A method for quantitatively evaluating the accessibility of a living space under a walking mode

Abstract

The application discloses a kind of based on walking mode under residential space accessibility quantitative evaluation method, the method includes: obtaining space-time big data;Space-time big data processing and fusion;Establish residential space quantitative accessibility evaluation model;Evaluation result pre-processing;Polygon clustering analysis based on de launay triangle;Divide residential space category.The present application comprehensively considers the facility convenience and public transport station accessibility;Introduce POI density and category, population age image and other parameters, weight division is carried out for different groups, the influence of space-time change on accessibility is considered, the accuracy and comprehensiveness of evaluation result are improved.

Description

Technical Field

[0001] This invention belongs to the field of urban planning technology, specifically a quantitative assessment method for the accessibility of residential spaces based on pedestrian patterns. Background Technology

[0002] In urban planning and transportation, assessing the accessibility of residential areas is crucial for improving residents' quality of life and promoting sustainable urban development. Current assessment methods primarily focus on public transportation accessibility, lacking a comprehensive evaluation of the accessibility of other amenities. Furthermore, existing walkability index models fail to adequately consider the diversity and density of surrounding facilities when assessing the accessibility of residential spaces, resulting in inaccurate and incomplete assessments. In recent years, location-based big data acquisition methods have become increasingly prevalent, including points of interest, instantaneous population data, and trending travel patterns. Numerous research findings on the cleaning, fusion, and analysis of big data are also available. Therefore, this paper aims to provide technical and data support for improving existing accessibility assessment methods, considering the impact of spatiotemporal variations on accessibility, and conducting quantitative analysis and assessment of accessibility.

[0003] Traditional methods for assessing residential accessibility suffer from a lack of evaluation indicators, particularly regarding walkability. They fail to consider factors such as the density and variety of surrounding amenities, population age, and consumer profiles, resulting in quantitative assessments that don't fully reflect reality. Public transportation accessibility and neighborhood assessment methods rely on simplistic indicators, typically using only distance, time, or mode of transport selection, failing to comprehensively consider multiple aspects of residential space, such as transportation convenience, service facilities, and community environment. Existing methods often treat residential accessibility as a static concept, ignoring the impact of spatiotemporal changes. However, changes in transportation networks, service facilities, and people's travel behaviors are dynamic, influencing residential accessibility and lacking a spatiotemporal dynamism. Furthermore, while there are connections and distinctions between different methods—walkability, public transportation accessibility, and neighborhood analysis—the assessment process lacks integration of multiple factors across these methods. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a distributed data synchronization system that overcomes or at least partially solves the above problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for quantitatively assessing the accessibility of residential spaces based on walking patterns, the method comprising: Acquire spatiotemporal big data; Spatiotemporal big data processing and fusion; Establish a quantitative accessibility assessment model for residential spaces; Preprocessing of evaluation results; Polygon clustering analysis based on Delaunay triangle; Classify living spaces.

[0006] Optionally, acquiring spatiotemporal big data includes: The target website was identified, and the POI, AOI, and traffic information came from the Gaode Map Open Platform, while the bus and subway station information came from Open Street Map. Analyze the target website's webpage structure, URL format, and data display method. Use Python and the Scrapy library to define crawling rules, process pages, extract data, and store data. Use the Requests library to send HTTP requests and parse the returned JSON or XML data to write a crawler program. Data extraction and storage: Extract POI, AOI, traffic flow and speed from HTML content. Store the extracted data in a MySQL database. POI and traffic station data are saved as points, AOI is saved as polygon data, and traffic information is saved in CSV format.

[0007] Optionally, after acquiring the spatiotemporal big data, the spatiotemporal big data may be cleaned, including: Select outlier identification and interpolation methods that match the data characteristics for identification, removal, and supplementation; Missing attribute names are supplemented, and the missing attribute values ​​are supplemented based on the attribute values ​​that are not missing at the same time on other dates using a linear interpolation method; Remove outliers, x i Let x represent the population of each branch at a specific time on a specific day. Calculate the expected value E(x) and standard deviation σ for each hour across all days. Values ​​within the interval (E(x)-σ, E(x)+σ) are considered normal, while those outside are considered outliers.

