Recreation facility site selection method, device, equipment and medium

Abstract

The invention provides a recreation facility site selection method, device and equipment and a medium, and relates to the technical field of urban planning, and the method comprises the steps: obtaining first spatial data of a recreation service blind area, second spatial data and population data of each residential land in the recreation service blind area, and road data corresponding to a research area; determining a region weight value corresponding to each residential land according to the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data; generating life circle data corresponding to each residential land according to the second spatial data and the road data; and extracting the target recreation facility site selection area based on the first spatial data, the area weight value and the life circle data. According to the method, the efficiency and accuracy of recreation facility site selection can be remarkably improved, recreation service blind areas are accurately and effectively reduced, and balanced distribution of urban recreation resources is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and in particular to a method, device, equipment and medium for selecting a site for recreational facilities. Background Art

[0002] In the current field of urban planning and green space construction, although technological progress has been significant, it mainly focuses on methods to identify blind spots in rest and recreation services. These technical means can accurately outline the blank areas of service coverage through big data analysis, spatial model construction and other means. However, there is still a lack of a mature and widely applicable solution for how to accurately eliminate these blind spots and achieve the optimal allocation of service resources. This leads to the challenge that decision makers often face when building new parks or green spaces, which is the difficulty in accurately locating the construction location in order to maximize service efficiency and optimize resource allocation, thereby effectively solving deep-seated problems such as insufficient services and spatial dislocation. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for site selection of recreational facilities, which can significantly improve the efficiency and accuracy of site selection of recreational facilities, accurately and effectively eliminate blind spots in recreational services, and achieve a balanced distribution of urban recreational resources.

[0004] In a first aspect, an embodiment of the present invention provides a method for selecting a site for a recreational facility, comprising: Obtaining first spatial data of recreational service blind spots within the study area, second spatial data and population data of each residential land within the recreational service blind spots, and road data corresponding to the study area; Determine the regional weight value corresponding to each residential land according to the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data; and generate the living circle data corresponding to each residential land according to the second spatial data and the road data; Based on the first spatial data, the regional weight value corresponding to each residential land and the living circle data, the target recreational facility site selection area is extracted from the recreational service blind spot.

[0005] In one embodiment, according to the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data, the regional weight value corresponding to each residential land is determined, including: Based on the first spatial data and the second spatial data, respectively determine a first geometric center point corresponding to the recreational service blind spot and a second geometric center point corresponding to each residential land; For each residential land, based on the road data and the relative positional relationship between the second geometric center point and the first geometric center point corresponding to the residential land, the location entropy of the residential land in the spatial dimension is determined; and the location entropy of the residential land in the population dimension is determined by the ratio between the population data corresponding to the residential land and the sum of the population data corresponding to each residential land in the recreational service blind area; The ratio between the location entropy in the population dimension and the location entropy in the spatial dimension is taken as the regional weight value corresponding to the residential land.

[0006] In one embodiment, determining the location entropy of the residential land in the spatial dimension based on the road data and the relative position relationship between the second geometric center point and the first geometric center point corresponding to the residential land includes: Determine the shortest distance from the recreational service blind spot to the residential land based on the road data and the relative positional relationship between the second geometric center point corresponding to the residential land and the first geometric center point; The ratio of the shortest distance from the recreational service blind spot to the residential land to the sum of the shortest distances from the recreational service blind spot to each residential land is determined as the location entropy of the residential land in the spatial dimension.

[0007] In one implementation, generating life circle data corresponding to each residential area according to the second spatial data and the road data includes: For each residential land, taking the second geometric center point corresponding to the residential land as the starting point, the reachable range after residents move for a specified period of time is determined based on the road data, and the reachable range is used as the living circle data corresponding to the residential land.

[0008] In one embodiment, based on the first spatial data, the regional weight value corresponding to each residential land, and the life circle data, extracting the target recreational facility site selection area from the recreational service blind area includes: Based on the first spatial data and the life circle data corresponding to each residential land, the candidate recreational facility site selection areas are extracted from the recreational service blind areas; According to the regional weight value corresponding to the residential land to which the alternative recreational facility site selection area belongs, the target recreational facility site selection area is screened out from the alternative recreational facility site selection areas.

