Urban green vision rate monitoring space investigation object sampling method and system

Abstract

The invention belongs to the technical field of surveying and mapping, and discloses an urban green vision rate monitoring space investigation object sampling method and system, and the method comprises the steps: carrying out the cutting of a whole-city road network through an urban development boundary, and obtaining a development boundary road with administrative region attributes; obtaining first road information based on the development boundary road with the administrative region attribute; calculating an average value of the number of active users in the mobile phone signaling data of all villages and towns of the whole city in a preset time period range, and obtaining second road information based on the average value of the number of the active users; and based on the first road information and the second road information, randomly selecting and sampling by using a random algorithm, and carrying out data quality inspection to obtain sampling road information of the green vision rate of the to-be-monitored area. According to the method, the non-uniformity of urban space people flow density distribution is fully considered, the representativeness and precision of a sampling result can be greatly improved, and high-quality sampling point data can be obtained.

Description

Technical Field

[0001] The invention belongs to the field of surveying and mapping technology, and in particular relates to a method and system for sampling space survey objects for urban green view rate monitoring. Background Art

[0002] Urban greening is an important part of urban ecological construction, and plays an important role in improving the ecological environment and improving the quality of life of citizens. Greening evaluation indicators are important standards for measuring the level of urban greening construction results. In my country, greening rate, green coverage rate and per capita park green area have been used as the main urban greening evaluation indicators for many years. Although they can make an intuitive quantitative evaluation of the two-dimensional plane greening effect of urban green space, they cannot accurately reflect the green resources and landscape effects of the three-dimensional space of the city. The green view rate evaluation index refers to the proportion of green plants in the human field of vision. Evaluating the green volume of urban public space from the perspective of pedestrians and the dimension of urban space can truly reflect the green resources in the three-dimensional space and be closer to people's real feelings.

[0003] The sampling method is an important part of evaluating the level of urban greening construction results. Due to the uneven distribution of green infrastructure in the city, the actual green area of ​​each block is different, so it is necessary to determine the number and distribution of sampling points according to the green area of ​​each block. Compared with the purely manual selection of sampling points, the development of a sampling method that uses a computer-assisted method to intelligently determine the sampling points of the sampling space can reduce personnel input while improving the objectivity and accuracy of the monitoring results, and can obtain high-quality sampling point data. It can be seen that a scientific and effective sampling method will help improve the accuracy and reliability of urban green monitoring and provide strong support for the ecological health assessment of urban sustainable development. Summary of the invention

[0004] (I) Purpose of the invention

[0005] The purpose of the present invention is to provide a method and system for sampling urban green view rate monitoring space survey objects. The method makes full use of the urban space pedestrian density distribution characteristics reflected by mobile phone signaling data, adopts stratified Poisson sampling method to reasonably arrange monitoring sampling points, reduces personnel input, and improves the objectivity and accuracy of monitoring results.

[0006] (II) Technical solution

[0007] In order to solve the above problems, the first aspect of the present invention provides a method for sampling survey objects in urban green view rate monitoring space, comprising:

[0008] The city's road network is cut using the designated urban development boundaries to obtain development boundary roads with administrative district attributes;

[0009] Select the spatial positions of the development boundary roads with administrative district attributes and the township roads to determine the roads with township attributes;

[0010] Revising the data of the road with township attributes to obtain first road information; the first road information is the revised road with township attributes;

[0011] Calculate the average number of active users in the mobile phone signaling data of each township in the city within a preset time period, calculate the pedestrian flow density of each township area according to the average number of active users, and determine the number of sampling points of each township road based on the pedestrian flow density of each township area;

[0012] Determine the second road information according to the number of sampling points of each township road and the preset selection ratio of different road types, wherein the second road information is the number of sampling points corresponding to different road types in each township road;

[0013] Based on the first road information and the second road information, a random algorithm is used to randomly select samples, and data quality inspection is performed to obtain the sampled road information of the green view rate of the area to be monitored.

