Method for configuring public service facilities in mountainous village community living circles based on supply and demand balance

Abstract

The present invention specifically relates to a method for configuring public service facilities in mountainous village and town community life circles based on supply and demand balance, comprising: calculating a resident travel OD cost matrix using a dataset of residents' travel spatiotemporal behavior; determining the community life circle category to which a target village and town community belongs; calculating a public service facility supply allocation index and a usage demand index using a dataset of residents' travel spatiotemporal behavior and a dataset of the current configuration of public service facilities; fitting the spatial matching relationship between public service facility supply and demand using a spatial matching degree assessment model combined with the public service facility supply allocation index and the usage demand index, thereby determining the type of public service supply and demand relationship; and determining an implementation configuration plan for public service facilities in the target village and town community based on a basic configuration plan for public service facilities and a configuration adjustment plan for public service facilities. The present invention can specifically plan and identify the category of the public service community life circle for mountainous village and town communities, without applying the identification and demarcation methods of the public service community life circle of urban communities.

Description

Technical Field

[0001] The present invention relates to the technical field of big data and public service facility planning, and in particular to a method for configuring public service facilities in a mountainous village and town community living circle based on supply and demand balance. Background Art

[0002] Currently, communities are the fundamental units of resident life. The configuration of community service facilities directly impacts residents' quality of life and lifestyle. They are crucial spatial units in urban stock renewal and a key focus of community planning. However, defining the boundaries of community living circles is a key and challenging area of ​​research in community planning. Only by accurately defining the boundaries of community living circles can we more rationally configure community public service facilities and enhance residents' living experiences.

[0003] The concept of "community living circle," derived from Japan's "living metropolitan circle" theory, refers to the spatial scope of residents' behavioral characteristics. A community living circle is the area that meets residents' most basic daily needs and where residents engage in the most short-term, regular behaviors.

[0004] Public service facilities in existing urban and rural community life circles are primarily allocated based on the "1,000-person index." However, when applied to mountainous rural communities, this existing allocation method tends to overlook the varying requirements for facility spatial layout due to varying regional structures and socioeconomic factors. It also struggles to reconcile the complex and conflicting distribution of public service facilities across regions, leading to problems such as an imbalance in the supply and demand of public service facilities, idle resources, and low efficiency. Furthermore, the unique geographical features of mountainous rural areas result in a more dispersed distribution of village communities and higher transportation costs for public service allocation, resulting in a generally low number and density of public service facilities and significant regional differentiation. Directly applying the identification and delineation methods used in urban communities for public service community life circles will be difficult to adapt to the actual needs of mountainous rural areas.

[0005] Therefore, how to design a method that can plan and identify the public service community living circle in mountainous villages and towns in a targeted manner is a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide a method for configuring public service facilities in the living circle of mountainous village and town communities based on supply and demand balance, identify the categories of the public service community living circles of mountainous village and town communities through spatial clustering analysis of the actual travel time and space behavior information of community residents, and propose targeted planning and configuration adjustment plans, without applying the identification and demarcation methods of urban community public service community living circles, and effectively realize the supply and demand balance correction of public service facilities in the community living circle, thereby improving the suitability and practicality of the planning of public service facilities in mountainous village and town communities.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The method for configuring public service facilities in mountainous village and town community living circles based on supply and demand balance includes:

[0009] S1: Predetermine the basic configuration plan of public service facilities corresponding to different community life circle categories, as well as the configuration adjustment plan of public service facilities corresponding to different types of public service supply and demand relationships in each community life circle category;

[0010] S2: Obtain the spatial and temporal travel behavior dataset of residents in the target village and town communities and the current status dataset of public service facilities;

[0011] S3: Perform distance spatial cluster analysis and road network topology analysis on the residents' travel spatiotemporal behavior dataset to calculate the OD cost matrix of residents' travel in the target village and town communities;

[0012] S4: Determine the community life circle category to which the target village or town community belongs based on the residents' travel OD cost matrix;

[0013] S5: Calculate the public service facilities supply allocation index of the target village and town communities based on the public service facilities configuration status dataset;

[0014] S6: Calculate the public service facilities usage demand index of the target village and town community based on the residents' travel time and space behavior dataset;

[0015] S7: The spatial matching relationship between the supply and demand of public service facilities is determined by combining the global Moran index model and the local Moran index model with the public service facility supply allocation index and the public service facility use demand index, thereby determining the type of public service supply and demand relationship in the community life circle to which the target village and town community belongs;

[0016] S8: Determine the implementation configuration plan for the public service facilities of the target village and town communities based on the basic configuration plan for public service facilities corresponding to the category of the community life circle to which the target village and town communities belong, and the public service facility configuration adjustment plan corresponding to the type of public service supply and demand relationship in the community life circle to which the target village and town communities belong.

[0017] Preferably, the residents' travel spatiotemporal behavior dataset is constructed through the following steps:

[0018] S201: Obtaining the spatiotemporal information data of residents' travel and activities in the target village and town community, including residents' residence information and travel activity information;

[0019] S202: Obtaining mobile phone signaling data of residents in the target village or town community;

[0020] S203: improving residents' travel-activity spatiotemporal information data through residents' mobile phone signaling data;

[0021] S204: Obtaining road traffic data of target villages and towns;

[0022] S205: Summarize the road traffic data in the improved residents' travel-activity spatiotemporal information data to generate a corresponding residents' travel spatiotemporal behavior dataset.

[0023] Preferably, distance space cluster analysis is achieved through the following steps:

[0024] S301: Extract distance values ​​from residents’ travel time and space behavior dataset;

[0025] S302: using the ArcGIS geographic information platform to call Ripley's K function to calculate the distance cluster density value based on the distance value;

[0026] S303: Calculate the average of all distance cluster density values ​​to obtain the expected cluster density value;

[0027] S304: Calculate the difference between each distance agglomeration density value and the expected agglomeration density value, and determine the agglomeration scale and intensity of each public service facility point;

[0028] S305: The distance cluster density value with the largest cluster scale and intensity is used as the optimal clustering distance for the target village and town community public service facility points.

[0029] Preferably, the road network topology analysis is achieved through the following steps:

[0030] S311: Extracting vehicle road traffic data from residents’ travel spatiotemporal behavior dataset;

[0031] S312: Create a fishing net to simulate the distribution of settlements in target villages and towns using the ArcGIS geographic information platform;

[0032] S313: Combining traffic data with residential distribution to construct a road topology network;

[0033] S314: Set the travel mode and cost constraints of the road topology network to build and generate a complete road topology network.

[0034] Preferably, the resident travel OD cost matrix is ​​calculated by the following steps:

[0035] S321: Extract the coordinates of residents’ residences and travel points from the residents’ travel spatiotemporal behavior dataset, and then construct the residents’ travel OD cost matrix based on the road topology network;

[0036] S322: The optimal clustering distance of the target village and town community public service facility points is used as the reachable boundary of the OD parameter in the residents' travel OD cost matrix;

[0037] S323: Calculate the minimum travel cost distance for residents to travel points through the resident travel OD cost matrix;

[0038] S324: Match the corresponding attribute information according to the resident travel OD cost matrix, link the resident residence coordinates and travel point coordinates, and screen the resident activity boundaries of the target village and town community based on the co-occurrence relationship of resident activities.

[0039] Preferably, the travel time T of residents to different public service facilities is calculated based on the OD cost matrix of residents' travel, and then the community life circle category to which the target village community belongs is determined:

[0040] If 0<T≤30min, the community life circle to which the target village or town community belongs is the commuting life circle;

[0041] If 30min<T≤60min, the community life circle to which the target village or town community belongs is the basic life circle;

[0042] If 60min<T≤90min, the community life circle to which the target village and town community belongs is the expanded life circle.