[0009] In one embodiment, based on the first spatial data and the life circle data corresponding to each residential land, extracting candidate recreational facility site selection areas from the recreational service blind area includes: Intersecting the first spatial data with the life circle data corresponding to each residential land, so as to screen out the target life circle data located in the blind area of ​​recreational services; Perform intersection processing on the data of each target life circle to extract the overlapping area data between the target life circles; The overlapping area data is used as alternative recreational facility site selection areas.

[0010] In one embodiment, according to the regional weight value corresponding to the residential land to which the candidate recreational facility site selection area belongs, the target recreational facility site selection area is screened out from the candidate recreational facility site selection area, including: For each candidate recreational facility site selection area, the sum of the regional weight values ​​corresponding to each residential land to which the candidate recreational facility site selection area belongs is used as the target weight value corresponding to the candidate recreational facility site selection area; The target recreation facility site selection area is selected from the alternative recreation facility site selection areas in order of target weight values ​​from large to small.

[0011] In a second aspect, an embodiment of the present invention further provides a recreational facility site selection device, comprising: A data acquisition module is used to acquire first spatial data of a recreational service blind spot in a study area, second spatial data and population data of each residential land in the recreational service blind spot, and road data corresponding to the study area; A weight and living circle determination module, for determining the regional weight value corresponding to each residential land according to the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data; and generating the living circle data corresponding to each residential land according to the second spatial data and the road data; The site selection determination module is used to extract the target recreation facility site selection area from the recreation service blind spot based on the first spatial data, the regional weight value corresponding to each residential land and the living circle data.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0014] A recreational facility site selection method, device, equipment and medium provided by an embodiment of the present invention first obtains first spatial data of a recreational service blind spot in a study area, second spatial data and population data of each residential land in the recreational service blind spot, and road data corresponding to the study area; then, based on the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data, determines the regional weight value corresponding to each residential land; and, based on the second spatial data and the road data, generates living circle data corresponding to each residential land; finally, based on the first spatial data, the regional weight value corresponding to each residential land and the living circle data, extracts the target recreational facility site selection area from the recreational service blind spot. The above method takes the residential land within the recreational service blind spot as the service object, uses road data as the medium, and combines the first spatial data of the recreational service blind spot, the second spatial data of the residential land and the population data to determine the regional weight value. Finally, based on the first spatial data of the recreational service blind spot, the regional weight value corresponding to the residential land and the living circle data, the area with the largest sum of the weight values ​​of the overlapping parts of the target area is determined, which is the optimal site selection area for recreational facilities. The embodiment of the present invention can significantly improve the efficiency and accuracy of the site selection of recreational facilities, so that the recreational service blind spot can be accurately and effectively reduced, and the balanced distribution of urban recreational resources can be achieved.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a recreational facility site selection method provided by an embodiment of the present invention; Figure 2 A technical circuit diagram of a recreational facility site selection method provided by an embodiment of the present invention; Figure 3A schematic diagram of a path from a first geometric center point of a recreational service blind area to a second geometric center point of a residential area within the recreational service blind area provided by an embodiment of the present invention; Figure 4 A schematic diagram of a 15-minute living circle provided by an embodiment of the present invention; Figure 5 A schematic diagram of the intersection of 15-minute living circle data and recreational service blind area data provided by an embodiment of the present invention; Figure 6 A schematic diagram of splitting data provided by an embodiment of the present invention; Figure 7 A schematic diagram of an optimal area for site selection of recreational facilities to reduce service blind spots provided by an embodiment of the present invention; Figure 8 A schematic structural diagram of a recreational facility site selection device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Recreational services, as an indispensable part of the urban functional framework, have a profound impact on the well-being of citizens and the shaping of the urban landscape through careful planning and layout. Identifying blind spots in recreational services, that is, areas where recreational facilities are urgently needed, is the first step to improving urban service functions. However, within these blind spots, how to use technology to accurately lock in the best spatial location to ensure the efficient use of service facilities and the precise connection with citizen needs is more critical. Setting up recreational facilities in blind spots of recreational services can achieve the following goals: (1) Improving the well-being of the people: The quality of life of residents in blind spots of recreational services is significantly restricted due to the lack of sufficient leisure places. Therefore, relying on spatial analysis technology to accurately eliminate these blind spots is the only way to improve the well-being of citizens and improve the quality of life. (2) Promoting balanced urban development: The balanced layout of recreational services is an important indicator of the level of urban development. The existence of blind spots reveals the imbalance of urban resource allocation and aggravates the development gap within the city. By accurately locating blind spots and implementing targeted resource allocation strategies, it is helpful to promote the coordinated development of various urban areas and achieve fairness and balance in recreational services. (3) Shaping a good city image: A city with a sound recreational service system is like a bright business card that can attract visitors from all directions and enhance the city's attractiveness and competitiveness. On the contrary, the existence of service blind spots not only weakens the citizens' sense of happiness, but also may reduce the tourists' experience and affect the city's external image. Therefore, actively identifying and eliminating service blind spots is of strategic significance for shaping and improving the city's image.