[0014] Furthermore, the city road network is clipped using the demarcated urban development boundary to obtain development boundary roads with administrative boundary attributes, including:

[0015] Taking the county or district-level administrative district as the dividing boundary, cutting the city's road network to obtain roads within each county or district-level administrative district, wherein the delineated urban development boundary includes multiple county or district-level administrative district dividing boundaries;

[0016] Assigning administrative district attributes to the roads in each county or district-level administrative district respectively and then merging them to form the city-wide road network data with administrative district attributes;

[0017] A data cleaning operation is performed on the city-wide road network data with administrative district attributes to obtain development boundary roads with administrative district attributes.

[0018] Furthermore, the data cleaning operation includes: removing fragmented roads within a preset threshold in the city-wide road network data with administrative district attributes.

[0019] Furthermore, the spatial position selection of the development boundary roads with administrative district attributes and the township roads to determine the roads with township attributes (sample roads) includes:

[0020] The township code attribute is assigned according to the township where the road centroid falls, and a road with township attributes is obtained.

[0021] Further, the pedestrian density of each township is calculated according to the average value of the number of active users, and the number of sampling points of each township road is determined based on the pedestrian density of each township, including:

[0022] Calculate the proportion of each township’s flow to the entire city based on the density of each township’s flow;

[0023] Determine the preset expected total number of sampling points;

[0024] According to the proportion of passenger flow in each township area in the entire city, the expected total number of sampling points is allocated to each township area road in proportion to obtain the number of sampling points in each township area road.

[0025] Furthermore, based on the first road information and the second road information, random sampling is randomly selected using a random algorithm, and data quality inspection is performed to obtain sampled road information of the green view rate of the area to be monitored, including:

[0026] Based on the second road information, a random algorithm is used to randomly select a sample to obtain first sampled road information;

[0027] Based on the first road information, assign township attributes to the corresponding first sampled road information to obtain second sampled road information;

[0028] The second sampled road information is subjected to data quality inspection to obtain sampled road information of the green visibility rate of the area to be monitored.

[0029] Furthermore, the different road types include urban roads and highways, the urban roads include at least one of main roads, secondary roads, branch roads and expressways; the highways include at least one of national highways, provincial highways, county roads, township roads, and special highways.

[0030] Furthermore, the calculation formula for the number of sampling points of the roads in each township area is:

[0031] n i =n*(λ i ·|r i | / (∑iλ i ·|r i |)) ; (1)

[0032] In formula (1), n i is the number of sampling points in the ith township, n is the expected total number of sampling points, λ i is the flow density of the ith township, r i is the area of ​​the ith township.

[0033] Another aspect of the present invention provides a system for sampling urban green view rate monitoring space survey objects, comprising:

[0034] The clipping module is used to clip the city's road network using the designated urban development boundaries to obtain development boundary roads with administrative district attributes;

[0035] A spatial position selection module, used for performing spatial position selection between the development boundary roads with administrative district attributes and the township roads, and determining the roads with township attributes;

[0036] A data revision module, used for revising the data of the road with township attributes to obtain first road information;

[0037] The first sampling point calculation module is used to calculate the average number of active users in the mobile phone signaling data of each township in the city within a preset time period, calculate the pedestrian flow density of each township area according to the average number of active users, and determine the number of sampling points of each township road based on the pedestrian flow density of each township area;

[0038] A second sampling point calculation module is used to determine the second road information according to the number of sampling points of each township road and the preset selection ratio of different road types;

[0039] The sampling road determination module is used to randomly select samples based on the first road information and the second road information using a random algorithm, and perform data quality inspection to obtain the sampling road information of the green visibility rate of the area to be monitored.

