Evaluation method of micro-renovation project based on spatial multidimensional data analysis

Abstract

The present invention provides a micro-renewal and transformation project evaluation method based on spatial multidimensional data analysis, which relates to the field of spatial evaluation technology, including: establishing a multidimensional space recognition result through a spatial data set of a preset analysis scale and a target space; using the multidimensional space recognition result to perform spatial shape aggregation, establishing a spatial shape aggregation result and its micro-renewal and transformation project identification; establishing a technical indicator set, reconstructing the weight of the technical indicator set through the micro-renewal and transformation project identification, generating an update evaluation indicator to evaluate the micro-renewal and transformation, and generating a first evaluation result; reading micro-renewal and transformation feedback data, establishing a second evaluation result; using the first evaluation result and the second evaluation result to generate a micro-renewal and transformation project evaluation result of the target space. The present application solves the technical problem that the existing method relies on subjective opinion collection and large-scale site selection, lacks spatial data support, and leads to limited site selection accuracy and transformation effect, and improves the scientificity and sustainability of the transformation project.

Description

Technical Field

[0001] The present application relates to the field of space assessment technology, and in particular to a micro-renovation and transformation project assessment method based on spatial multidimensional data analysis. Background Art

[0002] Micro-renewal is an important way of urban renewal in recent years. It aims to partially renovate old communities or public spaces in cities in a small-scale and low-cost way to improve spatial vitality and the quality of life of residents. Compared with large-scale urban transformation, micro-renewal is more flexible and can achieve continuous optimization of urban space without changing the urban structure to a large extent.

[0003] At present, the site selection methods for micro-renewal transformation mainly rely on methods such as soliciting opinions, government-led and unified large-scale site selection. These methods usually determine the areas that need to be transformed by publicly soliciting opinions from citizens or community residents. Although this method can reflect certain public opinions, it has many limitations. On the one hand, residents' feedback is often not professional, which can easily lead to one-sided and subjective site selection for renovation projects; on the other hand, the unified large-scale site selection method lacks refined spatial data support and easily ignores the differences in local space. In addition, the existing methods are difficult to effectively integrate the city's multidimensional spatial data, such as population density, traffic flow and land use, resulting in a lack of accuracy and scientific basis for the renovation plan, which in turn affects the sustainability and rationality of the renovation effect. Summary of the invention

[0004] This application provides a micro-renewal and renovation project evaluation method based on spatial multidimensional data analysis, which solves the technical problems that the existing micro-renewal and renovation site selection methods rely on subjective opinion collection and large-area site selection, lack of refined spatial data support, and lead to limited site selection accuracy and renovation effects, thereby achieving the technical effect of improving the scientificity and sustainability of renovation projects.

[0005] In view of the above problems, the present application provides a micro-renewal and transformation project evaluation method based on spatial multidimensional data analysis, the method comprising: establishing a spatial data set of a target space, establishing a multidimensional space recognition result of the target space through a preset analysis scale, the multidimensional space recognition result comprising a kernel density recognition result, a heat map recognition result, and a land use function recognition result, and the multidimensional space recognition result is constructed through a spatial data set; performing spatial shape aggregation using the multidimensional space recognition result, establishing a spatial shape aggregation result, and establishing a micro-renewal and transformation project identification of the spatial shape aggregation result; establishing a set of technical indicators, and reconstructing the weights of the set of technical indicators through the micro-renewal and transformation project identification to generate an update evaluation indicator; performing a micro-renewal and transformation evaluation corresponding to the spatial shape aggregation result based on the update evaluation indicator to generate a first evaluation result; reading micro-renewal and transformation feedback data of the spatial shape aggregation result, and establishing a second evaluation result based on the feedback data reading result, the second evaluation result being a questionnaire feedback result after weighted calculation based on age level; and generating a micro-renewal and transformation project evaluation result of the target space using the first evaluation result and the second evaluation result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Establish a spatial data set of the target space, perform kernel density recognition, heat map recognition and land use function recognition on the spatial data set through a preset analysis scale, and establish a multi-dimensional spatial recognition result of the target space to obtain accurate spatial features, provide basic data for subsequent spatial analysis, and ensure the objectivity and accuracy of subsequent analysis. Use the multi-dimensional spatial recognition results to aggregate spatial shapes, establish spatial shape aggregation results, and establish micro-renewal and transformation project identification of spatial shape aggregation results, transform scattered, high-dimensional data into intuitive and easy-to-understand spatial forms, and reveal the inherent connections and laws of space to accurately identify areas suitable for micro-renewal and transformation. Establish a set of technical indicators, and reconstruct the weights of the technical indicator set through micro-renewal and transformation project identification, adjust the evaluation standards according to the actual conditions of different projects, generate update evaluation indicators, and establish clear directions and standards for the evaluation work, so that it can focus on the core elements of micro-renewal, and dynamically respond to different project needs to ensure that the technical indicators are more flexible and more adaptable. Based on the renewal evaluation index, the micro-renewal transformation evaluation of the corresponding spatial shape aggregation results is carried out to generate the first evaluation result; the micro-renewal transformation feedback data of the spatial shape aggregation results is read, and the questionnaire feedback results are weighted based on the age level to determine the second evaluation result, and the first evaluation result and the second evaluation result are used to generate the micro-renewal transformation project evaluation result of the target space. The first evaluation result conducts the first quantitative test on the feasibility of micro-renewal transformation and provides a preliminary basis for decision-making, while the second evaluation result further incorporates the soft indicators of community feedback, especially considering the feelings of people of different age groups, so that the evaluation results are closer to the real user needs. This dual evaluation mechanism combines data analysis and citizen feedback to make the evaluation results more comprehensive.