[0021] With the increasing maturity of modern scientific and technological means such as GIS, big data analysis and accessibility assessment, the site selection process of recreational service facilities is becoming more intelligent and refined. The application of these technologies will help urban planners to more scientifically evaluate the potential value of different locations, ensure that recreational facilities can be accurately implemented, and effectively meet the diverse needs of citizens and tourists.

[0022] At present, there is still a lack of mature and widely applicable solutions for how to accurately eliminate these blind spots and achieve the optimal allocation of service resources. As a result, when building new parks or green spaces, decision makers often face the challenge of finding the right location to maximize service efficiency and optimize resource allocation, thereby effectively solving deep-seated problems such as insufficient services and spatial dislocation.

[0023] Specifically, current technical means focus on comprehensive analysis of data such as urban green space distribution, population density, and activity patterns through tools such as satellite remote sensing, geographic information systems (GIS), and big data analysis, so as to identify service blind spots. Although this method is efficient and scientific, it still has obvious deficiencies in converting it into a practical action guide to guide the precise site selection and scientific planning of new parks or green spaces. For example, in the process of promoting the construction of a "green city", a city identified multiple service blind spots through technical means and planned to build a series of community parks to fill these gaps. However, in the specific implementation process, due to the lack of effective methods to accurately reduce blind spots, it is difficult for decision-makers to accurately judge the potential and limitations of each candidate plot, resulting in the completion of some parks. Although it has alleviated the leisure needs of surrounding residents to a certain extent, it has not been able to maximize its comprehensive benefits in improving the ecological environment and promoting community integration. Some parks have even experienced spatial dislocation due to improper site selection, that is, their service scope does not match the actual needs of surrounding residents, resulting in a waste of resources. Therefore, exploring and developing a comprehensive technical system that integrates blind spot identification and precise planning has become a key issue that needs to be tackled urgently in current urban planning and green space construction. It is of great significance to improving the quality of life of urban residents and promoting the construction of ecological civilization.

[0024] Based on this, the implementation of the present invention provides a method, device, equipment and medium for site selection of recreational facilities, which can significantly improve the efficiency and accuracy of site selection of recreational facilities, accurately and effectively eliminate blind spots in recreational services, and achieve a balanced distribution of urban recreational resources.

[0025] To facilitate understanding of this embodiment, a method for selecting a location for recreational facilities disclosed in an embodiment of the present invention is first described in detail. Figure 1 The flowchart of a method for selecting a location for recreational facilities is shown in FIG. 1 , and the method mainly includes the following steps S102 to S106: Step S102, obtaining the first spatial data of the recreation service blind area in the study area, the second spatial data and population data of each residential land in the recreation service blind area, and the road data corresponding to the study area.

[0026] Among them, recreational service blind spots can be understood as areas that lack necessary recreational facilities, services or activities and cannot meet people's recreational needs. The first spatial data is the surface spatial data of recreational service blind spots; residential land includes urban residences, rural homesteads, and multi-functional land with residential functions as the main function. The second spatial data is the surface spatial data of residential land. The population data can be the number of permanent residents; the road data is the linear spatial data of all roads in the study area.

[0027] Step S104, determining the regional weight value corresponding to each residential land based on the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data; and generating the living circle data corresponding to each residential land based on the second spatial data and the road data.

[0028] Among them, the regional weight value is used to characterize the importance of a certain residential land among all residential lands; the living circle data is the reachable range after residents move for a specified period of time, starting from the second geometric center point corresponding to the residential land.

[0029] In one example, after determining the location entropy of residential land in the spatial dimension and the location entropy in the population dimension, the ratio between the location entropy in the population dimension and the location entropy in the spatial dimension can be used as the regional weight value corresponding to the residential land.