[0040] (III) Beneficial effects

[0041] The present invention provides a method and system for sampling objects of urban green view rate monitoring space survey. The method gives full play to the advantages of mobile phone signaling data, adopts a stratified Poisson sampling method, and performs a fusion and stratified design on the road network of the whole city. In the method: first, the urban development boundary (including the boundary of multiple county or district-level administrative districts) is used to cut the road network of the whole city, and the roads within the development boundary with administrative district attributes are obtained to meet the multi-scale green view rate evaluation needs; and the pedestrian density of each township area is calculated according to the average value of the number of active users, and the number of sampling points of each township area road is determined based on the pedestrian density of each township area to ensure that there are enough observation objects in the sample, so that the sample can reflect the green view rate characteristics of the overall objects within the urban development boundary, wherein the pedestrian density of each township obeys Poisson distribution, and the present invention calculates the sampling points based on the method of Poisson distribution; finally, based on the first road information and the second road information, a random algorithm is used to randomly select samples, and data quality inspection is performed to obtain the sampling road information of the green view rate of the monitored area. This step ensures that the sample points of the random sampling are not interfered by human factors, and ensures the objective authenticity of the sample. This method fully considers the non-uniformity of the distribution of pedestrian density in urban space, which can improve the representativeness and accuracy of the sampling results and provide support for related applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of a method for sampling urban green view rate monitoring space survey objects based on the present invention;

[0043] Figure 2 It is a flow chart of a sampling system for urban green view rate monitoring space survey objects based on the present invention;

[0044] Figure 3 It is a schematic diagram of a sampling process of a specific embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram based on the comparison between the method of the present invention and manual sampling. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0047] like Figure 1 As shown, the present invention provides a method for sampling survey objects in urban green view rate monitoring space, comprising:

[0048] S101, using the defined urban development boundary to cut the city's road network, to obtain development boundary roads with administrative district attributes;

[0049] S102, selecting the spatial positions of the development boundary roads with administrative district attributes and the township roads to determine the roads with township attributes;

[0050] S103, revising the data of the roads with township attributes to obtain first road information, where the first road information is the revised roads with township attributes, indicating which township in which administrative district each road belongs to;

[0051] S104, calculating the average number of active users in the mobile phone signaling data of each township in the city within a preset time period, calculating the pedestrian flow density of each township area according to the average number of active users, and determining the number of sampling points of each township road based on the pedestrian flow density of each township area;

[0052] S105, determining second road information according to the number of sampling points of each township road and the preset selection ratio of different road types, where the second road information is the number of sampling points corresponding to different road types in each township road;

[0053] S106, based on the first road information and the second road information, randomly select samples using a random algorithm, and perform data quality inspection to obtain sampled road information of the green visibility rate of the area to be monitored.

[0054] Furthermore, in S101, the method of clipping the city's road network using the demarcated urban development boundary to obtain development boundary roads with administrative boundary attributes includes the following steps:

[0055] S1011, using the county or district-level administrative district as the dividing boundary, cutting the entire city road network, dividing and obtaining the roads in each county or district-level administrative district, wherein the delineated urban development boundary includes a plurality of county or district-level administrative district dividing boundaries, and the present invention uses the district-level administrative district as the basic unit to ensure that each element is covered in an equal proportion in each district-level administrative district, each element refers to a different road type, and the equal proportion refers to a preset selection proportion of different road types;

[0056] S1012, assign administrative district attributes to the roads in each of the county or district-level administrative districts respectively and then merge them to form the city-wide road network data with administrative district attributes. Specifically, the roads in each of the county or district-level administrative districts obtained by segmentation in S1011 are clipped road vectors, and a new XZQ field is created in the road vector to store the attributes of the administrative district to which the sampled object belongs. Then, the corresponding county or district-level administrative district name is assigned, and then the administrative districts are merged to form the city-wide road network data with administrative district attributes. This step reflects the first stratification of the entire city, and then the assigned partition data is merged to form the city-wide road network data with administrative district attributes.

