Urban and rural space planning optimization method based on surveying and mapping data

Abstract

The invention discloses a method for introducing student-friendly facility planning in urban and rural space planning to facilitate students going to school, i.e., constructing a student going-to-school route endpoint data set in a GIS (Geographic Information System) constructed on the basis of surveying and mapping data by constructing a student going-to-school route endpoint data set; and overlapping and screening the school going routes of all the students to obtain intersection points and overlapped line segments exceeding a threshold value, further obtaining candidate places for setting the student-friendly facilities, and after comprehensive scoring is performed through multi-criterion decision analysis, determining an area finally used for building the student-friendly facilities. According to the method, an improved A star algorithm is adopted during calculation of the student going-to-school route, the distance factor, the safety factor and the comfort factor are considered, the distance, safety and comfort in the student going-to-school route are considered, and consideration of student safety and comfort is introduced when student-friendly facilities are introduced in urban and rural space planning; and the school going route of the student is safer and more comfortable.

Description

Technical Field

[0001] The present invention belongs to the technical field of spatial planning optimization methods, and in particular relates to an urban and rural spatial planning optimization method based on surveying and mapping data. Background Art

[0002] With the continuous advancement of urbanization, the core concept of urban and rural spatial planning has gradually shifted to people-oriented. In this process, one of the focuses of family concern is the education of children. Education not only occupies most of the time in the growth process of young people, but also is the key to improving the cultural quality and innovation ability of cities, and also provides solid support for the continued prosperity and progress of society. With the popularization of compulsory education, children in the compulsory education stage spend most of their time in school. We need to start from the three key links of family, school route and school to jointly create a student-friendly regional environment. Among them, the student school process is mainly divided into three parts: family one point, school route one line and school one point. In order to better help and benefit students and create a student-friendly area, the planning scheme should be optimized in the process of urban and rural spatial planning. Consider setting up quite a number of student-friendly facilities around the student school route, such as amusement facilities, sports areas, rest areas, public toilets, etc. Therefore, the optimized student-friendly facility planning should be introduced in the current conventional urban and rural spatial planning. Summary of the invention

[0003] In order to overcome the technical problems described in the above-mentioned background technology, the present invention provides an urban and rural spatial planning optimization method based on surveying and mapping data, focusing on the planning goal of developing the city through education, introducing student-friendly facility planning in urban and rural spatial planning, and facilitating students going to school, that is, by constructing a data set of endpoints of students' school routes, in a GIS system constructed based on surveying and mapping data, a data set of endpoints of students' school routes is constructed, and all students' school routes are superimposed and screened to obtain intersection points and coincident line segments exceeding a threshold, thereby obtaining candidate locations for setting up student-friendly facilities, and after comprehensive scoring through multi-criteria decision analysis, the area ultimately used for the construction of student-friendly facilities is determined.

[0004] The technical solution of the present invention is: a method for optimizing urban and rural spatial planning based on surveying and mapping data, comprising the following steps: S1. Obtain the residential addresses and school addresses of students in the compulsory education stage and before in the current administrative area from the education department, complete data desensitization and data cleaning, and construct a data set of students' school route endpoints; S2. Build a GIS system within the current administrative area based on surveying and mapping data, geocode the residential area addresses and school addresses in the student school route endpoint data set, and build each student's school route through the shortest path search algorithm; S3. Superimpose all the school routes of the students, and use GIS spatial analysis tools to calculate the intersection points and coincident line segments of the school routes. The attribute values ​​of the intersection points and coincident lines are the number of students passing through. S4, according to the preset threshold q of the number of students passing through, extract the intersection points and coincident line segments exceeding the threshold, and highlight them in the GIS system; S5, extracting candidate locations according to the intersection of the constructible area in the GIS system and the extracted intersection points and coincident line segments exceeding the threshold; S6. Based on the multi-criteria decision analysis (MCDA) method, the multi-criteria decision analysis tool of GIS is used to comprehensively score the candidate sites and select the planned predetermined number m areas with the highest comprehensive scores. This is to determine the areas that will eventually be used for the construction of student-friendly facilities.