[0030] In one example, for any residential land, the second geometric center point corresponding to the residential land is taken as the starting point, and the reachable range after residents move for a specified period of time is determined based on road data, and the reachable range is used as the living circle data corresponding to the residential land.

[0031] Step S106, based on the first spatial data, the regional weight value corresponding to each residential land and the living circle data, extract the target recreational facility site selection area from the recreational service blind spot.

[0032] The target recreational facility site selection area is used to indicate that recreational facilities can be arranged at the site selection area. In one example, based on the first spatial data of the recreational service blind area and the life circle data corresponding to the residential land, the candidate recreational facility site selection area located in the recreational service blind area can be screened out, and then the regional weight value corresponding to the residential land can be used to assign values ​​to each candidate recreational facility site selection area, and finally, according to the target area weight value corresponding to each candidate recreational facility site selection area, the target recreational facility site selection area can be screened out.

[0033] The recreational facility site selection method provided by the embodiment of the present invention takes the residential land within the recreational service blind spot as the service object, uses road data as the medium, and combines the first spatial data of the recreational service blind spot, the second spatial data of the residential land and the population data to determine the regional weight value. Finally, based on the first spatial data of the recreational service blind spot, the regional weight value corresponding to the residential land and the living circle data, the area with the largest sum of the weight values ​​of the overlapping parts of the target area is determined, which is the optimal site selection area for the recreational facilities. The embodiment of the present invention can significantly improve the efficiency and accuracy of the site selection of recreational facilities, so that the recreational service blind spot can be accurately and effectively reduced, and the balanced distribution of urban recreational resources can be achieved.

[0034] For ease of understanding, the present invention provides a specific implementation of a recreational facility site selection method. Figure 2 The technical roadmap of a recreational facility site selection method shown in the figure combines the road network and N residential areas located in the recreational service blind spot to calculate the service network and output the living circle; assigns weights to the living circle according to the location entropy Q; intersects the living circle with the recreational service blind spot, and calculates the sum of the overlapping parts of the living circle according to the weight. The overlapping surface with the maximum value is the most preferred site selection area, that is, the target recreational facility site selection area.

[0035] In one example, the data acquired in the aforementioned step S102 includes: (1.1) Obtaining recreational service blind spot data: Obtain the first spatial data of recreational service blind spots based on residential land within the urban area of ​​a certain city, named data M.

[0036] (1.2) Determine the service target: Obtain the second spatial data of all residential land within the city’s recreational service blind spot, named data J, including urban residences, rural homesteads, and multifunctional land with residential functions as the main function.

[0037] (1.3) Determine population size: Obtain the number of permanent residents R of all residential land within the city’s recreational service blind spot. This can be obtained through census data or big data.

[0038] (1.4) Obtain road network: Obtain linear spatial data of all roads within the city, named data L. The more complete and comprehensive the road levels contained in this data, the higher the accuracy of the calculation.

[0039] For the aforementioned step S104, the embodiment of the present invention provides a specific implementation method for determining the regional weight value corresponding to each residential land, including steps 2.1 to 2.3: Step 2.1, based on the first spatial data and the second spatial data, respectively determine the first geometric center point corresponding to the recreational service blind spot and the second geometric center point corresponding to each residential land.

[0040] In one example, the [Feature to Point] tool in the [Data Management] tool of ArcMap software was used to convert the surface data of each recreational service blind spot M within the study area into the first geometric center point, named data M1, M2, M3...Mn.

[0041] Similarly, use the [Feature to Point] tool in the [Data Management] tool to convert each residential land J data in the recreational service blind area into the second geometric center point, and name it data J1, J2, J3...Jn.

[0042] Step 2.2, for each residential land, based on the road data and the relative position relationship between the second geometric center point and the first geometric center point corresponding to the residential land, determine the location entropy of the residential land in the spatial dimension; and determine the location entropy of the residential land in the population dimension by the ratio between the population data corresponding to the residential land and the sum of the population data corresponding to each residential land in the recreational service blind spot; In one example, the process of determining the location entropy of the residential land in the spatial dimension is as follows: first, based on the road data and the relative position relationship between the second geometric center point and the first geometric center point corresponding to the residential land, the shortest distance from the recreational service blind spot to the residential land is determined; then, the ratio of the shortest distance from the recreational service blind spot to the residential land to the sum of the shortest distances from the recreational service blind spot to each residential land is determined as the location entropy of the residential land in the spatial dimension.

