Urban community public space residence vitality measurement method based on space-time behaviors

Abstract

The invention discloses an urban community public space residence vitality measurement method based on spatiotemporal behaviors, and the method is characterized in that the method comprises the following steps: S1, determining a sample; s2, defining a space range; s3, acquiring resident resident activity data; s4, determining community public space information; s5, calculating a residence vitality index; S6, constructing a residence vitality measurement model; and S7, introducing the data and calculating the residence vitality of the public space of each community. According to the method, data acquisition, statistics and analysis are carried out based on actual daily travel behaviors of community residents, and the resident activity distribution condition of the community public space obtained through representation can more truly describe the use condition of the residents on the community public space, so that the actual demands of the residents are mined, and the resident experience is improved. Therefore, the rationality and effectiveness of urban community public space planning are evaluated more accurately, and targeted technical guidance and theoretical support are provided for updating and optimization of the mountain community public space.

Description

Technical Field

[0001] The present invention relates to the technical field of urban area vitality detection statistics, and in particular to a method for measuring the resident vitality of urban community public spaces based on spatiotemporal behavior. Background Art

[0002] Community renewal is a major livelihood project and development project that the country attaches great importance to. Community public space is an important part of community renewal. Community public space is a complex spatial system composed of spatial units of different scales, functions and forms, including public spaces within the community and squares, parks, and lane spaces within the 15-minute community life circle, as well as small spaces such as street corner green spaces and informal gaps. The resident activities generated based on the preference for the environment can truly reflect the attractiveness and friendliness of the environment. The resident vitality describes the vitality of the space from the aspects of the intensity, time consumption and diversity of the resident activities, and is a key indicator that effectively reflects the quality of public space. Starting from the spatiotemporal behavior of micro-individual residents, measuring the resident vitality of community public space and exploring its spatial distribution characteristics can more accurately recognize the actual needs of residents for community public space, and then provide technical support for analysis and evaluation and theoretical guidance for strategy formulation for the renewal practice of community public space.

[0003] There are three main methods for measuring residence vitality. The first is the indicator method, which mainly examines the residence rate, residence density or number of residents in the area to be evaluated as the basis for judgment. The second is the assignment method, which mainly classifies and grades the residence activities and assigns values for calculation. The third is the residence time method, which describes the residence vitality by the length of time people stay in the evaluation area.

[0004] The above methods still have the following shortcomings: 1. In terms of perspective, they all evaluate the public spaces that have been determined, and fail to identify the actual use of community public spaces by residents from the perspective of residents' spatiotemporal behavior, which is not conducive to accurately exploring the real needs of residents for community public spaces. 2. In terms of data, although the currently commonly used big data methods such as Baidu heat map data and mobile phone signaling data can obtain a large amount of behavioral data in a short period of time, there are many missing items, such as the basic socioeconomic characteristics of the actors, such as age and gender, the length of time the users stay in a certain place, and the specific activities they carry out. Therefore, it is difficult to finely analyze the differences in spatiotemporal behavior patterns and activity space needs of different age groups, and it is impossible to identify the resident activities that are truly conducive to neighborhood interaction and then conduct analysis. 3. In terms of measurement, the measurement method of resident vitality considers fewer dimensions, and the accuracy of the measurement results needs to be improved. Summary of the invention

[0005] Aiming at the deficiencies of the above-mentioned existing technologies, the technical problem to be solved by the present invention is: how to provide a method for measuring the residence vitality of urban community public spaces based on spatio-temporal behavior, which can accurately measure the residence vitality of community public spaces, better reflect the use of community public spaces by residents, evaluate the quality of community public spaces, and better understand the activity needs of community residents, so as to provide targeted strategies for the renewal and optimization of urban community public spaces.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for measuring the residence vitality of urban community public spaces based on spatio-temporal behavior, characterized by including the following steps:

[0008] S1 Determine the samples. Using the method of stratified sampling, for the residents of the residential community (i.e., the community) to be studied, with age as the stratification variable, samples are taken at specific ratios respectively as the survey objects.

[0009] Further, in step S1, the age groups are stratified based on the population census data of the sub-district office where the residential community is located, and samples are taken at a ratio of not less than 1% for each age group, with an equal male-female ratio.