[0008] To sum up, this application deeply explores the value of spatial data through multi-dimensional spatial data analysis, combines technical indicator reconstruction and feedback mechanism, and realizes a comprehensive, accurate and scientific evaluation of urban micro-renewal and renovation projects, thereby improving the accuracy and work efficiency of site selection for micro-renewal and renovation projects, making them more in line with actual needs, and improving the scientificity and sustainability of the renovation effect.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a process flow of a micro-renovation and transformation project evaluation method based on spatial multidimensional data analysis provided in an embodiment of the present application;

[0011] Figure 2A schematic diagram of a process for establishing inclusiveness indicators in a micro-renovation and transformation project evaluation method based on spatial multidimensional data analysis provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of a process for establishing safety indicators in a micro-renovation and transformation project evaluation method based on spatial multidimensional data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The embodiment of the present application provides a micro-renewal and renovation project evaluation method based on spatial multi-dimensional data analysis, thereby solving the technical problems that the existing micro-renewal and renovation site selection method relies on subjective opinion collection and large-area site selection, lacks refined spatial data support, and leads to limited site selection accuracy and renovation effect, thereby achieving the technical effect of improving the scientificity and sustainability of renovation projects.

[0014] like Figure 1 As shown, the embodiment of the present application provides a micro-renovation project evaluation method based on spatial multidimensional data analysis, the method comprising:

[0015] A spatial data set of the target space is established, and a multidimensional spatial recognition result of the target space is established through a preset analysis scale. The multidimensional spatial recognition result includes a kernel density recognition result, a heat map recognition result, and a land use function recognition result. The multidimensional spatial recognition result is constructed through the spatial data set.

[0016] Specifically, the target space is a specific urban area or space that needs to be analyzed and transformed. This area can be a block, a community, or even a part of a city. A spatial dataset is a collection of spatial information that is stored and managed. It contains various data related to the target space, including geographic location, building distribution, population density, land use, etc. A city's geographic information system (GIS) data, traffic data, or land use data can all be part of a spatial dataset.

[0017] By collecting various types of spatial data of the target space, a spatial data set of the target space is established, and the details and scope of the analysis are determined by presetting the analysis scale. The preset analysis scale refers to the scale of observation or analysis defined in spatial analysis, which determines the accuracy and details of the analysis. The scale can be based on the size of the space or the fineness of the data, such as analyzing in units of 1000m×1000m grids.

[0018] Then, based on different data dimensions, kernel density identification, heat map identification and land use function identification are performed. Using the kernel density identification method, dense areas of population, store distribution or vehicles in the target space can be determined. Heat map identification can intuitively display the density distribution of certain elements in the space. Through the change of color, hot spots in certain areas can be quickly located, such as busy intersections or crowded public squares. Land use function identification is used to distinguish the uses of different areas, such as commercial areas, residential areas and industrial areas. The identification of different functional areas helps to determine which areas are suitable for specific micro-renewal renovations. The results of kernel density identification, heat map identification and land use function identification are summarized to establish multidimensional space identification results. These multidimensional space identification results together constitute a comprehensive understanding of the target space and provide a basis for the subsequent micro-renewal project evaluation.

[0019] The multi-dimensional space recognition results are used to perform spatial shape aggregation, establish spatial shape aggregation results, and establish micro-renewal and transformation project identification of the spatial shape aggregation results.

[0020] Specifically, according to the results of multidimensional spatial recognition, spatial areas with similar characteristics are grouped together to form a specific spatial shape. Spatial shape aggregation can use clustering algorithms or spatial aggregation tools. For example, the common K-means clustering method can be used to group multiple spatial units by similarity; or use the buffer analysis in the geographic information system to divide areas with similar functions into a larger aggregation area. For example, in the old city of a city, multiple residential communities with dense residential areas can be aggregated into a large "residential renewal block" through kernel density and functional identification. Through multidimensional spatial recognition, it is found that blocks A and B have high population density and heavy traffic, but low green space coverage; blocks C and D show intensive commercial activities, especially on weekends and during evening rush hours. Then, using the spatial shape aggregation method, blocks A and B are aggregated into a "dense residential area", while blocks C and D are aggregated into a "commercial area".

[0021] These aggregated areas constitute the spatial shape aggregation results, which provide the basic regional units for transformation. Based on the spatial shape aggregation results, a specific project label is assigned to each aggregated area, indicating the specific renewal and transformation needs and goals of the area, and establishing the corresponding micro-renewal and transformation project identification. For example, an area shows a dense population and lacks public green space in the aggregation, and is identified as a "community greening transformation project." Another area shows active commercial activities in the aggregation, and the existing facilities cannot meet the flow of people, so it is identified as a "commercial facility upgrade project."