[0043] In the specific implementation, the Network Analysis tool in ArcGIS was used. This method can perform accessibility analysis based on the direction of the road network. It is more accurate and more in line with the actual situation than the buffer analysis service area method. The data processing and analysis process is as follows: Use data L to construct a network dataset (New Network Dataset). Based on the network dataset constructed by data L, use the [New Nearest Facility Point] function of the Network Analyst toolbar to calculate the closest distance Dn from the first geometric center point Mn of each recreational service blind spot to the second geometric center point Jn of the residential land in the recreational service blind spot, such as Figure 3 The schematic diagram of the path from the first geometric center point of a recreational service blind area to the second geometric center point of a residential land in the recreational service blind area is shown. Based on this, the location entropy P of the distance from the center point to the residential land in the recreational service blind area is calculated as follows: ; in, is the location entropy of residential land in the spatial dimension, is the shortest distance from the first geometric center point Mn of the recreational service blind area to the second geometric center point Ji of the i-th residential land in the service blind area, It is the sum of the shortest distances from the first geometric center point Mn of the recreational service blind area to the second geometric center point Jn of a residential land in the service blind area. The total amount of residential land within the recreational service blind spot.

[0044] The embodiment of the present invention uses the distance between the residential area and the service blind area as the denominator. The closer the distance between the two is, the greater the weight is, so as to ensure that the newly built recreational facilities are set up as close as possible. In addition, it also combines the shortest distance calculation method based on the actual road network, rather than the point-to-point straight-line distance calculation method, so that the calculation is more in line with the actual situation and more accurate.

[0045] In one example, the location entropy of the residential land in the population dimension is determined according to the following formula: ; in, is the location entropy of residential land in the spatial dimension, is the number of permanent residents in the ith residential area, The sum of the permanent population of all residential land within the recreational service blind area. The total amount of residential land within the recreational service blind spot.

[0046] The embodiment of the present invention uses the population of residential areas as a weight, and calculates the proportion of the population of each residential area to the total population in the recreational service blind area as a numerator, so as to ensure that the newly built recreational facilities serve the residential population to the maximum extent.

[0047] Step 2.3, the ratio between the location entropy in the population dimension and the location entropy in the spatial dimension is used as the regional weight value corresponding to the residential land. The location entropy is calculated as the assigned weight value for the 15-minute living circle of each residential land in the recreational service blind spot, and the formula is as follows: ; in, is the regional weight value corresponding to residential land.

[0048] For the aforementioned step S104, an embodiment of the present invention provides a specific implementation method for determining the living circle data corresponding to the residential land, including: for each residential land, taking the second geometric center point corresponding to the residential land as the starting point, determining the reachable range after residents move for a specified period of time based on road data, and using the reachable range as the living circle data corresponding to the residential land.

[0049] Exemplarily, the living circle data can be a 15-minute living circle. The 15-minute living circle is generally surrounded by urban trunk roads or land boundaries, with a resident population of 50,000 to 100,000 people and a service radius of 800 to 1,000 meters. It is connected with the management and service scope of blocks and streets, connecting several residential communities, and coordinating supporting facilities such as primary and secondary schools, nursing homes, community hospitals, sports venues and parks. The calculation process of the 15-minute living circle is as follows: Based on the network dataset constructed by data L, use the service area analysis function (Service Area) of the Network Analyst toolbar. Create a new service area (New ServiceArea) and load the central dataset Jn. Set the interval Breaks parameter in the attribute to the 15-minute living circle service radius, and then solve to obtain the 15-minute living circle corresponding to the second geometric center point J of all residential land in the recreational service blind spot, named F. This calculation can obtain the 15-minute living circle F1, F2, F3...Fn corresponding to each residential land through the residential area number, such as Figure 4 A schematic diagram of a 15-minute life circle is shown.

[0050] For the aforementioned step S108, the embodiment of the present invention provides a specific implementation method for extracting the target recreational facility site selection area, including the following steps 3.1 to 3.2: Step 3.1, based on the first spatial data and the life circle data corresponding to each residential land, extract the alternative recreational facility site selection area from the recreational service blind spot.

