Community life circle identification method based on public service atlas

Abstract

The invention discloses a community life circle identification method based on a public service map. The method comprises the following steps: step 1, integrating a community public service database; 2, building a community basic information map; 3, building a public service facility map; step 4, community public service atlas integration; 5, dividing a community life circle; and step 6, carrying out visual integrated display. Attribute information about distribution, facility function types and service levels of public service facilities in an urban community is intelligently recognized through space calculation and a map technology and is translated into a division rule of a community life circle, and accurate recognition of the community life circle and service functions of the community life circle is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of urban planning and design, and specifically relates to a method for identifying community life circles based on a public service map. Background Art

[0002] Identifying community living circles can improve residents' quality of life and optimize urban spatial structure. By identifying and analyzing community living circles, we can clearly understand the distribution and accessibility of various public service facilities within and outside the community, helping governments and relevant agencies to rationally plan and optimize the allocation of public facilities, thereby better meeting residents' daily needs and improving convenience. Furthermore, identifying community living circles can promote social equity and sustainable development. This process can accurately assess whether public service facilities are evenly distributed and meet the needs of residents in different areas, facilitating policy adjustments and service additions. It can also more accurately analyze service gaps for vulnerable groups, identify regional disparities and imbalances in urban development, and subsequently adjust resource allocation and implement targeted facility additions. Furthermore, identifying community living circles can improve transportation and environmental planning. By optimizing travel routes and public transportation station locations, it can alleviate traffic pressure, reduce congestion, and provide environmental benefits such as improved air quality.

[0003] Public service facilities improve the convenience of life. Supermarkets, schools, hospitals, cultural facilities, and other public service facilities are essential to residents' daily lives. A well-designed layout and good accessibility can significantly enhance residents' convenience, reduce travel time, and improve their quality of life. In emergency situations, such as medical emergencies and disaster relief, a well-placed public service facility layout can speed up response times and alleviate residents' stress. Public service facilities also promote economic and social development. The construction of public service facilities not only directly improves residents' living environment but also boosts regional economic development. For example, facilities like schools and hospitals can attract more families to settle down, driving real estate, retail, and commercial development in surrounding areas. Cultural and entertainment facilities (such as libraries, museums, and stadiums) can help improve the cultural literacy of community residents. Public service facilities can also ensure social equity. The equitable distribution of public service facilities ensures the fair allocation of social resources. This is particularly true in resource-poor, densely populated urban areas. Effective management of public service facilities can ensure that residents of impoverished and remote communities enjoy equal living conditions with those in central urban areas.

[0004] At present, the identification of community living circles mainly relies on methods such as accessibility analysis and LBS identification. The accessibility analysis method identifies which facilities are accessible to specific community residents by calculating the accessible time, accessible distance, and transportation mode between residents and public service facilities. However, it ignores other factors affecting accessibility and has a strong dependence on traffic data. Incomplete or inaccurate data will affect the accuracy of the results. The LBS identification method uses mobile devices and location data to analyze the activity trajectories of community residents and infer their living circles and service needs. However, it depends on users' personal devices and network coverage. Residents who do not use location services cannot participate in the identification. At the same time, the data acquisition cost is high, and a large amount of data cleaning and processing are required, making it difficult to ensure the accuracy of the data and the identification results. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method for identifying community living circles based on a public service map. The present invention intelligently identifies the attribute information of the distribution, facility function types, and service levels of public service facilities in urban communities through spatial calculation and map technology, and translates it into the division rules of community living circles to achieve the accurate identification of community living circles and their service functions.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for identifying community living circles based on a public service map includes the following steps:

[0008] Step 1: Integration of the community public service database

[0009] Obtain the public service facility points, administrative community areas, residential community points, community population, and graded road data of the target urban area, unify the data coordinate system, integrate the geographical coordinates and facility types of the public service facility points into the service facility database, and integrate the geographical coordinates, community population, and road geometric vectors of the residential community points into the community basic database;

[0010] Step 2: Construction of the community basic information map

[0011] Based on the community basic database in Step 1, import the residential community points into the geographic information system, and calculate the adjacent relationship and distance between the residential community points through the spatial proximity analysis algorithm; use the residential community points as nodes, the distance between adjacent communities as relationships, and the community point ID, geographical coordinates, and community population as attributes to establish a community basic information map;