[0008] Optional spatiotemporal big data processing and fusion include: Traffic network spatial data processing and traffic network model construction. Traffic network models are used to describe the topology of road networks, the distribution of traffic flow, and the spatiotemporal changes in traffic speed. Traffic flow data addition and configuration is used to configure traffic flow data to the road network, thereby realizing the dynamic and regular simulation of traffic flow patterns in the road network. Big data batch processing and spatial fusion: Develop a Python batch processing program to perform spatial data fusion according to analysis requirements.

[0009] Optionally, spatial data processing and traffic network model construction for traffic networks include: Organize and process the spatial components of the topology, including points, lines, and surfaces, to create a thematic dataset on transportation networks; Construct a spatial topology data structure; the spatial data of the transportation network dataset is used to build topological relationships based on topological rules. Add network dataset attributes and historical traffic feature attributes; Flow characterization curve fitting and feature extraction.

[0010] Optional, flow characterization curve fitting and feature extraction include: Flow characterization curve fitting: Distinguishing between weekends and weekdays, speed data for each road was extracted for 24 hours a day from Monday to Sunday, and for one month. A scatter plot was created with time on the x-axis and speed at any given moment on the y-axis, and linear fitting was performed using the least squares method. in, It is the dependent variable. It is the independent variable. and These are the coefficients of the fitted curve, which are confirmed by the least squares method by minimizing the sum of squared errors. and The value of, that is: Where n is the number of data points. and These represent the velocities at 24 time points and their corresponding times. The error of the fitted curve is evaluated by calculating the sum of squared residuals, which is the residual between the time-stamped velocity and the measured velocity using the least squares method. in, This is the actual measured speed. For the least squares simulation speed, SSR is controlled within [ 5, 5] Within 5, Expected speeds for different dates at the same time; Curve similarity determination and extraction of representative flow characterization curves: The Pearson correlation coefficient algorithm is used to determine the degree of linear correlation in the flow characterization curves. First, the mean of each curve is subtracted, and the results are denoted as follows: Then calculate the normalized covariance. Divide by the product of the standard deviations of the two curves value range The range is between -1 and 1, with the closer to 1 indicating a higher correlation, the closer to -1 indicating a negative correlation, and the closer to 0 indicating no correlation. Only one curve is retained if it is identified as similar. The flow characterization curve with typical characteristics is selected and assigned a unique ID.

[0011] Optionally, establishing a quantitative accessibility assessment model for residential spaces includes: Calculate the service area coverage of public transportation stations, and select residential communities within the coverage area as the research object; Based on differences in human physical function, walking difference coefficients were set for different age groups. : Where T refers to the time required to walk the same distance on a basic basis. The age-related adjustment factor is a factor adjusted for differences in walking ability among different age groups. Calculate the intersection density (number of points in the circle / circle area), average block length, POI density (number of POI points in the circle / circle area), and POI richness (total number of POI types in the circle / circle area) within each circle. The average block length is the average length of each road segment within the circle. ,in The total length of the intersecting streets. Based on quantity and travel habits, the more intersections and the longer the road, the fewer the number and types of POIs, and the lower the willingness to walk. The attenuation rate is defined as the sum of the attenuation rates of each component, and the attenuation coefficient is calculated. : The average intersection density attenuation rate, The average street length attenuation rate, POI density decay rate The POI richness decay rate; Following the three concentric circles, calculate the shortest distance from the point to each point of each type of facility, find the shortest distance length threshold, and obtain the shortest distance attenuation rate; Calculate the accessibility index of living space; accessibility index for: * ,in As the weight of the indicator category, For the weight of the indicator sub-category, To hinder the decay rate, For short-path attenuation rate, This is the walking difference coefficient; The accessibility score of public transport stations is calculated by using the Dijkstra algorithm to calculate the average distance from the subway station to the neighborhood within a 1000-meter walking distance of the public transport station, thus obtaining the average public transport station accessibility index.

[0012] Optionally, preprocessing of the evaluation results includes: Matrix standardization, denoted as . , For each element in the matrix, the standardized matrix is ​​obtained by applying a standardization algorithm: The normalized matrix is ​​minimized and normalized to the interval [0, 100] using the following function: f(x) represents the positiveized result, and x represents the public transportation station accessibility index.