[0005] Furthermore, the calculation formula for the preset threshold q of the number of students is: , where μ is the mean and σ is the standard deviation. The number of students passing through all routes is counted, and the mean μ and standard deviation σ are calculated. k is a control parameter, usually adjusted between 1 and 3, representing the proportion of the routes you want to choose. If you want to concentrate on planning a few high-frequency route areas, you can set a larger k; if you want to cover more routes, you can set a smaller k.

[0006] Furthermore, in step S2, the shortest path search algorithm is replaced by the improved A-star algorithm. The main improvements of the A-star algorithm include the following steps: S201. Constructing an improved multi-factor comprehensive cost function , instead of the traditional single cost function, , ,in , , are the weight coefficients of distance factor, safety factor and comfort factor respectively, and satisfy , To reflect the distance cost from the starting point to node n, To reflect the safety risk of the route, The comfort cost reflects the walking comfort of the route; S202. Improve the traditional heuristic function that usually uses straight-line distance, consider safety and comfort, and construct an improved heuristic function , ,in , , are the weight coefficients of distance factor, safety factor and comfort factor respectively, and satisfy , is the Euclidean distance from node n to the target node, is the average security risk estimate of the area surrounding node n, is the average comfort estimate of the area surrounding node n.

[0007] Furthermore, in step S201, the distance cost The calculation formula is: , where k is the number of edges from the starting point to node n, For edge The length can be automatically calculated by the GIS system based on the topological structure of the road network.

[0008] Furthermore, in step S201, the security cost The calculation formula is: , where k is the number of edges from the starting point to node n, For edge The safety score of the network is usually in the range of [0, 1], and the larger the value, the safer it is.

[0009] Furthermore, the safety score of edge e The calculation formula is: ,in, is the frequency of accidents or events on edge e, and are the minimum and maximum accident frequencies of all edges respectively.

[0010] Furthermore, the frequency of accidents or events on edge e is obtained by the following steps: S701. Obtain the archive data of traffic accidents and public security incidents in the current area over the years through the public security department; S702, using natural language processing technology to perform word segmentation, complete time recognition, place name recognition and direction recognition of traffic accidents and public security incidents; S703, performing data cleaning and standardization on the extracted place names, directions and time information, removing invalid data and duplicates, and ensuring that the data format is consistent; S704, using the geocoding technology in the Geographic Information System (GIS) to convert the standardized place names and location information into corresponding longitude and latitude coordinates; S705, spatially matching the longitude and latitude coordinates with the road network data in the GIS system, and mapping the accident and incident locations to corresponding roads, road sections or intersections using spatial association analysis; S706. Use the GIS system to count the cumulative number of accidents and events in the edge attributes, and divide the cumulative number of occurrences by the cumulative number of all accidents and events to obtain the accident or event frequency on edge e. , the frequency data Write the attribute fields of edge e.

[0011] Furthermore, in step S201, the comfort cost The calculation formula is: , where k is the number of edges from the starting point to node n, For edge The comfort score ranges from [0,1], and the larger the value, the more comfortable it is.

[0012] Furthermore, the comfort score of edge e The calculation formula is: ,in, , , , are the weight coefficients of sidewalk width, greening, noise, and air quality, respectively, and , Score the width of the sidewalk, Score greenery. Score the noise, Rate the air quality.

[0013] Furthermore, the sidewalk width score and green rating They are obtained through normalization based on the existing planning data, and the noise score and air quality rating They are obtained through normalization based on the monitoring data of the environmental monitoring department.

[0014] The present invention has the following beneficial effects due to the adoption of the above technology.

[0015] 1. The present invention improves the student-friendliness in urban and rural spatial planning. By comprehensively considering students' school routes, home addresses and school locations, it can provide more targeted and optimized student-friendly facility planning for urban and rural planning, and promote the safety and convenience of students in the process of going to school.

[0016] 2. By introducing the GIS system and the shortest path search algorithm, the present invention can accurately geocode and visualize the routes of students to school, ensuring the accurate identification and marking of route intersections and coincident line segments during the planning process. In particular, the sections and intersections where the number of students passing through exceeds the threshold are highlighted, so that planners can quickly identify important nodes and give priority to the construction of facilities in these areas.