[0010] S2 Define the spatial range. Considering the influence of the urban terrain and transportation network on the walking living circle, the network analysis method in Arc GIS (geographic information system software) is used, and a terrain model is introduced. Starting from the main entrance of the community to be studied, the boundary of the area reachable by a specific walking path distance is calculated as the research spatial range.

[0011] Further, in step S2, the boundary of the 1000-meter walking path distance area is used as the research spatial range. Such a 1-kilometer distance is exactly the distance that a person walks in about 15 minutes, which is a suitable distance for community residents to use as a surrounding activity, leisure and entertainment place.

[0012] S3 Obtain residents' residence activity data. For the survey objects selected in step S1, GPS recorders are distributed. The GPS recorders immediately record the spatial coordinates of the survey objects, and obtain the activity data information during non-working hours on at least one working day and one non-working day. The activities with a residence duration exceeding the specified time are identified as residence activities, and the corresponding spatial points are identified as residence points; then, taking the selected survey objects as interviewees, their demographic information is obtained, including age, gender, household income, education level, and activity content at the residence points, to construct a residence activity database for mountain community public spaces.

[0013] Further, in step S3, the activities with a residence duration exceeding 10 minutes are identified as residence activities. If the duration is too short, it is easy to cause misjudgment and reduce the accuracy.

[0014] S4 determines the information of community public spaces. Based on the spatial scope defined in step S2, through the vector map data and POI data (Point of Interest data, which refers to the data used to describe physical locations with specific meanings or attractions in a geographic information system. These locations can be commercial facilities, public facilities, transportation facilities, cultural attractions, etc., such as restaurants, gas stations, bus stops, parks, museums, etc.) of the global open-source map platform (OpenStreetMap), identify the stay points recorded in step S3 and name them, determine them as the community public spaces to be counted, obtain their geographical coordinates and spatial boundaries, and determine their regional areas in the geographic information system software (Arc GIS Pro);

[0015] Furthermore, in step S4, for the stay points whose names cannot be confirmed by the map data and POI data, name them according to the agreed names of the survey objects. In this way, starting from the actual living conditions of residents, the statistical objects of this method can better cover the locations that are not reflected in the map and POI data but have actually become the public activity spaces of residents, making the measurement results obtained by this method better reflect the real usage situation of urban community public spaces.

[0016] S5 calculates the stay vitality indicators. Based on the resident stay activity data obtained in step S3, calculate the 6 associated stay vitality indicators from three aspects: stay intensity, stay duration, and stay diversity. The stay vitality indicators include the stay rate and stay density representing stay intensity, the overall stay duration and average stay duration representing stay duration, and the population diversity and activity diversity representing stay diversity;

[0017] Furthermore, the calculation methods for the 6 stay vitality indicators in step S5 are as follows:

[0018] (1) Calculate the stay rate: Based on the community public spaces summarized in step S4, apply the stay activity data obtained in step S3, count the number of interviewees who have stayed in each community public space, and divide it by the total number of interviewees in the corresponding community. The calculation formula is as follows,

[0019]

[0020] where, l_pct i represents the stay rate of the i-th community public space, ∑ i Lin_num represents the total number of interviewees who have had stay activities in the i-th community public space, and ∑Sum_num represents the total number of interviewees in the community to be studied;

[0021] (2) Calculate the residence density: Based on the area of the community public space summarized in step S4 and the residence activity data obtained in step S3, count the number of interviewees who have stayed in each community public space, and divide it by the area of the corresponding community public space. The calculation formula is as follows,

[0022]

[0023] Among them, l_dens i represents the residence density of the i-th community public space, ∑ i Lin_num represents the total number of interviewees who have had residence activities in the i-th community public space, s i represents the area of the i-th community public space;

[0024] (3) Calculate the total residence time: Based on the residence activity data obtained in step S3, calculate the sum of the durations of all residence activities in each community public space (the duration unit is hours); the calculation formula is as follows,

[0025]

[0026] Among them, l_sum_t i represents the total residence time of the i-th community public space, t ki represents the duration of the k-th residence activity in the i-th community public space, and x represents the total number of residence activities in the i-th community public space;

[0027] (4) Calculate the average residence time: Based on the total residence time of each community public space, calculate the average residence time of each residence activity. The calculation formula is as follows,

[0028]

[0029] Among them, l_avgt i represents the average residence time of the i-th community public space, l_sum_t i represents the total residence time of the i-th community public space, and x represents the total number of residence activities in the i-th community public space;