[0051] In one example, the process for identifying candidate recreation facility siting areas is as follows: First, the first spatial data and the life circle data corresponding to each residential area are intersected to filter out the target life circle data located in the recreational service blind spot. Figure 5 A schematic diagram of the intersection of 15-minute living circle data and recreational service blind area data is shown, which intersects the 15-minute living circle data Fn with the recreational service blind area data M to ensure that the site selection area is located within the recreational service blind area.

[0052] Then, the data of each target living circle is intersected to extract the overlapping area data between the target living circles, and the overlapping area data is used as the alternative recreational facility site selection area. Specifically, the overlapping area data of the 15-minute living circle in the recreational service blind spot is extracted. Using the [Feature to Polygon] tool in [Data Management Tools], the input element is the 15-minute living circle of all residential land, and the output element is the splitting of the overlapping area of ​​the living circle, and the layer data containing the overlapping area is obtained. Open the [Spatial Join] tool in [Analysis Tools], set the target element to the splitting data of the overlapping area of ​​the living circle, and set the connection element to the original 15-minute living circle data containing weight assignment, and obtain the split data with the attributes of the original 15-minute living circle.

[0053] For example, see Figure 6 A schematic diagram of split data is shown. The left figure shows two overlapping 15-minute living circles 1 and 15-minute living circles 2. The overlapping area 3 is obtained by intersecting the data. The data after the intersection processing can be split to obtain split data 1, split data 2 and split data 3. Split data 3 is the site selection area for alternative recreational facilities.

[0054] Step 3.2, based on the regional weight value corresponding to the residential land to which the alternative recreational facility site selection area belongs, select the target recreational facility site selection area from the alternative recreational facility site selection area.

[0055] In one example, the process of determining the target recreation facility site area is as follows: First, for each candidate recreational facility site selection area, the sum of the regional weight values ​​corresponding to each residential land to which the candidate recreational facility site selection area belongs is used as the target weight value corresponding to the candidate recreational facility site selection area. Figure 6 Taking split data 3 as an example, the target weight value corresponding to split data 3 is the sum of the regional weight values ​​of split data 1 and split data 2.

[0056] Then, the target recreational facility site selection area is selected from the candidate recreational facility site selection areas in the order of target weight values ​​from large to small. In practical applications, the area with the highest total weight value among all split data is the optimal site selection area for recreational facilities to reduce service blind spots, such as Figure 7 A schematic diagram of the optimal area for site selection of recreational facilities to reduce service blind spots is shown.

[0057] The embodiment of the present invention uses the network analysis method of ArcGIS, combined with actual road data to identify the 15-minute living circle of each residential land, and to identify the overlapping areas of the 15-minute living circles of different residential lands. The analysis method of location entropy is used to balance the population and distance parameters, and by assigning values ​​to the overlapping parts of the living circles, the area with the highest weight value is calculated, thereby identifying the optimal location area for the new recreational service facilities.

[0058] In summary, the recreational facility site selection method provided by the embodiment of the present invention has at least the following characteristics: First, after implementing the method for optimal site selection of new parks and green spaces, the embodiments of the present invention can accurately and effectively reduce blind spots in recreational services, achieve a balanced distribution of urban recreational resources, and greatly improve the quality of life of residents in areas that originally lacked recreational facilities.

[0059] Secondly, through the application and analysis tools of ArcGIS, the layout optimization of recreational facilities has a scientific basis. The precise matching of these facilities with the needs of residential areas can not only improve the efficiency of facility use and avoid waste of resources, but also enable residents to enjoy high-quality recreational services more conveniently.

[0060] Furthermore, this method also promotes the intensive use of urban space. With limited land resources, through scientific planning and precise site selection, the harmonious coexistence of recreational facilities and the surrounding environment can be achieved, which not only meets the leisure needs of residents, but also does not have a negative impact on the overall appearance of the city. This efficient use of space has laid a solid foundation for the sustainable development of the city.

[0061] Finally, from a more macro perspective, the optimal location method for new parks and green spaces also promotes the green development and ecological civilization construction of the city. By increasing the area of ​​urban green space and improving the quality of the ecological environment, we have created a more livable and harmonious urban environment for residents. At the same time, this has also injected new impetus into the sustainable development of the city and enhanced the overall competitiveness and attractiveness of the city.