[0057] S1013, perform data cleaning operation on the city-wide road network data with administrative district attributes to obtain development boundary roads with administrative district attributes. The data cleaning operation includes: in the city-wide road network data with administrative district attributes, remove the fragmented roads with a preset threshold, the preset threshold is, for example, fragmented roads less than 200 meters, and delete the fragmented roads less than 200 meters during data cleaning to obtain development boundary roads with administrative district attributes. This step cleans the roads that are too short to ensure that the selected sampling objects meet the subsequent green view rate calculation evaluation.

[0058] Furthermore, S102 specifically includes:

[0059] According to the township where the road centroid falls, the township code attribute is assigned to obtain the sample road with township attributes. In this step, centroid selection is a method of selecting the patch according to the spatial position between vectors. The road centroid is the geometric center of each road segment. For example, when a trunk road passes through the two townships A1 and A2 in administrative area A, if the centroid of the trunk road is located in A1, the trunk road is assigned the A1 town attribute;

[0060] Furthermore, S103 specifically includes: revising the data of the road with township attributes to obtain the first road sample information. In this step, a new OSTCD field is created for the road (road vector) within the development boundary with administrative district attributes obtained in step S1013 to store the first road information (the first road information is the revised road with township attributes), so as to prepare for the subsequent division of different selection ratios for different hot townships.

[0061] Further, in S104, the average value of the number of active users in the mobile phone signaling data of each township in the city within the preset time period is calculated (for example, the preset time period is February-March 2024, the parameter values ​​of active users in the mobile phone signaling data of each township for one consecutive month are obtained, and the obtained parameter values ​​are arithmetic averaged to obtain the average value of the number of active users in each township), and the pedestrian flow density of each township is calculated according to the average value of the number of active users (the average value of the number of active users in each township is divided by the area of ​​each township, that is, the pedestrian flow density of each township), and the number of sampling points of each township road is determined based on the pedestrian flow density of each township; this step specifically includes:

[0062] According to the pedestrian density of each township road, calculate the proportion of pedestrian flow of each township road in the whole city;

[0063] Determine the preset expected total number of sampling points;

[0064] According to the proportion of the pedestrian flow of each township road in the whole city, the expected total number of sampling points is allocated to each township road in proportion to obtain the number of sampling points of each township road;

[0065] Assume that the urban space area is divided into i sub-areas r1, r2, ..., r i Each sub-region (township) r i The crowd density follows the Poisson distribution Poisson(λ i );

[0066] The calculation formula for the number of sampling points of each township road is:

[0067] n i =n*(λ i ·|r i | / (∑iλ i ·|r i |)) ; (1)

[0068] Among them, n i is the number of sampling points in the ith township, n is the expected total number of sampling points, λ i is the flow density of the ith township, r i is the area of ​​the ith township, (∑iλ i·|r i |) is the sum of the product of the flow density and area of ​​all townships in the city, which is used for normalization.

[0069] Further, in S105, second road information is determined according to the number of sampling points of each township road and the preset selection ratio of different road types, where the second road information is the number of sampling points corresponding to different road types in each township road, specifically including:

[0070] The preset selection ratios of different road types within the same county or district-level administrative area are determined by sorting according to the average number of active users. Townships with many active users will have a larger manually determined selection ratio. For example, they can be divided into high-hotspot townships, medium-hotspot townships and low-hotspot townships according to size. The preset selection ratio for high-hotspot townships is 70%, the preset selection ratio for medium-hotspot townships is 20%, and the preset selection ratio for low-hotspot townships is 10%.

[0071] The number of sampling points of each township road is allocated according to the preset selection ratio of different road types, so as to obtain the number of sampling points (second road information) corresponding to different road types in each township road.

[0072] In this step, different road types include urban roads and highways, the urban roads include at least one of main roads, secondary roads, branch roads and expressways; the highways include at least one of national highways, provincial highways, county roads, township roads, and special highways.