[0022] Through spatial shape aggregation, areas that exhibit similar attributes or trends in multidimensional spatial identification results can be automatically merged into larger and more meaningful geographic units through algorithms, reducing the complexity of data analysis while highlighting key geographic patterns and correlations, ensuring that the specific needs of each area are accurately identified and marked, and ultimately achieving efficient management and precise transformation of spatial resources.

[0023] Establish a set of technical indicators, reconstruct the weights of the set of technical indicators through micro-renovation and transformation project identification, and generate update evaluation indicators.

[0024] Specifically, in micro-renewal projects, in order to scientifically evaluate the project effects, it is necessary to first establish a set of technical indicators. This set includes multiple indicators for measuring different dimensions of the project. The determination of technical indicators can refer to the evaluation indicators and evaluation dimensions in the Public Space Index (PSI) model, such as inclusiveness indicators, accessibility indicators, safety indicators, etc.

[0025] Then, according to the micro-renewal renovation project identification, the renovation goal of each aggregation area is clarified, and the importance of each technical indicator is adjusted based on the renovation goal, and the indicator weight is reconstructed. The weight adjustment process can be completed using data analysis, such as the analytic hierarchy process (AHP), by comparing different indicators pairwise and assigning their relative importance. For example, in an area identified as a "community greening renovation project", the weight of the green area indicator may be set very high (such as 40%), while the weight of traffic improvement is relatively low (such as 10%). In another area identified as a "commercial facility upgrade project", the weight of traffic flow and commercial facilities may occupy a larger proportion. After the weight reconstruction, the renewal evaluation indicators for each project are formed. These evaluation indicators integrate multiple dimensions of the technical indicator set and can be used to evaluate the renovation effect of the project.

[0026] Through weight reconstruction and flexible adjustment of technical indicators, the evaluation framework of each micro-renewal and renovation project can accurately reflect the actual needs of the project, ensure that the results of micro-renewal and renovation are more in line with the special needs of the target area, and improve the pertinence and effectiveness of the evaluation results.

[0027] A micro-update transformation evaluation is performed on the corresponding spatial shape aggregation result based on the update evaluation index to generate a first evaluation result.

[0028] Specifically, for each aggregation area in the spatial shape aggregation results, spatial data simulation tools and statistical analysis tools (such as R language or Python) are used to perform data analysis and prediction on the effect of the transformation plan. These tools can predict the possible effect after the transformation based on current data and indicators. For example, GIS tools can help simulate the impact of the layout of new green spaces on surrounding areas and evaluate the degree of environmental improvement in the area after the addition of green spaces; traffic flow models can be used to evaluate whether adding new roads or adjusting traffic flow lines can effectively alleviate congestion.

[0029] According to the updated evaluation indicators determined above, relevant data is collected from the predicted effects. For example, for an area that aims to improve the walking environment, data such as sidewalk width, pedestrian flow, and barrier-free facility coverage need to be collected; while for an area that aims to promote community interaction, data such as the efficiency of public space utilization and the frequency of events held need to be focused on.

[0030] Evaluation criteria and scoring rules are set for each indicator. For example, if the width of the sidewalk exceeds 2 meters, full marks will be awarded, while if it is less than 1.5 meters, points will be deducted; if the average daily use time of the public space exceeds 5 hours, it will be rated as "excellent", while if it is less than 2 hours, it will be rated as "needing improvement". According to the evaluation criteria, the renovation plans of each area are scored, and the scores of all evaluation indicators are comprehensively considered using methods such as comprehensive scoring method and hierarchical analysis method (AHP) to obtain a preliminary comprehensive evaluation result, that is, the first evaluation result. This first evaluation result is a comprehensive assessment of the preliminary feasibility and effect of the micro-renovation renovation plan, which provides an important reference for subsequent project optimization and implementation.

[0031] The feedback data reading of the spatial shape aggregation result is slightly updated and transformed, and a second evaluation result is established based on the feedback data reading result. The second evaluation result is the questionnaire feedback result after weighted calculation based on age level.

[0032] Specifically, micro-renewal and transformation feedback data refers to the feedback information on micro-renewal and transformation plans from residents, merchants and other stakeholders collected from each area in the spatial shape aggregation results, including questionnaires, interviews, online comments and other forms.

[0033] The renovation area (i.e., the spatial shape aggregation result) is divided into different areas, and feedback data on micro-renovation renovation is collected from residents of all ages through questionnaires, online platform feedback, face-to-face interviews, etc. The questionnaire design can include evaluation options for the functional use, aesthetics, convenience, etc. of the renovation area. According to the age of the feedback group, they are divided into different levels such as children, teenagers, middle-aged, and elderly. Different groups of people have different concerns about micro-renovation renovation. The elderly may pay more attention to the convenience and safety of facilities, while young people may pay more attention to innovation and interactive experience. Therefore, when processing data, the weighted average method is used to give different weights to feedback from different age groups to ensure that the evaluation results can more comprehensively and fairly reflect the opinions of all groups. For example, a micro-renovation renovation project in a certain area collected a total of 1,000 valid feedbacks, including 100 feedbacks from children, 300 feedbacks from teenagers, 400 feedbacks from middle-aged, and 200 feedbacks from the elderly. When calculating the overall satisfaction, the weight of children's feedback is set to 0.1, teenagers to 0.3, middle-aged to 0.4, and elderly to 0.2. The final satisfaction calculation needs to consider the specific feedback and respective weights of each age group.

