by each user, completing the cross-coding between behavior and place significance; The number of cross-occurrences of different behavioral activities and place meaning categories within each main category is classified and counted to obtain the number of different types of place meanings carried by different behavioral activities.

5. A user behavior activity and place significance feature analysis system based on NVivo, characterized by: The system comprises: A collection unit, used to collect text information about user behavior and the significance of the place; A database construction unit, used for constructing a text database for behavior activities and place significance according to text information; The unit for constructing the model of constituent elements of behavior activities and place meaning is used to construct the model of constituent elements of behavior activities and place meaning using the data randomly extracted from the 2 / 3 behavior activities and place meaning text database, including: encoding the 2 / 3 original text data of the behavior activities and place meaning of users randomly extracted, obtaining the summary of the keywords, phrases or short sentences of the behavior activities and place meaning, and forming the third-level nodes; According to the similarity of the third-level nodes in the conceptual dimension and the possibility of merging and classifying, the third-level nodes with similar meanings and repeated occurrences are classified and integrated to form the second-level nodes; Compare and analyze the third-level nodes, second-level nodes and original text data, classify the second-level nodes according to the principle of the same field, and integrate them into the corresponding first-level nodes to complete the construction of the model of the elements of behavioral activities and place meaning; Verification unit, used to verify the reliability and validity of the coding and extraction results of the model of behavioral activities and place meaning components; A feature analysis unit, for performing feature analysis on the interactive relationship between the behavior activity and the place meaning by using the verified model of the constituent elements of the behavior activity and the place meaning; In the model of behavioral activities and place significance components, behavioral activities include three main categories: participatory observation behavior, place attention behavior, and high-intensity connection behavior with the place, as well as nine sub-dimensions: walking or running, facility activities, site activities, free activities, appreciating nature, relaxation and static rest, social interaction, regional characteristic activities, and cultural experience activities; place significance includes three main categories: sensory enjoyment significance, emotional connection significance, and self-exploration significance, as well as seven sub-dimensions: place adaptation, place characteristics, personal emotions, memory and nostalgia, cultural connection, strengthening of external social connections, and internal self-value exploration.

6. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a method for analyzing user behavior activities and place significance characteristics based on NVivo as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program executes the method for analyzing user behavior activities and place significance characteristics based on NVivo as described in any one of claims 1 to 4.

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