[0062] Based on the above embodiments, the present invention provides a recreational facility site selection device. Figure 8 The schematic diagram of the structure of a recreational facility site selection device shown in FIG. 1 mainly includes the following parts: The data acquisition module 802 is used to acquire the first spatial data of the recreation service blind area in the study area, the second spatial data and population data of each residential land in the recreation service blind area, and the road data corresponding to the study area; The weight and living circle determination module 804 is used to determine the regional weight value corresponding to each residential land according to the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data; and generate the living circle data corresponding to each residential land according to the second spatial data and the road data; The site selection determination module 806 is used to extract the target recreation facility site selection area from the recreation service blind area based on the first spatial data, the area weight value corresponding to each residential land and the life circle data.

[0063] The recreational facility site selection device provided by the embodiment of the present invention takes the residential land within the recreational service blind spot as the service object, uses road data as the medium, and determines the regional weight value in combination with the first spatial data of the recreational service blind spot, the second spatial data of the residential land and the population data. Finally, based on the first spatial data of the recreational service blind spot, the regional weight value corresponding to the residential land and the living circle data, the area with the largest sum of the weight values ​​of the overlapping parts of the target area is determined, which is the optimal site selection area for the recreational facilities. The embodiment of the present invention can significantly improve the efficiency and accuracy of the site selection of recreational facilities, so that the recreational service blind spot can be accurately and effectively reduced, and the balanced distribution of urban recreational resources can be achieved.

[0064] In one implementation, the weight and life circle determination module 804 is specifically used to: Based on the first spatial data and the second spatial data, respectively determine a first geometric center point corresponding to the recreational service blind spot and a second geometric center point corresponding to each residential land; For each residential land, based on the road data and the relative positional relationship between the second geometric center point and the first geometric center point corresponding to the residential land, the location entropy of the residential land in the spatial dimension is determined; and the location entropy of the residential land in the population dimension is determined by the ratio between the population data corresponding to the residential land and the sum of the population data corresponding to each residential land in the recreational service blind area; The ratio between the location entropy in the population dimension and the location entropy in the spatial dimension is taken as the regional weight value corresponding to the residential land.

[0065] In one implementation, the weight and life circle determination module 804 is specifically used to: Determine the shortest distance from the recreational service blind spot to the residential land based on the road data and the relative position relationship between the second geometric center point corresponding to the residential land and the first geometric center point; The ratio of the shortest distance from the recreational service blind spot to the residential land to the sum of the shortest distances from the recreational service blind spot to each residential land is determined as the location entropy of the residential land in the spatial dimension.

[0066] In one implementation, the weight and life circle determination module 804 is specifically used to: For each residential land, taking the second geometric center point corresponding to the residential land as the starting point, the reachable range after residents move for a specified period of time is determined based on the road data, and the reachable range is used as the living circle data corresponding to the residential land.

[0067] In one implementation, the site selection determination module 806 is specifically configured to: Based on the first spatial data and the life circle data corresponding to each residential land, the candidate recreational facility site selection areas are extracted from the recreational service blind areas; According to the regional weight value corresponding to the residential land to which the alternative recreational facility site selection area belongs, the target recreational facility site selection area is screened out from the alternative recreational facility site selection areas.

[0068] In one implementation, the site selection determination module 806 is specifically configured to: Intersecting the first spatial data with the life circle data corresponding to each residential land, so as to screen out the target life circle data located in the blind area of ​​recreational services; Perform intersection processing on the data of each target life circle to extract the overlapping area data between the target life circles; The overlapping area data is used as alternative recreational facility site selection areas.

[0069] In one implementation, the site selection determination module 806 is specifically configured to: For each candidate recreational facility site selection area, the sum of the regional weight values ​​corresponding to each residential land to which the candidate recreational facility site selection area belongs is used as the target weight value corresponding to the candidate recreational facility site selection area; The target recreation facility site selection area is selected from the alternative recreation facility site selection areas in order of target weight values ​​from large to small.

[0070] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0071] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0072] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0073] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0074] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for selecting a location for recreational facilities, characterized in that: include: Acquire first spatial data of a recreational service blind spot in a study area, second spatial data and population data of each residential land in the recreational service blind spot, and road data corresponding to the study area; Determine the regional weight value corresponding to each of the residential lands according to the location entropy of the residential lands in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential lands in the population dimension represented by the population data; and generate the living circle data corresponding to each of the residential lands according to the second spatial data and the road data; Based on the first spatial data, the area weight value corresponding to each of the residential lands and the living circle data, a target recreational facility site selection area is extracted from the recreational service blind spot.