[0073] Furthermore, in S106, based on the first road information and the second road information, randomly selecting samples using a random algorithm and performing data quality inspection to obtain sampled road information of the green view rate of the area to be monitored includes:

[0074] S1061, based on the second road information, a random algorithm is used to randomly select samples to obtain first sampled road information; the first sampled road information includes the coordinates of each sampling point. In this step, each sub-area r i (township area), its interior belongs to the Poisson point distribution Poisson (λ i ·|r i |), combined with the number of sampling points corresponding to different road types in each township road in step S105, randomly select n i sampling points.

[0075] The coordinates of each sampling point (x i ,y i ) can be generated by the following formula:

[0076] x i =u i +a i ; (2)

[0077] y i =v i +b i ; (3)

[0078] Among them, (u i ,v i ) is the sub-region r i The lower left corner coordinates, (a i ,b i ) is subject to uniform distribution U(0,|r i |), taking the lower left corner as the reference, and combining ai and bi to achieve random sampling.

[0079] S1062, assign township attributes to the first sampled road information through the first road information to obtain the second sampled road information. In this step, a new Year field is created in the first road information to assign a unique value to the first sampled road information, which is used to distinguish between samples and non-samples in the sample area. The sample is the selected sampling area. In addition, in this step, there are some special situations where the first sampled road information needs to be assigned administrative district attributes. For example, if there is a problem with the administrative district attributes assigned to each township in the second road information, such as some roads involving two administrative districts, manual revision is required before calculation.

[0080] S1063, performing data quality inspection on the second sampled road information to obtain sampled road information of the green view rate of the area to be monitored (corresponding to Figure 2 (Routinely adjust regional road samples after quality inspection in this step). During the data quality inspection process in this step, the surveyed roads that are too narrow, military-related, confidential, and inaccessible are replaced with the same hotspot and the same type; if the number of a certain type of road is less than one after proportional calculation, it is supplemented manually. The unique value of the Year field needs to be assigned and replaced simultaneously. In this step, the stratified Poisson sampling method ensures that the sample points of random sampling are not interfered with by human factors and ensures the objective authenticity of the sample.

[0081] In addition, if Figure 2 As shown, the present invention also provides a system for sampling urban green view rate monitoring space survey objects, the system comprising:

[0082] The clipping module 201 is used to clip the city's road network using the demarcated urban development boundary to obtain development boundary roads with administrative district attributes;

[0083] A spatial position selection module 202 is used to select the development boundary roads with administrative district attributes and township roads in spatial positions to determine the roads with township attributes;

[0084] A data revision module 203 is used to revise the data of the road with township attributes to obtain first road information;

[0085] The first sampling point calculation module 204 is used to calculate the average number of active users in the mobile phone signaling data of each township in the city within a preset time period, calculate the pedestrian flow density of each township area according to the average number of active users, and determine the number of sampling points of each township road based on the pedestrian flow density of each township area;

[0086] The second sampling point number calculation submodule 205 is used to determine the second road information according to the sampling point number of each township road and the preset selection ratio of different road types;

[0087] The sampling road determination module 206 is used to randomly select samples based on the first road information and the second road information using a random algorithm, and perform data quality inspection to obtain the sampling road information of the green visibility rate of the area to be monitored.

[0088] Combine the following Figure 3 Taking the sampling method of survey objects of green view rate monitoring space in Beijing as an example, we will explain it in detail:

[0089] Step 1-clipping: Use the urban development boundary to clip the complete road data of Beijing to obtain the road data within the urban development boundary; the urban development boundary includes multiple county or district-level administrative areas;

[0090] Step 2-Clipping: To meet the needs of multi-scale green view rate assessment, the road data within the urban development boundary is clipped using the district (county) level administrative division data to obtain the road data of each administrative district within the urban development boundary (roads within each county or district level administrative district);

[0091] Step 3-Attribute Assignment: Create a new XZQ field in the result data of Step 2 (roads in each county or district-level administrative area). The XZQ field is used to store the attributes of the administrative area to which the sampled object belongs (after clipping, the roads in each county or district-level administrative area will become a separate data. Create a new XZQ field in this data and assign values ​​according to the clipping range), reflecting the concept of the first stratification of the entire city. This step assigns administrative area attributes to the roads in each county or district-level administrative area;

