Public service facility site selection planning method, device, equipment, medium and product

Abstract

The invention discloses a public service facility site selection planning method and device, equipment, a medium and a product, and the method comprises the steps: obtaining multi-source spatio-temporal data of a target public service facility, and carrying out the preprocessing of the multi-source spatio-temporal data, and obtaining related multi-source data; determining site selection related factors according to the related multi-source data; eliminating invalid site selection related factors by adopting a geographic detector model to obtain final site selection related factors and calculating weight values of the final site selection related factors; pearson correlation analysis is adopted to determine that the final site selection correlation factors are positive / negative correlation, and objective weight values of the final site selection correlation factors are calculated; and calculating a site selection result of the target public service facility according to the final site selection related factors and the corresponding objective weight values. According to the method, the site selection result of the public service facility can be evaluated based on the multi-source spatio-temporal data of the public service facility in combination with the Pearson correlation analysis and geographic detector model, and a scientific basis is provided for site selection layout planning of the public service facility.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and particularly relates to a method, device, equipment, medium and product for site selection planning of public service facilities. Background Art

[0002] An AED (Automated External Defibrillator) is a device used for emergency cardiac arrest situations, which can provide timely medical assistance in emergency situations, especially when cardiac arrest occurs. A large number of medical studies have shown that using an AED within 5 minutes during out-of-hospital cardiac arrest can significantly improve the survival rate of patients. It can be seen that this first-aid measure can save lives and reduce the mortality and disability rates caused by cardiac arrest. Therefore, the reasonable layout and site selection planning of AED facilities are particularly important.

[0003] Currently, most public facility site selection technologies evaluate the importance of location-related factors, determine the weights of various factors by combining methods such as the analytic hierarchy process, and then calculate the comprehensive suitability score of candidate locations. However, current public facility site selection technologies still have limitations: (1) ignoring the association between various factors and the object to be evaluated, and the strength of this association has not been considered in the site selection evaluation; (2) traditional methods such as the expert scoring method and the analytic hierarchy process have the disadvantage of subjectivity. Therefore, it is necessary to improve the research work on urban public facility selection prediction. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium and product for site selection planning of public service facilities. Based on the multi-source spatio-temporal data of public service facilities, combined with Pearson correlation analysis and the geographical detector model, the site selection results of public service facilities are evaluated to provide a scientific basis for the site selection layout planning of public service facilities.

[0005] To achieve the above object, an embodiment of the present invention provides a method for site selection planning of public service facilities, including:

[0006] Obtain the multi-source spatio-temporal data of the target public service facility in the target area, and preprocess the multi-source spatio-temporal data to obtain the relevant multi-source data of the target public service facility; wherein, the multi-source spatio-temporal data includes the target public service facility data, mobile phone signaling population data, and POI data;

[0007] Determine the site selection-related factors of the target public service facility according to the relevant multi-source data; use the geographical detector model to exclude invalid site selection-related factors, obtain the final site selection-related factors of the target public service facility, and calculate the weight values of the final site selection-related factors;

[0008] Pearson correlation analysis is used to determine whether the factors related to the final site selection are positively / negatively correlated, and the objective weight values of the factors related to the final site selection are calculated.

[0009] Based on the factors related to the final site selection and the corresponding objective weight values, the site selection result of the target public service facility is calculated.

[0010] As an improvement to the above solution, the use of the geographical detector model to exclude invalid factors related to site selection, obtain the factors related to the final site selection of the target public service facility, and calculate the weight values of the factors related to the final site selection includes:

[0011] On the 100-meter grid of the target area, taking the stock data of the target public service facility in the grid as the dependent variable and the factors related to site selection as the independent variables, a geographical detector model is constructed.

[0012] Based on the q value and significance level p value of the result of the geographical detector model, invalid factors related to site selection are excluded, the factors related to the final site selection of the target public service facility are obtained, and the corresponding weight values of the factors related to the final site selection are calculated.

[0013] As an improvement to the above solution, the use of Pearson correlation analysis to determine whether the factors related to the final site selection are positively / negatively correlated, and calculate the objective weight values of the factors related to the final site selection includes:

[0014] Pearson correlation analysis is used to calculate the Pearson correlation coefficient between each factor related to the final site selection and the dependent variable.