2. The recreational facility site selection method according to claim 1, characterized in that: Determining a regional weight value corresponding to each residential land according to the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data, including: Based on the first spatial data and the second spatial data, respectively determine a first geometric center point corresponding to the recreational service blind spot and a second geometric center point corresponding to each of the residential lands; For each of the residential lands, based on the road data and the relative positional relationship between the second geometric center point corresponding to the residential land and the first geometric center point, the location entropy of the residential land in the spatial dimension is determined; and the location entropy of the residential land in the population dimension is determined by the ratio between the population data corresponding to the residential land and the sum of the population data corresponding to each of the residential lands in the recreational service blind area; The ratio between the location entropy in the population dimension and the location entropy in the space dimension is used as the regional weight value corresponding to the residential land.

3. The method for selecting a location for recreational facilities according to claim 2, characterized in that: Determining the location entropy of the residential land in a spatial dimension based on the road data and the relative position relationship between the second geometric center point corresponding to the residential land and the first geometric center point includes: Determine the shortest distance from the recreational service blind spot to the residential land based on the road data and the relative positional relationship between the second geometric center point corresponding to the residential land and the first geometric center point; The ratio of the shortest distance from the recreational service blind spot to the residential land to the sum of the shortest distances from the recreational service blind spot to each of the residential lands is determined as the location entropy of the residential land in the spatial dimension.

4. The method for selecting a location for recreational facilities according to claim 2, characterized in that: Generating living circle data corresponding to each of the residential areas according to the second spatial data and the road data includes: For each of the residential land, taking the second geometric center point corresponding to the residential land as the starting point, the reachable range after residents move for a specified period of time is determined based on the road data, and the reachable range is used as the living circle data corresponding to the residential land.

5. The recreational facility site selection method according to claim 1, characterized in that: Based on the first spatial data, the regional weight value corresponding to each of the residential lands and the living circle data, extracting a target recreational facility site selection area from the recreational service blind area includes: Extracting candidate recreational facility site selection areas from the recreational service blind area based on the first spatial data and the life circle data corresponding to each of the residential lands; A target recreation facility site selection area is screened out from the candidate recreation facility site selection areas according to the area weight value corresponding to the residential land to which the candidate recreation facility site selection area belongs.

6. The method for selecting a location for recreational facilities according to claim 5, characterized in that: Extracting candidate recreational facility site selection areas from the recreational service blind area based on the first spatial data and the life circle data corresponding to each of the residential lands, including: Intersecting the first spatial data with the life circle data corresponding to each of the residential lands to filter out target life circle data located in the recreational service blind spot; Performing intersection processing on each of the target life circle data to extract overlapping area data between the target life circles; The overlapping area data is used as an alternative recreational facility site selection area.

7. The method for selecting a location for recreational facilities according to claim 5, characterized in that: According to the area weight value corresponding to the residential land to which the candidate recreational facility site selection area belongs, selecting a target recreational facility site selection area from the candidate recreational facility site selection area includes: For each of the candidate recreational facility site selection areas, the sum of the area weight values ​​corresponding to the various residential lands to which the candidate recreational facility site selection area belongs is used as the target weight value corresponding to the candidate recreational facility site selection area; The target recreation facility site selection area is screened out from the candidate recreation facility site selection areas in the order of the target weight values ​​from large to small.

8. A device for selecting a location for recreational facilities, characterized in that: include: A data acquisition module, used to acquire first spatial data of a recreational service blind spot in a study area, second spatial data and population data of each residential land in the recreational service blind spot, and road data corresponding to the study area; A weight and living circle determination module, for determining the regional weight value corresponding to each residential land according to the location entropy of the residential land in the spatial dimension represented by the first spatial data, the second spatial data and the road data, and the location entropy of the residential land in the population dimension represented by the population data; and generating the living circle data corresponding to each residential land according to the second spatial data and the road data; A site selection determination module is used to extract the target recreational facility site selection area from the recreational service blind spot based on the first spatial data, the area weight value corresponding to each residential land and the living circle data.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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