[0017] 3. The present invention helps to concentrate limited public resources in places where students need them, such as public toilets, rest areas and other facilities, by superimposing and spatially analyzing the densely populated routes to school, thereby improving the efficiency of resource utilization and the utilization rate of facilities.

[0018] 4. When calculating the student's route to school, the present invention adopts an improved A-star algorithm, taking into account distance factors, safety factors and comfort factors, and taking into account the distance, safety and comfort of the student's route to school. It also helps to introduce student safety and comfort considerations when introducing student-friendly facilities in urban and rural space planning, making the student's route to school safer and more comfortable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the first embodiment of the present invention.

[0020] Figure 2 It is a flowchart of the main steps of the main improvement of the A-star algorithm in the second embodiment of the present invention.

[0021] Figure 3 is the frequency of traffic accidents or public safety incidents on edge e in the GIS system of the present invention Schematic diagram of the obtained process.

[0022] Figure 4 It is a flowchart of the third embodiment of the present invention. DETAILED DESCRIPTION

[0023] Embodiment 1: Figure 1 As shown, the present invention provides an urban and rural spatial planning optimization method based on surveying and mapping data, comprising the following steps: S1. Obtain the residential addresses and school addresses of students in the compulsory education stage and before in the current administrative area from the education department, that is, the residential addresses and school addresses of students in kindergarten, primary school and junior high school, complete data desensitization and data cleaning, and construct a data set of endpoints of students' school routes; there are two ways to deal with data desensitization. One is to directly delete student privacy-related information such as name, student number, class, and home house number; the other is to desensitize the address information through a hash algorithm to prevent sensitive information leakage, and to retain the latitude and longitude data of the student's address with high precision to ensure that the accuracy of the path calculation is not affected during geocoding; when cleaning the data, remove invalid data, duplicate data and erroneous data. If necessary, introduce a GIS system to verify whether the address exists to ensure that the address data is valid; S2. Build a GIS system in the current administrative area based on surveying and mapping data, geocode the residential area addresses and school addresses in the endpoint dataset of the student's school route, calculate the shortest path from home to school through the classic Dijkstra algorithm or A* algorithm and other path search algorithms, and build each student's school route; S3. Superimpose all the school routes of the students, and use GIS spatial analysis tools to calculate the intersection points and coincident line segments of the school routes. The attribute values ​​of the intersection points and coincident lines are the number of students passing through. S4, according to the preset threshold q of the number of students passing through, extract the intersection points and coincident line segments exceeding the threshold, and highlight them in the GIS system; S5. According to the intersection of the constructible area in the GIS system and the extracted intersection points and coincident line segments exceeding the threshold, that is, spatially superimposing the intersection points and coincident line segments exceeding the threshold with the constructible area in the GIS system, filtering out the non-constructible areas (such as water bodies, nature reserves, etc.), and extracting candidate sites; S6. Based on the multi-criteria decision analysis (MCDA) method, the multi-criteria decision analysis tool of GIS is used to comprehensively score the candidate sites and select the planned predetermined number m areas with the highest comprehensive scores. This is to determine the areas that will eventually be used for the construction of student-friendly facilities.

[0024] The calculation formula for the preset threshold q of the number of students passing through is: , where μ is the mean and σ is the standard deviation. The number of students passing through all routes is counted, and the mean μ and standard deviation σ are calculated. k is a control parameter, usually adjusted between 1 and 3, representing the proportion of the routes you want to choose. If you want to concentrate on planning a few high-frequency route areas, you can set a larger k; if you want to cover more routes, you can set a smaller k.

[0025] In step S6, the MCDA method is introduced into the GIS system for comprehensive scoring. The scoring criteria should at least include traffic convenience, safety, and construction cost. For traffic convenience, the score is based on the distance between the candidate location and the main traffic nodes. The formula is: ,in is the distance between the candidate location and the nearest traffic node; for safety, consider the surrounding safety factors of the candidate location (such as traffic accident rate, public security, etc.), and the score is based on historical data, such as the safety score of edge e in Example 2 The calculation method is as follows: for construction cost, based on factors such as land price and construction difficulty, the lower the cost, the higher the score; a comprehensive score is obtained by weighted calculation based on traffic convenience, safety and construction cost. , ,in , , They are the weights of traffic convenience, safety, and construction cost. The specific values ​​are determined by the planning objectives. For example, , , are 0.4, 0.3, 0.3 respectively, and .

