CN116451931A - Data processing method for public service facility site selection administrative management decision - Google Patents
Data processing method for public service facility site selection administrative management decision Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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- G06Q10/06313—Resource planning in a project environment
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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- G06Q50/26—Government or public services
Abstract
The invention provides a data processing method for public service facility site selection administrative management decision, which belongs to the technical field of data processing methods for administrative purposes, and comprises the following steps: s1, drawing a plane axis map according to a central line of a map section; s2, carrying out space syntactic analysis on the research area to obtain global integration data and local integration (R=3) data; s3, obtaining a topological distance model; s4, dividing road sections into six types from high to low by an equal interval method; s5, taking a first type of road section, namely an ultrahigh type of road section, and carrying out Dijkstra algorithm analysis; s6, selecting the maximum value of all shortest practical distances corresponding to the intersection as the physical distance of the intersection for any intersection; s7, selecting a road section with the smallest physical data as the optimal site selection position. The technical scheme of the invention solves the problem that the randomness and uncertainty of public service facility site selection cannot be scientifically decided in the prior art.
Description
Technical Field
The invention relates to the technical field of data processing methods for administrative purposes, in particular to a data processing method for public service facility site selection administrative management decisions.
Background
The public service facility is used as a public product with a sharing attribute, the site selection of the public service facility relates to public interests and civil problems, and the good site selection of the public service facility can obviously improve the happiness index of residents, ensure social fairness and maintain the benefits of citizens. Public facilities can be divided into entertainment facilities, medical facilities, cultural facilities and the like according to different properties, hospitals are used as medical facilities for the life health of people, and the rescue rate of people can be directly influenced by the position relationship of the public facilities and the layout of residential areas. The concept of a fifteen minute community life circle is becoming more and more interesting.
The planning and site selection problems contain rich contents, and the position determination from the industrial area, the commercial area and the living area to the traffic site and public facilities belong to the site selection problems; the covered disciplines are wide in content and relate to sociology, operation and research, economics, mathematics, urban and rural planning and the like. As early as the 17 th century, the math Pierre de ferman presented the well-known ferman problem of finding a specified point in a plane that minimizes the sum of the distances of the specified point from any other three points of demand. Then in 1909 Alfred Weber upgrades the ferman problem to the Weber problem: and (3) popularizing the specified point and the demand point into a plurality of points, giving weight to each demand point, and calculating the optimal site selection position of the specified point. With the development of Weber's problem, the problem of addressing began to get the attention of the scholars in various fields and intensive research was conducted. The traditional research site selection coverage model is used for determining site selection positions of facilities on the premise of meeting the requirements of all users in a service range, wherein the more the users covered by the facilities are, the better the site selection positions are; the median model addresses the facility location with the average distance of all users to the facility, the smaller the average distance of the users to the facility, the better the addressed location. The coverage model and the median model consider too single factors, and the selection of sites is less reasonable.
In traditional urban planning, public service facility center site selection sets strict coverage standard, and land scale is configured according to service radius and service population number, so that scientific decision on site selection randomness and uncertain factors and other problems can not be carried out by combining real and effective data and models. Because the formation elements of urban space are complex, the matching of public service facilities is also irregular, and the fifteen-minute life circle matching public service facilities are required to be built.
Thus, there is a need for a data processing method that combines a variety of factors to provide a more rational choice of location selection for public service facilities for location selection administration decisions.
Disclosure of Invention
The invention mainly aims to provide a data processing method for public service facility site selection administrative management decision, which aims to solve the problem that the prior art cannot scientifically decide the randomness, uncertainty factors and the like of site selection by combining real and effective data and models.