[0092] Step 4-Merge: Merge the road data of each district with administrative district attributes in Step 3 to obtain the roads within the urban development boundary with administrative district attributes in the whole city (the whole city road network data with administrative district attributes);

[0093] Step 5-Cleaning: Carry out data cleaning, delete the fragmented roads less than 200 meters in Step 4, and obtain the road sample selection area (development boundary roads with administrative district attributes);

[0094] Step 6-Selection: Create a new OSTCD field in the result data of Step 5, and then select the spatial position with the township-level administrative district boundary (township road), and assign the township boundary code attribute (road with township attribute) according to the township where the road centroid falls. After the attribute is assigned, a manual inspection is carried out, and the roads with incorrect township codes are manually revised to correct the possible problems in the automatic attribute assignment, and the first road information (i.e. Figure 2 Sample area roads that are given township boundary attributes after revision);

[0095] Step 7-Calculation of spatial area sampling points: Use the average value of the number of active users in the mobile phone signaling data of each township in Beijing within the specified time period (for example, the preset time period is 2023.4-2024.6), calculate the pedestrian density of each township road according to the average value of the number of active users, and determine the number of sampling points of each township road (sub-area) based on the pedestrian density of each township road; then determine the second road information according to the number of sampling points of each township road and the preset selection ratio of different road types (sort according to the average value of the number of active users, and determine the preset selection ratio of different road types in the same county or district-level administrative area), the second road information is the number of sampling points corresponding to different road types in each township road.

[0096] Step8-Sampling: Combine the result data of Step6 and Step7 (the result data of Step6 corresponds to the first road information, and the result data of Step7 corresponds to the second road information), perform random sampling according to the sampling ratio, and obtain the first sampled road information. Then, create a new Year field in the sample area road vector with township boundary attributes in Step6 (first road sample information), and assign a unique value to the extracted first sampled road information to distinguish between samples and non-samples in the sample area. The road vector is usually composed of a series of nodes and line segments connecting the nodes. Each node represents a specific location on the road, and the line segment represents the direction and connection relationship of the road. The sample area road vector with township boundary attributes in Step6 is the total vector, and a new Year field can be created in the total vector.

[0097] Step 9-Quality Inspection: Manually inspect the road samples of each township area in Step 8, and replace the surveyed roads that are too narrow, military-related, confidential, and inaccessible with the same hotspot and the same type; if the number of roads of a certain type is less than one after proportional calculation, they will be supplemented manually. The unique value of the Year field needs to be assigned and replaced synchronously to obtain the sampled road information of the green view rate of the area to be monitored. The sampled road information of the green view rate of the area to be monitored includes the flow density of each hotspot township, the number of sampling points, and the coordinates of each sampling point. The same hotspot in the same hotspot and the same type replacement refers to the distinction between different townships in the same administrative area as high, medium, and low hotspot townships; the same type refers to the type of sample road.

[0098] The calculation process of each index of the sampled road information is as shown in formulas (1)-(3).

[0099] This method fully considers the non-uniformity of the distribution of pedestrian density in urban space, which can improve the representativeness and accuracy of the sampling results and provide support for related applications.

[0100] In order to fully verify the scientificity and rationality of the method of the present invention, this study is based on public open road network data, and selects two representative areas of Beijing, Chaoyang District and Changping District, as test areas. Figure 4 As shown. To ensure the wide applicability and generalization of the test results, the test area in Chaoyang District was deliberately set within the prosperous built-up area to fully verify the performance of the method in a highly urbanized environment; while the test area in Changping District was located in a typical township gathering area to examine the applicability of the method in a relatively rural environment. The scope of the two test areas was precisely set to an area of ​​5 kilometers by 5 kilometers to ensure the accuracy of the test. The specific process of the experiment is as follows:

[0101] 1. Clipping road network data: Using the selected test area, clip the public open road network data to obtain the test area road network data in Chaoyang District and Changping District.