[0034] Based on the questionnaire feedback results weighted by age level, the acceptance and actual effect of the micro-renovation project among people of different age groups are comprehensively analyzed to form the second evaluation result. This evaluation result not only includes the satisfaction score, but also may include improvement suggestions, future optimization directions, etc., which can more objectively reflect the actual feelings of different groups on the project and provide reference for further adjustment and optimization of the renovation plan to make it more in line with the needs of residents.

[0035] The first evaluation results and the second evaluation results are used to generate the evaluation results of the micro-renovation project of the target space.

[0036] Specifically, the first evaluation result and the second evaluation result are integrated. Comparative analysis, weight allocation or other statistical methods can be used to ensure that the two evaluation results can be effectively combined. Through comprehensive analysis, the evaluation results of the micro-renewal renovation project of the target space are generated. This result will provide comprehensive information about the renovation project, including its technical feasibility, spatial adaptability and public acceptance. Exemplarily, using weight allocation, different weights are assigned to the technical function evaluation and user satisfaction evaluation according to the project goals and community needs, and the comprehensive score of each renovation area is calculated according to the weights. First, the data of the first evaluation results and the second evaluation results are merged and standardized to ensure comparability between different indicators. For example, a standardized interval of 0 to 1 is used to compare technical indicators and satisfaction indicators in the same dimension. The two scores of Park A are 0.8 and 0.6 respectively. The weight of the first evaluation result is 0.7, and the weight of the second evaluation result is 0.3. The comprehensive score is 0.7×0.8+0.3×0.6=0.74.

[0037] The comprehensive evaluation result generated by combining the first and second evaluation results can comprehensively reflect the renovation effect of the micro-renewal renovation project, guide the specific implementation and continuous optimization of the renovation plan, and thus improve the scientificity and sustainability of the micro-renewal renovation project.

[0038] Further, such as Figure 2 As shown, the establishment of a technical indicator set in the embodiment of the present application also includes:

[0039] Establish an inclusive index, and take the inclusive index as a technical indicator in the technical indicator set. The inclusive index is constructed as follows: configure a crowd evaluation function, which includes age distribution evaluation characteristics, gender ratio evaluation characteristics, barrier-free evaluation characteristics, and occupation evaluation characteristics; configure an activity and behavior range evaluation function, which includes activity diversity evaluation characteristics and spatial flexibility evaluation characteristics; use the crowd evaluation function and the activity and behavior range evaluation function to establish the inclusive index.

[0040] Specifically, the inclusive index is an evaluation standard used to measure the effectiveness of micro-renewal and transformation programs in serving different groups of people and promoting diverse activities and behaviors. It aims to build a more equitable, diverse and accessible public space. Therefore, the inclusive index is used as one of the technical indicators when establishing a set of technical indicators.

[0041] To establish inclusiveness indicators, we first need to configure a population evaluation function, which is used to evaluate the adaptability of the renovation project to different populations. It covers multiple dimensions of the population, including age distribution evaluation characteristics, gender ratio evaluation characteristics, barrier-free evaluation characteristics, and occupational evaluation characteristics. Through the comprehensive evaluation of these dimensions, the population evaluation function can determine whether the design of the project meets the needs of different populations. Among them, the age distribution evaluation characteristics are determined by evaluating whether the space design takes into account the needs of different age groups such as children, teenagers, middle-aged people, and the elderly, such as the setting of children's play areas and elderly rest areas. The gender ratio evaluation characteristics are determined by evaluating the friendliness of the space design to men and women, such as the layout of toilets and dressing room designs. The barrier-free evaluation characteristics are determined by evaluating the friendliness of the space to people with disabilities, such as ramps, elevators, barrier-free signs, etc. The occupational evaluation characteristics are determined by analyzing whether the space can meet the rest, communication, and work needs of various occupational groups, such as whether suitable office space or rest areas are provided for office workers.

[0042] Secondly, it is necessary to configure the activity and behavior range evaluation function, which is used to evaluate whether the space can support a variety of activities and behaviors to meet different needs. By comprehensively evaluating the activity diversity evaluation characteristics and the spatial flexibility evaluation characteristics, an activity and behavior range evaluation function is established to measure the flexibility of the project space and the diversity of activities. Among them, the activity diversity evaluation characteristics are determined by evaluating whether the space can support a variety of activities, such as sports, performances, and learning. The spatial flexibility evaluation characteristics are determined by evaluating whether the spatial layout has the ability to convert multiple functions and whether it can be adjusted according to different activity needs, such as whether a piece of grass can be used as a leisure venue and for community activities.