[0012] Step 3: Construction of the public service facility map

[0013] Based on the service facility database in step 1, the service function type and service level of the facility points are divided according to the facility type and quantity; the facility points are imported into the geographic information system to calculate the adjacent relationship and distance; with the facility points as nodes, the distance between adjacent facilities as relationships, and the facility ID, geographic coordinates, facility function and level classification as attributes, a public service facility map is established;

[0014] Step 4: Community public service graph integration

[0015] Extract the geographic coordinates of residential community nodes and facility nodes from the community basic information map and public service facility map, as well as the road geometry vectors from the basic database; analyze the correlation between community points and facility points in the geographic information system, calculate and identify the correlation threshold using the DBSCAN (clustering) algorithm, and then set extraction rules to extract the associated facilities around the residential community to form the community's initial facility cluster; integrate the community basic information map and the public service facility map, and add association relationships between community points and facility points in the map that are in the same community's initial facility cluster to form a community public service map;

[0016] Step 5: Community Living Circle Division

[0017] The functional level classification and geographic coordinate attributes of facility nodes in the community public service map are extracted, and spatially associated facility clusters are identified through a community detection algorithm. The community population attributes of residential community nodes and the reachable time attributes of facility nodes in the community public service map are extracted to calculate the service demand of the community, and facility points that meet the corresponding needs are extracted to form a community living facility cluster. The community living facility cluster and related spatially associated facility clusters are integrated through the K-means algorithm, and the community living circle is divided according to its spatial boundaries. The spatial boundaries are matched according to the facility classification to form a multi-level and multi-functional community living circle system.

[0018] Step 6: Visual integration display

[0019] Through the digital projector, the community basic information map, public service facilities map and community public service map are integrated and displayed, and interactive community life circle system visualization information is superimposed, and their real spatial relationships are displayed in the geographic information system.

[0020] Furthermore, the service function type and service level of the facility points are divided according to the facility type and quantity in step 3, which means that the various types of facilities are divided into five functional types: commercial entertainment, cultural and sports activities, education and medical care, road transportation, and administrative office according to the facility type information. The division rules are shown in the following table:

[0021]

[0022] Dividing the service levels of facility points according to the number of facilities means obtaining the number of various facilities under each functional type. Through the natural breaks classification method, the facilities are divided into three facility levels: high-level facilities, medium-level facilities, and low-level facilities according to the ascending order of the number of facilities.

[0023] Further, analyzing the correlation between community points and facility points in step 4 means calculating the accessible time and azimuth angle between community points and facility points; among them, the accessible time T refers to the shortest travel time calculated based on the road network topology from the community point to the facility point; the azimuth angle θ refers to the geographical direction from the community point to the facility point; First, construct the correlation matrix of the community point set C = {c1, c2, …, c m} and the facility point set F = {f1, f2, …, f n}; for each community point c i calculate its accessible time T i and azimuth angle θ ij to all facility points f ij , forming the data point set D = {(T ij , θ ij )}.

[0024] Further, calculating and identifying the correlation threshold in step 4 refers to 1; let the weight coefficient of the accessible time T be w T , and the weight coefficient of the azimuth angle θ be w θ , calculate the correlation between the community point and the facility point, that is, the weighted distance d(c i , f i ), and construct the distance matrix;

[0025] d(c i , f i ) = w T ·|T ij - T kl | + w θ ·min(|θ ij - θ kl |, 2π - |θ ij - θ kl )

[0026] w T + w θ = 1

[0027] Among them, c i and f i respectively refer to the community point and the facility point; T ij refers to the accessible time between the community point c i and the facility point f i , T kl refers to the accessible time between the community point c k and the facility point f lReachable time; θ ij Refers to the community point c i and the facility point f i The azimuth angle between them, θ kl Refers to the community point c k and the facility point f l The azimuth angle between them.

[0028] Furthermore, in the fourth step, extraction rules are respectively set to extract the associated facilities around the residential community, which means setting the average reachable time as the reachable time threshold T threshold , setting the standard deviation of the average azimuth angle as the azimuth uniformity threshold θ uniformity , calculating the weighted distance threshold d threshold , that is, the correlation threshold:

[0029] d threshold = w T ·T threshold + w θ ·θ uniformity

[0030] If the weighted distance d(c i and the facility point f i ) < d i , f i ), then the facility has a strong correlation with the community, meets the extraction conditions, and belongs to the associated facilities of community c threshold i i of the associated facilities.