[0043] Preferably, the public service facility supply allocation index is calculated by the following steps:

[0044] S501: extracting the number and coordinate points of various public service facilities from the public service facility configuration status dataset;

[0045] S502: Acquire spatial information data of the community life circle to which the target village or town belongs;

[0046] S503: Using the ArcGIS geographic information platform, the number of public service facilities in the community life circle of the target village or town community and their coordinate points are connected with their spatial information data by grid ID, and a corresponding resident public service facility accessibility network is established;

[0047] S504: Using the ArcGIS geographic information platform, the grid ID of the target village and town community's public service facility accessibility grid and the public service facility usage duration attribute are connected to establish a corresponding public service facility accessibility-usage duration grid;

[0048] S505: Overlaying the target village and town community's resident population parameters on the resident public service facility accessibility-usage duration grid to generate corresponding supply allocation indices for various types of public service facilities;

[0049] The calculation formula for the supply and distribution index of various public service facilities in target villages and towns is:

[0050]

[0051] Where: represents the supply allocation level of K-type public service facilities at location i in the target village or town community, that is, the supply allocation index of the K-th type of public service facilities; k represents the total amount of K-type public service facilities; j(k) represents the j-th specific facility spatial point of the K-th type of public service facilities; represents the usage time of j(k) type public service facilities at location i; d ij(k) represents spatial isolation, which is the straight-line distance from the resident i to the specific j(k) type of public service facilities; n represents the population of the target village or town community;

[0052] S506: performing weighted summation of the supply allocation indexes of various public service facilities according to the preset supply service weights corresponding to the various public service facilities to obtain the public service facility supply allocation index of the target village and town community;

[0053] The calculation formula for the public service facilities supply allocation index of the target village and town communities is:

[0054]

[0055] Where: S represents the public service facilities supply allocation index of the target village and town community; represents the supply allocation level of K-type public service facilities at location i in the target village or town community, that is, the supply allocation index of the K-th type of public service facilities; q k Represents the supply service weight of K-type public service facilities in the target village and town community.

[0056] Preferably, the public service facility usage demand index is calculated by the following steps:

[0057] S601: Extract relevant data information including resident travel point coordinates, grid ID information, activity duration, and number of participants from the resident travel spatiotemporal behavior dataset;

[0058] S602: Connect relevant data information by grid ID through the ArcGIS geographic information platform to form a dataset of residents' public service facility usage needs in the target village and town communities;

[0059] S603: extracting the average usage time of residents' public service facilities, residents' population parameters, and usage demand weights of various public service facilities from the residents' public service facility usage demand dataset;

[0060] S604: Using the ArcGIS geographic information platform, calculate the usage demand index of various public service facilities based on the average usage time of residents' public service facilities and resident population parameters;

[0061] The calculation formula for the demand index of various public service facilities in target villages and towns is:

[0062]

[0063] Where: represents the use demand level of K-type public service facilities at location i in the target village or town community, that is, the use demand index of the K-th public service facility; k represents the total number of K-type public service facilities; j(k) represents the j-th specific facility spatial point of the K-th public service facility; represents the average usage time of j(k) type public service facilities at location i; n represents the population of the target village or town community; A i represents the area of ​​the domain at location i;

[0064] S605: performing weighted summation of the usage demand indexes of various public service facilities according to the usage demand weights of various public service facilities to obtain the public service facility usage demand index of the target village or town community;

[0065] The calculation formula for the public service facilities use demand index of the target village and town communities is:

[0066]

[0067] Where: D represents the demand index for public service facilities in the target village and town community; Q represents the use demand level of K-type public service facilities at location i in the target village or town community, that is, the use demand index of K-th type public service facilities; k Represents the usage demand weight of K-type public service facilities in the target village and town communities.

[0068] Preferably, the spatial matching relationship between supply and demand of public service facilities is fitted through the following steps:

[0069] S701: Divide the target village and town community into several spatial grid units;

[0070] S702: Calculate the corresponding global Moran index based on the public service facility supply distribution index and public service facility use demand index of the target village and town community to represent the spatial correlation of the public service facility supply or demand intensity;

[0071] The calculation formula of the global Moran index is:

[0072]

[0073] Where: I represents the global Moran index, i.e., the spatial correlation of the intensity of supply or demand for public service facilities; N represents the total number of spatial grid cells in the village community; i and j represent the index numbers of two random spatial grid cells in the target village community; Z represents the standard value of the relevant variables, i.e., the standard value of the public service facility supply allocation index and the public service facility use demand index after normalization; wij represents the spatial matrix weights of spatial grid cells i and j; W represents the sum of all w in the target rural community ij and;

[0074] Among them, the value range of the global Moran's I is [-1, 1];

[0075] If 0 < Moran's I ≤ 1, it indicates that the relevant variable elements have positive agglomeration, that is, high supply or high demand of public service facilities;

[0076] If 0 = Moran's I, it indicates that there is no spatial correlation relationship between the relevant variable elements;

[0077] If -1 ≤ Moran's I < 0, it indicates that the relevant variable elements have negative agglomeration, that is, low supply or low demand of public service facilities;

[0078] At the same time, the larger the absolute value of Moran's I, the stronger the agglomeration;

[0079] S703: After determining the spatial correlation of the supply or demand intensity of public service facilities, further calculate the bivariate local Moran's index of the public service facilities in the target rural community to analyze the spatial matching degree of the supply-demand configuration of public service facilities;

[0080] The calculation formula of the bivariate local Moran's index is:

[0081]

[0082] In the formula: I' represents the bivariate local Moran's index, that is, the spatial matching degree of the supply-demand configuration of public service facilities in the target rural community; i and j respectively represent the index numbers of two random spatial grid cells in the target rural community; Z represents the standard value of the relevant variable, that is, the standard value after the normalization processing of the public service facility supply distribution index and the public service facility usage demand index, where represents the standard value of the public service facility supply distribution index at the spatial grid cell i in the target rural community, represents the standard value of the public service facility usage demand index at the spatial grid cell j in the target rural community; w ij represents the spatial matrix weights of spatial grid cells i and j;

[0083] Among them, the value range of the bivariate local Moran's I' is [-1, 1];

[0084] If 0 < Moran's I' ≤ 1, it indicates that there is a spatial positive correlation relationship between the relevant variable elements, that is, the public service supply-demand relationship in the target rural community is matched: high supply - high demand, or low supply - low demand;

[0085] If 0 = moran's I ′, it means there is no spatial correlation between the relevant variable elements;

[0086] If -1≤moran′sI′<0, it means that there is a spatial negative correlation between the relevant variable elements, that is, the supply and demand of public services in the target village and town communities are mismatched: high supply-low demand, or low supply-high demand;

[0087] At the same time, a larger absolute value of moran′sI′ indicates a stronger correlation;

[0088] S704: Use GeoDa software to calculate the bivariate local Moran index of each spatial grid unit in the target village and town community to generate a Moran scatter plot, and use the Moran scatter plot to determine the spatial matching relationship between the supply and demand of public service facilities.

[0089] Preferably, the Moran scatter plot uses the public service facility use demand index as the horizontal axis and the public service facility supply distribution index as the vertical axis;

[0090] First, the Moran scatter plot is divided into four quadrants according to the spatial matching relationship between the supply and demand of public service facilities.

[0091] Then, by scattering in Moran Scatter Figure 4 The points in the four quadrants form four types of supply and demand relationships for public service facilities: the first quadrant is high demand-high supply, the second quadrant is low demand-high supply, the third quadrant is low demand-low supply, and the fourth quadrant is high demand-low supply.

[0092] Finally, observe the quadrant distribution of the Moran scatter plot of the target village and town community:

[0093] 1) If the Moran scatter point of the target village or town community falls in the first or third quadrant, the public service supply and demand relationship type of the community life circle to which the target village or town community belongs is the supply and demand balance type;

[0094] 2) If the Moran scatter point of the target village or town community falls in the second quadrant, the public service supply and demand relationship type of the community life circle to which the target village or town community belongs is supply > demand;

[0095] 3) If the Moran scatter point of the target village or town community falls in the fourth quadrant, the public service supply and demand relationship type of the community life circle to which the target village or town community belongs is supply < demand.

[0096] Compared with the prior art, the method for configuring public service facilities in mountainous village and town community living circles based on supply and demand balance provided by the present invention has the following beneficial effects:

[0097] The present invention first sets the correspondence between the category of community life circle and the type of public service supply and demand relationship and the basic configuration plan and optimized configuration plan of public service facilities. Secondly, the residents' travel spatiotemporal behavior dataset and the public service facility configuration status dataset of the target village and town community are obtained. Then, based on the information dataset, distance space clustering analysis, road network topology analysis and resident travel OD cost matrix calculation are performed to identify the category of public service community life circle. Subsequently, the public service facility supply allocation index and usage demand index of the community life circle are calculated, and the public service facility supply and demand configuration matching degree is generated by fitting. Finally, the public service facility supply and demand configuration matching degree is determined to determine the type of public service supply and demand relationship of the community life circle, and the implementation configuration plan of the public service facilities of the target village and town community is determined according to the public service facility basic configuration plan and configuration adjustment plan.

[0098] On the one hand, the present invention constructs a dataset of residents' travel time-space behavior in village and town communities, and identifies the categories of public service community life circles through distance space clustering analysis, road network topology analysis and resident travel OD cost matrix calculation, so that the actual travel time-space behavior information of community residents can be analyzed through spatial clustering to plan and identify the categories of public service community life circles in mountainous village and town communities in a targeted manner, instead of applying the identification and demarcation methods of public service community life circles in urban communities. That is, it can better adapt to the special geographical and geomorphological characteristics and obvious regional differentiation problems of mountainous village and town communities, thereby improving the effectiveness of public service facility planning in mountainous village and town communities.