[0015] Based on the Pearson correlation coefficient, it is determined whether the factors related to the final site selection are positively / negatively correlated, and the objective weight values of the factors related to the final site selection are calculated.

[0016] As an improvement to the above solution, the use of the q value and significance level p value of the result of the geographical detector model to exclude invalid factors related to site selection, obtain the factors related to the final site selection of the target public service facility, and calculate the corresponding weight values of the factors related to the final site selection includes:

[0017] Based on the geographical detector model, the q value and significance level p value corresponding to each factor related to site selection are calculated.

[0018] All factors related to site selection are traversed, the significance level p value of each factor related to site selection is compared with a preset threshold group, and invalid factors related to site selection are excluded according to the comparison result to obtain the factors related to the final site selection of the target public service facility.

[0019] Calculate the weight value of each final site selection related factor according to the comparison result and the result q value of each final site selection related factor.

[0020] As an improvement of the above solution, determining that the final site selection related factor is positively / negatively correlated according to the Pearson correlation coefficient and calculating the objective weight value of the final site selection related factor includes:

[0021] If the Pearson correlation coefficient is positive, there is a positive correlation between the final site selection related factor and the dependent variable, determine that the final site selection related factor is positively correlated, and calculate the objective weight value of the final site selection related factor;

[0022] If the Pearson correlation coefficient is negative, there is a negative correlation between the final site selection related factor and the dependent variable, determine that the final site selection related factor is negatively correlated, and calculate the objective weight value of the final site selection related factor.

[0023] As an improvement of the above solution, obtaining multi-source spatio-temporal data of the target public service facilities in the target area and preprocessing the multi-source spatio-temporal data to obtain relevant multi-source data of the target public service facilities includes:

[0024] Obtain multi-source spatio-temporal data of the target public service facilities in the target area, and convert the text address in the target public service facilities data into longitude and latitude coordinates;

[0025] According to the POI data, check the longitude and latitude coordinates, delete the incorrect and redundant multi-source spatio-temporal data, and obtain the relevant multi-source data of the target public service facilities.

[0026] To achieve the above object, an embodiment of the present invention provides a public service facility site selection planning device, including:

[0027] A facility data acquisition module, configured to acquire multi-source spatio-temporal data of the target public service facilities in the target area, and preprocess the multi-source spatio-temporal data to obtain relevant multi-source data of the target public service facilities; wherein, the multi-source spatio-temporal data includes the target public service facilities data, mobile phone signaling population data, and POI data;

[0028] A relevant factor screening module, configured to determine the site selection related factors of the target public service facilities according to the relevant multi-source data; use a geographical detector model to exclude invalid site selection related factors, obtain the final site selection related factors of the target public service facilities, and calculate the weight value of the final site selection related factors;

[0029] An objective weight calculation module, which is used to determine whether the factors related to the final site selection are positively / negatively correlated by using Pearson correlation analysis, and calculate the objective weight values of the factors related to the final site selection;

[0030] A site selection result calculation module, which is used to calculate the site selection result of the target public service facility according to the factors related to the final site selection and the corresponding objective weight values.

[0031] To achieve the above object, an embodiment of the present invention correspondingly provides a public service facility site selection and planning device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above public service facility site selection and planning method is implemented.

[0032] To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above public service facility site selection and planning method.

[0033] To achieve the above object, an embodiment of the present invention further provides a computer program product. The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the above public service facility site selection and planning method.