[0102] 2. Data cleaning: To ensure the comparability of subsequent test data, the cropped test area road network data is cleaned in combination with publicly developed street view images, and road objects that do not have corresponding street view images are deleted.

[0103] 3. Survey sample selection: According to the same sample selection ratio, pure manual method and invented method were used to select survey samples respectively.

[0104] 4. Calculate the green view rate value: According to the survey samples selected by the two methods, match the corresponding public open street view images and calculate the corresponding green view rate value.

[0105] 5. Comparative analysis: Compare the relationship between the green view rate values ​​of the samples selected by the two methods and the green view rate values ​​of the full test area data to evaluate the impact of different sample selection methods on the green view rate calculation. Both randomly selected (method of the present invention) and manually selected road samples must be associated with the corresponding street view images to calculate the green view rate value of the sample road. The green view rate value of the sample must be compared and analyzed with the total green view rate value of all roads to determine whether the sample can reflect the whole and whether the selected sample is representative.

[0106] In this experiment, a total of 117 road objects with street view images were covered in the two test areas, and the comprehensive green view rate was 20.31%. Samples were selected based on the principle that different hotspot areas have different selection ratios, while different types of roads in the same hotspot area have the same ratio. The details of the survey samples selected manually and by the method of the present invention are shown in Table 1 below.

[0107] Table 1 Comparison of results between the method of the present invention and the manual method

[0108] Sample selection method Time Sample comprehensive green viewing rate value Purely manual selection 52 minutes 17.46％ The method of the present invention selects 7 minutes 20.87％

[0109] By comparing the above test data, it can be clearly seen that the method of the present invention takes less time and is more efficient in selecting survey samples, and the comprehensive green viewing rate value of the samples obtained is closer to the green viewing rate value of the entire objects in the test area, showing higher accuracy and representativeness.

[0110] The present invention provides a method and system for sampling survey objects in urban green view rate monitoring space. The method makes full use of the distribution characteristics of urban space human flow density reflected by mobile phone signaling data, adopts stratified Poisson sampling method to reasonably arrange monitoring sampling points, and realizes comprehensive and accurate monitoring of urban ecological elements. The present invention has the following advantages:

[0111] (1) Through the collection and analysis of mobile phone signaling data, the Poisson distribution parameters of urban spatial pedestrian density are extracted; through stratified Poisson sampling, the monitoring sampling points are optimized based on the spatial pedestrian density stratification;

[0112] (2) Through the data collection and fusion processing process, the ecological indicator data such as green view rate are integrated to generate analysis results that are closely related to the urban ecological space distribution and human flow.

[0113] The present invention makes full use of the advantages of mobile phone signaling data and adopts stratified Poisson sampling method to improve the representativeness and accuracy of monitoring results. At the same time, it combines the distribution of human flow density with ecological element monitoring data to provide a scientific basis for urban planning, environmental management, etc.

Claims

1. A method for sampling survey objects in urban green view rate monitoring space, characterized in that: include: The city's road network is cut using the designated urban development boundaries to obtain development boundary roads with administrative district attributes; Select the spatial positions of the development boundary roads with administrative district attributes and the township roads to determine the roads with township attributes; Revising the data of the road with township attributes to obtain first road information, where the first road information is the revised road with township attributes; Calculate the average number of active users in the mobile phone signaling data of each township in the city within a preset time period, calculate the pedestrian flow density of each township area according to the average number of active users, and determine the number of sampling points of each township road based on the pedestrian flow density of each township area; Determine the second road information according to the number of sampling points of each township road and the preset selection ratio of different road types, wherein the second road information is the number of sampling points corresponding to different road types in each township road; Based on the first road information and the second road information, a random algorithm is used to randomly select samples, and data quality inspection is performed to obtain the sampled road information of the green view rate of the area to be monitored.