[0043] According to the project characteristics, weights are set for the combination of the crowd evaluation function and the activity and behavior range evaluation function, and the crowd evaluation function and the activity and behavior range evaluation function are integrated according to the corresponding weights to form a comprehensive inclusiveness index, ensuring that the index system can fully reflect the inclusiveness of the space.

[0044] Furthermore, the establishment of a technical indicator set described in the embodiment of the present application also includes:

[0045] A reachability indicator is established, and the reachability indicator is used as a technical indicator in the technical indicator set. The reachability indicator is as follows:

[0046] .

[0047] in, Characterize the accessibility index, is the actual walking time, For the ideal walking time, is the actual average vehicle speed, The maximum design speed of the road. is the actual number of public transportation facilities in the target space, is the ideal number of public transportation facilities for the target space, , , They represent the weight factors of walking evaluation characteristics, traffic fluency characteristics, and public transportation convenience characteristics respectively.

[0048] Specifically, the accessibility index is a technical indicator used to evaluate the traffic convenience of the target space. It combines walking time, vehicle speed and the number of public transportation facilities to quantify the accessibility of the area. By constructing the accessibility index formula, the traffic convenience and mobility of the target space can be evaluated.

[0049] In the above accessibility index formula, the actual walking time Refers to the actual walking time from the starting point to the target space, reflecting the convenience of the walking environment. Ideal walking time Refers to the walking time under ideal conditions (e.g. no obstacles, smooth roads). Usually used as a reference value for comparison with actual walking time. The formula calculates the actual walking time and ideal walking time If the actual walking time is close to the ideal walking time, it means that the walking environment is good and the walking convenience is high.

[0050] Actual average speed Refers to the actual average speed of vehicles traveling on the road. This value can be obtained through road traffic data or monitoring data, reflecting the traffic fluidity. It refers to the maximum speed of the road design, that is, the maximum speed that vehicles can reach under the best conditions. Maximum design speed of the road The closer the actual speed is to the design speed, the higher the traffic flow.

[0051] Actual number of public transportation facilities Refers to the number of public transportation facilities that actually exist near the target space, such as bus stops, subway stations, etc. Ideal number of public transportation facilities Refers to the number of public transportation facilities ideally required in the target area, used to measure the completeness of actual facilities. and the ideal number of facilities The closer the actual number of facilities is to the ideal number of facilities, the more convenient the public transportation is.

[0052] , , Represents the weights of walking evaluation features, traffic flow features and public transportation convenience features. These weights can be set according to the actual situation of each area in the spatial shape aggregation results to balance the importance of different indicators.

[0053] Further, such as Figure 3 As shown, the establishment of a technical indicator set in the embodiment of the present application also includes:

[0054] Establish a security index and use the security index as a technical indicator in the technical indicator set. The security index is constructed as follows: configure daytime security indicators. The evaluation characteristics of daytime security indicators include daytime anomaly rate characteristics, visualization characteristics, and daytime monitoring facility coverage characteristics; configure nighttime security indicators. The evaluation characteristics of nighttime security indicators include nighttime anomaly rate characteristics, nighttime lighting facility characteristics, and nighttime monitoring facility coverage characteristics; use daytime security indicators and nighttime security indicators to construct security indicators.

[0055] Specifically, the safety index is a set of indicators that evaluate the level of safety of personnel and property that can be guaranteed in daily operations of micro-renovation and transformation projects, including daytime safety indicators and nighttime safety indicators. These two indicators focus on the safety status of the space during the day and night, respectively, and cover multi-dimensional evaluations such as abnormal prevention, visual transparency, and monitoring systems. The safety index is used as a technical indicator in the technical indicator set.

[0056] The evaluation characteristics of daytime safety indicators include daytime anomaly rate characteristics, visualization characteristics, and daytime monitoring facility coverage characteristics. The daytime anomaly rate characteristics are determined by collecting the number of abnormal events such as lost items that occurred during the day in the target space in the past year through statistical reports from the public security department or public data; the visualization characteristics are obtained by evaluating the degree of openness of the field of vision inside and around the space; the daytime monitoring facility coverage characteristics are determined by using technologies such as geographic information systems to count the number of cameras and their coverage.

[0057] The evaluation characteristics of nighttime safety indicators include nighttime abnormality rate characteristics, nighttime lighting facility characteristics, and nighttime monitoring facility coverage characteristics. Among them, the nighttime abnormality rate characteristics are determined by counting the statistical data of abnormal events at night in the same time period; the nighttime lighting facility characteristics are determined by evaluating the distribution and brightness of street lights and public lighting facilities; the nighttime monitoring facility coverage characteristics are determined by counting the number of normal operation of nighttime monitoring equipment and its coverage range.

[0058] Collect the corresponding data according to the characteristics of the defined daytime and nighttime safety indicators, and set evaluation criteria and thresholds for the daytime and nighttime safety indicators. For example, an abnormality rate below a certain value is considered safe, and 100% monitoring coverage is the best practice. Then, determine the relative importance of daytime and nighttime safety indicators based on the transformation needs of each area, and assign different weights to the two. Combine the daytime and nighttime safety indicators according to the weights to construct a safety indicator.