[0034] Compared with the prior art, a public service facility site selection and planning method, device, equipment, medium and product disclosed in an embodiment of the present invention obtains multi-source spatio-temporal data of a target public service facility in a target area, preprocesses the multi-source spatio-temporal data to obtain relevant multi-source data of the target public service facility; wherein, the multi-source spatio-temporal data includes the target public service facility data, mobile phone signaling population data, and POI data; determines the factors related to the site selection of the target public service facility according to the relevant multi-source data; uses the geographical detector model to exclude invalid factors related to the site selection, obtains the final factors related to the site selection of the target public service facility and calculates the weight values of the final factors related to the site selection; uses Pearson correlation analysis to determine whether the final factors related to the site selection are positively / negatively correlated, and calculates the objective weight values of the final factors related to the site selection; calculates the site selection result of the target public service facility according to the final factors related to the site selection and the corresponding objective weight values. It can combine the geographical detector model with Pearson correlation analysis, and calculate the objective weights of the public service facility site selection factors based on the influence size and significance level of the site selection factors on the site selection object, and their correlation direction, which has objectivity and quantifiability from the aspect of site selection factor selection to weight calculation, and provides a scientific basis for the site selection layout planning of public service facilities. Description of the Drawings

[0035] Figure 1 is a schematic flowchart of a method for site selection and planning of public service facilities provided by an embodiment of the present invention;

[0036] Figure 2 is a schematic structural diagram of a device for site selection and planning of public service facilities provided by an embodiment of the present invention;

[0037] Figure 3 is a structural block diagram of a device for site selection and planning of public service facilities provided by an embodiment of the present invention. Detailed implementation manners

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] It should be noted that the terms "including" and "specific" in the present invention and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0040] Please refer to Figure 1 , Figure 1 is a schematic flowchart of a method for site selection and planning of public service facilities provided by an embodiment of the present invention. The method for site selection and planning of public service facilities includes:

[0041] S1. Obtain multi-source spatio-temporal data of the target public service facilities in the target area, and preprocess the multi-source spatio-temporal data to obtain relevant multi-source data of the target public service facilities; wherein, the multi-source spatio-temporal data includes the target public service facility data, mobile phone signaling population data, and POI data;

[0042] S2. Determine the site selection related factors of the target public service facilities according to the relevant multi-source data; use the geographical detector model to exclude invalid site selection related factors, obtain the final site selection related factors of the target public service facilities, and calculate the weight values of the final site selection related factors;

[0043] S3. Use Pearson correlation analysis to determine whether the final site selection related factors are positively / negatively correlated, and calculate the objective weight values of the final site selection related factors;

[0044] S3. Calculate the siting result of the target public service facility based on the siting-related factors and the corresponding objective weight values.

[0045] Exemplarily, the public service facility siting planning method described in the embodiments of the present invention is implemented by a facility siting planning server. The facility siting planning server can be installed in a city planning server or exist independently of the city planning server. The facility siting planning server can interact with the target user and the city planning server. The facility siting planning server obtains multi-source spatio-temporal data of the target public service facility in the target area (such as target public service facility data, mobile phone signaling population data, POI data (Point of Interest)), deletes the incorrect and redundant multi-source spatio-temporal data, and obtains the relevant multi-source data of the target public service facility; determines the siting-related factors of the target public service facility from three aspects of traffic accessibility, social economy, and urban function based on the relevant multi-source data (such as the distance from the nearest subway station exit, the density of the surrounding road network, the population mobility within the grid, etc.); uses the geographical detector model to exclude the invalid siting-related factors, obtains the final siting-related factors of the target public service facility and calculates the weight values of the final siting-related factors; uses Pearson correlation analysis to determine whether the final siting-related factors are positively / negatively correlated and calculates the objective weight values of the final siting-related factors; calculates the siting result of the target public service facility based on the final siting-related factors and the corresponding objective weight values. Guide the siting layout of the incremental target public service facility according to the predicted siting result of the target public service facility layout. In the embodiments of the present invention, through multi-source spatio-temporal big data, a comprehensive data basis is provided for evaluating the siting prediction of the target public service facility. The multi-source data fusion can effectively solve the deviation problem caused by a single data source; applying Pearson correlation analysis and the geographical detector model to the siting prediction can effectively avoid subjectivity in the siting process.

[0046] Specifically, in step S2, the process of using the geographical detector model to exclude the invalid siting-related factors, obtaining the final siting-related factors of the target public service facility, and calculating the weight values of the final siting-related factors includes:

[0047] S21. On the 100-meter grid in the target area, use the stock data of the target public service facility in the grid as the dependent variable and the siting-related factors as the independent variables to construct a geographical detector model.