[0030] (5) Calculate population diversity: Based on the information of the interviewees collected in step S3, use the Shannon diversity index (an existing diversity calculation method) to calculate the diversity of the four characteristic indicators of the gender, age, education level, and annual household income of the resident population respectively, and calculate the average value; among them, gender is classified into male and female; age is classified into age groups of under 18 years old, 18 - 44 years old, 45 - 59 years old, 60 - 74 years old, and over 75 years old; education level is classified into junior high school and below, high school or secondary school, junior college, undergraduate, and master's degree and above; annual household income is classified into below 100,000 yuan, 100,000 - 400,000 yuan, 400,000 - 2,000,000 yuan, and above 2,000,000 yuan; the calculation formula is as follows,

[0031]

[0032] In the formula, P ij is the diversity of the jth characteristic indicator of the resident population in the ith community public space, y is the total number of types of this characteristic indicator (the value in this method is 4), p jl is the proportion of the lth type of population in the jth characteristic indicator; P i is the diversity of the resident population in the ith community public space;

[0033] (6) Calculate activity diversity: Based on the resident activity data obtained in step S3, classify the resident activities into 6 types of resident activity types according to the content, including waiting types, watching types, health types, social types, entertainment types, and commercial types, and use the Shannon diversity index to calculate the diversity of the resident activity types; among them, the waiting types include but are not limited to waiting for a bus and waiting for someone, the watching types include but are not limited to sitting idle, taking pictures, and viewing scenery, the health types include but are not limited to taking walks and exercising, the social types include but are not limited to chatting, the entertainment types include but are not limited to playing chess, singing and dancing, and playing games, and the commercial types include but are not limited to shopping and selling; the calculation formula is as follows,

[0034]

[0035] Among them, A i is the diversity of the resident activity types in the ith community public space, m is the number of evaluation indicators (m = 6 in this method), p k is the proportion of the kth type of resident activity type. In this way, the above 6 indicators cover all aspects of the residents' activities, deeply explore the deep logic of different residents' resident activities under different time and space conditions, and the statistical results can better reflect the diverse resident needs of the residents.

[0036] S6 Construct a resident vitality measurement model, perform standardization processing on the calculation results of all resident vitality indicators, and then use the entropy weight method (an existing objective weighting method) to determine the weights of the 6 resident vitality indicators in step S5, and construct a multi-dimensional resident vitality measurement model for urban community public spaces;

[0037] The dwelling vitality measurement model is constructed as follows:

[0038]

[0039] Among them, LV i represents the dwelling vitality of the i-th community public space, u j represents the weight of the j-th index, a ij represents the value of the j-th index of the i-th community public space, and n represents the number of community public spaces in the community to be studied;

[0040] Furthermore, in step S6, the normalization process is carried out using the range normalization method, and its calculation formula is as follows (all 6 indicators are positive indicators):

[0041]

[0042] In the formula, Y ij is the normalized value, X ij is the sample data to be normalized, X min is the minimum value of the sample data of this index, X max is the maximum value of the sample data of this index. In this way, the range normalization method is an existing data normalization method, and using this method can better eliminate the influence of different dimensions.

[0043] Furthermore, in step S6, the calculation method for determining the weights of the 6 dwelling vitality indicators using the entropy weight method is as follows:

[0044] The first step is to normalize the judgment matrix to obtain the standard matrix R, and calculate the proportion of the index values. The formula is as follows,

[0045] R = (r ij ) m×n

[0046]

[0047] In the formula, R is the standard matrix, r ij is the value of the j-th index of the i-th community public space, n is the number of community public spaces in the community to be studied, m is the number of evaluation indicators (m = 6 in this method), f ij is the proportion of the j-th index of the i-th community public space, a ij is the value of the j-th index of the i-th community public space;

[0048] The second step is to calculate the information entropy value e j of the j-th index. The calculation formula is as follows,

[0049]

[0050] where e j (0 ≤ e j ≤ 1) is the entropy value of the j-th index; is the information entropy coefficient;

[0051] Step 3, calculate the weight u of the j-th index j (0 ≤ u j ≤ 1), and the calculation formula is as follows,

[0052]

[0053] S7. According to the data obtained in steps S3 and S4, calculate the relevant indicators of each community public space according to step S5, substitute them into step S6 to calculate the weights of each indicator and construct a residence vitality measurement model, and then calculate the residence vitality of each community public space. In this way, the entropy weight method is used to calculate the index weights. As a mature index weight calculation method, the entropy weight method has high feasibility and good objectivity.