In order to achieve the above object, the present invention provides a data processing method for public service facility site selection administrative management decision, comprising the following steps:
s1, acquiring a study area map and length data of each road section, importing the study area map and the length data into AutoCAD, drawing a plane axis map according to the center line of the map road section, cutting off each straight line at the intersection, performing head processing, namely, enabling an axis port to exceed an axis interface, marking each intersection from 1, namely, the intersection is gathered as I= {1,2,3, … … }, and naming each road section by adding "-" in the middle of intersection numbers at two ends of each road section, such as 1-2, 2-3;
s2, carrying out space syntactic analysis on the research area, exporting the drawn dwg file into a dxf file, importing depthmap space syntactic analysis software, and carrying out global integration degree analysis and local integration degree (R=3) analysis on the research area to obtain global integration data and local integration degree (R=3) data;
s3, carrying out weighted superposition on the global integration degree and the local integration degree (R=3), and setting reasonable weighting coefficients to obtain a topological distance model:
M＝aA+bB
wherein M is the topological distance of the road section, a is the global integration degree, B is the local integration degree (r=3), a and B are weighting coefficients, a+b=1, and the topological distance data of each road section can be obtained according to the formula;
s4, taking the topological distance data obtained in the step S3 as parameters, and dividing road sections into six types by an equal interval method from high to low: the method comprises the steps of ultrahigh, high, middle-upper, middle-lower, low and ultralow, and generating a road section classification map, wherein the specific expression formula is as follows:
wherein Q is the size of a topological distance data classification interval, M max For maximum topological distance data in all road sections, M min The minimum topological distance data in all road sections;
s5, classifying in the step S4, carrying out Dijkstra algorithm analysis on the road sections of the first type, namely the super-high type, constructing a weighted adjacency matrix w according to the road section length data acquired in the step S1, calling a Matlab program for calculating the shortest path, and generating a shortest path matrix by using the Dijkstra algorithm to obtain the shortest practical distance from each intersection to all other intersections;
s6, selecting the maximum value in the shortest actual distances to all the intersections corresponding to the intersection as the physical distance of the intersection for any intersection, wherein the set of the physical distances of all the intersections is H= { E 1 ，E 2 ，E 3 … … }, and converts the physical distance of the intersection into the physical distance of the road segment:
wherein N is m—n For the physical distance of m-n road sections E m For the physical distance of m crossing E n The physical distance data of each road section can be obtained by the formula for the physical distance of the n road junctions;
and S7, after the steps are completed, successfully constructing a community health service center site selection model, selecting a road section with the minimum physical data as an optimal site selection position of the community health service center by comparing the physical distance data of each road section, and generating an optimal site selection position schematic diagram.
Further, in step S3, global integration degree and local integration degree (r=3) data are imported into the splppro for CRITIC weight analysis to obtain a and b.
The invention has the following beneficial effects:
1. the method of the invention is based on space syntax, takes the integration degree as the quantization standard of site selection, and fuses the global integration degree and the local integration degree (R=3), so that the space syntax analysis is more comprehensive and accurate.
2. The method not only considers the space syntax analyzed by the topological structure, but also introduces Dijkstra algorithm analysis analyzed by the physical distance, and combines the topological distance and the physical distance, so that the determination of the site selection position is more scientific and reasonable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a data processing method of a public service facility site selection administrative decision of the present invention.
Fig. 2 is a plan CAD axis of a new area Xin An street map of the west coast of the Qingdao city, drawn by the method of the present invention.
Fig. 3 is an analysis chart of the local integration degree (r=3) of the Dethmap of fig. 2 drawn by the method according to the present invention.
Fig. 4 is a graph of the index importance using splsppro output as proposed by the present invention.
Fig. 5 is a road segment classification diagram drawn according to topological distance data by using the present invention.