[0048] S22. According to the q value and significance level p value of the result of the geographical detector model, exclude the invalid siting-related factors, obtain the final siting-related factors of the target public service facility, and calculate the weight values corresponding to the final siting-related factors.

[0049] More specifically, in step S22, it includes:

[0050] S221, according to the geographical detector model, calculate the result q value and significance level p value corresponding to each location-related factor;

[0051] S222, traverse all location-related factors, compare the significance level p value of each location-related factor with a preset threshold group, and exclude invalid location-related factors according to the comparison result to obtain the final location-related factors of the target public service facility;

[0052] S223, calculate the weight value of each final location-related factor according to the comparison result and the result q value of each final location-related factor.

[0053] Exemplarily, taking the AED facility as an example, based on relevant multi-source data, 20 potential location-related factors are selected from three aspects: traffic accessibility, social economy, and urban function. The detailed information of the location-related factors is shown in Table 1, specifically including aspects such as population density, public places, historical first aid events, proportion of the elderly, geographical location, and traffic convenience. For example, the POI type classification is based on the classification standard of Amap, and 10 types of POIs such as catering services, shopping services, and life services are selected and their kernel densities are calculated respectively as location-related factors.

[0054] On the basis of a 100-meter grid, select the existing number of AEDs in the grid as the dependent variable Y, and select the above 20 location-related factors as the independent variable X to construct a geographical detector model. The factor detector sub-model of this model is mainly used, and the q value calculation formula is as follows:

[0055]

[0056] where h = 1, 2,..., L, L is the number of factor types; N h is the number of unit areas corresponding to each factor type, N is the number of unit areas corresponding to the number of AED facilities to be analyzed, is the variance of the average value of each factor within each factor type, σ 2 is the variance of the number of AED facilities to be analyzed. The value of the result q is between 0 and 1. The larger the value, the higher the influence or explanatory power of the variable on the dependent variable. The significance level is judged by the size of the p value.

[0057] Calculate the factor weights based on the q - value and significance level p - value of the results of the Geodetector model. First, screen the effective variables. If the p - value of a variable (location - related factor) is greater than 0.05, it means that it fails the significance test statistically, so this variable is excluded; if the p - value of a variable is less than 0.05, it means that it passes the significance level test but at a relatively low level, then this variable is included, and the weight value assigned to this variable is the q - value of this variable multiplied by 0.5; if the p - value of a variable is less than 0.01, it means that it passes the significance level test and the level is relatively medium - low, then this variable is included, and the weight assigned to this variable is the q - value of this variable multiplied by 0.75; if the p - value of a variable is less than 0.001, it means that it passes the significance level test and the significance level is relatively high, then this variable is included, and the weight assigned to this variable is the q - value of this variable multiplied by 1.

[0058] Table 1 Location - related factors for the layout of AED facilities

[0059]

[0060]

[0061] It should be noted that dividing the target area into grid levels not only provides a unified scale for comparing spatial positions but also facilitates the aggregation or decomposition of multi - source spatial data, thus providing an effective data integration method. According to relevant research, combined with the actual situation and accuracy requirements, grids with a resolution of 100m×100m were selected for data processing and analysis. The Geodetector model is a statistical tool for analyzing the relationships between variables in geospatial data. It reveals the spatial distribution patterns of geographical phenomena or processes by identifying spatial heterogeneity. The Geodetector model is mainly applied in fields such as environmental science, public health, and urban planning to help researchers understand how geographical variables affect the spatial distribution of specific phenomena. Specifically, the Geodetector evaluates the influence degree of variables on the spatial distribution by calculating indicators such as the spatial autocorrelation and stratified heterogeneity of variables. This model helps to identify key influencing factors and provides a scientific basis for policy - making and resource management. The purposes of using the Geodetector model in the embodiments of the present invention are: (1) to quantify the influence of relevant factors on the spatial distribution of the location - selection object through the q - value of the model results; (2) to identify effective significant factors and eliminate ineffective non - significant factors through the significance level p - value of the model results.