[0099] On the other hand, the present invention reflects the supply and demand of public services in the community life circle by calculating the public service facility supply distribution index and usage demand index of the target village and town community, and then determines the type of public service supply and demand relationship of the community life circle by fitting the spatial matching relationship between the supply and demand of public service facilities to determine the public service facility configuration adjustment plan, and determines the implementation configuration plan of the public service facilities of the target village and town community based on the basic configuration plan and the configuration adjustment plan of the public service facilities, so as to effectively realize the supply and demand balance correction of the public service facilities in the community life circle, and can take into account the requirements of the regional structure and socio-economic factors of mountain village and town communities on the spatial layout of facilities, adjust the complexity and contradiction of public service facilities between regions, thereby improving the suitability and practicality of the public service facility planning in mountain village and town communities. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] In order to make the purpose, technical solutions and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0101] Figure 1 A logical framework for optimizing the community life circle of public services;

[0102] Figure 2 A block diagram of the principles of the method for optimizing the life circle of a public service community;

[0103] Figure 3 A block diagram of the principles for identifying the life circle of a public service community;

[0104] Figure 4 To determine the other principle framework of the target village and town community;

[0105] Figure 5 A schematic diagram for determining the living circle of the target village and town community;

[0106] Figure 6 Moran's scatter plot. DETAILED DESCRIPTION

[0107] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0108] It should be noted that similar reference numerals and letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it does not require further definition or explanation in subsequent figures. In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" indicate positions or relationships based on the positions or relationships shown in the figures, or the positions or relationships in which the inventive product is typically placed when in use. These terms are intended solely to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation, and are therefore not to be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third," etc., are used solely to distinguish descriptions and are not to be construed as indicating or implying relative importance. Furthermore, terms such as "horizontal" and "vertical" do not imply that a component is absolutely horizontal or overhanging, but rather may be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but rather may be slightly tilted. In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0109] The following is a further detailed description through specific implementation methods:

[0110] Example:

[0111] This embodiment discloses a method for configuring public service facilities in a mountainous village community living circle based on supply and demand balance.

[0112] like Figure 1 and Figure 2 As shown in the figure, the configuration method of public service facilities in mountainous village and town community life circle based on supply and demand balance includes:

[0113] Before implementing the technical solution of the present invention, it is necessary to first determine the planning area of ​​the target village or town community. The planning area refers to the administrative spatial boundaries of the target village or town community. The administrative spatial boundary information of mountain villages and towns can be obtained through the National Geographic Information Public Service Platform, or the corresponding boundary data can be manually input based on relevant planning materials.

[0114] The "public service" facilities mentioned in this article refer to public service facilities. Public service facilities are built in proportion to the population size to meet the material and cultural needs of residents and provide public services to them. They specifically include various public and service facilities that provide public service products to residents. Based on the specific characteristics of the projects, they can be divided into education, medical care, culture and entertainment, transportation, sports, social welfare and security, administrative management and community services, postal and telecommunications, and commercial and financial services.

[0115] S1: Predetermine the basic configuration plan of public service facilities corresponding to different community life circle categories, as well as the configuration adjustment plan of public service facilities corresponding to different types of public service supply and demand relationships in each community life circle category;

[0116] It should be noted that the public service facility basic configuration scheme and public service facility configuration adjustment scheme described in the present invention are generated based on existing research. In order to better introduce the technical solution of the present invention, the following examples are introduced in this embodiment:

[0117] 1) In this embodiment, the community life circle of the village and town community is divided into three categories: commuting life circle, basic life circle and expanded life circle. The specific basic configuration plans of public service facilities in the three categories can be generated by referring to Table 1.

[0118] Table 1 Basic configuration plan of public service facilities in the living circle

[0119]

[0120]

[0121]

[0122] 2) In this embodiment, the public service supply and demand relationship in village and town communities is divided into three types: ① balanced supply and demand, ② "supply > demand", and ③ "supply < demand". Each community life circle type has a corresponding public service facility configuration adjustment plan for different public service supply and demand relationship types, as detailed below:

[0123] As shown in Table 2, when the community life circle category of the village community is a commuting life circle, the public service facility configuration adjustment plan corresponding to different types of public service supply and demand relationships in mountain village communities.

[0124] Table 2 Adjustment plan for public service facilities configuration in commuting living circle

[0125]

[0126]

[0127]

[0128] As shown in Table 3, when the community life circle category to which the village community belongs is taken as the basic life circle, the public service facility configuration adjustment plan corresponding to different types of public service supply and demand relationships in mountain village communities.

[0129] Table 3 Adjustment plan for public service facilities configuration in basic living circle

[0130]

[0131]

[0132] As shown in Table 4, when the community life circle category of the village and town community is the expanded life circle, the public service facility configuration adjustment plan corresponding to different types of public service supply and demand relationships in mountain village and town communities.

[0133] Table 4 Adjustment plan for public service facilities configuration to expand living area

[0134]

[0135]

[0136] S2: Obtain the spatial and temporal travel behavior dataset of residents in the target village and town communities and the current status dataset of public service facilities;

[0137] S3: Perform distance spatial cluster analysis and road network topology analysis on the residents' travel spatiotemporal behavior dataset to calculate the OD cost matrix of residents' travel in the target village and town communities;

[0138] S4: Determine the community life circle category of the target village or town community based on the residents' travel OD cost matrix; and then obtain the basic configuration plan of public service facilities corresponding to the community life circle category of the target village or town community;

[0139] S5: Calculate the public service facilities supply allocation index of the target village and town communities based on the public service facilities configuration status dataset;

[0140] S6: Calculate the public service facilities usage demand index of the target village and town community based on the residents' travel time and space behavior dataset;

[0141] S7: The spatial matching relationship between the supply and demand of public service facilities is determined by combining the global Moran index model and the local Moran index model with the public service facility supply allocation index and the public service facility use demand index, thereby determining the type of public service supply and demand relationship in the community life circle to which the target village and town community belongs;

[0142] In this embodiment, the spatial matching evaluation model that combines the global Moran's index model and the local Moran's index model is also called the spatial autocorrelation model, which is a statistical model for evaluating the spatial correlation of geospatial data. It is based on the concept of the Moran's index, which is an indicator for measuring the spatial correlation of geospatial data. Among them, the global Moran's index model is used to measure the degree of spatial correlation of the entire study area, which takes into account the data distribution of the entire area and its spatial position relationship. The local Moran's index model focuses on the pattern of local spatial correlation within the study area. It divides the study area into multiple small areas and calculates the Moran's index of each small area separately, thereby revealing the data aggregation and dispersion in different geographical locations. Combining the global Moran's index model with the local Moran's index model can comprehensively consider the spatial correlation of the entire area and local areas, thereby more comprehensively evaluating the spatial matching degree. This model can help us determine the areas of aggregation and dispersion in the data set, and reveal the influencing factors of spatial patterns and spatial correlation.

[0143] S8: Determine the implementation configuration plan for the public service facilities of the target village and town communities based on the basic configuration plan for public service facilities corresponding to the category of the community life circle to which the target village and town communities belong, and the public service facility configuration adjustment plan corresponding to the type of public service supply and demand relationship in the community life circle to which the target village and town communities belong.

[0144] The present invention first sets the correspondence between the category of community life circle and the type of public service supply and demand relationship and the basic configuration plan and optimized configuration plan of public service facilities. Secondly, the residents' travel spatiotemporal behavior dataset and the public service facility configuration status dataset of the target village and town community are obtained. Then, based on the information dataset, distance space clustering analysis, road network topology analysis and resident travel OD cost matrix calculation are performed to identify the category of public service community life circle. Subsequently, the public service facility supply allocation index and usage demand index of the community life circle are calculated, and the public service facility supply and demand configuration matching degree is generated by fitting. Finally, the public service facility supply and demand configuration matching degree is determined to determine the type of public service supply and demand relationship of the community life circle, and the implementation configuration plan of the public service facilities of the target village and town community is determined according to the public service facility basic configuration plan and configuration adjustment plan.

[0145] On the one hand, the present invention constructs a dataset of residents' travel time-space behavior in village and town communities, and identifies the categories of public service community life circles through distance space clustering analysis, road network topology analysis and resident travel OD cost matrix calculation, so that the actual travel time-space behavior information of community residents can be analyzed through spatial clustering to plan and identify the categories of public service community life circles in mountainous village and town communities in a targeted manner, instead of applying the identification and demarcation methods of public service community life circles in urban communities. That is, it can better adapt to the special geographical and geomorphological characteristics and obvious regional differentiation problems of mountainous village and town communities, thereby improving the effectiveness of public service facility planning in mountainous village and town communities.