[0059] For example, in the process of renovating an old commercial district, define the daytime safety indicators: ensure that the daytime anomaly rate is less than 0.5%, at least 85% of the area is clearly visible under natural light or artificial light, and at the same time ensure 100% daytime monitoring coverage. Construct nighttime safety indicators: control the nighttime anomaly rate within 0.2%, ensure that the nighttime lighting facilities are intact, the lighting intensity is moderate and the coverage is wide, and there are no blind spots for nighttime monitoring. Set the weights of daytime and nighttime safety indicators to 0.6 and 0.4 respectively, and calculate the overall safety score by weighted average method.

[0060] Furthermore, the multidimensional space recognition result of establishing the target space by presetting the analysis scale described in the embodiment of the present application also includes:

[0061] Use the preset analysis scale to configure the bandwidth and dimension of the kernel function, and use the configured bandwidth and dimension to perform kernel density identification, as follows:

[0062] .

[0063] in, Characterizes the kernel density estimate, is the total number of data points, is the bandwidth, For the dimension, Characterize the target point, Characterize the dataset data points, Characterize the kernel function.

[0064] Specifically, the kernel density recognition is performed on the spatial data set of the target space by using a preset analysis scale to obtain the kernel density recognition result in the multidimensional space recognition result. Kernel density recognition estimates the data distribution by applying the kernel function to each data point and summing up all the kernel function results. This process first configures the appropriate bandwidth according to the preset analysis scale. and dimensions Among them, bandwidth Determines the smoothing range around each data point. The choice of bandwidth can be determined automatically by methods such as cross-validation, or a suitable value can be selected based on empirical rules. A larger bandwidth will result in smooth transitions in the data, resulting in loss of details; a smaller bandwidth will produce overfitting noise. Dimension Represents the dimension of the data. In one dimension, the bandwidth determines the range of influence of each data point; in two or more dimensions, the choice of bandwidth is more complicated and needs to adapt to the changes in each dimension. For example, the bandwidth in two-dimensional data may be a matrix that represents the smoothing range of each dimension.

[0065] Then select the kernel function, which is a weighted function used to assign different weights to data points with different distances from the target point. Common kernel functions include Gaussian kernel, uniform kernel, and triangular kernel. The choice of kernel function will affect the result of density estimation. The weight distribution of Gaussian kernel decays exponentially, that is, the farther away from the target point, the smaller the weight. The weight of triangular kernel decays linearly, that is, when the distance is within 1, the weight of the data point decreases linearly with the distance, and when it exceeds 1, the weight is 0. The uniform kernel assigns the same weight to all points within the bandwidth.

[0066] For the target point , calculate each data point With target point The distance between them and the bandwidth Normalize the distance and substitute the standardized distance into the kernel function , calculate the kernel function value of each point.

[0067] Finally, add up the kernel function values ​​for all data points and divide the sum by Standardize and get the target point The estimated density of is the total number of data points, is the bandwidth, For dimension.

[0068] Through the above steps, kernel density estimation is used to analyze the density distribution of specific areas in urban micro-renewal projects, such as population density, building density, etc., so as to provide data support for the renovation projects. By reasonably selecting bandwidth and dimension, more accurate and reliable analysis results can be obtained, providing a scientific basis for decision-making.

[0069] Furthermore, the multidimensional space recognition result of establishing the target space by presetting the analysis scale described in the embodiment of the present application also includes:

[0070] Configure the distance attenuation parameters through the pre-analysis scale; use the configured distance attenuation parameters to perform heat map recognition as follows:

[0071] .

[0072] in, Representation in position The heat recognition value at is the data point index, is the total number of data points, For the The weight of the data point, Characterizing Data Points With location The distance is the distance attenuation parameter.

[0073] Specifically, the distance attenuation parameter is set according to the pre-analysis scale If you want the influence of a data point to be effective only within a small range, choose a smaller If you want to consider the impact in a wider range, select a larger value. value.

[0074] For the target location , calculate its correlation with each data point The distance between The greater the distance, the smaller the impact on the position. According to the formula For each data point, according to its distance to The smaller the weight decay result is, the smaller the contribution of the data point to the target position is. Multiply the decay weight of each data point by its own weight. , and add the results of all data points to get the target position The higher the heat value, the higher the density or importance of the data points near the location.

[0075] By using the configured distance attenuation parameters for heat map recognition, we can intuitively understand the spatial data and identify areas that need special attention or transformation, thereby providing strong data support for urban micro-renewal and transformation projects.

[0076] Furthermore, the method of establishing a second evaluation result based on the feedback data reading result described in the embodiment of the present application further includes:

[0077] Establish age grade classification, and use micro-renovation project identification to conduct age grade classification fit evaluation, and establish fit weights; perform feedback weighted calculation based on the fit weights and feedback data reading results, and establish a second evaluation result.

[0078] Specifically, the feedback samples are divided into different levels according to different age groups, for example, children (0 to 12 years old), teenagers (13 to 18 years old), middle-aged (19 to 60 years old), and the elderly (over 60 years old). For each age level, the matching degree between the micro-renovation project logo and its preferences and adaptability is evaluated. Based on the fit evaluation results, different weights are set for each age level, namely the fit weight, which reflects the coefficient of the proportion of different age levels in the feedback of the micro-renovation project, and is used to adjust the age bias of the evaluation results. The real feedback information on the micro-renovation project from different age groups is summarized, and the feedback information within each age group is averaged to obtain the average feedback score of each group. The average feedback score of each age group is weighted according to the fit weight to determine the second evaluation result, highlighting the value of the opinions of the key age groups.

