A method for verifying the layout of public facilities based on population distribution and road network

Abstract

The present invention discloses a method for checking the layout of public facilities based on population distribution and road network. The method comprises the following steps: firstly, classifying and screening points of interest (POIs), extracting long-term densely-staying points such as settlements, and estimating the population in the area of ​​interest using population density data and mobile phone signaling data, obtaining accurate population distribution grid data, and then using a path planning algorithm to calculate the shortest path between the entrance and exit and the public facilities; using Thiessen polygons to segment the population distribution grid data in the region, and assigning the corresponding population value as an attribute to each entrance and exit; and finally, in order to measure the coordinated relationship between the supply of urban public service facilities and the spatial distribution of population in the same period, a coordination degree model is used to evaluate the rationality of the original layout of public facilities. The present invention proposes suggestions for optimizing the reasonable layout of public facilities, and solves the problem that the traditional layout method using buffer zones ignores the spatial heterogeneity of population distribution.

Description

Technical Field

[0001] The invention relates to a public facility layout inspection method based on population distribution and road network, belonging to the technical field of position correction methods. Background Art

[0002] The layout of public facilities should take into account the requirements of urban landscape organization and create an urban landscape with local features. The distribution of public facilities should consider a reasonable construction sequence and leave room for arranging the construction sequence of public facility projects, so as to ensure the necessary public facilities configuration in different construction periods and avoid premature or excessive construction, which will cause investment waste. However, the current layout of public facilities mainly includes the following:

[0003] (I) Strip layout along the street

[0004] 1. When public service facilities are laid out along urban roads:

[0005] (1) Based on the nature and direction of urban roads, it is advisable to layout along living roads and ensure that the houses have a good orientation.

[0006] (2) When public buildings are arranged at road intersections, attention should be paid to the rational organization of pedestrian and vehicle flows. Generally, public service facilities with large pedestrian flows should not be arranged at intersections with heavy traffic. Instead, some public service facilities that attract fewer pedestrian flows can be arranged, and the buildings should be appropriately retreated to leave a small square as a buffer for pedestrian gathering and dispersion.

[0007] 2. When public service facilities are arranged along residential roads (double-sided, single-sided, commercial pedestrian streets)

[0008] (1) When the street is not wide and the traffic volume is not heavy, a double-sided layout is adopted. Stores are concentrated and the commercial atmosphere is strong. Residents go shopping on both sides of the street. The traffic volume is not heavy and it is safer and more convenient.

[0009] (2) When the street is wide and the main road in a residential area is more than 20 meters, the relevant commercial facilities frequently used by residents can be arranged on one side, while the shops that are not frequently used can be placed on the other side. This can reduce the intersection of pedestrian and vehicle traffic, reduce the number of residents crossing the road, and increase safety.

[0010] (3) In order to fully ensure the safety of residents and create a residential center full of life, public service facilities should be arranged along both sides of the road in a pedestrian manner, so that the commercial and service environment is relatively peaceful and residents can move freely without being disturbed.

[0011] (II) Centralized layout

[0012] 1. Courtyard type, square type, mixed type, combined with greening

[0013] 2. Enclose a small square

[0014] (III) Mixed arrangement

[0015] It is not difficult to see that the current layout method still takes the regional structure as the main reference, and does not take population as an important condition, and cannot maximize the effect of a public facility. Therefore, based on the spatial distribution of population, it is of great significance to analyze and plan the optimal location of public facilities at the entrances and exits through the Dijkstra network. Summary of the invention

[0016] In view of the problems existing in the above-mentioned prior art, the present invention provides a method for inspecting the layout of public facilities based on population distribution and road network, thereby solving the above-mentioned technical problems.

[0017] In order to achieve the above-mentioned object, the technical solution adopted by the present invention is: a method for inspecting the layout of public facilities based on population distribution and road network, characterized in that it comprises the following steps;

[0018] S1: Obtain public facility planning information of the area to be inspected through remote sensing images, obtain POI (point of interest) data of the area to be inspected, estimate the population based on building structure and population density, and obtain population distribution data;

[0019] S2: Obtain the entrance and exit data of the AOI of the area of ​​interest, and re-divide the population according to the entrance and exit data;

[0020] S3: Use the Dijkstra algorithm to plan the shortest path from each entrance to the public facility location and calculate the actual number of service objects of each public facility;

[0021] S4: Establish a coordination model based on the results of the redivision to verify the rationality of the layout of public facilities.