[0054] Furthermore, this method also includes:

[0055] S8 Residence vitality characterization. Based on the map display function of geographic information system software, select the heat map mode. Use the residence vitality values of each community public space measured in step S7 as the weight field, determine the color scheme, use cold colors to represent low residence vitality, the higher the brightness, the lower the value, and use warm colors to represent high residence vitality, the higher the brightness, the higher the value, and realize the visual expression of the spatial distribution of residence vitality in the community public space on the map.

[0056] In this way, it can more intuitively reflect and display the vitality of the public space around the community.

[0057] Therefore, this method constructs a complete spatio-temporal behavior chain of residents by using the spatio-temporal behavior data of micro individuals, explores the deep logic of residents' residence activities under different spatio-temporal conditions, can more accurately understand the diverse residence needs of residents in the mountain community public space, and explores the residence activity patterns and diverse residence needs of different age groups. At the same time, this method constructs a multi-dimensional residence vitality measurement model including residence intensity, residence time consumption, and residence diversity, and uses the objective weighting method to determine the index weights, which can measure the residence vitality more comprehensively and accurately.

[0058] In summary, this method collects, counts, and analyzes data based on the actual daily travel behavior of community residents. The characterized distribution of residence vitality in the community public space can more realistically depict the use of the community public space by residents, and then explore the actual needs of residents, so as to more accurately evaluate the rationality and effectiveness of urban community public space planning. Description of the Drawings

[0059] Figure 1 It is a schematic diagram of the content obtained through investigation in step S3 for constructing a database of residence activities in the public space of mountain communities.

[0060] Figure 2 It is an example schematic diagram of determining the research space range around the community based on the geographic information system software in step S2.

[0061] Figure 3 It is an example schematic diagram of determining the distribution information of community public space in step S4.

[0062] Figure 4 It is an example schematic diagram of the visualization of residence vitality corresponding to the locations of each community public space in the map in step S8. Detailed implementation method

[0063] The present invention will be further described in detail below in conjunction with the accompanying drawings and the detailed implementation method.

[0064] Implementation method: A method for measuring the residence vitality of urban community public space based on spatio-temporal behavior, characterized by including the following steps:

[0065] S1 Determine the samples. Using the method of stratified sampling, for the residents of the residential community to be studied (i.e., the community), with age as the stratification variable, samples are taken at specific ratios respectively as the survey objects.

[0066] During implementation, in step S1, the population census data of the sub-district office where the residential community is located is used as the statistical basis for age stratification, and samples are taken at a ratio of not less than 1% for each age group, with an equal male-female ratio.

[0067] S2 Define the space range. Considering the influence of urban terrain and traffic network on the walking living circle, the network analysis method in Arc GIS (geographic information system software) is used, and a terrain model is introduced. Starting from the main entrance of the community to be studied, the boundary of the area reachable by a specific walking path distance is calculated as the research space range. See Figure 2 Example: The dotted line in the figure represents the determined boundary range of the area.

[0068] During implementation, in step S2, the boundary of the area with a walking path distance of 1000 meters is used as the research space range. Such a 1-kilometer distance is exactly the distance that a person walks in about 15 minutes, which is a suitable distance for the residents of the community to use as a surrounding activity and entertainment place.

[0069] S3 Obtain the data of residents' staying activities. For the selected survey objects in step S1, distribute GPS recorders. The GPS recorders immediately record the spatial coordinates of the survey objects, obtain the activity data information during non-working hours on at least one working day and one non-working day, identify the activities with a staying duration exceeding the specified time as staying activities, and identify the corresponding spatial points as staying points; then, taking the selected survey objects as interviewees, obtain their demographic information, including age, gender, household income, education level, and the activities at the staying points, and construct a database of staying activities in the public spaces of mountain communities, see Figure 1 ;

[0070] During implementation, in step S3, identify the activities with a staying duration exceeding 10 minutes as staying activities.