[0013] Optional polygon clustering analysis based on Delaunay triangles includes: Thiessen polygons are constructed based on a threshold between points. The definition of the distance threshold during clustering is as follows: in It is the midpoint or centroid of the first kind. Distance For the farthest point, the range where η is a constant is 0 < η < 1; The midpoint within a class is calculated as follows: in It is a set of data The number of midpoints or objects, and ≥1; It is a data group The point in; X i and y i , , are the x and y coordinates of station i, respectively, and represent the normalized values ​​of the mean accessibility index of surrounding residential areas and the normalized values ​​of public transportation station accessibility, respectively, which are mapped to the horizontal and vertical coordinates in a two-dimensional map coordinate system.

[0014] Optionally, based on the results of polygon clustering analysis, the living space can be divided into mature, balanced, nurturing, and deficient types.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention comprehensively considers facility convenience and public transportation station accessibility; it introduces parameters such as POI density and type, population age profile, etc., and assigns weights to different groups of people, taking into account the impact of spatiotemporal changes on accessibility, thereby improving the accuracy and comprehensiveness of the assessment results.

[0016] 2. This invention uses an improved clustering algorithm to group the clustering results—residential area types—based on the clustering results and Delaunay triangles, quantitatively and intuitively classifying the convenience level of residential spaces, providing valuable information for urban planning and residents.

[0017] 3. The convenience analysis is based on a high-precision dynamic traffic network model, which includes multiple modes of transportation such as vehicles, pedestrians, and public transportation. It fully considers various spatial factors such as underground passages and pedestrian overpasses. The living circle is obtained based on the network analysis model, which strengthens the data foundation of the model. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a method for quantitatively assessing the accessibility of residential spaces based on a walking pattern, provided for an embodiment of this application; Figure 2 A roadmap for big data cleaning technologies; Figure 3 A roadmap for big data fusion technologies; Figure 4 Flowchart for a quantitative accessibility assessment model for residential spaces; Figure 5 A schematic diagram showing the service areas at 500 meters and 1000 meters for 50 stations; Figure 6 A diagram illustrating the walking index of a residential community; Figure 7 This is a schematic diagram of public transportation accessibility analysis based on the shortest path. Figure 8 This is a schematic diagram of the results of cluster analysis of residential spaces. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This embodiment provides a method for quantitatively assessing the accessibility of residential spaces based on walking patterns, the method comprising: S1. Obtain spatiotemporal big data.

[0021] Based on compliance with the target website's regulations, privacy policy, and terms of use, web crawling technology is used to obtain POI and AOI points of interest, public transportation stops (bus stops, subway stations), traffic flow, and traffic speed data. The specific steps are as follows: The target website was identified, and the POI, AOI, and traffic information came from the Gaode Map Open Platform, while the bus and subway station information came from Open Street Map.

[0022] Analyze the target website's webpage structure, URL format, and data display method. Using Python, the Scrapy library is used to define crawling rules, process pages, extract data, and store data. The Requests library is used to send HTTP requests and parse the returned JSON or XML data to write a web crawler program.

[0023] Data extraction and storage: POI, AOI, traffic flow, and traffic speed are extracted from HTML content. The extracted data is stored in a MySQL database. POI and traffic station data are saved as points, AOI is saved as polygonal spatial data, and traffic information is saved in CSV format and associated with a unique ID of the road name.

[0024] The remaining data include residential communities and individual buildings (area data, core area survey data), instantaneous population and population profile data, and permanent resident population (from the seven national censuses) from census surveys or purchases.

[0025] After acquiring spatiotemporal big data, the spatiotemporal big data is cleaned, including: Outlier identification and interpolation methods that meet the data characteristics are selected for identification, removal, and supplementation.

[0026] Analysis revealed issues with the population and traffic flow data, including missing attribute names (such as "travel mode" and "age group" in population profile data, and "date" and "hour" in traffic flow data), missing attribute values, and abnormal values. Based on the metadata description, missing attribute names were supplemented, and the missing attribute values ​​were supplemented using linear interpolation based on the attribute values ​​that were not missing at the same time on other dates.