[0022] Furthermore, the specific steps of S1 are:

[0023] S11: Divide the study area and extract ROI samples of the study area, that is, extract the public facilities in the area to be detected through supervised classification and obtain their distribution characteristics;

[0024] S12: Find and collect relevant information about public facilities in the area to understand the number of planned service objects;

[0025] S13: Obtain POI data of the study area, and use a web crawler to access the client API developer port to batch obtain POI data of the study area;

[0026] S14: Filtering POIs by their category labels to find out points where people stay for a long time and in large numbers;

[0027] S15: Obtain the specific structure of the building through the cadastral data of the area to be inspected;

[0028] S16: Obtain population density data, i.e., obtain spatial open source data on population numbers in each 1km*1km area, supplemented by client API location service check-in data obtained by web crawlers; estimate the population number of the study area based on the population density data;

[0029] S17: According to the research area categories screened out from the POI data, i.e., points where people stay for a long time and densely, population distribution estimation models are established respectively, and finally a high-precision population distribution raster result is obtained.

[0030] Furthermore, the specific steps of step S2 are as follows:

[0031] S21: Use a web crawler to access the client API developer port to obtain AOI entrance and exit data;

[0032] S22: Divide the estimated population according to the entrance and exit data of the AOI;

[0033] S23: Use the Voronoi diagram to divide the study area, with each entrance and exit as the only point in each Thiessen polygon;

[0034] S24: Calculate the population covered by the Thiessen polygons of each entrance and exit based on population distribution and population density, and assign each entrance and exit with the value of the population covered by the Thiessen polygons to which the entrance and exit belong as an attribute.

[0035] Furthermore, the specific steps of step S3 are as follows:

[0036] S31: Write a Dijkstra algorithm program to calculate the shortest path between the AOI entrance and exit and public facilities;

[0037] S32: extracting the road network connecting the AOI entrance and exit and public facilities in the detection area;

[0038] S33: Calculate the distances from all entrances and exits to each similar public facility in the detection area, and assign the associated population quantity attribute to the nearest public facility;

[0039] S34: Recalculate the actual number of service objects of each public facility according to the population numerical attribute assigned by S32.

[0040] Furthermore, the specific steps of step S4 are as follows:

[0041] Analyze the actual number of service recipients of various public facilities, and use the coordination model (used to measure the coordination relationship between the supply of urban public service facilities and the spatial distribution of population in the same period) to evaluate the rationality of the original layout of public facilities;

[0042] The calculation formula is:

[0043]

[0044] In the formula, C i is the coordination degree between public facilities supply and population distribution in region i, C i ∈[0, 1], when C i = 1, the coordination between public service facilities supply and population distribution is the greatest; conversely, when C i = 0 when the coordination degree is the smallest, g i (y) is the population development level of region i, which in this method refers to the proportion of the population of region i to the total population, and k is the coordination coefficient (k ≥ 2, generally k = 2). i is the comprehensive evaluation index of the public service facilities supply level and population development level in region i, α and β are specific coefficients, generally α=β=0.5; D i It is the coordinated development degree of the urban public service facilities supply level and population distribution, which essentially reflects the coordination degree of the development level of the public service facilities supply level and population size level when the comprehensive benefits are the largest under certain conditions; Q i is the matching degree between facilities and population in region i, when Q i =1, it means that public service facilities are basically matched with the population size; when Q i ∈[0,0.8], the public service facilities in region i lag behind the population size; when Q i ∈[1.2,+∞], the scale of public service facilities in region i is in excess, and the population size lags behind the scale of public service facilities.

[0045] The beneficial effects of the present invention are as follows: the present invention first classifies and screens points of interest (POIs) to extract settlements and other points where people stay for a long time. The population density data and mobile phone signaling data are used to estimate the population in the area of ​​interest to obtain accurate population distribution raster data. Secondly, the entrance and exit data of the buildings in the area of ​​interest are obtained, and the path planning algorithm is used to calculate the shortest path between the entrance and exit and the public facilities based on the road network. Thirdly, the population distribution raster data in the area is segmented using Thiessen polygons, and the corresponding population value after segmentation is assigned to each entrance and exit as an attribute. Finally, in order to measure the coordinated relationship between the supply of urban public service facilities and the spatial distribution of the population in the same period, the coordination degree model is used to evaluate the rationality of the original layout of public facilities. Suggestions are put forward to optimize the reasonable layout of public facilities, which can specifically solve the planning rationality evaluation of fitness plazas, charging piles, parks, etc., and solve the problem that the traditional layout method of using buffer zones ignores the spatial heterogeneity of population distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the inspection method according to an embodiment of the present invention;

[0047] Figure 2 An accurate population distribution grid data map of an embodiment of the present invention;

[0048] Figure 3 A Thiessen polygon segmentation graph according to an embodiment of the present invention;

[0049] Figure 4 This is the shortest path planning diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below through the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0052] Example:

[0053] like Figure 1-Figure 4 As shown, a public facility layout inspection method based on population distribution and road network of the present invention comprises the following steps:

[0054] Step S1: Obtain public facility planning information of the area to be inspected through remote sensing images. Obtain POI data of the area to be inspected, estimate the population based on building structure and population density, and obtain population distribution data;

[0055] S11: Extract ROI samples from the study area, extract public facilities in the area to be detected through supervised classification, and obtain their distribution characteristics;

[0056] S12: Find and collect relevant information about public facilities in the area to understand the number of planned service objects;

[0057] S13: Obtain POI data of the study area, and use a web crawler to access the Baidu Map API developer port to batch obtain POI data of the study area, including location information, category labels, etc.;

[0058] S14: Filter POIs by their category labels. Points where people stay for a long time and in large numbers include residential areas, office buildings, schools, large shopping districts, etc.