[0031] Furthermore, in the fifth step, identifying the spatial associated facility clusters means classifying different types of facilities based on the facility proximity calculation method and the K-Means clustering algorithm; First, for each community C, use the formula Calculate the average adjacent distance between different types of facilities f i and f j within its range to quantify the spatial correlation degree between different facility types, where F i , F j respectively represent the facility sets of facility types i and j, and d(p,q) is the Euclidean distance between facility points p and q; Secondly, since different types of facilities have different service capabilities for the community, it is necessary to construct a facility proximity weight matrix according to the calculated proximity mean where γ is a regulation parameter used to control the influence of distance on the weight; adjust the influence factor when clustering facilities through proximity weights, so that the spatial distance and functional category are balanced when identifying facility clusters; After obtaining the weights, construct a geographical coordinate (x i , y i ) of the facility point and the facility function type φ i iThe eigenvector v i =(x i , y i , φ i ), and the K-Means clustering method is used to identify spatial associated facility clusters with spatial compactness and service function similarity.

[0032] Furthermore, in step five, calculate the service demand degree of the community, extract facility points that meet the corresponding requirements, and form a community living facility group, which means extracting facility points according to the ratio of the population quantity to the facility quantity, comprehensively considering the facility quantity and accessibility to measure the service demand degree of the community, and judging the quantity of various required facilities; in terms of the facility quantity, for different types of facilities f, define the demand degree of each type of facility where is the total quantity of facility type f in community C; when exceeds a certain threshold , it indicates that this type of facility is relatively scarce and needs to be given priority to supplement this type of facility during the identification process of the community living circle; further calculate the accessibility A C,f , and the shortest path time t(C, f) from facility point f to the community center C is calculated by the formula A C,f =e -αt(C,f) ; calculate the comprehensive applicability score of the facility point through the formula , where λ1 and λ2 are weight parameters to control the influence of demand degree and accessibility on facility screening; according to the ranking of S f , extract the facility points with higher scores, and screen out the facility points that meet the community needs by comprehensively considering the quantity and accessibility of the facilities to form a community living facility group.

[0033] Furthermore, in step five, integrate the community living facility group and the relevant spatial associated facility clusters, which means that in spatial association analysis, if it is judged through the formula that is, there is a spatial intersection between the community living facility group F L and a certain spatial associated facility cluster F S , where d(f i , f j ) represents the Euclidean distance between facilities f i and f j , and ∈ is the set merging threshold, then merge the facility cluster F S into the community living facility group of this community to form an extended community living facility group F' L =F L ∪F S ; to further clarify the boundary of the community living circle and the ownership relationship of the facilities, add living circle information to the community public service atlas, and enhance the atlas through the link of attribution attributes and connection relationships; specifically, for the living circle L within community Ck , add the living circle attribution attribute to each facility node f in the graph Meanwhile, introduce a new living circle connection relationship between facility nodes Among them, ∈′ is the maximum visual connection distance of the facilities within the living circle; to more intuitively express the structure of the community living circle, use the force-directed algorithm and hierarchical layout to adjust the graph so that it conforms to the structure of the community living circle cluster, that is, through the formula E = ∑ i<j k ij (||p i -p j || - d ij ) 2 Calculate to minimize E and achieve a reasonable layout of the facility clusters. Among them, p i and p j are the coordinates of facilities i and j in the visualization space respectively, d ij is the distance between the two in the original space, and k ij is the elastic coefficient used to control the stretching force of the edge; in addition, further adopt a hierarchical layout to optimize the organizational structure of the community living circle, arrange facilities at different levels in layers to ensure that the hierarchical relationship of the community living circle is clearly visible, and finally obtain the level and distribution characteristics of the community living circle cluster.

[0034] Furthermore, in the multi-level and multi-functional community living circle system in step five, multi-level refers to three living circle levels: high-level living circle, middle-level living circle, and low-level living circle, and multi-functional means that each level includes five different public service function living circles: commercial and entertainment living circle, cultural and sports activity living circle, education and medical living circle, road traffic living circle, and administrative office living circle. Thus, a community living circle system composed of 1 community overall living circle, 3 multi-level living circles, and 15 multi-functional living circles is formed.

[0035] Advantages of the present invention

[0036] 1. The present invention identifies the community living circle based on the public service graph, which helps to check and supplement the community living circle from an objective perspective of the distribution of service facilities on the basis of administrative community division, and identify problems in the community public service system such as insufficient service types and too long service distances.