[0026] Embodiment 2: Based on Embodiment 1, Figure 2 As shown, in step S2, the shortest path search algorithm is replaced by the improved A-star algorithm. The main improvements of the A-star algorithm include the following steps: S201. Constructing an improved multi-factor comprehensive cost function , instead of the traditional single cost function, , , in , , are the weight coefficients of distance factor, safety factor and comfort factor respectively, and satisfy , determined by specific planning objectives, such as setting , , They are 0.4, 0.3, and 0.3 respectively. To reflect the distance cost from the starting point to node n, To reflect the safety risk of the route, The comfort cost reflects the walking comfort of the route; S202. Improve the traditional heuristic function that usually uses straight-line distance, consider safety and comfort, and construct an improved heuristic function , , in , , are the weight coefficients of distance factor, safety factor and comfort factor respectively, and satisfy , , , The specific value of is determined according to the planning objectives, for example, , , They are 0.4, 0.3, and 0.3 respectively. is the Euclidean distance from node n to the target node, is the average security risk estimate of the area surrounding node n, is the average comfort estimate of the area surrounding node n.

[0027] In step S201, the distance cost The calculation formula is: , where k is the number of edges from the starting point to node n, For edge The length can be automatically calculated by the GIS system based on the topological structure of the road network.

[0028] Among them, in step S201, the security cost The calculation formula is: , where k is the number of edges from the starting point to node n, For edge The safety score of the network is usually in the range of [0, 1], and the larger the value, the safer it is.

[0029] Among them, the safety score of edge e The calculation formula is: ,in, is the frequency of accidents or events on edge e, and are the minimum and maximum accident frequencies of all edges respectively.

[0030] like Figure 3 As shown, the frequency of traffic accidents or public safety incidents on edge e The acquisition is done through the following steps: S701. Obtain the archive data of traffic accidents and public security incidents in the current area in the past ten years through the public security department; S702, using natural language processing technology to perform word segmentation, complete time recognition, place name recognition and direction recognition of traffic accidents and public security incidents; S703, performing data cleaning and standardization on the extracted place names, directions and time information, removing invalid data and duplicates, and ensuring that the data format is consistent; S704, using the geocoding technology in the Geographic Information System (GIS) to convert the standardized place names and location information into corresponding longitude and latitude coordinates; S705, spatially matching the longitude and latitude coordinates with the road network data in the GIS system, and mapping the accident and incident locations to corresponding roads, road sections or intersections using spatial association analysis; S706. Use the GIS system to count the cumulative number of accidents and events in the edge attributes, and divide the cumulative number of occurrences by the cumulative number of all accidents and events to obtain the accident or event frequency on edge e. , the frequency data Write the attribute fields of edge e.

[0031] In step S702, the API of large models such as Wenxin Yiyan and Tongyi Qianwen can be directly called to complete the time recognition, place name recognition and direction recognition of traffic accidents and public security incidents, and export data in xslx format or json format with three fields of time, place and direction.

[0032] Among them, in step S201, the comfort cost The calculation formula is: , where k is the number of edges from the starting point to node n, For edge The comfort score ranges from [0,1], and the larger the value, the more comfortable it is.

[0033] Comfort rating of the edge The calculation formula is: , in, , , , are the weight coefficients of sidewalk width, greening, noise, and air quality, respectively, and , , , , The specific value is determined according to the planning objectives. Score the width of the sidewalk, Score greenery. Score the noise, Rate the air quality.

[0034] Sidewalk Width Rating and green rating They are obtained through normalization based on the existing planning data, and the noise score and air quality rating They are obtained through normalization based on the monitoring data of the environmental monitoring department.