[0071] S4 Determine the information of community public spaces. Based on the spatial scope defined in step S2, through the vector map data and POI data (which refers to the data used to describe physical locations with specific meanings or attractions in a geographic information system. These locations can be commercial facilities, public facilities, transportation facilities, cultural attractions, etc., such as restaurants, gas stations, bus stops, parks, museums, etc.) of the global open-source map platform (OpenStreetMap), identify and name the staying points recorded in step S3, determine them as the community public spaces to be counted, obtain their geographic coordinates and spatial boundaries, and determine their regional areas in the geographic information system software (Arc GISPro), see Figure 3 as shown, where the blackened position points are the determined positions of community public spaces;

[0072] During implementation, in step S4, for the staying points whose names cannot be confirmed by the map data and POI data, name them according to the agreed names of the survey objects.

[0073] S5 Calculate the staying vitality indicators. Based on the residents' staying activity data obtained in step S3, calculate 6 associated staying vitality indicators from three aspects: staying intensity, staying time consumption, and staying diversity. The staying vitality indicators include the staying rate and staying density representing staying intensity, the overall staying time consumption and average staying time consumption representing staying time consumption, and the population diversity and activity diversity representing staying diversity;

[0074] During implementation, the calculation methods for the 6 staying vitality indicators in step S5 are as follows:

[0075] (1) Calculate the staying rate: Based on the community public spaces summarized in step S4, apply the staying activity data obtained in step S3, count the number of interviewees who have stayed in each community public space, and divide it by the total number of interviewees in the corresponding community. The calculation formula is as follows,

[0076] where, l_pcti represents the residence rate of the i-th community public space, ∑ i Lin_num represents the total number of interviewees who have had residence activities in the i-th community public space, ∑ i Sum_num represents the total number of interviewees in the community to be studied;

[0077] (2) Calculate the residence density: Based on the area of the community public space summarized in step S4 and the residence activity data obtained in step S3, count the number of interviewees who have stayed in each community public space, and divide it by the area of the corresponding community public space. The calculation formula is as follows,

[0078]

[0079] where, l_dens i represents the residence density of the i-th community public space, ∑ i Lin_num represents the total number of interviewees who have had residence activities in the i-th community public space, s i represents the area of the i-th community public space;

[0080] (3) Calculate the total residence time consumption: Based on the residence activity situation data obtained in step S3, calculate the sum of the durations of all residence activities in each community public space (the duration unit is hours); the calculation formula is as follows,

[0081]

[0082] where, l_sum_t i represents the total residence time consumption of the i-th community public space, t ki represents the duration of the k-th residence activity in the i-th community public space, and x represents the total number of residence activities in the i-th community public space;

[0083] (4) Calculate the average residence time consumption: Based on the total residence time consumption of each community public space, calculate the average time consumption of each residence activity. The calculation formula is as follows,

[0084]

[0085] where, l_avgt i represents the average residence time consumption of the i-th community public space, l_sum_t i represents the total residence time consumption of the i-th community public space, and x represents the total number of residence activities in the i-th community public space;

[0086] (5) Calculate population diversity: Based on the interviewee information collected in step S3, use the Shannon diversity index (an existing diversity calculation method) to calculate the diversity of the four characteristic indicators of the gender, age, education level, and annual household income of the resident population respectively, and then calculate the average value; among them, gender is classified into male and female; age is classified into age groups of under 18 years old, 18 - 44 years old, 45 - 59 years old, 60 - 74 years old, and over 75 years old; education level is classified into junior high school and below, high school or secondary school, junior college, undergraduate, and master's degree and above; annual household income is classified into below 100,000, 100,000 - 400,000, 400,000 - 2,000,000, and above 2,000,000; the calculation formula is as follows,

[0087]

[0088] In the formula, P ij is the diversity of the jth characteristic indicator of the resident population in the ith community public space, y is the total number of types of this characteristic indicator (taking the value of 4 in this method), p jl is the proportion of the kth type of population in the jth characteristic indicator; P i is the diversity of the resident population in the ith community public space;

[0089] (6) Calculate activity diversity: Based on the resident activity data obtained in step S3, classify the resident activities into 6 types of resident activity types according to the content, including waiting types, watching types, health types, social types, entertainment types, and commercial types, and use the Shannon diversity index to calculate the diversity of the resident activity types; among them, the waiting types include but are not limited to waiting for a bus and waiting for someone, the watching types include but are not limited to sitting idle, taking pictures, and viewing scenery, the health types include but are not limited to taking a walk and doing sports, the social types include but are not limited to chatting, the entertainment types include but are not limited to playing chess, singing and dancing, and playing games, and the commercial types include but are not limited to shopping and selling; the calculation formula is as follows,