[0146] On the other hand, the present invention reflects the supply and demand of public services in the community life circle by calculating the public service facility supply distribution index and usage demand index of the target village and town community, and then determines the type of public service supply and demand relationship of the community life circle by fitting the spatial matching relationship between the supply and demand of public service facilities to determine the public service facility configuration adjustment plan, and determines the implementation configuration plan of the public service facilities of the target village and town community based on the basic configuration plan and the configuration adjustment plan of the public service facilities, so as to effectively realize the supply and demand balance correction of the public service facilities in the community life circle, and can take into account the requirements of the regional structure and socio-economic factors of mountain village and town communities on the spatial layout of facilities, adjust the complexity and contradiction of public service facilities between regions, thereby improving the suitability and practicality of the public service facility planning in mountain village and town communities.

[0147] Before constructing the dataset, the present invention needs to first obtain the basic data required for identifying the community life circle of village and town community public service facilities, and preprocess the basic data to achieve unified registration and transformation of geographic coordinate information, so as to facilitate the construction of subsequent related datasets for analysis.

[0148] It should be noted that the relevant basic data required for identifying the public service community living circle in mountainous villages and towns in this embodiment includes but is not limited to:

[0149] 1) Basic geographic information data: basic geographic spatial data in 250*250 grid Shp. format, serving as the basic unit for research and analysis; geographic information data includes topographic data, geographic coordinates, and spatial extent of planned villages, towns, and communities, as well as their counties (districts), towns (townships), and other information.

[0150] 2) Rural community census data, census attribute information statistics, population age, gender, population size, income and other information in rural communities, and analysis of socioeconomic attributes.

[0151] 3) Road traffic related data, road transportation GPS data, and road traffic topology network construction.

[0152] 4) Mobile phone signaling data, GPS (Global Positioning System) activity trajectories and length of stay of village and town community populations, and other data information to analyze the demand for public service facilities.

[0153] 5) Public service facility POI (Point of Information) data, including the current number of various public service facilities, as well as the facility name, classification, geographic coordinates, etc., to analyze the current status of public service facility supply.

[0154] 6) Resident travel questionnaire survey data is used to understand the current usage of public service facilities by community residents, including frequency of use, duration of use, usage preferences, etc., and to analyze the demand for public service facilities; the data is initially paper-based and obtained through manual entry and analysis without any restrictions.

[0155] Specifically, the residents' travel spatiotemporal behavior dataset is constructed through the following steps:

[0156] S201: Obtaining the spatiotemporal information data of residents' travel and activities in the target village and town community, including residents' residence information and travel activity information;

[0157] In this embodiment, a travel activity survey form for residents in villages and towns as shown in Table 5 can be designed and a questionnaire survey can be conducted to obtain residents' travel activity survey data. Then, the existing MYSQL database management system tool can be used to extract residents' residence information (residence coordinates) and travel information (travel point coordinates, travel frequency, travel purpose, and activity duration) from the travel activity survey data. Finally, the ArcGIS geographic information platform is used to realize the unified registration and conversion of the geographic coordinate information of the data.

[0158] Table 5 Questionnaire on residents' travel activities

[0159]

[0160]

[0161] In this embodiment, the ArcGIS geographic information platform is a comprehensive geographic information system (GIS) software platform developed by Esri, Inc. in the United States. It provides a wide range of functions and tools for creating, managing, analyzing, and visualizing spatial data, supports multiple data formats and standards, and has powerful geoprocessing capabilities and visualization effects. The ArcGIS geographic information platform includes multiple components and applications, the main ones of which are:

[0162] ArcMap: The primary application for creating, editing, analyzing, and visualizing geographic data. It provides powerful layer management, spatial analysis, map building, and data query capabilities suitable for a wide range of GIS tasks.

[0163] ArcCatalog: An application for managing and organizing geographic data. It provides a centralized interface for viewing and managing geographic data, including functions such as browsing data, creating metadata, and creating geographic databases.

[0164] ArcGIS Pro: A next-generation application for the ArcGIS platform, it provides advanced 3D visualization, 3D analysis, and geographic data processing capabilities. With a modern user interface and faster performance, ArcGIS Pro is ideal for professional GIS workflows.

[0165] ArcGIS Online: A cloud-based GIS platform that can be accessed and used through a browser. Users can publish, share, and manage geographic data, maps, applications, and spatial analysis tools, and collaborate and share with others.

[0166] ArcGIS Server: is software for building enterprise-level GIS applications and services. It allows users to deploy and manage GIS services on an organization's local servers to provide access to geographic data and functionality.

[0167] S202: Obtaining mobile phone signaling data of residents in the target village or town community;

[0168] In this embodiment, mobile phone signaling data includes GPS (Global Positioning System) activity trajectories and length of stay data for village and town community residents, which is used to analyze demand for public service facilities. Residents' mobile phone signaling data includes acquired signaling location data (a time-space location data sequence with base station coordinates) and residents' GPS movement trajectory data. The data's geographic coordinate information is uniformly aligned and converted using the ArcGIS geographic information platform.

[0169] S203: improving residents' travel-activity spatiotemporal information data through residents' mobile phone signaling data;

[0170] S204: Obtaining road traffic data of target villages and towns;

[0171] In this embodiment, the road traffic data includes road transportation GPS data.

[0172] S205: Summarize the road traffic data in the improved residents' travel-activity spatiotemporal information data (match the residents' travel-activity spatiotemporal information data with the road traffic paths) to generate a corresponding residents' travel spatiotemporal behavior dataset.

[0173] As shown in Table 6, the residents’ travel spatiotemporal behavior dataset includes information such as the residents’ residence coordinates, travel point coordinates, travel frequency, travel distance, travel time, travel purpose, activity duration (stay duration), activity trajectory, traveler information, and road traffic data.

[0174] Table 6 Residents' travel time and space behavior dataset

[0175] A dataset of spatial and temporal travel behavior information of residents in rural communities ·Residential coordinates Travel point coordinates Travel frequency Travel distance Travel time Purpose of travel Activity duration (stay duration) ·Movement tracking Traveler information Village and town community road traffic data ·……

[0176] During the specific implementation process, the public service facility configuration status dataset mainly includes information such as the name, category, quantity and coordinate locations of various public service facilities.

[0177] In a specific implementation, the distance space cluster analysis described in the present invention is a method for identifying similar patterns and cluster structures in spatial data. It measures the relationship between objects based on their distance or similarity and clusters similar objects together. The main goal of distance space cluster analysis is to find clusters of closely connected objects that are separated from other clusters or objects. Cluster analysis can identify hot spots, agglomeration phenomena, spatial distribution patterns, etc. in space. Among them, existing methods for distance space cluster analysis include:

[0178] K-means clustering: It is a distance-based clustering algorithm that divides objects into K clusters such that the similarity of objects in the same cluster is maximized.

[0179] DBSCAN: It is a density-based clustering algorithm that identifies clusters by detecting the density of objects. This algorithm can find clusters of arbitrary shapes and sizes without being restricted by the prior K value.

[0180] Agglomerative hierarchical clustering: This method initially treats each object as a separate cluster, and then gradually merges the most similar clusters by calculating the distance between objects until all objects are in one cluster.

[0181] Divisive Hierarchical Clustering: This is a bottom-up clustering method that starts with each object as a separate cluster and then gradually splits the objects with the largest distance into different clusters until every object is in a cluster.

[0182] Combine Figure 3 As shown in the figure, distance space cluster analysis is achieved through the following steps:

[0183] S301: Extract distance values ​​from residents’ travel time and space behavior dataset;

[0184] Specifically, the distance values ​​are included in the residents’ travel spatiotemporal behavior dataset, including the residents’ initial travel distance, number of travel steps, and travel duration.

[0185] S302: using the ArcGIS geographic information platform to call Ripley's K function to calculate the distance cluster density value based on the distance value;

[0186] In this embodiment, the Ripley's K function used is a spatial statistical analysis method for evaluating the spatial autocorrelation of point patterns. It can help us determine whether a point set presents an agglomerated or discrete pattern, which is of great significance in many practical applications. The key steps of the Ripley's K function algorithm are as follows: 1) Assume that we have a point pattern containing N points, and we need to calculate the distance between each point and all other points. 2) For each point, calculate its distance to all other points and sort them according to the distance. 3) For each distance value, calculate the number of point pairs that are less than or equal to the distance, that is, the number of points within the distance. 4) Calculate the ratio of the number of point pairs, that is, the ratio of the number of points within the distance range to the total number of points. 5) For each distance value, calculate the cumulative ratio of the number of point pairs. 6) Depending on the needs of the specific problem, very few curves can be standardized, for example, using the Monte Carlo simulation method to generate point patterns on a random distribution.