2. The urban green view rate monitoring space survey object sampling method according to claim 1 is characterized in that: The city road network is clipped using the demarcated urban development boundary to obtain development boundary roads with administrative district attributes, including: Taking the county or district-level administrative district as the dividing boundary, cutting the city's road network to obtain roads within each county or district-level administrative district, wherein the delineated urban development boundary includes multiple county or district-level administrative district dividing boundaries; Assigning administrative district attributes to the roads in each county or district-level administrative district respectively and then merging them to form the city-wide road network data with administrative district attributes; A data cleaning operation is performed on the city-wide road network data with administrative district attributes to obtain development boundary roads with administrative district attributes.

3. The urban green view rate monitoring space survey object sampling method according to claim 2 is characterized in that: The data cleaning operation includes: removing fragmented roads within a preset threshold value in the city-wide road network data with administrative district attributes.

4. The urban green view rate monitoring space survey object sampling method according to claim 1 is characterized in that: The step of selecting the spatial positions of the development boundary roads with administrative district attributes and the township roads to determine the roads with township attributes includes: The township code attribute is assigned according to the township where the road centroid falls, and a road with township attributes is obtained.

5. The urban green view rate monitoring space survey object sampling method according to claim 1 is characterized in that: Calculating the pedestrian density of each township area according to the average value of the number of active users, and determining the number of sampling points of each township area road based on the pedestrian density of each township area, including: Calculate the proportion of each township to the city's population density based on the population density of each township; Determine the preset expected total number of sampling points; According to the proportion of passenger flow in each township area in the entire city, the expected total number of sampling points is allocated to each township area road in proportion to obtain the number of sampling points in each township area road.

6. The urban green view rate monitoring space survey object sampling method according to claim 1 is characterized in that: The method of randomly selecting samples based on the first road information and the second road information by using a random algorithm and performing data quality inspection to obtain the sampled road information of the green view rate of the area to be monitored includes: Based on the second road information, a random algorithm is used to randomly select a sample to obtain first sampled road information; Based on the first road information, assign township attributes to the corresponding first sampled road information to obtain second sampled road information; The second sampled road information is subjected to data quality inspection to obtain sampled road information of the green visibility rate of the area to be monitored.

7. The urban green view rate monitoring space survey object sampling method according to claim 1 is characterized in that: The different road types include urban roads and highways, the urban roads include at least one of main roads, secondary roads, branch roads and expressways; the highways include at least one of national roads, provincial roads, county roads, township roads, and special roads.

8. The method for sampling urban green view rate monitoring space survey objects according to claim 5 is characterized in that: The calculation formula for the number of sampling points of each township road is: n i =n*(λ i ·|r i | / (∑iλ i ·|r i |)); Among them, n i is the number of sampling points in the ith township, n is the expected total number of sampling points, λ i is the flow density of the ith township, r i is the area of ​​the ith township.

9. A system for sampling urban green view rate monitoring space survey objects, characterized in that: include: The clipping module is used to clip the city's road network using the designated urban development boundaries to obtain development boundary roads with administrative district attributes; A spatial position selection module, used for performing spatial position selection between the development boundary roads with administrative district attributes and the township roads, and determining the roads with township attributes; A data revision module, used for revising the data of the road with township attributes to obtain first road information; The first sampling point calculation module is used to calculate the average number of active users in the mobile phone signaling data of each township in the city within a preset time period, calculate the pedestrian flow density of each township area according to the average number of active users, and determine the number of sampling points of each township road based on the pedestrian flow density of each township area; A second sampling point number calculation module is used to determine the second road information according to the number of sampling points of each township road and the preset selection ratio of different road types; The sampling road determination module is used to randomly select samples based on the first road information and the second road information using a random algorithm, and perform data quality inspection to obtain the sampling road information of the green view rate of the monitored area.

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