[0090]

[0091] Among them, A i is the diversity of the resident activity types in the ith community public space, p k is the proportion of the kth type of resident activity type;

[0092] S6 Construct a resident vitality measurement model, perform standardization processing on the calculation results of all resident vitality indicators, and then use the entropy weight method (an existing objective weighting method) to determine the weights of the 6 resident vitality indicators in step S5, and construct a multi-dimensional resident vitality measurement model for urban community public spaces;

[0093] The resident vitality measurement model is constructed as follows:

[0094]

[0095] Among them, LV i represents the residence vitality of the i-th community public space, and u j represents the weight of the j-th index, and a ij represents the value of the j-th index of the i-th community public space;

[0096] During implementation, in step S6, the normalization process is carried out using the range normalization method, and its calculation formula is as follows (all 6 indicators are positive indicators):

[0097]

[0098] In the formula, Y ij is the normalized value, X ij is the sample data to be normalized, X min is the minimum value of the sample data of this indicator, and X max is the maximum value of the sample data of this indicator. In this way, the range normalization method is an existing data normalization processing method, and using this method can better eliminate the influence of different dimensions.

[0099] During implementation, in step S6, the calculation method for determining the weights of the 6 residence vitality indicators using the entropy weight method is as follows:

[0100] First step, normalize the judgment matrix to obtain the standard matrix R, and calculate the proportion of the indicator values. The formula is as follows,

[0101] R = (r ij ) m×n

[0102]

[0103] In the formula, R is the standard matrix, and r ij is the indicator value of the i-th community public space under the j-th indicator, n is the number of community public spaces in the community to be studied, m is the number of evaluation indicators (m = 6 in this method), and f ij is the proportion of the j-th indicator of the i-th community public space, and a ij is the value of the j-th indicator of the i-th community public space;

[0104] Second step, calculate the information entropy value e j of the j-th indicator, and the calculation formula is as follows,

[0105]

[0106] In the formula, e j (0 ≤ e j ≤ 1) is the entropy value of the j-th item indicator; is the information entropy coefficient;

[0107] Step 3: Calculate the entropy weight u of the j-th index j (0 ≤ u j ≤ 1), and the calculation formula is as follows

[0108]

[0109] S7. According to the data obtained in step S3 and step S4, calculate the relevant indicators of each community public space according to step S5, substitute them into step S6 to calculate the weights of each indicator and construct a residence vitality measurement model, and then calculate the residence vitality of each community public space.

[0110] During implementation, this method further includes

[0111] S8 Residence vitality characterization: Based on the map display function of geographic information system software, select the heat map mode, use the residence vitality values of each community public space measured in step S7 as the weight field, determine the color matching scheme, use cold colors to represent low residence vitality, the higher the brightness, the lower the value, and use warm colors to represent high residence vitality, the higher the brightness, the higher the value, and realize the visual expression of the spatial distribution of residence vitality of community public spaces in the map.

[0112] See Figure 4 Example, the brighter the color of the community public space in the figure, the higher the residence vitality of the community public space.

[0113] This method constructs a complete spatio-temporal behavior chain of residents through the spatio-temporal behavior data of micro individuals, and explores the deep logic of residents' residence activities under different spatio-temporal conditions, which can more accurately understand the diverse residence needs of residents in mountain community public spaces and explore the residence activity patterns and diverse residence needs of different age groups. At the same time, this method constructs a multi-dimensional residence vitality measurement model including residence intensity, residence duration, and residence diversity, and uses the objective weighting method to determine the indicator weights, which can measure the residence vitality more comprehensively and accurately.