[0187] The rare curve measures spatial autocorrelation by showing the cumulative proportion of point pairs within each distance range in the point pattern. If the rare curve exceeds the boundary of the random model, it indicates the presence of a clustered pattern; if the rare curve is below the boundary of the random model, it indicates the presence of a dispersed pattern.

[0188] S303: Calculate the average of all distance cluster density values ​​to obtain the expected cluster density value;

[0189] S304: Calculate the difference between each distance agglomeration density value and the expected agglomeration density value, and determine the agglomeration scale and intensity of each public service facility point;

[0190] In this embodiment, the smaller the difference between the distance agglomeration density value and the expected agglomeration density value, the greater the agglomeration scale and intensity of the corresponding public service facility points.

[0191] S305: The distance cluster density value with the largest cluster scale and intensity is used as the optimal clustering distance for the target village and town community public service facility points.

[0192] In the specific implementation process, the road network topology analysis described in the present invention is a method for studying and analyzing the road network structure. It mainly focuses on the connection relationship between roads, the topological characteristics of intersections and road sections, and the impact of these characteristics on traffic flow and road network performance. Among them, the existing methods of road network topology analysis include: 1) Node and edge analysis: statistical analysis of nodes and edges in the road network, such as calculating the degree and connectivity of nodes, finding and analyzing important nodes and edges, etc. 2) Road connectivity analysis: studying the connection relationship between roads, including the type and structure of road intersections, the connection method of roads, the directionality of roads, etc. 3) Geographical spatial analysis: through geographic information system (GIS) technology, geographic data is combined with road network data for spatial analysis. For example, the distribution, density and aggregation degree of roads can be analyzed to study the road network characteristics in different geographical areas. 4) Path analysis: based on the path planning algorithm, the optimal path from the starting point to the end point in the road network is analyzed, considering path selection and route optimization under different conditions.

[0193] Specifically, the road network topology analysis is achieved through the following steps:

[0194] S311: Extracting vehicle road traffic data from residents’ travel spatiotemporal behavior dataset;

[0195] In this embodiment, the vehicle road traffic data includes urban roads, national roads, provincial roads, county roads, township roads, and rural roads.

[0196] S312: Create a fishing net to simulate the distribution of settlements in target villages and towns using the ArcGIS geographic information platform;

[0197] S313: Combining traffic data with residential distribution to construct a road topology network;

[0198] S314: Set the travel mode and cost constraints of the road topology network to build and generate a complete road topology network.

[0199] In this embodiment, the travel mode and cost constraints of the road topology network include: travel mode, shortest path constraint, road capacity and congestion constraint, and cost constraint.

[0200] In practice, the resident travel OD cost matrix describes the cost of traveling between different origin and destination points. This cost can be a combination of multiple factors, such as time, money, and traffic congestion. Each OD corresponds to a matrix element, representing the travel cost from the origin to the destination. This matrix can be used to analyze residents' travel patterns and choices, as well as assess cost differences between different travel modes and routes.

[0201] The establishment of a resident travel OD cost matrix can help transportation planners better understand travelers' travel decisions, assess the attractiveness and costs of different transportation modes, and provide a scientific basis for transportation planning. Furthermore, the resident travel OD cost matrix can be used in areas such as traffic simulation and forecasting, providing support for traffic management and operations.

[0202] Existing methods for constructing a resident travel OD cost matrix primarily include: 1) Collecting traffic network data: Obtaining accurate traffic network data, including road networks and public transportation routes, can be obtained from local transportation management departments, mapping applications, transportation planning agencies, or third-party data providers. 2) Collecting traffic flow data: Obtaining traffic flow data can be obtained from various sources, such as sensors, traffic monitoring equipment, and GPS data. This data is used to calculate indicators such as traffic congestion levels and travel speeds. 3) Determining travel time: Based on traffic flow data and road characteristics, traffic models or predictive models based on historical traffic data are used to estimate the time consumed between different ODs. 4) Calculating travel costs: Considering the costs of different travel modes, such as public transportation fares, fuel costs, and parking fees. This data can be obtained from public transportation companies, gas stations, and parking lots. 5) Considering other influencing factors: In addition to time and cost, other factors influencing resident travel choices need to be considered, such as accessibility to the destination, personal preferences, and traffic safety. 6) Data integration and calculation: Integrating the above data, the travel cost corresponding to each OD is calculated to form a travel OD cost matrix.

[0203] Specifically, the present invention implements the calculation of the OD cost matrix of residents' travel through the following steps:

[0204] S321: Extract the coordinates of residents’ residences and travel points from the residents’ travel spatiotemporal behavior dataset, and then construct the OD (origin-destination) cost matrix of residents’ travel based on the road topology network;

[0205] S322: The optimal clustering distance of the target village and town community public service facility points is used as the reachable boundary of the OD parameter in the residents' travel OD cost matrix;

[0206] In this embodiment, the reachable boundary of the resident travel OD cost matrix refers to the range of target locations that the residents can reach within a certain travel cost range.

[0207] S323: Calculate the minimum travel cost distance for residents to travel points (public service facilities) through the residents' travel OD cost matrix;

[0208] In this embodiment, the minimum travel cost distance refers to selecting the travel distance corresponding to the OD with the minimum travel distance cost among all travel OD pairs in the resident travel OD cost matrix.

[0209] S324: Match the corresponding attribute information according to the resident travel OD cost matrix, link the resident residence coordinates and travel point coordinates, and screen the resident activity boundaries of the target village and town community based on the co-occurrence relationship of resident activities.

[0210] In this embodiment, the calculation of the reachable boundary, minimum travel cost distance, and resident activity boundary of the OD parameters is to determine the boundary of the isochronous circle and prepare for the subsequent division of the living circle boundary.

[0211] First, obtain the OD cost matrix of residents' travel (the matrix records the travel costs between different travel OD pairs, and obtains the attribute information of residents, such as the coordinates of the residence and the coordinates of the travel point), then link the coordinates of the residents' residence and travel points (use the residents' attribute information to link the coordinates of the residence and travel points), and then calculate the co-occurrence relationship of residents' activities (according to the OD cost matrix, the travel frequency or travel quantity information between different residents is obtained. Analysis of these data can identify the co-occurrence relationship between residents, that is, which residents often travel together or travel to the same destination), and then screen the activity boundaries of village and town residents (based on the co-occurrence relationship, residents can be clustered according to their residences, and residents with higher co-occurrence can be gathered together to form the activity boundaries of village and town residents), and finally output the activity boundaries of village and town residents (output the screened village and town community activity boundaries).

[0212] During the specific implementation process, the spatial travel time T of residents to different public service facilities is calculated based on the residents' travel OD cost matrix (that is, the isochronous circle is used as the judgment standard for the village and town community living circle), and then the community living circle category to which the target village and town community belongs is determined.

[0213] In this embodiment, when calculating the travel time distance: first obtain the residents' travel OD cost matrix (the matrix records the travel costs between different travel OD pairs), then determine the public service facilities (determine the public service facilities for which the travel time distance needs to be calculated, such as schools, hospitals, shopping centers, etc.), then extract the OD cost from residents to public service facilities (extract the OD cost from residents to public service facilities from the residents' travel OD cost matrix), and finally calculate the travel time distance: calculate the travel time distance from residents to public service facilities based on the extracted OD cost from residents to public service facilities (select usage time for calculation).

[0214] Specifically, such as Figure 5 As shown:

[0215] If the travel time range is 0<T≤30min, the community life circle to which the target village and town community belongs is the commuting life circle; that is, the scope of the community commuting life circle is defined with the travel time of 30min as the boundary.

[0216] If the travel time range is 30min<T≤60min, the community life circle to which the target village or town community belongs is the basic life circle; that is, the scope of the community basic life circle is delineated with the travel time of 60min as the boundary.

[0217] If the travel time is 60 minutes < T ≤ 90 minutes, the community living circle to which the target village or town community belongs is the extended living circle. That is, the extended living circle is defined with the travel time of 90 minutes as the boundary.

[0218] It should be noted that if T>90min, it does not belong to the three types of community life circles described in the present invention and will not be discussed or studied.

[0219] Combine Figure 4 As shown in Figure 2, the public service facilities supply allocation index is calculated through the following steps:

[0220] S501: extracting the number and coordinate points of various public service facilities from the public service facility configuration status dataset;

[0221] S502: Acquire spatial information data of the community life circle to which the target village or town belongs;

[0222] In this embodiment, spatial information data may include public service facilities (such as the location information of public service facilities such as schools, hospitals, shopping centers, parks, etc.), transportation networks, land use data, population data, facility and equipment data, infrastructure data, geographic boundary data, spatial analysis results (such as the spatial travel time of residents to public facilities, connectivity between regions, and other analysis results), etc.