[0062] Specifically, step S3 includes:

[0063] S31, adopt Pearson correlation analysis to calculate the Pearson correlation coefficient between each final location - related factor and the dependent variable;

[0064] S32. Determine whether the relevant factors for the final site selection are positively or negatively correlated according to the Pearson correlation coefficient, and calculate the objective weight value of the relevant factors for the final site selection.

[0065] More specifically, step S32 includes:

[0066] S321. If the Pearson correlation coefficient is positive, there is a positive correlation between the relevant factors for the final site selection and the dependent variable. Determine that the relevant factors for the final site selection are positively correlated, and calculate the objective weight value of the relevant factors for the final site selection;

[0067] S322. If the Pearson correlation coefficient is negative, there is a negative correlation between the relevant factors for the final site selection and the dependent variable. Determine that the relevant factors for the final site selection are negatively correlated, and calculate the objective weight value of the relevant factors for the final site selection.

[0068] Exemplarily, judge the positive / negative weight of the relevant factors for the final site selection based on Pearson correlation analysis. By calculating the Pearson correlation coefficient between each relevant factor for the final site selection and the dependent variable one by one, if the Pearson correlation coefficient is positive, it indicates a positive correlation between the relevant factor for the final site selection and the dependent variable, then the weight is positive, and the weight value is multiplied by 1; if the Pearson correlation coefficient is negative, it indicates a negative correlation between the relevant factor for the final site selection and the dependent variable, then the weight is positive, and the weight value is multiplied by (-1); finally, calculate the suitability result S of the AED facility site selection at the 100-meter grid scale: S = X1×W1 + X2×W2 + … + X n ×W n , where, X n is the value of the nth relevant factor for the final site selection, W n is the objective weight value of the nth relevant factor for the final site selection, and the value of n is the number of relevant factors for the final site selection. It can more accurately predict and optimize the site selection layout of AED facilities, and improve the allocation efficiency and effect of public first aid resources.

[0069] It should be noted that Pearson correlation analysis is a commonly used statistical method for measuring the strength and direction of the linear relationship between two continuous variables. By calculating the Pearson correlation coefficient (usually denoted as Pearson’s R), this method can quantify the degree of correlation between variables, and its value ranges from -1 to 1. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and the closer the absolute value is to 1, the stronger the correlation. The prerequisite for using the Pearson correlation coefficient is that the data conforms to a normal distribution and the relationship between variables is linear. Although the geographical detector model has the ability to identify the explanatory power / influence of independent variables on the dependent variable, it can only identify the magnitude level of this influence and fails to judge whether the influence of the independent variable on the dependent variable is positive or negative. Therefore, combine Pearson correlation analysis simultaneously to complement the limitations of the geographical detector.

[0070] Specifically, step S1 includes:

[0071] S11, obtaining multi-source spatio-temporal data of target public service facilities in the target area, and converting the text addresses in the target public service facility data into longitude and latitude coordinates;

[0072] S12, checking the longitude and latitude coordinates according to the POI data, deleting the incorrect and redundant multi-source spatio-temporal data, and obtaining the relevant multi-source data of the target public service facilities.

[0073] Exemplarily, taking the AED facility as an example, obtain AED facility data through the information published on the official website of the Health Commission and the "AED First Aid Map" WeChat mini-program, including information such as ID number, text address, and affiliated department; convert the text address into longitude and latitude coordinates through the Baidu Map Geocoding API, and finally manually visually check by overlaying the geocoding result coordinate points with the online map to delete the invalid and redundant data with incorrect positioning.

[0074] A public service facility site selection and planning method disclosed in an embodiment of the present invention, by obtaining multi-source spatio-temporal data of target public service facilities in the target area, preprocessing the multi-source spatio-temporal data to obtain relevant multi-source data of the target public service facilities; wherein, the multi-source spatio-temporal data includes the target public service facility data, mobile phone signaling population data, and POI data; determining the site selection related factors of the target public service facilities according to the relevant multi-source data; using the geographical detector model to exclude invalid site selection related factors, obtaining the final site selection related factors of the target public service facilities and calculating the weight values of the final site selection related factors; using Pearson correlation analysis to determine whether the final site selection related factors are positively / negatively correlated, and calculating the objective weight values of the final site selection related factors; calculating the site selection result of the target public service facilities according to the final site selection related factors and the corresponding objective weight values. It can combine the geographical detector model with Pearson correlation analysis, and based on the influence size and significance level of the site selection factors on the site selection object, and their correlation direction, calculate the objective weights of the public service facility site selection factors, which is objective and quantifiable from the aspect of site selection factor selection to weight calculation, and provides a scientific basis for the site selection layout planning of public service facilities.