[0059] S15: Obtain the specific structure of the building through the cadastral data of the area to be inspected;

[0060] S16: Obtain population density data, that is, through the spatial open source data of population numbers in each 1km*1km area, supplemented by the Sina Weibo API location service check-in data obtained by web crawlers, combined with mobile phone signaling data, to improve the reliability of population density data;

[0061] S17: Estimate the population of the study area based on population density data, taking residential areas as an example, with floors, number of households, area of ​​each household, and family size as the main indicators;

[0062] S18: The population distribution data is refined to each residential building to improve the inspection accuracy;

[0063] S19: Based on the research area categories selected from the POI data, residential areas and office buildings are mainly used as research objects, and population distribution estimation models are established respectively. For residential areas, the number of family members is divided by area based on the floor and house type, and the unit entrance is used as the entrance and exit of the experimental area. For office areas such as office buildings, the number, scale, and personnel of companies registered in the building are used as the basis. Finally, a high-precision population distribution grid result is obtained; (such as Figure 2 )

[0064] Step S2: obtaining the entrance and exit data of the area of ​​interest AOI, and re-dividing the population according to the entrance and exit data;

[0065] S21: Use a web crawler to access the Baidu Map API developer port to obtain AOI entrance and exit data.

[0066] S22: Divide the estimated population according to the entrance and exit data of the AOI;

[0067] S23: Use the Voronoi diagram to divide the study area, with each entrance and exit as the only point in each Thiessen polygon;

[0068] S24: Calculate the population covered by the Thiessen polygons of each entrance and exit according to population distribution and population density, and assign the population covered by the Thiessen polygons of the entrance and exit as an attribute to each entrance and exit; (e.g. Figure 3 )

[0069] Step S3: Use the Dijkstra algorithm to plan the shortest path from each entrance to the public facility location and calculate the actual number of service objects of each public facility;

[0070] S31: Write a Dijkstra algorithm program to calculate the shortest path between the AOI entrance and exit and public facilities; (e.g. Figure 4 )

[0071] S32: extracting the road network connecting the AOI entrance and exit and public facilities in the detection area;

[0072] S33: Calculate the distances from all entrances and exits to each similar public facility in the detection area, and assign the associated population quantity attribute to the nearest public facility;

[0073] S34: recalculate the actual number of service objects of each public facility according to the population numerical attribute assigned by S32;

[0074] Step S4: Establish a coordination model based on the results of the redivision to verify the rationality of the layout of public facilities;

[0075] S41: Analyze the actual number of service objects of various public facilities, and use the coordination model (used to measure the coordination relationship between the supply of urban public service facilities and the spatial distribution of population in the same period) to evaluate the rationality of the original layout of public facilities. The calculation formula is:

[0076] The calculation formula is:

[0077]

[0078] In the formula, C i is the coordination degree between public facilities supply and population distribution in region i, C i ∈[0, 1], when C i = 1, the coordination between public service facilities supply and population distribution is the greatest; conversely, when C i = 0 when the coordination degree is the smallest, g i(y) is the population development level of region i, which in this method refers to the proportion of the population of region i to the total population, and k is the coordination coefficient (k ≥ 2, generally k = 2). i is the comprehensive evaluation index of the public service facilities supply level and population development level in region i, α and β are specific coefficients, generally α=β=0.5; D i It is the coordinated development degree of the urban public service facilities supply level and population distribution, which essentially reflects the coordination degree of the development level of the public service facilities supply level and population size level when the comprehensive benefits are the largest under certain conditions; Q i is the matching degree between facilities and population in region i, when Q i =1, it means that public service facilities are basically matched with the population size; when Q i ∈[0,0.8], the public service facilities in region i lag behind the population size; when Q i ∈[1.2,+∞], the scale of public service facilities in region i is in excess, and the population size lags behind the scale of public service facilities.