[0223] S503: Using the ArcGIS geographic information platform, the number of public service facilities in the community life circle of the target village or town community and their coordinate points are connected with their spatial information data by grid ID, and a corresponding resident public service facility accessibility network is established;

[0224] S504: Using the ArcGIS geographic information platform, the grid ID of the target village and town community's public service facility accessibility grid and the public service facility usage duration attribute are connected to establish a corresponding public service facility accessibility-usage duration grid;

[0225] S505: Overlaying the target village and town community's resident population parameters on the resident public service facility accessibility-usage duration grid to generate corresponding supply allocation indices for various types of public service facilities;

[0226] The calculation formula for the supply distribution index of various types of public service facilities is:

[0227]

[0228] Where: represents the supply allocation level of K-type public service facilities at location i in the target village or town community, that is, the supply allocation index of the K-th type of public service facilities; k represents the total amount of K-type public service facilities; j(k) represents the j-th specific facility spatial point of the K-th type of public service facilities; represents the usage time of j(k) type public service facilities at location i; d ij(k) represents spatial isolation, which is the straight-line distance from residents at location i to specific j(k) type public service facilities; n represents the population of the target village or town community;

[0229] S506: Based on the preset supply service weights corresponding to various public service facilities (the weights can be set according to actual needs or calculated using the spatiotemporal dataset), the supply allocation index of various public service facilities is weighted and summed to obtain the public service facility supply allocation index of the target village and town community;

[0230] The calculation formula for the public service facilities supply allocation index of the target village and town communities is:

[0231]

[0232] Where: S represents the public service facilities supply allocation index of the target village and town community; represents the supply allocation level of K-type public service facilities at location i in the target village or town community, that is, the supply allocation index of the K-th type of public service facilities; q k Represents the supply service weight of K-type public service facilities in the target village and town community.

[0233] Combine Figure 4 As shown in the figure, the public service facility usage demand index is calculated through the following steps:

[0234] S601: Extract relevant data information including resident travel point coordinates, grid ID information, activity duration, and number of participants from the resident travel spatiotemporal behavior dataset;

[0235] S602: Connect relevant data information by grid ID through the ArcGIS geographic information platform to form a dataset of residents' public service facility usage needs in the target village and town communities;

[0236] S603: Extract the average usage time of public service facilities, resident population parameters, and usage demand weights of various public service facilities from the resident public service facility usage demand dataset (the weights are set based on actual needs or can be calculated using the spatiotemporal dataset);

[0237] S604: Using the ArcGIS geographic information platform, calculate the usage demand index of various public service facilities based on the average usage time of residents' public service facilities and resident population parameters;

[0238] S604: Using the ArcGIS geographic information platform, calculate the usage demand index of various public service facilities based on the average usage time of residents' public service facilities and resident population parameters;

[0239] The calculation formula for the usage demand index of various public service facilities is:

[0240]

[0241] Where: represents the use demand level of K-type public service facilities at location i in the target village or town community, that is, the use demand index of the K-th public service facility; k represents the total number of K-type public service facilities; j(k) represents the j-th specific facility spatial point of the K-th public service facility; represents the average usage time of j(k) type public service facilities at location i; n represents the population of the target village or town community; A i represents the area of ​​the domain at location i;

[0242] S605: performing weighted summation of the usage demand indexes of various public service facilities according to the usage demand weights of various public service facilities to obtain the public service facility usage demand index of the target village or town community;

[0243] The calculation formula for the public service facilities use demand index of the target village and town communities is:

[0244]

[0245] Where: D represents the demand index for public service facilities in the target village and town community; Q represents the use demand level of K-type public service facilities at location i in the target village or town community, that is, the use demand index of K-th type public service facilities; k Represents the usage demand weight of K-type public service facilities in the target village and town communities.

[0246] It should be noted that the principle of use demand index analysis - the distribution of residents' demand for public services is the result of the interaction between the aggregation of people and socio-economic elements (POI points, that is, public service facilities), and it shows a positive correlation distribution and uneven distribution in the urban spatial structure. The demand for public service facilities by residents in the target village and town units can be understood as the attraction of public service facilities to residents. To put it further, the density analysis of residents' demand for public service facilities is used to reflect the intensity of residents' demand for public service facilities in a certain spatial unit, that is, the public service facility demand index. Point density analysis is usually used to characterize this feature in ArcGIS. The principle is to divide the research scope into several square spatial analysis units with a side length of l, and the area radius r is defined around the center I of the spatial analysis unit. i , calculate the total number of public service facilities in the neighborhood, and then divide it by the area of ​​the domain to get the data density evaluation, which is the public service facility use demand index In this case, the village community is used as the analysis unit, so A i is the area of ​​the domain at location i.

[0247] During the specific implementation process, the spatial matching relationship between the supply and demand of public service facilities is fitted through the following steps:

[0248] S701: Divide the target village and town community into several spatial grid units;

[0249] S702: Calculate the corresponding global Moran index based on the public service facility supply distribution index and public service facility use demand index of the target village and town community to represent the spatial correlation of the public service facility supply or demand intensity;

[0250] The calculation formula of the global Moran index is:

[0251]

[0252] Where: I represents the global Moran index, that is, the spatial correlation of the supply or demand intensity of public service facilities in the target village and town community (the degree of spatial intensity agglomeration); N represents the total number of spatial grid cells in the village and town community; i and j represent the index numbers of two random spatial grid cells in the target village and town community; Z represents the standard value of the relevant variables, that is, the standard value of the public service facility supply allocation index and the public service facility use demand index after normalization; w ij represents the spatial matrix weights of spatial grid cells i and j; w represents all w in the target village community ij sum;

[0253] Spatial autocorrelation analysis of the agglomeration intensity of the supply or demand of public service facilities in mountainous rural communities is carried out through GeoDa software. Among them, the value range of the global Moran's I is [-1, 1];

[0254] In this embodiment, GeoDa software is a free and open-source geographic data analysis software, which can be used for visualizing, exploring, and analyzing various geographic data. It provides many geostatistical methods and tools, including spatial autocorrelation analysis, spatial clustering, pattern recognition, geographically weighted regression, etc. GeoDa also has an intuitive interface and easy-to-use functions, enabling users to explore and analyze geographic data in a graphical and interactive way.

[0255] If 0 < Moran's I ≤ 1, it indicates that the relevant variable elements have positive agglomeration, that is, high supply or high demand of public service facilities;

[0256] If 0 = Moran's I, it indicates that there is no spatial correlation relationship between the relevant variable elements;

[0257] If -1 ≤ Moran's I < 0, it indicates that the relevant variable elements have negative agglomeration, that is, low supply or low demand of public service facilities;

[0258] At the same time, the larger the absolute value of Moran's I, the stronger the agglomeration;

[0259] S703: After determining the spatial correlation of the supply or demand intensity of public service facilities, further calculate the bivariate local Moran's index of the public service facilities in the target village or town to analyze the spatial matching degree of the supply-demand configuration of public service facilities;

[0260] The calculation formula of the bivariate local Moran's index is:

[0261]

[0262] In the formula: I' represents the bivariate local Moran's index, that is, the spatial matching degree of the supply-demand configuration of public service facilities in the target village or town community; i and j respectively represent the index numbers of two random spatial grid cells in the target village or town community; Z represents the standard value of the relevant variable, that is, the standard value after the normalization processing of the public service facility supply distribution index and the public service facility usage demand index, where [[ID=二十九]]represents the standard value of the public service facility supply distribution index at the spatial grid cell i in the target village or town community, represents the standard value of the public service facility usage demand index at the spatial grid cell j in the target village or town community; w ij represents the spatial matrix weight of the spatial grid cells i and j;

[0263] The spatial autocorrelation analysis of the supply-demand matching of public service facilities in rural and urban communities was carried out through GeoDa software. Among them, the value range of the bivariate local Moran's I' is [-1, 1];

[0264] If 0 < Moran's I' ≤ 1, it indicates that there is a positive spatial correlation between the related variable elements, that is, the supply-demand relationship of public services in the target rural and urban communities is matched: high supply - high demand, or low supply - low demand;

[0265] If 0 = Moran's I', it indicates that there is no spatial correlation between the related variable elements;

[0266] If -1 ≤ Moran's I' < 0, it indicates that there is a negative spatial correlation between the related variable elements, that is, the supply-demand relationship of public services is mismatched: high supply - low demand, or low supply - high demand;

[0267] At the same time, the larger the absolute value of Moran's I', the stronger the correlation;

[0268] S704: Calculate the bivariate local Moran's index of the public services in the target rural and urban communities through GeoDa software to generate a Moran scatter plot, and judge the spatial matching relationship between the supply and demand of public service facilities according to the Moran scatter plot.