[0079] By introducing the age level grading and fit evaluation mechanism, the second evaluation results can reasonably summarize the feedback results of people of different age groups according to the key target groups of the micro-renewal and renovation projects, and highlight the opinions of people of key age groups, so as to more accurately and comprehensively reflect the real feelings of different age groups and provide strong support for the optimization of micro-renewal and renovation projects.

[0080] Furthermore, the method described in the embodiment of the present application also includes:

[0081] An evaluation feedback database is established, and the evaluation feedback database is used to read the actual micro-renovation execution effect corresponding to the micro-renovation project evaluation result, and a feedback data set is established based on the deviation between the execution effect reading result and the micro-renovation project evaluation result; and the feedback data set is used to perform subsequent evaluation optimization of the micro-renovation project.

[0082] Specifically, a database is designed to store basic information, evaluation indicators, expected and actual effect data of micro-renewal and renovation projects, and the historical evaluation results and implementation effect feedback of micro-renewal and renovation projects are entered into the database to establish an evaluation feedback database. The database collects and stores the evaluation results and actual implementation effect feedback data of micro-renewal and renovation projects for subsequent analysis and optimization. New project evaluation results and the latest implementation effect feedback are collected regularly, and the evaluation feedback database is updated to ensure the timeliness of the data in the database.

[0083] The actual micro-renovation transformation execution effect is read from the database, compared with the evaluation results, the difference between the evaluation results and the execution effect is identified, and the cause of the deviation is analyzed. All evaluation deviation cases are organized into a feedback data set. This feedback data set is used to analyze the accuracy of the evaluation results and improve the setting of evaluation technical indicators.

[0084] The feedback data set is classified by technical indicators, project type, regional environment, etc., and the feedback data set is deeply analyzed to identify the key factors that affect the accuracy of the assessment. Based on the analysis results of the feedback data set, the weights, thresholds and other parameters in the assessment model are revised. For example, if it is found that the accessibility indicators are too optimistic in the assessment, its weight can be adjusted to ensure that future assessments are closer to reality. Use the revised assessment model to re-evaluate historical projects and verify the improved assessment results. If the improved assessment results are close to the actual micro-renewal and transformation execution results, the optimized assessment model will be applied to new projects. At the same time, new data will be collected during the application process to continuously optimize the assessment system.

[0085] By establishing an evaluation feedback database and using it to optimize the evaluation results of micro-renovation projects, the accuracy and practicality of the entire project evaluation process can be effectively improved, ensuring that the project evaluation results are closer to reality, and promoting the continuous optimization and successful implementation of micro-renovation projects.

[0086] In summary, the micro-renovation project evaluation method based on spatial multidimensional data analysis provided by the embodiment of the present application has the following technical effects:

[0087] Establish a spatial data set of the target space, perform kernel density identification, heat map identification and land use function identification on the spatial data set through a preset analysis scale, and establish a multi-dimensional spatial identification result of the target space to obtain accurate spatial features, provide basic data for subsequent spatial analysis, and ensure the objectivity and accuracy of subsequent analysis. Use the multi-dimensional spatial identification results to aggregate spatial shapes, establish spatial shape aggregation results, and establish micro-renewal and transformation project identification of spatial shape aggregation results, transforming scattered, high-dimensional data into intuitive and easy-to-understand spatial forms, while revealing the inherent connections and laws of space, so as to accurately identify areas suitable for micro-renewal and transformation. Establish a set of technical indicators including inclusiveness indicators, accessibility indicators and safety indicators, and reconstruct the weights of the technical indicator set through micro-renewal and transformation project identification, adjust the evaluation standards according to the actual conditions of different projects, generate update evaluation indicators, and establish clear directions and standards for the evaluation work, so that it can focus on the core elements of micro-renewal, while dynamically responding to different project needs to ensure that the technical indicators are more flexible and more adaptable. Based on the renewal evaluation index, the micro-renewal transformation evaluation of the corresponding spatial shape aggregation results is carried out to generate the first evaluation result; the micro-renewal transformation feedback data of the spatial shape aggregation results is read, and the questionnaire feedback results are weighted based on the age level to determine the second evaluation result, and the first evaluation result and the second evaluation result are used to generate the micro-renewal transformation project evaluation result of the target space. The first evaluation result conducts the first quantitative test on the feasibility of micro-renewal transformation and provides a preliminary basis for decision-making, while the second evaluation result further incorporates the soft indicators of community feedback, especially considering the feelings of people of different age groups, so that the evaluation results are closer to the real user needs. This dual evaluation mechanism combines data analysis and citizen feedback to make the evaluation results more comprehensive.