[0035] For example, according to a more balanced planning goal, , , , The values ​​of are 0.3, 0.3, 0.2, and 0.2 respectively. Assuming that the indicators of a roadside e are: sidewalk width 3 meters (minimum 1 meter, maximum 5 meters), green coverage 40% (minimum 10%, maximum 60%), noise 65 decibels (minimum 50 decibels, maximum 80 decibels), air pollution index (AQI) 75 (minimum 50, maximum 150), then after normalization, , , , The values ​​of are 0.5, 0.6, 0.5, and 0.75 respectively, then =0.3×0.5+0.3×0.6+0.2×0.5+0.2×0.75=0.58, that is, the comprehensive comfort score of the road side is 0.58, indicating that its comfort level is at a medium level.

[0036] Embodiment 3: Based on Embodiment 2, considering that many regions currently adopt the school district method, and students' residential addresses are generally around schools, the planning optimization method includes the following steps: S1. Obtain the residential addresses and school addresses of students in the compulsory education stage and before from the education department in the current administrative area. According to the three types of schools, kindergarten, primary school and junior high school, select the residential addresses and school addresses of students in the three stages respectively, complete data desensitization and data cleaning, and construct the endpoint data set of kindergarten students' school route, primary school students' school route endpoint data set, and junior high school students' school route endpoint set; S2. Build a GIS system in the current administrative area based on surveying and mapping data, geocode the residential area addresses and school addresses in the endpoint dataset of the student's school route, calculate the shortest path from home to school through the classic Dijkstra algorithm or A* algorithm and other path search algorithms, and build each student's school route; S3, superimpose the school routes of students in the three stages respectively, and superimpose all the school routes of students in the same stage together, and use GIS spatial analysis tools to calculate the intersection points and coincident line segments of the school routes. The attribute values ​​of the intersection points and coincident lines are the number of students passing through, thus obtaining three types of intersection points and coincident line segments; S4. Preset different thresholds for the number of students passing through according to different stages of students , the values ​​of i are 1, 2, and 3, that is, is the threshold corresponding to kindergarten students, is the threshold corresponding to primary school students, To correspond to the thresholds of junior high school students, three types of intersection points and coincident line segments exceeding the corresponding thresholds are extracted and highlighted in three different colors in the GIS system; S5. According to the intersection of the constructible area in the GIS system and the three extracted intersection points and coincident line segments exceeding the threshold, three candidate locations are extracted. For example, a small amusement park can be built at the candidate point corresponding to kindergarten students, a table tennis court can be built at the candidate point corresponding to primary school students, and a small basketball court can be built at the candidate point corresponding to junior high school students, which is helpful for the development of junior high school students in adolescence to meet the different needs of students at different stages. S6. Based on the multi-criteria decision analysis (MCDA) method, the multi-criteria decision analysis tool of GIS is used to comprehensively score the candidate sites, and the planned predetermined number m areas with the highest comprehensive scores are selected. This is to determine the three areas that will eventually be used for the construction of student-friendly facilities.

Claims

1. A method for optimizing urban and rural spatial planning based on surveying and mapping data, characterized in that: The steps include: S1. Obtain the residential addresses and school addresses of students in the compulsory education stage and before in the current administrative area from the education department, complete data desensitization and data cleaning, and construct a data set of students' school route endpoints; S2. Geocode the residential area addresses and school addresses in the student school route endpoint dataset, and construct each student's school route through the shortest path search algorithm based on the GIS system in the current administrative area; S3. Superimpose all the school routes of the students, and use GIS spatial analysis tools to calculate the intersection points and coincident line segments of the school routes. The attribute values ​​of the intersection points and coincident lines are the number of students passing through. S4, according to the preset threshold q of the number of students passing through, extract the intersection points and coincident line segments exceeding the threshold, and highlight them in the GIS system; S5, extracting candidate locations according to the intersection of the constructible area in the GIS system and the extracted intersection points and coincident line segments exceeding the threshold; S6. Based on the multi-criteria decision analysis (MCDA) method, the multi-criteria decision analysis tool of GIS is used to comprehensively score the candidate sites and select the planned predetermined number m areas with the highest comprehensive scores. This is to determine the areas that will eventually be used for the construction of student-friendly facilities.