[0037] 2. The present invention classifies public service facilities into 5 service functions and 3 service levels according to the facility type and quantity characteristics, and then forms 15 facility classifications, which helps to carry out refined management of various facilities, can more accurately analyze the service relationship between different facilities and community residents in living circles at different levels, so as to provide personalized and differentiated public services and ensure that the facility resources can meet the actual needs of the community.

[0038] 3. The present invention innovatively proposes a "multi - level and multi - functional" community living circle system, which can divide the community living circle into high - level living circles, medium - level living circles and low - level living circles, and cover different service functions such as commercial entertainment, cultural and sports activities, education and medical care, road traffic and administrative office according to functional requirements at each level. This method enables the division of living circles to not only meet the needs of communities of different scales, but also take into account various public service functions, ensuring that the living circle functions are perfect and the services are balanced at different levels.

[0039] 4. By combining geographic information systems and big data analysis, the present invention realizes the dynamic identification and optimization of community living circles. With the help of the K - Means clustering algorithm and other spatial analysis methods, it can dynamically adjust the boundaries of living circles according to community needs and facility distributions, and visually display their distribution characteristics in the atlas. This innovation provides real - time and scientific support for community planning and public service decision - making, further promoting the construction of smart cities and smart communities.

[0040] 5. The present invention adopts the method of facility proximity merging. By the spatial relationship and functional matching between facilities, it optimizes the spatial layout of public service facilities in the community; combined with accessibility analysis and demand calculation, it closely connects service facilities with residents' needs, solves the problem of over - concentration or over - dispersion of facilities, enables residents to enjoy equal services within a reasonable spatial range, and improves the utilization efficiency and accessibility of public resources.

[0041] 6. Through the integrated display of geographic information systems, the present invention visually displays the community basic information atlas, public service facility atlas and community public service atlas. Through an interactive interface, residents and managers can intuitively view the distribution of service facilities in the community, the living circle structure and service coverage, providing intuitive information support for the planning and optimization of community services, and enhancing the convenience of community management and residents' participation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is the flowchart of the method of the present invention;

[0043] Figure 2 is the spatial effect of the multi - level community living circle. DETAILED DESCRIPTION OF THE INVENTION

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment

[0046] Taking the central urban area of Nanjing as an example, the technical solution of the present invention will be described in detail below.

[0047] A method for identifying a community living circle based on a public service map, as Figure 1-2 shown, includes the following steps:

[0048] (1) Integration of the community public service database. Obtain the public service facility points, administrative community areas, residential community points, community population, and hierarchical road data in the central urban area of Nanjing, unify the data coordinate system, integrate the geographical coordinates and facility types of the public service facility points into the service facility database of the central urban area of Nanjing, and integrate the geographical coordinates, community population, and road geometric vectors of the residential community points into the community basic database of the central urban area of Nanjing.

[0049] (2) Construction of the community basic information map. Based on the community basic database of the central urban area of Nanjing constructed in step (1), import the residential community points into the geographic information system, and calculate the adjacent relationship and distance between the residential community points through the spatial proximity analysis algorithm. Taking the residential community points as nodes, the distance between adjacent communities as the relationship, and the community point ID, geographical coordinates, and community population as attributes, establish the community basic information map of the central urban area of Nanjing.

[0050] (3) Construction of the public service facility map. Based on the service facility database of the central urban area of Nanjing in step (1), divide the service function types and service levels of the facility points according to the facility type and quantity. Import the facility points into the geographic information system to calculate the adjacent relationship and distance. Taking the facility points as nodes, the distance between adjacent facilities as the relationship, and the facility ID, geographical coordinates, facility function, and grade classification as attributes, establish the public service facility map of the central urban area of Nanjing.

[0051] Among them, the division of the service function types and service levels of the facility points according to the facility type and quantity means that various facilities are divided into five function types: commercial entertainment, cultural and sports activities, education and medical care, road traffic, and administrative office according to the facility type information, and the division rules are shown in the following table:

[0052]

[0053] The division of the service levels of the facility points according to the facility quantity means obtaining the quantity of various facilities under each function type, and through the natural break classification method, dividing the facilities into three facility levels: high-level facilities, medium-level facilities, and low-level facilities according to the ascending arrangement of the facility quantity.