FIG. 6 is a schematic diagram of the best addressing location of FIG. 2 found using the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Taking a community health service center as an example for selecting a new area Xin An street in Qingdao city and west coast, a data processing method for public service facility site selection administrative decision shown in fig. 1 includes the following steps:
s1, acquiring a map of a research area, namely a new Qingdao west coast area Xin An street and length data of each road section, introducing the map into AutoCAD for drawing, drawing a plane axis map according to the central line of the map road section, cutting off each straight line at an intersection, and performing head processing, namely that an axis port exceeds an axis interface, so as to obtain a plane CAD axis map of the new Qingdao west coast area Xin An street map shown in FIG. 2, wherein each intersection is marked from 1, namely the intersection is gathered as I= {1,2,3, … … }, and each road section is named as "-" in the middle of the numbers of the intersections at two ends of the road section, such as 1-2, 2-3;
s2, as shown in fig. 3, performing space syntactic analysis on the research area, exporting the drawn dwg file into a dxf file, importing depthmap X space syntactic analysis software, and performing global integration analysis and local integration (R=3) analysis on the research area to obtain global integration data and local integration (R=3) data; global integration represents the degree of integration of a certain road segment in the whole system, and local integration (r=3) represents the degree of integration of a certain road segment in a spatial range within 3 steps adjacent around.
S3, carrying out weighted superposition on the global integration degree and the local integration degree (R=3), and setting reasonable weighting coefficients to obtain a topological distance model:
M＝aA+bB
wherein M is the topological distance of a road section, namely the difficulty level of other road sections to reach the road section, a is the global integration degree, B is the local integration degree (r=3), a and B are weighting coefficients, a+b=1, and the topological distance data of each road section can be obtained according to the formula;
s4, taking the topological distance data obtained in the step S3 as parameters, and dividing road sections into six types by an equal interval method from high to low: superhigh, high, middle-upper, middle-lower, low and ultralow, and generates a road section classification chart shown in fig. 5, wherein the specific expression formula is as follows:
wherein Q is the size of a topological distance data classification interval, M max For maximum topological distance data in all road sections, M min The minimum topological distance data in all road sections; where "ultrahigh" means that it is easiest for any other road segment to reach that road segment, and the meaning of the remaining categories is analogized.
S5, classifying in the step S4, carrying out Dijkstra algorithm analysis on the road sections of the first type, namely the super-high type, constructing a weighted adjacency matrix w according to the road section length data acquired in the step S1, calling a Matlab program for calculating the shortest path, and generating a shortest path matrix by using the Dijkstra algorithm to obtain the shortest practical distance from each intersection to all other intersections; the code in which Dijkstra algorithm is used for shortest path analysis is as follows:
the constructed weighted adjacency matrix w is as follows:
the first column and the first row of data "0" in the matrix w represent that the actual distance from the first intersection to the first intersection is 0, the first column and the second row of data "400" represent that the actual distance from the first intersection to the second intersection is 400, the first column and the third row of data "inf" represent that the first intersection is not adjacent to the third intersection, and the meaning of other position data is similar.
S6, selecting the maximum value in the shortest actual distances to all the intersections corresponding to the intersection as the physical distance of the intersection for any intersection, wherein the set of the physical distances of all the intersections is H= { E 1 ，E 2 ，E 3 … … }, and converts the physical distance of the intersection into the physical distance of the road segment:
wherein N is m—n For the physical distance of m-n road sections E m For the physical distance of m crossing E n The physical distance data of each road section can be obtained by the formula for the physical distance of the n road junctions;
and S7, after the steps are completed, successfully constructing a community health service center site selection model, selecting a road section with the smallest physical data as the optimal site selection position of the community health service center by comparing the physical distance data of each road section, and generating an optimal site selection position schematic diagram shown in FIG. 6, wherein the position V shown in FIG. 6 is the optimal site selection position.
Specifically, in step S3 shown in fig. 4, global integration degree and local integration degree (r=3) data are imported into the splppro for CRITIC weight analysis to obtain a and b. The weighting coefficient a= 43.782%, b= 56.218% in this embodiment.
It should be understood that the above description is not intended to limit the invention to the particular embodiments disclosed, but to limit the invention to the particular embodiments disclosed, and that the invention is not limited to the particular embodiments disclosed, but is intended to cover modifications, adaptations, additions and alternatives falling within the spirit and scope of the invention.