[0027] Remove outliers, x i Let x represent the population (resident population or instantaneous population) of each branch at a specific time on a specific day. Calculate the expected value E(x) and standard deviation σ for all days in each hour: Values ​​within the interval (E(x)-σ, E(x)+σ) are considered normal; values ​​outside this interval are considered outliers. Data cleaning was implemented using a Python program, and the technical approach is as follows: Figure 2 As shown.

[0028] S2, Spatiotemporal Big Data Processing and Fusion.

[0029] Based on the cleaned big data results and combined with the needs of the analysis objectives, a unified spatial big data result with a unified spatial benchmark is formed through traffic network model construction, batch spatialization and attribute binding, and coordinate transformation.

[0030] Spatiotemporal big data processing and fusion includes: Traffic network spatial data processing and traffic network model construction: Traffic network models are used to describe the topology of road networks, the distribution of traffic flow, and the spatiotemporal changes in traffic speed.

[0031] Traffic flow data is added and configured by linking historical traffic flow data to the road network spatial data using a unique ID, and configuring traffic flow data to the road network at 1-hour intervals. This enables dynamic simulation of traffic flow patterns in the road network.

[0032] Big data batch processing and spatial fusion: After cleaning the big data resources, a Python batch processing program is developed to perform spatial data fusion according to the technical route of spatialization, spatial reference system unification, and fusion between spatial data based on analysis requirements. Text data is spatialized in batches through gridding, name association, ID association, and coordinate representation of attribute values.

[0033] This study investigates multi-source spatial reference ellipsoids and projection parameters. Three types of coordinate systems are first converted to a geographic coordinate system. Then, the geographic coordinate system ellipsoid is transformed to the CGCS2000 ellipsoid, and finally converted to the CGCS2000 projected coordinate system using the transverse Mercator projection algorithm. The big data fusion technology roadmap is as follows: Figure 3 As shown.

[0034] After unifying spatial references, we integrate population-residential space, traffic flow and road space data, and profile data with POIs and urban planning map patches to form a spatiotemporal big data fusion thematic dataset.

[0035] Traffic network spatial data processing and traffic network model construction include: The spatial components of the topology are organized and processed, including points, lines, and surfaces, to establish a thematic dataset on transportation networks. Points include obstacle points, corner points (turns, directions, and time constraints), centers (locations that receive or allocate resources), and stations (bus and subway stations, warehouses), etc. Lines include urban roads, bus routes, subway routes, pedestrian overpasses, underpasses, sidewalks, and connecting routes to ensure the accuracy of road connections. Turns represent changes in the direction of flow at intersections. Key attributes include: connectivity (accessibility, restrictions, prohibition), directionality, and consumption (time, speed, distance).

[0036] A spatial topology data structure is constructed, and the spatial data of the transportation network dataset is used to build topological relationships based on topological rules; the specific content of the topology rules is shown in Table 1: Table 1 Topology Rules Content Add attributes to the network dataset, which include four categories: cost, hierarchy, constraints, and description. As needed, the road network model adds cost attributes including road speed, bus speed, subway speed, travel time (road travel time, station transfer and waiting time), and path length; hierarchy attributes include one-way constraints and vehicle height constraints.

[0037] Add historical traffic characteristic attributes, and add a traffic profile table and a profile allocation table. The added attribute items in the traffic profile table include the traffic characterization curve ID, 24-hour time interval, and start and end time nodes. The profile allocation table includes unobstructed driving speed and the traffic characterization curve IDs corresponding to each of the seven days from Monday to Sunday.

[0038] Flow characterization curve fitting and feature extraction.

[0039] Specifically, flow characterization curve fitting and feature extraction include: Flow characterization curve fitting: Distinguishing between weekends and weekdays, speed data for each road was extracted for 24 hours a day from Monday to Sunday, and for one month. A scatter plot was created with time on the x-axis and speed at any given moment on the y-axis, and linear fitting was performed using the least squares method. in, It is the dependent variable. It is the independent variable. and These are the coefficients of the fitted curve, which are confirmed by the least squares method by minimizing the sum of squared errors. and The value of, that is: Where n is the number of data points. and These represent the velocities at 24 time points and their corresponding times. The error of the fitted curve is evaluated by calculating the sum of squared residuals, which is the residual between the time-stamped velocity and the measured velocity using the least squares method. in, This is the actual measured speed. For the least squares simulation speed, SSR is controlled within [ 5, 5] Within 5, The expected speed at the same time on different dates.