[0054] (4) Integration of community public service maps. Extract the geographical coordinates of residential community nodes and facility nodes from the community basic information map of the central urban area of Nanjing and the public service facility map of the central urban area of Nanjing, as well as the road geometric vectors in the basic database. Analyze the relevance between community points and facility points in the geographic information system, calculate and identify the relevance threshold through the DBSCAN (clustering) algorithm, and then set extraction rules respectively to extract the associated facilities around the residential community to form an initial community facility group. Integrate the community basic information map and the public service facility map, and add an association relationship to the community points and facility points in the same initial community facility group within the map to form the community public service map of the central urban area of Nanjing.

[0055] Among them, the analysis of the association relationship between community points and facility points refers to calculating the reachable time and azimuth angle between community points and facility points. The reachable time T refers to the shortest passing time calculated based on the road network topology from the community point to the facility point; the azimuth angle θ refers to the geographical direction from the community point to the facility point. First, construct an association matrix of the community point set C = {c1, c2,..., c m} and the facility point set F = {f1, f2,..., f n}. For each community point c i , calculate its reachable time T i and azimuth angle θ ij to all facility points f ij , and form a data point set D = {(T ij , θ ij ).

[0056] Among them, the calculation and identification of the relevance threshold refers to 1; let the reachable time T weighting coefficient be w T , and the azimuth angle θ weighting coefficient be w θ , calculate the relevance between community points and facility points, that is, the weighted distance d(c i , f i ), and construct a distance matrix.

[0057] d(c i , f i ) = w T ·|T ij - T kl | + w θ ·min(|θ ij - θ kl |, 2π - |θ ij - θ kl |)

[0058] w T + w θ = 1

[0059] Among them, c i and fi respectively refer to the community point and the facility point; T ij refers to the community point c i and the facility point f i of the reachable time, T kl refers to the community point c k and the facility point f l of the reachable time; θ ij refers to the community point c i and the facility point f i of the azimuth angle between them, θ kl refers to the community point c k and the facility point f l of the azimuth angle between them.

[0060] Among them, the above-mentioned respectively set extraction rules for extracting the associated facilities around the residential community mean setting the average reachable time as the reachable time threshold T threshold , setting the average azimuth angle standard deviation as the azimuth uniformity threshold θ uniformity , calculating the weighted distance threshold d threshold , that is, the correlation threshold:

[0061] d threshold =w T ·T threshold +w θ ·θ uniformity

[0062] If the weighted distance d(c i and the facility point f i )<d i ,f i )<d threshold , then the facility has a strong correlation with the community, meets the extraction conditions, and belongs to the associated facilities of community c i .

[0063] (5) Community living circle division. Extract the functional level classification and geographical coordinate attributes of the facility nodes in the community public service atlas of the central urban area of Nanjing, and identify the spatial associated facility clusters through the community detection algorithm. Extract the community population attributes of the residential community nodes and the reachable time attributes of the facility nodes in the community public service atlas of the central urban area of Nanjing, calculate the service demand degree of the community, and extract the facility points that meet the corresponding demands to form a community living facility group. Integrate the community living facility group and the relevant spatial associated facility clusters through the K-means algorithm, divide the community living circle by its spatial boundary, and match the spatial boundary according to the facility classification respectively to form a multi-level and multi-functional community living circle system.

[0064] Among them, the above-mentioned identifying the spatial associated facility clusters refers to classifying different types of facilities based on the facility proximity calculation method and the K-Means clustering algorithm. First, for each community C, use the formula Calculate the average proximity distance between different types of facilities f within its range to quantify the degree of spatial association between different facility types, where F i and f j respectively represent the sets of facilities of facility types i and j, and d(p,q) is the Euclidean distance between facility points p and q. Secondly, since different types of facilities have different service capabilities for the community, it is necessary to construct a facility proximity weight matrix according to the calculated proximity mean i ,F j where γ is a regularization parameter used to control the influence of distance on the weight. By the proximity weight, the influence factor in facility clustering can be adjusted so that the spatial distance and functional category are balanced when identifying facility clusters. After obtaining the weight, construct a feature vector v containing the geographical coordinates (x of the facility point,y i ,y i ) and the facility function type φ i , and use the K-Means clustering method to identify spatially associated facility clusters with spatial compactness and service function similarity. i =(x i ,y i ,φ i ),