Claims (2)
1. A data processing method for public service facility site selection administrative management decisions, comprising the steps of:
s1, acquiring a study area map and length data of each road section, importing the study area map and the length data into AutoCAD, drawing a plane axis map according to the center line of the map road section, cutting off each straight line at the intersection, performing head processing, namely, enabling an axis port to exceed an axis interface, marking each intersection from 1, namely, the intersection is gathered as I= {1,2,3, … … }, and naming each road section by adding "-" in the middle of intersection numbers at two ends of each road section, such as 1-2, 2-3;
s2, carrying out space syntactic analysis on the research area, exporting the drawn dwg file into a dxf file, importing depthmap space syntactic analysis software, and carrying out global integration degree analysis and local integration degree (R=3) analysis on the research area to obtain global integration data and local integration degree (R=3) data;
s3, carrying out weighted superposition on the global integration degree and the local integration degree (R=3), and setting reasonable weighting coefficients to obtain a topological distance model:
M＝aA+bB
wherein M is the topological distance of the road section, a is the global integration degree, B is the local integration degree (r=3), a and B are weighting coefficients, a+b=1, and the topological distance data of each road section can be obtained according to the formula;
s4, taking the topological distance data obtained in the step S3 as parameters, and dividing road sections into six types by an equal interval method from high to low: the method comprises the steps of ultrahigh, high, middle-upper, middle-lower, low and ultralow, and generating a road section classification map, wherein the specific expression formula is as follows:
wherein Q is the size of a topological distance data classification interval, M max For maximum topological distance data in all road sections, M min The minimum topological distance data in all road sections;
s5, classifying in the step S4, carrying out Dijkstra algorithm analysis on the road sections of the first type, namely the super-high type, constructing a weighted adjacency matrix w according to the road section length data acquired in the step S1, calling a Matlab program for calculating the shortest path, and generating a shortest path matrix by using the Dijkstra algorithm to obtain the shortest practical distance from each intersection to all other intersections;
s6, selecting the maximum value in the shortest actual distances to all the intersections corresponding to the intersection as the physical distance of the intersection for any intersection, wherein the set of the physical distances of all the intersections is H= { E 1 ，E 2 ，E 3 … … }, and converts the physical distance of the intersection into the physical distance of the road segment:
wherein N is m—n For the physical distance of m-n road sections E m For the physical distance of m crossing E n The physical distance data of each road section can be obtained by the formula for the physical distance of the n road junctions;
and S7, after the steps are completed, successfully constructing a public service facility site selection model, selecting a road section with the minimum physical data as an optimal site selection position of the public service facility by comparing the physical distance data of each road section, and generating an optimal site selection position schematic diagram.
2. The method according to claim 1, wherein in step S3, global integration and local integration (r=3) data are imported into the splsspro for CRITIC weight analysis to obtain a and b.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117252745A (en) * | 2023-11-20 | 2023-12-19 | 河北省交通规划设计研究院有限公司 | Public service facility site selection method and device and computer equipment |
| CN117274004A (en) * | 2023-11-20 | 2023-12-22 | 武汉市规划编审中心(武汉规划展示馆) | Primary school address selection method based on shortest path planning and space syntax |
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- 2023-03-15 CN CN202310245462.9A patent/CN116451931A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117252745A (en) * | 2023-11-20 | 2023-12-19 | 河北省交通规划设计研究院有限公司 | Public service facility site selection method and device and computer equipment |
| CN117274004A (en) * | 2023-11-20 | 2023-12-22 | 武汉市规划编审中心(武汉规划展示馆) | Primary school address selection method based on shortest path planning and space syntax |
| CN117274004B (en) * | 2023-11-20 | 2024-02-27 | 武汉市规划编审中心(武汉规划展示馆) | Primary school address selection method based on shortest path planning and space syntax |
| CN117252745B (en) * | 2023-11-20 | 2024-03-12 | 河北省交通规划设计研究院有限公司 | Public service facility site selection method and device and computer equipment |
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