[0040] Curve similarity determination and extraction of representative flow characterization curves: The Pearson correlation coefficient algorithm is used to determine the degree of linear correlation in the flow characterization curves. First, the mean of each curve is subtracted, and the results are denoted as follows: Then calculate the normalized covariance. Divide by the product of the standard deviations of the two curves value range The range is between -1 and 1. The closer to 1, the higher the correlation; the closer to -1, the negative correlation; and the closer to 0, the no correlation.

[0041] Only one curve is retained if it is identified as similar. The flow characterization curve with typical characteristics is selected and assigned a unique ID.

[0042] S3. Establish a quantitative accessibility assessment model for residential spaces.

[0043] Based on the fusion of spatiotemporal data, evaluation indicators and algorithms were established for the accessibility of surrounding facilities and public transportation stations in residential spaces. Combined with spatial analysis models, an evaluation model was constructed, outlining the process of the residential space accessibility evaluation model. Figure 4 As shown.

[0044] Establishing a quantitative accessibility assessment model for residential spaces specifically includes: Calculate the service area coverage of public transportation stations, and select residential communities within the coverage area as the research object; Based on differences in human physical function, walking difference coefficients were set for different age groups (old, middle-aged, and young). : Where T refers to the time required to walk the same distance on a basic basis. The age-related adjustment factor is a factor adjusted based on the differences in walking ability among different age groups; it can be set according to actual conditions to represent the differences in walking ability relative to the baseline walking time among different age groups.

[0045] Calculate the intersection density (number of points in the circle / circle area), average block length, POI density (number of POI points in the circle / circle area), and POI richness (total number of POI types in the circle / circle area) within each circle. The average block length is the average length of each road segment within the circle. ,in The total length of the intersecting streets. Based on quantity and travel habits, the more intersections and the longer the road, the fewer the number and types of POIs, and the lower the willingness to walk. The attenuation rate is defined as the sum of the attenuation rates of each component, and the attenuation coefficient is calculated. : The average intersection density attenuation rate, The average street length attenuation rate, POI density decay rate The decay rate is the POI richness; the decay rates of each parameter are shown in Table 2. Table 2 Attenuation Rate of Each Parameter According to the POI weight table (Table 3), the shortest distance from each point to each point of each type of facility is calculated sequentially according to three concentric circles. The shortest distance length threshold is then determined, and the shortest distance attenuation rate is obtained. Weights are assigned according to the primary category to which the POI facility belongs. Basic living facilities are the most important for daily life and have the highest weight coefficient. Safety and security facilities, including fire-fighting facilities and police stations, are of secondary importance. Quality assurance facilities, under the protection of the first two categories, are used to improve the quality of life, such as fitness plazas, parks and green spaces, and art galleries. For POI subcategories, weights are assigned based on their importance to the elderly, middle-aged, and young adults. Table 3 POI Weight Allocation Table The distance attenuation rate calculation was based on a Python program developed using Dijkstra's shortest path algorithm. This program batch-calculated the shortest paths from over 5000 residential communities to approximately 60,000 points across 27 POI categories, involving approximately 360,000,000 calculations. The shortest distance between residential communities and subway stations was calculated. The shortest distance threshold was divided into three intervals: less than 500m, 500-1000m, and greater than 1000m. A path attenuation coefficient was assigned to each interval based on a piecewise function. Represents path decay rate, variable Represents each shortest path, These are the values ​​of the path attenuation rate for the three intervals.

[0046] Calculate the accessibility index of living space; accessibility index for: * ,in As the weight of the indicator category, For the weight of the indicator sub-category, To hinder the decay rate, For short-path attenuation rate, This represents the walking difference coefficient.

[0047] Accessibility score for residential space = weight of major category of indicator * weight of minor category of indicator * (1 - obstacle attenuation rate) * (1 - shortest path attenuation rate).