Claims

1. A method for measuring the residence vitality of urban community public space based on spatio-temporal behavior, characterized in that, It includes the following steps: S1 Determine the samples. Using the method of stratified sampling, for the residents of the residential community to be studied, with age as the stratification variable, sample at specific ratios respectively as the survey objects; S2 Define the spatial scope. Considering the influence of urban terrain and traffic network on the walking living circle, use the network analysis method in geographic information system software, introduce the terrain model, and take the main entrance of the community to be studied as the starting point to calculate the regional boundary reachable by a specific walking path distance as the research spatial scope; S3 Obtain the data of residents' staying activities. For the survey objects selected in step S1, distribute GPS recorders. The GPS recorders immediately record the spatial coordinates of the survey objects, obtain the activity data information during non-working hours of at least one working day and one non-working day, identify the activities with a staying duration exceeding the specified time as staying activities, and the corresponding spatial points as staying points; then take the selected survey objects as interviewees to obtain their demographic information, including age, gender, family income, education level, and activity content at the staying points, and construct a database of staying activities in the public space of mountain communities; S4 Determine the information of community public space. Based on the spatial scope defined in step S2, through the vector map data and POI data of the global open-source map platform, identify and name the staying points recorded in step S3, determine them as the community public space to be counted, obtain their geographical coordinates and spatial boundaries, and determine their regional area in geographic information system software; S5 Calculate the staying vitality indicators. Based on the residents' staying activity data obtained in step S3, calculate 6 related staying vitality indicators respectively from three aspects: staying intensity, staying time consumption, and staying diversity. The staying vitality indicators include the staying rate and staying density representing staying intensity, the overall staying time consumption and average staying time consumption representing staying time consumption, and the population diversity and activity diversity representing staying diversity; S6 Construct a staying vitality measurement model. Standardize the calculation results of all staying vitality indicators, and then use the objective weighting method to determine the weights of the 6 staying vitality indicators in step S5 to construct a multi-dimensional staying vitality measurement model for the public space of urban communities; The construction of the staying vitality measurement model is as follows: In the formula, LV i represents the residence vitality of the i-th community public space, u j represents the weight of the j-th index, a ij represents the value of the j-th index of the i-th community public space; n represents the number of community public spaces in the community to be studied; S7 According to the data obtained in step S3 and step S4, calculate the relevant indicators of each community public space according to step S5, substitute them into step S6 to calculate the weights of each indicator and construct a staying vitality measurement model, and then calculate the staying vitality of each community public space.

2. The method for measuring the residence vitality of urban community public space based on spatio-temporal behavior according to claim 1, wherein, In step S1, stratify by age group based on the population census data of the sub-district office where the residential community is located, and sample each age group at a ratio of not less than 1%, with equal male and female ratios.

3. The method for measuring the residence vitality of urban community public space based on spatio-temporal behavior according to claim 1, wherein, In step S2, take the regional boundary of a 1000-meter walking path distance as the research spatial scope.

4. The method for measuring the residence vitality of urban community public space based on spatio-temporal behavior according to claim 1, characterized in that, In step S3, identify the activities with a staying duration exceeding 10 minutes as staying activities.

5. The method for measuring the resident vitality of urban community public space based on spatio-temporal behavior according to claim 1, wherein In step S4, for the staying points whose names cannot be confirmed by the map data and POI data, name them according to the agreed names of the survey objects.