2. The urban and rural spatial planning optimization method based on surveying and mapping data according to claim 1 is characterized in that: The calculation formula for the preset threshold q of the number of students passing is: , Among them, μ is the mean and σ is the standard deviation. The number of students passing through all routes is counted, and the mean μ and standard deviation σ are calculated. k is a control parameter, usually adjusted between 1 and 3, representing the proportion of the routes you want to choose. If you want to concentrate on planning a few high-frequency route areas, you can set a larger k; if you want to cover more routes, you can set a smaller k.

3. The urban and rural spatial planning optimization method based on surveying and mapping data according to claim 1 or 2, characterized in that: In step S2, the shortest path search algorithm is replaced by the improved A-star algorithm. The main improvements of the A-star algorithm include the following steps: S201. Constructing an improved multi-factor comprehensive cost function , instead of the traditional single cost function, ， ， in , , are the weight coefficients of distance factor, safety factor and comfort factor respectively, and satisfy , To reflect the distance cost from the starting point to node n, To reflect the safety risk of the route, The comfort cost reflects the walking comfort of the route; S202. Improve the traditional heuristic function that usually uses straight-line distance, consider safety and comfort, and construct an improved heuristic function , ， in , , are the weight coefficients of distance factor, safety factor and comfort factor respectively, and satisfy , is the Euclidean distance from node n to the target node, is the average security risk estimate of the area surrounding node n, is the average comfort estimate of the area surrounding node n.

4. The method for optimizing urban and rural spatial planning based on surveying and mapping data according to claim 3 is characterized in that: In step S201, the distance cost The calculation formula is: ， Where k is the number of edges from the starting point to node n, For edge The length can be automatically calculated by the GIS system based on the topological structure of the road network.

5. The method for optimizing urban and rural spatial planning based on surveying and mapping data according to claim 3 is characterized in that: In step S201, the security cost The calculation formula is: ， Where k is the number of edges from the starting point to node n, For edge The safety score of the network is usually in the range of [0, 1], and the larger the value, the safer it is.

6. The urban and rural spatial planning optimization method based on surveying and mapping data according to claim 5 is characterized in that: Bian e's safety score The calculation formula is: , in, is the frequency of accidents or events on edge e, and are the minimum and maximum accident frequencies of all edges respectively.

7. The method for optimizing urban and rural spatial planning based on surveying and mapping data according to claim 6, characterized in that: The frequency of accidents or events on edge e is obtained through the following steps: S701. Obtain the archive data of traffic accidents and public security incidents in the current area over the years through the public security department; S702, using natural language processing technology to perform word segmentation, complete time recognition, place name recognition and direction recognition of traffic accidents and public security incidents; S703, performing data cleaning and standardization on the extracted place names, directions and time information, removing invalid data and duplicates, and ensuring that the data format is consistent; S704, using the geocoding technology in the Geographic Information System (GIS) to convert the standardized place names and location information into corresponding longitude and latitude coordinates; S705, spatially matching the longitude and latitude coordinates with the road network data in the GIS system, and mapping the accident and incident locations to corresponding roads, road sections or intersections using spatial association analysis; S706. Use the GIS system to count the cumulative number of accidents and events in the edge attributes, and divide the cumulative number of occurrences by the cumulative number of all accidents and events to obtain the accident or event frequency on edge e. , the frequency data Write the attribute fields of edge e.

8. The method for optimizing urban and rural spatial planning based on surveying and mapping data according to claim 3 is characterized in that: In step S201, the comfort cost The calculation formula is: ， Where k is the number of edges from the starting point to node n, For edge The comfort score ranges from [0,1], and the larger the value, the more comfortable it is.

9. The method for optimizing urban and rural spatial planning based on surveying and mapping data according to claim 8, characterized in that: Comfort rating of the edge The calculation formula is: , in, , , , are the weight coefficients of sidewalk width, greening, noise, and air quality, respectively, and , Score the width of the sidewalk, Score greenery. Score the noise, Rate the air quality.

10. The urban and rural spatial planning optimization method based on surveying and mapping data according to claim 9 is characterized in that: Sidewalk Width Rating and green rating They are obtained through normalization based on the existing planning data, and the noise score and air quality rating They are obtained through normalization based on the monitoring data of the environmental monitoring department.

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