[0048] The accessibility score of public transport stations is calculated by using the Dijkstra algorithm to calculate the average distance from the subway station to the neighborhood within a 1000-meter walking distance of the public transport station, thus obtaining the average public transport station accessibility index.

[0049] S4. Preprocessing of evaluation results.

[0050] Preprocessing of evaluation results includes: Matrix standardization, denoted as . , For each element in the matrix, the standardized matrix is ​​obtained by applying a standardization algorithm: The normalized matrix is ​​minimized and normalized to the interval [0, 100] using the following function: f(x) represents the positiveized result, and x represents the public transportation station accessibility index.

[0051] S5. Polygon clustering analysis based on Delaunay triangle.

[0052] Polygon clustering analysis based on Delaunay triangles includes: Thiessen polygons are constructed based on a threshold between points. The definition of the distance threshold during clustering is as follows: in It is the midpoint or centroid of the first kind. Distance For the farthest point, the range where η is a constant is 0 < η < 1.

[0053] The midpoint within a class is calculated as follows: in It is a set of data The number of midpoints or objects, and ≥1; It is a data group The point in X. i and y i, , are the x and y coordinates of a certain station i, respectively, and represent the normalized average value of the accessibility index of surrounding communities and the normalized value of public transportation station accessibility, respectively, which are mapped to the horizontal and vertical coordinates in a two-dimensional map coordinate system.

[0054] The algorithm idea is as follows: Step 1: Input dataset S and clustering threshold .

[0055] Step 2: Construct Thiessen polygons based on dataset S.

[0056] Step 3: Combining functions Calculate the distance between every two points (or two classes), and store the resulting distances in a matrix. middle.

[0057] Step 4: Convert the matrix Sort the data and select the minimum distance value. When the minimum distance min(dis) < Merge two points (or two matrices) to form a new class. .

[0058] Step 5: Calculate the new class Find the centroid and add its coordinates to matrix S.

[0059] Step 6: Recursively repeat steps 3-5 until min(dis) > Stop. The spatial clustering results of the living spaces are shown in Table 4: S6. Classify living spaces.

[0060] Based on the results of polygon clustering analysis, the living space is divided into four types: mature (Level 1), balanced (Level 2), developing (Level 3), and deficient (Level 4). Table 4 Classification of Residential Space Clustering Results This embodiment comprehensively considers facility convenience and public transportation station accessibility; it introduces parameters such as POI density and type, population age profile, etc., and assigns weights to different groups of people, taking into account the impact of spatiotemporal changes on accessibility, thereby improving the accuracy and comprehensiveness of the assessment results.

[0061] This embodiment uses an improved clustering algorithm to group the clustering results by residential area type using Delaunay triangles, which quantitatively and intuitively classifies the convenience level of residential spaces, providing valuable information for urban planning and residents.

[0062] The convenience analysis is based on a high-precision dynamic traffic network model, which includes multiple modes of transportation such as vehicles, pedestrians, and public transportation. It fully considers various spatial factors such as underground passages and pedestrian overpasses. The living circle is obtained based on the network analysis model, which strengthens the data foundation of the model.

[0063] The following example will further verify this method.

[0064] Taking the core functional area of ​​Beijing (Dongcheng and Xicheng districts) as a case study, this study selects 50 subway stations in the core area as examples of public transportation stations, and uses 500-meter walking and 1000-meter cycling ranges as examples of living circles. A spatiotemporal database of residential space accessibility is established by processing and organizing the core area's transportation network, public transportation, subway, residential communities, Gaode Maps POIs, and Baidu Insight location big data.

[0065] Service area calculation within a living circle. Using ArcGIS Service Area analysis tool and based on a traffic and road network model, service areas within a 500-meter walking distance and a 1000-meter cycling distance of 50 subway stations were calculated. Figure 5 As shown.

[0066] Analyze the population and demographics of residential communities surrounding the service area. Calculate the walking distance difference coefficient around subway stations in the core population area. To determine the differences in walking ability relative to baseline walking time among different age groups.

[0067] We selected road features and POI features for pedestrian and cycling service areas of 50 subway stations respectively.