[0075] See Figure 2 , Figure 2 is a schematic structural diagram of a public service facility site selection and planning device 10 provided by an embodiment of the present invention. The public service facility site selection and planning device 10 includes:

[0076] The facility data acquisition module 11 is configured to acquire multi-source spatio-temporal data of target public service facilities in a target area, preprocess the multi-source spatio-temporal data, and obtain relevant multi-source data of the target public service facilities. Among them, the multi-source spatio-temporal data includes the target public service facility data, mobile signaling population data, and POI data.

[0077] The relevant factor screening module 12 is configured to determine the location-related factors of the target public service facilities according to the relevant multi-source data; use the geographical detector model to exclude invalid location-related factors, obtain the final location-related factors of the target public service facilities, and calculate the weight values of the final location-related factors.

[0078] The objective weight calculation module 13 is configured to use Pearson correlation analysis to determine whether the final location-related factors are positively / negatively correlated, and calculate the objective weight values of the final location-related factors.

[0079] The location result calculation module 14 is configured to calculate the location result of the target public service facilities according to the final location-related factors and the corresponding objective weight values.

[0080] A public service facility location planning device 10 provided by an embodiment of the present invention can implement all processes of the public service facility location planning method in the above embodiment. The functions of each module in the device and the achieved technical effects are respectively the same as the functions and achieved technical effects of the public service facility location planning method in the above embodiment, and will not be elaborated here.

[0081] See Figure 3 , Figure 3 FIG. is a schematic structural diagram of a public service facility location planning device 20 provided by an embodiment of the present invention. The public service facility location planning device 20 in this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the embodiment of the above public service facility location planning method are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module in the embodiment of the above public service facility location planning device are implemented.

[0082] Exemplarily, the computer program can be divided into one or more modules. The one or more modules are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the public service facility location planning device 20.

[0083] The public service facility location planning device 20 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The public service facility location planning device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the public service facility location planning device 20, and does not constitute a limitation on the public service facility location planning device 20. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the public service facility location planning device 20 may also include input / output devices, network access devices, buses, etc.

[0084] The so-called processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 21 is the control center of the public service facility location planning device 20, and connects all parts of the entire public service facility location planning device 20 through various interfaces and lines.

[0085] The memory 22 can be used to store the computer programs and / or modules. The processor 21 realizes various functions of the public service facility location planning device 20 by running or executing the computer programs and / or modules stored in the memory 22, and by calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0086] Among them, if the modules integrated in the public service facility site selection planning device 20 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 such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0087] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0088] The embodiment of the present invention also provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. Among them, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the public service facility site selection planning method as described in the above embodiment.

[0089] In addition, the embodiment of the present invention also provides a computer program product. The computer program product is stored in a storage medium. The program product is executed by at least one processor to implement the steps of the public service facility site selection planning method as described in the above embodiment.

[0090] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A method for site selection and planning of public service facilities, characterized in that: include: Acquire multi-source spatiotemporal data of target public service facilities in a target area, pre-process the multi-source spatiotemporal data, and obtain relevant multi-source data of the target public service facilities; wherein the multi-source spatiotemporal data includes the target public service facility data, mobile phone messenger population data, and POI data; Determine the location-related factors of the target public service facility according to the relevant multi-source data; use a geographic detector model to eliminate invalid location-related factors, obtain the final location-related factors of the target public service facility and calculate the weight value of the final location-related factors; Using Pearson correlation analysis, determine whether the final site selection related factors are positively / negatively correlated, and calculate the objective weight values ​​of the final site selection related factors; The site selection result of the target public service facility is calculated based on the final site selection related factors and the corresponding objective weight values.