[0269] As Figure 6 shown, the generated Moran scatter plot uses the public service usage demand index as the abscissa and the public service supply distribution index as the ordinate.

[0270] First, divide the Moran scatter plot into four quadrants according to the fitted spatial matching relationship between the supply and demand of public service facilities;

[0271] Then, four types of supply-demand relationships of public service facilities are formed by the points scattered in the Figure 4 quadrants of the Moran scatter plot: the first quadrant is high demand - high supply (G - G), the second quadrant is low demand - high supply (L - G), the third quadrant is low demand - low supply (L - L), and the fourth quadrant is high demand - low supply (G - L);

[0272] Finally, observe the quadrant distribution of the Moran scatter points of the target rural and urban communities in the Moran scatter plot:

[0273] 1) If the Moran scatter points of the target rural and urban communities fall in the first quadrant or the third quadrant, the type of supply-demand relationship of public services in the community living circle to which the target rural and urban communities belong is the supply-demand balance type;

[0274] 2) If the Moran scatter points of the target rural and urban communities fall in the second quadrant, the type of supply-demand relationship of public services in the community living circle to which the target rural and urban communities belong is the type of "supply > demand";

[0275] 3) If the Moran scatter point of the target village or town community falls in the fourth quadrant, the public service supply and demand relationship type of the community life circle to which the target village or town community belongs is "supply < demand".

[0276] In such Figure 6 In the illustrated embodiment, the Moran scatter point of the target village community a falls in the second quadrant, and the public service supply and demand relationship type of the village community a is "supply > demand".

[0277] During the implementation process, the basic configuration plan for public service facilities is determined according to the type of community public service life circle to which the target village and town community belongs, and then the corresponding public service facility configuration adjustment plan is determined according to the type of its public service supply and demand relationship. Finally, the implementation configuration plan for public service facilities in the target mountainous village and town communities is formed by superimposing the basic configuration plan for public service facilities and the configuration adjustment plan for public service facilities (that is, on the basis of Table 1, the configuration plan is adjusted in combination with the contents of Table 2, Table 3 or Table 4).

[0278] Taking the target village community a as an example, during the implementation process, firstly based on Figure 5 As shown in the example of life circle determination, assuming that the spatial time distance from the target village and town community a to the public service facility configuration point is within the range of T1, the public service life circle of the target village and town community a belongs to the commuting life circle range. The basic plan for the public service configuration of the village and town community is determined according to Table 1; secondly, according to Figure 6 As shown in the Moran scatter plot, the public service relationship of target village and town community a belongs to the "supply > demand" type, so the adjustment of its public service facility configuration should refer to the contents shown in Table 2; finally, based on the social, economic, demographic and other basic conditions of target village and town community a, as well as the current configuration status of various public service facilities, supply and demand relationship, etc., the implementation configuration plan of public service facilities in target village and town community a is determined.

[0279] In summary, the present invention constructs a dataset of residents' travel spatiotemporal behavior in village and town communities, and identifies the categories of public service community life circles through distance space clustering analysis, road network topology analysis and resident travel OD cost matrix calculation, so that the actual travel spatiotemporal behavior information of community residents can be analyzed through spatial clustering to plan and identify the categories of public service community life circles in mountainous village and town communities in a targeted manner, without applying the identification and demarcation methods of public service community life circles in urban communities. That is, it can better adapt to the special geographical and geomorphological characteristics and obvious regional differentiation problems of mountainous village and town communities, thereby improving the effectiveness of public service facility planning in mountainous village and town communities. At the same time, the present invention reflects the supply and demand situation of public services in the community life circle by calculating the public service facility supply distribution index and usage demand index of the target village and town community, and then determines the type of public service supply and demand relationship of the community life circle by fitting the spatial matching relationship between the supply and demand of public service facilities to determine the public service facility configuration adjustment plan, and determines the implementation configuration plan of the public service facilities of the target village and town community according to the basic configuration plan and the configuration adjustment plan of the public service facilities, so as to effectively realize the supply and demand balance correction of the public service facilities in the community life circle, and can take into account the requirements of the regional structure and socio-economic factors of mountain village and town communities on the spatial layout of facilities, adjust the complexity and contradiction of public service facilities between regions, thereby improving the suitability and practicality of the public service facility planning in mountain village and town communities.

[0280] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the technical solutions. Those skilled in the art should understand that modifications or equivalent replacements of the technical solutions of the present invention that do not depart from the purpose and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for configuring public service facilities in mountainous village and town community living circles based on supply and demand balance, characterized by: include: S1: Predetermine the basic configuration plan of public service facilities corresponding to different community life circle categories, as well as the configuration adjustment plan of public service facilities corresponding to different types of public service supply and demand relationships in each community life circle category; S2: Obtain the spatial and temporal travel behavior dataset of residents in the target village and town communities and the current status dataset of public service facilities; In step S2, the residents' travel spatiotemporal behavior dataset is constructed through the following steps: S201: Obtaining the spatiotemporal information data of residents' travel and activities in the target village and town community, including residents' residence information and travel activity information; S202: Obtaining mobile phone signaling data of residents in the target village or town community; S203: improving residents' travel-activity spatiotemporal information data through residents' mobile phone signaling data; S204: Obtaining road traffic data of target villages and towns; S205: Summarizing the road traffic data in the improved resident travel-activity spatiotemporal information data to generate a corresponding resident travel spatiotemporal behavior dataset; S3: Perform distance spatial cluster analysis and road network topology analysis on the residents' travel spatiotemporal behavior dataset to calculate the OD cost matrix of residents' travel in the target village and town communities; S4: Determine the community life circle category to which the target village or town community belongs based on the residents' travel OD cost matrix; S5: Calculate the public service facilities supply allocation index of the target village and town communities based on the public service facilities configuration status dataset; S6: Calculate the public service facilities usage demand index of the target village and town community based on the residents' travel time and space behavior dataset; In step S6, the demand for public service facilities by residents in the target village or town unit is the attraction of the public service facilities to the residents. The density analysis of residents' demand for public service facilities is used to reflect the intensity of residents' demand for public service facilities in a certain spatial unit, which is the public service facility demand index. The public service facility usage demand index is calculated through the following steps: S601: Extract relevant data information including resident travel point coordinates, grid ID information, activity duration, and number of participants from the resident travel spatiotemporal behavior dataset; S602: Connect relevant data information by grid ID through the ArcGIS geographic information platform to form a dataset of residents' public service facility usage needs in the target village and town communities; S603: extracting the average usage time of residents' public service facilities, residents' population parameters, and usage demand weights of various public service facilities from the residents' public service facility usage demand dataset; S604: Using the ArcGIS geographic information platform, calculate the usage demand index of various public service facilities based on the average usage time of residents' public service facilities and resident population parameters; The calculation formula for the demand index of various public service facilities in target villages and towns is: Where: represents the use demand level of K-type public service facilities at location i in the target village or town community, that is, the use demand index of the K-th public service facility; k represents the total number of K-type public service facilities; j(k) represents the j-th specific facility spatial point of the K-th public service facility; represents the average usage time of j(k) type public service facilities at location i; n represents the population of the target village or town community; A i represents the area of ​​the domain at location i; S605: performing weighted summation of the usage demand indexes of various public service facilities according to the usage demand weights of various public service facilities to obtain the public service facility usage demand index of the target village or town community; The calculation formula for the public service facilities use demand index of the target village and town communities is: Where: D represents the demand index for public service facilities in the target village and town community; Q represents the use demand level of K-type public service facilities at location i in the target village or town community, that is, the use demand index of K-th type public service facilities; k represents the weight of the demand for K-type public service facilities in the target village and town community; S7: The spatial matching relationship between the supply and demand of public service facilities is determined by combining the global Moran index model and the local Moran index model with the public service facility supply allocation index and the public service facility use demand index, thereby determining the type of public service supply and demand relationship in the community life circle to which the target village and town community belongs; S8: Determine the implementation configuration plan for the public service facilities of the target village and town communities based on the basic configuration plan for public service facilities corresponding to the category of the community life circle to which the target village and town communities belong, and the public service facility configuration adjustment plan corresponding to the type of public service supply and demand relationship in the community life circle to which the target village and town communities belong.