[0068] Accessibility calculation of facilities around residential communities. Using an improved walking index model, calculate the intersection density (number of points within the circle / circle area), average block length, POI density (number of POI points within the circle / circle area), and POI richness (total number of POI types within the circle / circle area). Set attenuation coefficients and barrier rates, and assign POI weights according to the proportion of elderly, middle-aged, and young people in the population profile. Calculate the walking index for young, elderly, and middle-aged people in residential communities around subway stations, such as... Figure 6 As shown.

[0069] Subway station accessibility calculation. Using the Dijkstra algorithm, the average distance from a residential area within a 1000-meter walking distance to a subway station is calculated, yielding the average public transportation accessibility index for that area. For example... Figure 7 As shown.

[0070] Preprocessing of residential accessibility and public transportation accessibility assessment results. The public transportation accessibility results are reverse-engineered and standardized, and the residential accessibility results are standardized to the [0,100] range.

[0071] The standardized results for residential accessibility and public site accessibility are based on polygon clustering using Delaunay's triangle. The results of the residential space clustering analysis are as follows: Figure 8 As shown, ① represents the mature type, ② the balanced type, ③ the gestation type, and ④ the deficient type.

[0072] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for quantitatively assessing the accessibility of residential spaces based on walking patterns, characterized in that, The method includes: Acquire spatiotemporal big data; Spatiotemporal big data processing and fusion; Establish a quantitative accessibility assessment model for residential spaces; Preprocessing of evaluation results; Polygon clustering analysis based on Delaunay triangle; Residential spaces are categorized, including: Establishing a quantitative accessibility assessment model for residential spaces includes: Calculate the service area coverage of public transportation stations, and select residential communities within the coverage area as the research object; Based on differences in human physical function, walking difference coefficients were set for different age groups. : Where T refers to the time required to walk the same distance on a basic basis. The age-related adjustment factor is a factor adjusted for differences in walking ability among different age groups. Calculate the intersection density (number of points in the circle / circle area), average block length, POI density (number of POI points in the circle / circle area), and POI richness (total number of POI types in the circle / circle area) within each circle. The average block length is the average length of each road segment within the circle. ,in The total length of the intersecting streets. Based on quantity and travel habits, the more intersections and the longer the road, the fewer the number and types of POIs, and the lower the willingness to walk. The attenuation rate is defined as the sum of the attenuation rates of each component, and the attenuation coefficient is calculated. : The average intersection density attenuation rate, The average street length attenuation rate, POI density decay rate The POI richness decay rate; Following the three concentric circles, calculate the shortest distance from the point to each point of each type of facility, find the shortest distance length threshold, and obtain the shortest distance attenuation rate; Calculate the accessibility index of living space; accessibility index for: * ,in As the weight of the indicator category, For the weight of the indicator sub-category, To hinder the decay rate, For short-path attenuation rate, This is the walking difference coefficient; The accessibility score of public transport stations is calculated by using the Dijkstra algorithm to calculate the average distance from the subway station to the neighborhood within a 1000-meter walking distance of the public transport station, thus obtaining the average public transport station accessibility index. Polygon clustering analysis based on Delaunay triangles includes: Thiessen polygons are constructed based on a threshold between points. The definition of the distance threshold during clustering is as follows: in It is the midpoint or centroid of the first kind. Distance The farthest point, where η is a constant, is in the range of 0 < η < 1, and Dis() is a function that calculates the distance between two points; The midpoint within a class is calculated as follows: in It is a set of data The number of midpoints or objects, and ≥1; It is a data group The point in; X i and y i , , are the x and y coordinates of station i, respectively, and represent the normalized values ​​of the mean accessibility index of surrounding residential areas and the normalized values ​​of public transportation station accessibility, respectively, which are mapped to the horizontal and vertical coordinates in a two-dimensional map coordinate system.

2. The method for quantitatively assessing the accessibility of residential spaces based on walking patterns as described in claim 1, characterized in that, Acquiring spatiotemporal big data includes: The target website was identified, and the POI, AOI, and traffic information came from the Gaode Map Open Platform, while the bus and subway station information came from Open Street Map. Analyze the target website's webpage structure, URL format, and data display method. Use Python and the Scrapy library to define crawling rules, process pages, extract data, and store data. Use the Requests library to send HTTP requests and parse the returned JSON or XML data to write a crawler program. Data extraction and storage: Extract POI, AOI, traffic flow and speed from HTML content. Store the extracted data in a MySQL database. POI and traffic station data are saved as points, AOI is saved as polygon data, and traffic information is saved in CSV format.