[0088] Overall, the embodiment of the present application deeply explores the value of spatial data through multi-dimensional spatial data analysis, combines technical indicator reconstruction and feedback mechanism, and realizes a comprehensive, accurate and scientific evaluation of urban micro-renewal and renovation projects, thereby improving the accuracy and work efficiency of site selection for micro-renewal and renovation projects, making them more in line with actual needs, so as to improve the scientificity and sustainability of the renovation effect.

[0089] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A micro-renovation project evaluation method based on spatial multidimensional data analysis, characterized in that: The method comprises: Establishing a spatial data set of the target space, establishing a multidimensional spatial recognition result of the target space by presetting an analysis scale, wherein the multidimensional spatial recognition result includes a kernel density recognition result, a heat map recognition result, and a land use function recognition result, and summarizing the kernel density recognition result, the heat map recognition result, and the land use function recognition result to establish a multidimensional spatial recognition result; Use the multi-dimensional space recognition results to perform spatial shape aggregation, establish spatial shape aggregation results, and establish micro-renewal and transformation project identification of the spatial shape aggregation results; Establish a set of technical indicators, and reconstruct the weights of the technical indicator set through micro-renovation and transformation project identification to generate renewal evaluation indicators; Based on the update evaluation index, a micro-update transformation evaluation of the corresponding spatial shape aggregation result is performed to generate a first evaluation result; The feedback data reading of the spatial shape aggregation result is slightly updated and modified, and a second evaluation result is established based on the feedback data reading result, wherein the second evaluation result is the questionnaire feedback result after weighted calculation based on age level; Generate a micro-renovation project evaluation result of the target space using the first evaluation result and the second evaluation result; The multi-dimensional space recognition result of the target space is established by presetting the analysis scale, and further includes: Use the preset analysis scale to configure the bandwidth and dimension of the kernel function, and use the configured bandwidth and dimension to perform kernel density identification, as follows: ； Among them, f(x) represents the kernel density estimate, n is the total number of data points, h is the bandwidth, d is the dimension, x represents the target point, x i represents the i-th data point in the data set, Characterize the kernel function; The method further comprises: Configure distance decay parameters through pre-analysis scale; Use the configured distance attenuation parameters for heat map recognition as follows: ； Among them, H(P x , P y ) is characterized in (P x , P y ) is the heat recognition value at the position, j is the data point index, m is the total number of data points, is the weight of the jth data point, Characterizing Data Points and position (P x , P y ), and r is the distance attenuation parameter.

2. The micro-renovation project evaluation method based on spatial multidimensional data analysis according to claim 1 is characterized in that: The establishing of the technical indicator set further includes: Establish an inclusive index, and take the inclusive index as a technical indicator in the technical indicator set. The inclusive index is constructed as follows: Configuring a crowd evaluation function, wherein the crowd evaluation function includes an age distribution evaluation feature, a gender ratio evaluation feature, an accessibility evaluation feature, and an occupation evaluation feature; configuring an activity and behavior range evaluation function, wherein the activity and behavior range evaluation function includes an activity diversity evaluation feature and a spatial flexibility evaluation feature; The inclusiveness index is established using crowd evaluation function, activity and behavior range evaluation function.

3. The micro-renovation and transformation project evaluation method based on spatial multidimensional data analysis according to claim 2 is characterized in that: The method further comprises: A reachability indicator is established, and the reachability indicator is used as a technical indicator in the technical indicator set. The reachability indicator is as follows: ； Among them, S represents the accessibility index, is the actual walking time, is the ideal walking time, V a is the actual average vehicle speed, V max is the maximum design speed of the road, P actual is the actual number of public transportation facilities in the target space, P ideal is the ideal number of public transportation facilities for the target space, , , They represent the weight factors of walking evaluation characteristics, traffic fluency characteristics, and public transportation convenience characteristics respectively.

4. The micro-renovation project evaluation method based on spatial multidimensional data analysis according to claim 2 is characterized in that: The method further comprises: Establish a safety index, and take the safety index as a technical indicator in the technical indicator set. The safety index is constructed as follows: Configure daytime safety indicators. The evaluation characteristics of daytime safety indicators include daytime abnormality rate characteristics, visualization characteristics, and daytime monitoring facility coverage characteristics. Configure night safety indicators. The evaluation characteristics of night safety indicators include night abnormality rate characteristics, night lighting facility characteristics, and night monitoring facility coverage characteristics. The safety index is constructed using the daytime safety index and the nighttime safety index.

5. The micro-renovation and transformation project evaluation method based on spatial multidimensional data analysis according to claim 1 is characterized in that: The establishing of the second evaluation result based on the feedback data reading result further includes: Establish age-grade classification, and use micro-renovation project identification to evaluate the fit of age-grade classification and establish fit weights; Feedback weighted calculation is performed based on the fit weight and the feedback data reading result to establish a second evaluation result.

6. The micro-renovation project evaluation method based on spatial multidimensional data analysis according to claim 1 is characterized in that: The method further comprises: Establish an evaluation feedback database, use the evaluation feedback database to read the actual micro-renovation and transformation execution effect corresponding to the micro-renovation and transformation project evaluation results, and establish a feedback data set based on the deviation between the execution effect reading result and the micro-renovation and transformation project evaluation result; The feedback data set is used to perform subsequent evaluation and optimization of the micro-renovation project.

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