6. The method for measuring the residence vitality of urban community public space based on spatio-temporal behavior according to claim 1, wherein The calculation methods for the 6 staying vitality indicators in step S5 are as follows: (1) Calculate the residence rate: Based on the community public spaces summarized in step S4 and applying the residence activity data obtained in step S3, count the number of interviewees who have stayed in each community public space and divide it by the total number of interviewees in the corresponding community. The calculation formula is as follows: where \(l_{pct}\) i represents the residence rate of the \(i\)-th community public space, \(\sum\) i Lin_num represents the total number of interviewees who have had residence activities in the \(i\)-th community public space, and \(\sum\)Sum_num represents the total number of interviewees in the community to be studied; (2) Calculate the residence density: Based on the area of the community public spaces summarized in step S4 and the residence activity data obtained in step S3, count the number of interviewees who have stayed in each community public space and divide it by the area of the corresponding community public space. The calculation formula is as follows: where \(l_{dens}\) i represents the residence density of the \(i\)-th community public space, \(\sum\) i Lin_num represents the total number of interviewees who have had residence activities in the \(i\)-th community public space, \(s\) i represents the area of the \(i\)-th community public space; (3) Calculate the total residence time consumption: Based on the residence activity situation data obtained in step S3, calculate the sum of the durations of all residence activities in each community public space. The calculation formula is as follows: Where, l_sum_t i represents the total residence time of the i-th community public space, and t ki represents the duration of the k-th residence activity in the i-th community public space, and x represents the total number of residence activities in the i-th community public space; (4) Calculate the average residence time consumption: Based on the total residence time consumption of each community public space, calculate the average time consumption of each residence activity. The calculation formula is as follows: where \(l_{avgt}\) i represents the average residence time consumption of the \(i\)-th community public space, and \(l_{sumt}\) i represents the total residence time consumption of the \(i\)-th community public space, and \(x\) represents the total number of residence activities in the \(i\)-th community public space; (5) Calculate the population diversity: Based on the interviewee situation collected in step S3, use the Shannon diversity index to calculate the diversity of four characteristic indicators of the resident population, namely gender, age, education level, and household annual income, and find the average value. Among them, gender is classified as male and female; age is classified into age groups of under 18 years old, 18 - 44 years old, 45 - 59 years old, 60 - 74 years old, and over 75 years old; education level is classified as junior high school and below, high school or secondary school, junior college, undergraduate, and master's degree and above; household annual income is classified as below 100,000 yuan, 100,000 - 400,000 yuan, 400,000 - 2,000,000 yuan, and above 2,000,000 yuan. The calculation formula is as follows: Wherein, P ij is the diversity of the j-th characteristic index of the people staying in the i-th community public space, y is the total number of types of this characteristic index, which takes the value of 4 in this method, and p jl is the proportion of the l-th type of people in the j-th characteristic index; P i is the diversity of the people staying in the i-th community public space; (6) Calculate the activity diversity: Based on the residence activity data obtained in step S3, classify the residence activities into 6 types of residence activity types according to the content, including waiting, observing, health care, socializing, entertainment, and commerce. Use the Shannon diversity index to calculate the diversity of the residence activity types. Among them, the waiting category includes but is not limited to waiting for a vehicle and waiting for someone; the observing category includes but is not limited to sitting idle, taking pictures, and viewing scenery; the health care category includes but is not limited to taking a walk and exercising; the socializing category includes but is not limited to chatting; the entertainment category includes but is not limited to playing chess, singing and dancing, and playing games; the commerce category includes but is not limited to shopping and selling. The calculation formula is as follows: Where A i is the diversity of the i-th community public space residence activity type, m is the number of evaluation indicators, and in this method m = 6, p k is the proportion of the k-th type of residence activity type.

7. The method for measuring the residence vitality of urban community public space based on spatio-temporal behavior according to claim 1, wherein In step S6, the standardization process is carried out using the range standardization method, and its calculation formula is as follows: where Y ij is the normalized value, X i j is the sample data to be normalized, X min is the minimum value of the sample data of this indicator, X max is the maximum value of the sample data of this indicator.

8. The method for measuring the residence vitality of urban community public space based on spatio-temporal behavior according to claim 1, wherein In step S6, the entropy weight method is used to determine the weights of the 6 residence vitality indicators in step S5. The specific calculation method is as follows: In the first step, standardize the judgment matrix to obtain the standard matrix R and calculate the proportion of the index values. The formula is as follows: R＝(r ij ) m×n where R is the standard matrix, and r ij is the index value of the i-th community public space under the j-th index, n is the number of community public spaces in the community to be studied, m is the number of evaluation indexes, and in this method, m = 6, f ij is the proportion of the j-th index of the i-th community public space, a ij is the value of the j-th index of the i-th community public space; Step 2: Calculate the information entropy value e of the j-th indicator j , and the calculation formula is as follows where e j (0 ≤ e j ≤ 1) is the entropy value of the j-th index; is the information entropy coefficient; Step 3: Calculate the weight u of the j-th indicator j (0 ≤ u j ≤ 1), and the calculation formula is as follows 9. The method for measuring the resident vitality of urban community public space based on spatio-temporal behavior according to claim 1, wherein, This method also includes the steps: S8 Residence vitality characterization: Based on the map display function of the geographic information system software, select the heat map mode. Use the residence vitality values of each community public space measured in step S7 as the weight field, determine the color scheme, use cold colors to represent low residence vitality, and the higher the brightness, the lower the value; use warm colors to represent high residence vitality, and the higher the brightness, the higher the value. Realize the visual expression of the spatial distribution of the residence vitality of community public spaces in the map.

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