3. The method for quantitatively assessing the accessibility of residential spaces based on walking patterns as described in claim 1, characterized in that, After acquiring spatiotemporal big data, the spatiotemporal big data is cleaned, including: Select outlier identification and interpolation methods that match the data characteristics for identification, removal, and supplementation; Missing attribute names are supplemented, and the missing attribute values ​​are supplemented based on the attribute values ​​that are not missing at the same time on other dates using a linear interpolation method; Remove outliers, x i Let x represent the population of each branch at a specific time on a given day. Calculate the expected value E(x) and standard deviation σ for each hour across all days. Values ​​within the interval (E(x)-σ, E(x)+σ) are considered normal, while those outside are considered outliers.

4. The method for quantitatively assessing the accessibility of residential spaces based on walking patterns as described in claim 1, characterized in that, Spatiotemporal big data processing and fusion includes: Traffic network spatial data processing and traffic network model construction. Traffic network models are used to describe the topology of road networks, the distribution of traffic flow, and the spatiotemporal changes in traffic speed. Traffic flow data addition and configuration is used to configure traffic flow data to the road network, thereby realizing the dynamic and regular simulation of traffic flow patterns in the road network. Big data batch processing and spatial fusion: Develop a Python batch processing program to perform spatial data fusion according to analysis requirements.

5. The method for quantitatively assessing the accessibility of residential spaces based on walking patterns as described in claim 4, characterized in that, Traffic network spatial data processing and traffic network model construction include: Organize and process the spatial components of the topology, including points, lines, and surfaces, to create a thematic dataset on transportation networks; Construct a spatial topology data structure; the spatial data of the transportation network dataset is used to build topological relationships based on topological rules. Add network dataset attributes and historical traffic feature attributes; Flow characterization curve fitting and feature extraction.

6. The method for quantitatively assessing the accessibility of residential spaces based on walking patterns as described in claim 5, characterized in that, Flow characterization curve fitting and feature extraction include: Flow characterization curve fitting: Distinguishing between weekends and weekdays, speed data for each road was extracted for 24 hours a day from Monday to Sunday, and for one month. A scatter plot was created with time on the x-axis and speed at any given moment on the y-axis, and linear fitting was performed using the least squares method. in, It is the dependent variable. It is the independent variable. and These are the coefficients of the fitted curve, which are confirmed by the least squares method by minimizing the sum of squared errors. and The value of, that is: Where n is the number of data points. and These represent the velocities at 24 time points and their corresponding times. The error of the fitted curve is evaluated by calculating the sum of squared residuals, which is the residual between the time-stamped velocity and the measured velocity using the least squares method. in, This is the actual measured speed. For the least squares simulation speed, SSR is controlled within [ 5, 5] Within 5, Expected speeds for different dates at the same time; Curve similarity determination and extraction of representative flow characterization curves: The Pearson correlation coefficient algorithm is used to determine the degree of linear correlation in the flow characterization curves. First, the mean of each curve is subtracted, and the results are denoted as follows: Then calculate the normalized covariance. Divide by the product of the standard deviations of the two curves value range The range is between -1 and 1, with the closer to 1 indicating a higher correlation, the closer to -1 indicating a negative correlation, and the closer to 0 indicating no correlation. Only one curve is retained if it is identified as similar. The flow characterization curve with typical characteristics is selected and assigned a unique ID.

7. The method for quantitatively assessing the accessibility of residential spaces based on walking patterns as described in claim 1, characterized in that, Preprocessing of evaluation results includes: Matrix standardization, denoted as . , For each element in the matrix, the standardized matrix is ​​obtained by applying a standardization algorithm: The normalized matrix is ​​minimized and normalized to the interval [0, 100] using the following function: f(x) represents the positiveized result, and x represents the public transportation station accessibility index.

8. The method for quantitatively assessing the accessibility of residential spaces based on walking patterns as described in claim 1, characterized in that, Based on the results of polygon clustering analysis, the living spaces are divided into mature, balanced, nurturing, and deficient types.

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