2. The method for site selection and planning of public service facilities according to claim 1, characterized in that: The method of using the geographic detector model to eliminate invalid site selection related factors, obtaining the final site selection related factors of the target public service facility and calculating the weight values ​​of the final site selection related factors includes: On a 100-meter grid of the target area, a geographic detector model is constructed by taking the stock data of the target public service facilities in the grid as the dependent variable and the site selection related factors as the independent variables; According to the result q value and the significance level p value of the geographic detector model, invalid site selection related factors are eliminated to obtain the final site selection related factors of the target public service facility, and the weight values ​​corresponding to the final site selection related factors are calculated.

3. The method for site selection and planning of public service facilities according to claim 2, characterized in that: The Pearson correlation analysis is used to determine whether the final site selection related factors are positively / negatively correlated, and the objective weight values ​​of the final site selection related factors are calculated, including: Pearson correlation analysis was used to calculate the Pearson correlation coefficient between each final site selection related factor and the dependent variable; According to the Pearson correlation coefficient, it is determined whether the final site selection related factors are positively / negatively correlated, and the objective weight values ​​of the final site selection related factors are calculated.

4. The method for site selection and planning of public service facilities according to claim 2, characterized in that: The step of eliminating invalid location-related factors based on the result q value and the significance level p value of the geographic detector model, obtaining the final location-related factors of the target public service facility, and calculating the weight values ​​corresponding to the final location-related factors includes: According to the geographic detector model, the result q value and the significance level p value corresponding to each location-related factor are calculated; Traversing all the location-related factors, comparing the significance level p value of each location-related factor with a preset threshold group, eliminating invalid location-related factors according to the comparison results, and obtaining the final location-related factors of the target public service facility; According to the comparison result and the result q value of each final site selection related factor, the weight value of each final site selection related factor is calculated.

5. The method for site selection and planning of public service facilities according to claim 3, characterized in that: Determining whether the final site selection related factors are positively / negatively correlated according to the Pearson correlation coefficient, and calculating the objective weight values ​​of the final site selection related factors, includes: If the Pearson correlation coefficient is positive, there is a positive correlation between the final site selection related factor and the dependent variable, the final site selection related factor is determined to be positively correlated, and the objective weight value of the final site selection related factor is calculated; If the Pearson correlation coefficient is negative, there is a negative correlation between the final site selection related factor and the dependent variable, the final site selection related factor is determined to be negatively correlated, and the objective weight value of the final site selection related factor is calculated.

6. The method for site selection and planning of public service facilities according to claim 1, characterized in that: The step of acquiring multi-source spatiotemporal data of target public service facilities in the target area and preprocessing the multi-source spatiotemporal data to obtain relevant multi-source data of the target public service facilities includes: Acquire multi-source spatiotemporal data of target public service facilities in a target area, and convert text addresses in the target public service facility data into latitude and longitude coordinates; The latitude and longitude coordinates are checked according to the POI data, and erroneous and redundant multi-source spatiotemporal data are deleted to obtain relevant multi-source data of the target public service facility.

7. A public service facility site selection and planning device, characterized in that: include: A facility data acquisition module is used to acquire multi-source spatiotemporal data of target public service facilities in a target area, and pre-process the multi-source spatiotemporal data to obtain relevant multi-source data of the target public service facilities; wherein the multi-source spatiotemporal data includes the target public service facility data, mobile phone messenger population data, and POI data; A relevant factor screening module is used to determine the site selection related factors of the target public service facility according to the relevant multi-source data; use a geographic detector model to eliminate invalid site selection related factors, obtain the final site selection related factors of the target public service facility and calculate the weight value of the final site selection related factors; An objective weight calculation module, used to determine whether the final site selection related factors are positively / negatively correlated by using Pearson correlation analysis, and calculate the objective weight values ​​of the final site selection related factors; The site selection result calculation module is used to calculate the site selection result of the target public service facility according to the final site selection related factors and the corresponding objective weight values.

8. A public service facility site selection and planning device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for site selection and planning of a public service facility as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the public service facility site selection planning method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the public service facility site selection planning method as described in any one of claims 1-6.

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