2. The method for configuring public service facilities in a mountainous village community living circle based on supply and demand balance according to claim 1, characterized in that: In step S3, distance space cluster analysis is implemented through the following steps: S301: Extract distance values ​​from residents’ travel time and space behavior dataset; S302: using the ArcGIS geographic information platform to call Ripley's K function to calculate the distance cluster density value based on the distance value; S303: Calculate the average of all distance cluster density values ​​to obtain the expected cluster density value; S304: Calculate the difference between each distance agglomeration density value and the expected agglomeration density value, and determine the agglomeration scale and intensity of each public service facility point; S305: The distance cluster density value with the largest cluster scale and intensity is used as the optimal clustering distance for the target village and town community public service facility points.

3. The method for configuring public service facilities in a mountainous village community living circle based on supply and demand balance as claimed in claim 2, characterized in that: In step S3, the road network topology analysis is performed through the following steps: S311: Extracting vehicle road traffic data from residents’ travel spatiotemporal behavior dataset; S312: Create a fishing net to simulate the distribution of settlements in target villages and towns using the ArcGIS geographic information platform; S313: Combining traffic data with residential distribution to construct a road topology network; S314: Set the travel mode and cost constraints of the road topology network to build and generate a complete road topology network.

4. The method for configuring public service facilities in a mountainous village community living circle based on supply and demand balance according to claim 3, characterized in that: In step S3, the resident travel OD cost matrix is ​​calculated by the following steps: S321: Extract the coordinates of residents’ residences and travel points from the residents’ travel spatiotemporal behavior dataset, and then construct the residents’ travel OD cost matrix based on the road topology network; S322: The optimal clustering distance of the target village and town community public service facility points is used as the reachable boundary of the OD parameter in the residents' travel OD cost matrix; S323: Calculate the minimum travel cost distance for residents to travel points through the resident travel OD cost matrix; S324: Match the corresponding attribute information according to the resident travel OD cost matrix, link the resident residence coordinates and travel point coordinates, and screen the resident activity boundaries of the target village and town community based on the co-occurrence relationship of resident activities.

5. The method for configuring public service facilities in mountainous village and town community living circles based on supply and demand balance as claimed in claim 4, characterized in that: In step S4, the travel time T of residents to different public service facilities is calculated based on the residents' travel OD cost matrix, and then the community life circle category to which the target village community belongs is determined: If 0<T≤30min, the community life circle to which the target village or town community belongs is the commuting life circle; If 30min<T≤60min, the community life circle to which the target village or town community belongs is the basic life circle; If 60min<T≤90min, the community life circle to which the target village and town community belongs is the expanded life circle.

6. The method for configuring public service facilities in a mountainous village community living circle based on supply and demand balance according to claim 1, characterized in that: In step S5, the supply distribution index of public service facilities is calculated through the following steps: S501: Extract the quantity and coordinate points of various public service facilities from the current situation dataset of public service facility configuration; S502: Obtain the spatial information data of the community living circle to which the target village or town belongs; S503: Through the ArcGIS geographic information platform, connect the quantity and coordinate points of public service facilities in the community living circle to which the target village or town community belongs with its spatial information data by grid ID, and establish a corresponding grid of accessibility of public service facilities for residents; S504: Through the ArcGIS geographic information platform, connect the grid of accessibility of public service facilities for residents in the target village or town community with the attribute of public service facility usage duration by grid ID, and establish a corresponding grid of accessibility - usage duration of public service facilities for residents; S505: Overlay the resident population parameters of the target village or town community in the grid of accessibility - usage duration of public service facilities for residents to generate the supply distribution index of corresponding various public service facilities; The calculation formula for the supply distribution index of various public service facilities in the target village or town community is: Where: represents the supply allocation level of K-type public service facilities at location i in the target village or town community, that is, the supply allocation index of the K-th type of public service facilities; k represents the total amount of K-type public service facilities; j(k) represents the j-th specific facility spatial point of the K-th type of public service facilities; represents the usage time of j(k) type public service facilities at location i; d ij(k) represents spatial isolation, which is the straight-line distance from the resident i to the specific j(k) type of public service facilities; n represents the population of the target village or town community; S506: According to the preset supply service weights corresponding to various public service facilities, perform weighted summation on the supply distribution indexes of various public service facilities to obtain the supply distribution index of public service facilities in the target village or town community; The calculation formula for the supply distribution index of public service facilities in the target village or town community is: Where: S represents the public service facilities supply allocation index of the target village and town community; represents the supply allocation level of K-type public service facilities at location i in the target village or town community, that is, the supply allocation index of the K-th type of public service facilities; q k Represents the supply service weight of K-type public service facilities in the target village and town community.

7. The method for configuring public service facilities in mountainous village and town community living circles based on supply and demand balance as claimed in claim 1, characterized in that: In step S7, the spatial matching relationship between the supply and demand of public service facilities is fitted through the following steps: S701: Divide the target village or town community into several spatial grid units; S702: Calculate the corresponding global Moran's I index through the supply distribution index and the usage demand index of public service facilities in the target village or town community to represent the spatial correlation of the intensity of public service facility supply or demand; The calculation formula for the global Moran's I index is: Where: I represents the global Moran index, i.e., the spatial correlation of the intensity of supply or demand for public service facilities; N represents the total number of spatial grid cells in the village community; i and j represent the index numbers of two random spatial grid cells in the target village community; Z represents the standard value of the relevant variables, i.e., the standard value of the public service facility supply allocation index and the public service facility use demand index after normalization; w ij represents the spatial matrix weights of spatial grid cells i and j; W represents all w in the target village community ij sum; Among them, the value range of the global Moran's I index moran′sI is [-1, 1]; If 0 < moran′sI ≤ 1, it indicates that the relevant variable elements have positive agglomeration, that is, high supply or high demand of public service facilities; If 0 = moran′sI, it indicates that there is no spatial correlation between the relevant variable elements; If -1 ≤ moran′sI < 0, it indicates that the relevant variable elements have negative agglomeration, that is, low supply or low demand of public service facilities; At the same time, the larger the absolute value of moran′sI, the stronger the agglomeration; S703: After determining the spatial correlation of the intensity of public service facility supply or demand, further calculate the bivariate local Moran's I index of public service facilities in the target village or town to analyze the spatial matching degree of the supply - demand configuration of public service facilities; The calculation formula for the bivariate local Moran's I index is: Where: I′ represents the bivariate local Moran index, i.e., the spatial matching degree of the supply-demand configuration of public service facilities in the target village and town community; i and j represent the index numbers of two random spatial grid cells in the target village and town community; Z represents the standard value of the relevant variables, i.e., the standard value of the public service facility supply allocation index and the public service facility use demand index after normalization. represents the standard value of the public service facilities supply allocation index at spatial grid cell i in the target village and town community, represents the standard value of the public service facility usage demand index at spatial grid unit j in the target village or town community; w ij represents the spatial matrix weights of spatial grid cells i and j; Among them, the value range of the bivariate local Moran's I index moran′sI′ is [-1, 1]; If 0 < moran′sI′ ≤ 1, it indicates that there is a positive spatial correlation between the relevant variable elements, that is, the public service supply - demand relationship in the target village or town community is matched: high supply - high demand, or low supply - low demand; If 0 = moran′sI′, it indicates that there is no spatial correlation between the relevant variable elements; If -1≤moran′sI′<0, it means that there is a spatial negative correlation between the relevant variable elements, that is, the supply and demand of public services in the target village and town communities are mismatched: high supply-low demand, or low supply-high demand; At the same time, a larger absolute value of moran′sI′ indicates a stronger correlation; S704: Use GeoDa software to calculate the bivariate local Moran index of each spatial grid unit in the target village and town community to generate a Moran scatter plot, and use the Moran scatter plot to determine the spatial matching relationship between the supply and demand of public service facilities.

8. The method for configuring public service facilities in mountainous village and town community living circles based on supply and demand balance as claimed in claim 7, characterized in that: The Moran scatter plot uses the public service facility use demand index as the horizontal axis and the public service facility supply distribution index as the vertical axis; First, the Moran scatter plot is divided into four quadrants according to the spatial matching relationship between the supply and demand of public service facilities. Then, four types of public service facility supply and demand relationships were formed through the points scattered in the four quadrants of the Moran scatter plot: the first quadrant is high demand-high supply, the second quadrant is low demand-high supply, the third quadrant is low demand-low supply, and the fourth quadrant is high demand-low supply; Finally, observe the quadrant distribution of the Moran scatter plot of the target village and town community: 1) If the Moran scatter point of the target village or town community falls in the first or third quadrant, the public service supply and demand relationship type of the community life circle to which the target village or town community belongs is the supply and demand balance type; 2) If the Moran scatter point of the target village or town community falls in the second quadrant, the public service supply and demand relationship type of the community life circle to which the target village or town community belongs is supply > demand; 3) If the Moran scatter point of the target village or town community falls in the fourth quadrant, the public service supply and demand relationship type of the community life circle to which the target village or town community belongs is supply < demand.

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