[0079] The present invention first classifies and screens points of interest (POIs) to extract settlements and other places where people stay for a long time. The population density data and mobile phone signaling data are used to estimate the population in the area of ​​interest to obtain accurate population distribution raster data. Secondly, the entrance and exit data of the buildings in the area of ​​interest are obtained, and the path planning algorithm is used to calculate the shortest path between the entrance and exit and the public facilities based on the road network. Thirdly, the population distribution raster data in the area is segmented using Thiessen polygons, and the corresponding population value after segmentation is assigned to each entrance and exit as an attribute. Finally, in order to measure the coordinated relationship between the supply of urban public service facilities and the spatial distribution of the population in the same period, the coordination degree model is used to evaluate the rationality of the original layout of public facilities. Suggestions are put forward to optimize the reasonable layout of public facilities, which can specifically solve the planning rationality evaluation of fitness plazas, charging piles, parks, etc., and solve the problem that the traditional layout method of using buffer zones ignores the spatial heterogeneity of population distribution.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent substitution or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for checking the layout of public facilities based on population distribution and road network, characterized in that: The steps include: S1: Obtain public facility planning information of the area to be inspected through remote sensing images, obtain POI (point of interest) data of the area to be inspected, estimate the population based on building structure and population density, and obtain population distribution data; S2: Obtain the entrance and exit data of the AOI (Area of ​​Interest), and re-divide the population according to the entrance and exit data; S3: Use the Dijkstra algorithm to plan the shortest path from each entrance to the public facility location and calculate the actual number of service objects of each public facility; The details of the S3 step are: S31: Write a Dijkstra algorithm program to calculate the shortest path between the AOI entrance and exit and public facilities; S32: extracting the road network connecting the AOI entrance and exit and public facilities in the detection area; S33: Calculate the distances from all entrances and exits to each similar public facility in the detection area, and assign the associated population quantity attribute to the nearest public facility; S34: recalculate the actual number of service objects of each public facility according to the population numerical attribute assigned by S32; S4: Establish a coordination model based on the results of the redivision to verify the rationality of the layout of public facilities; The details of step S4 are as follows: Analyze the actual number of service objects of various public facilities, and use the coordination model to evaluate the rationality of the original layout of public facilities; the coordination model is used to measure the coordination relationship between the supply of urban public service facilities and the spatial distribution of population in the same period; The calculation formula is: T i =αf i (x)+βg i (y) In the formula, C i is the coordination degree between public facilities supply and population distribution in region i, C i ∈[0, 1], when C i = 1, the coordination between public service facilities supply and population distribution is the greatest; conversely, when C i = 0 when the coordination degree is the smallest, g i (y) is the population development level of region i, where the population development level is the proportion of the population of region i to the total population, k is the coordination coefficient, k≥2, T i is the comprehensive evaluation index of the public service facilities supply level and population development level in region i, α and β are specific coefficients; D i It is the coordinated development degree between the supply level of urban public service facilities and population distribution, reflecting the degree of coordination between the development levels of public service facilities and population size when the comprehensive benefits are maximized under certain conditions; Q i is the matching degree between facilities and population in region i, when Q i =1, indicating that public service facilities match the population size; when Q i ∈[0,0.8], the public service facilities in region i lag behind the population size; when Q i ∈[1.2,+∞], the scale of public service facilities in region i is in excess, and the population size lags behind the scale of public service facilities.

2. A method for checking the layout of public facilities based on population distribution and road network according to claim 1, characterized in that: The specific steps of S1 are: S11: Divide the study area and extract ROI samples of the study area, where the ROI samples are public facilities areas selected in the remote sensing image using the ROI tool, that is, extract the public facilities in the area to be detected through supervised classification to obtain their distribution characteristics; S12: Find and collect relevant information about public facilities in the area to understand the number of planned service objects; S13: Obtain POI data of the study area, and use a web crawler to access the client API developer port to batch obtain POI data of the study area; S14: Filtering POIs by their category labels to find out points where people stay for a long time and in large numbers; S15: Obtain the specific structure of the building through the cadastral data of the area to be inspected; S16: Obtain population density data, i.e., obtain spatial open source data on population numbers in each 1km*1km area, supplemented by client API location service check-in data obtained by web crawlers; estimate the population number of the study area based on the population density data; S17: According to the research area categories screened out from the POI data, i.e., points where people stay for a long time and densely, population distribution estimation models are established respectively, and finally a high-precision population distribution raster result is obtained.

3. A method for checking the layout of public facilities based on population distribution and road network according to claim 1, characterized in that: The details of step S2 are as follows: S21: Use a web crawler to access the client API developer port to obtain AOI entrance and exit data; S22: Divide the estimated population according to the entrance and exit data of the AOI; S23: Use the Voronoi diagram to divide the study area, with each entrance and exit as the only point in each Thiessen polygon; S24: Calculate the population covered by the Thiessen polygons of each entrance and exit based on population distribution and population density, and assign each entrance and exit with the value of the population covered by the Thiessen polygons to which the entrance and exit belong as an attribute.

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