[0065] Among them, calculating the service demand degree of the community and extracting facility points that meet the corresponding needs to form a community living facility group means extracting facility points by considering the ratio of the population quantity to the facility quantity and comprehensively considering the facility quantity and accessibility, so as to measure the service demand degree of the community and judge the quantity of various required facilities. In terms of the facility quantity, for different types of facilities f, define the demand degree of each type of facility where is the total quantity of facility type f in community C. When exceeds a certain threshold , it indicates that this type of facility is relatively scarce and needs to be given priority to supplement this type of facility in the process of identifying the community living circle. Further calculate the accessibility A C,f of the facility, which can be calculated by the formula A C,f =e -αt(C,f) from the shortest path time t(C,f) from the facility point f to the community center C. Calculate the comprehensive applicability score of the facility point through the formula , where λ1 and λ2 are weight parameters that control the influence of the demand degree and accessibility on facility screening. According to the ranking of S f , extract the facility points with higher scores, and comprehensively screen the facility points that meet the community needs considering the quantity and accessibility of the facilities to form a community living facility group.

[0066] Among them, the integrated community living facility group and the related spatial association facility clusters refer to that in spatial association analysis, if judged by the formula That is, the community living facility group F L and a certain spatial association facility cluster F S have a spatial intersection. Among them, d(f i , f j ) represents the Euclidean distance between facilities f i and f j . ∈ is the set merging threshold, then the facility cluster F S is merged into the community living facility group of this community to form an extended community living facility group F' L = F L ∪F S . To further clarify the boundary of the community living circle and the ownership relationship of facilities, living circle information is added to the community public service atlas, and the atlas is enhanced through the link of attribution attributes and connection relationships. Specifically, for the living circle L k within the community C, a living circle ownership attribute is added to each facility node f in the atlas At the same time, a new living circle connection relationship is introduced between facility nodes Among them, ∈' is the maximum visual connection distance of facilities within the living circle. To more intuitively express the structure of the community living circle, the force-directed algorithm and hierarchical layout are used to adjust the atlas to make it conform to the structure of the community living circle cluster group, that is, through the formula E = ∑ i<j k ij (||p i - p j || - d ij ) 2 is calculated to minimize E and achieve a reasonable layout of facility clusters. Among them, p i and p j are the coordinates of facilities i and j in the visualization space respectively, d ij is the distance between the two in the original space, and k ij is the elastic coefficient used to control the stretching force of the edge. In addition, the hierarchical layout is further adopted to optimize the organizational structure of the community living circle, and facilities at different levels are arranged in layers to ensure that the hierarchical relationship of the community living circle is clearly visible, and finally the hierarchical and distribution characteristics of the community living circle cluster group are obtained.

[0067] Among them, the multi-level and multi-functional community life circle system has multi-levels referring to three levels of high-level life circles, medium-level life circles, and low-level life circles, and multi-functional meaning that each level includes life circles with five different public service functions: commercial and entertainment life circles, cultural and sports activity life circles, education and medical life circles, road traffic life circles, and administrative office life circles. Thus, a community life circle system composed of 1 overall community life circle, 3 multi-level life circles, and 15 multi-functional life circles is formed.

[0068] (6) Visual integrated display. Integrate and display the community basic information map, public service facility map, and community public service map of the central urban area of Nanjing through a digital projector, and overlay the visual information of the interactive community life circle system. At the same time, display their real spatial relationships in the geographic information system.

Claims

1. A method for identifying a community living circle based on a public service map, characterized in that It includes the following steps: Step 1: Integration of the community public service database Obtain the public service facility points, administrative community areas, residential community points, community population, and graded road data of the target urban area, unify the data coordinate system, integrate the geographical coordinates and facility types of the public service facility points into the service facility database, and integrate the geographical coordinates, community population, and road geometric vectors of the residential community points into the community basic database; Step 2: Construction of the community basic information graph Based on the community basic database in Step 1, import the residential community points into the geographic information system, and calculate the adjacent relationship and distance between the residential community points through the spatial proximity analysis algorithm; Taking the residential community points as nodes, the distance between adjacent communities as the relationship, and the community point ID, geographical coordinates, and community population as attributes, establish the community basic information graph; Step 3: Construction of the public service facility graph Based on the service facility database in Step 1, divide the service function types and service levels of the facility points according to the facility type and quantity; Import the facility points into the geographic information system to calculate the adjacent relationship and distance; taking the facility points as nodes, the distance between adjacent facilities as the relationship, and the facility ID, geographical coordinates, facility function, and grade classification as attributes, establish the public service facility graph; Step 4: Integration of the community public service graph Extract the geographical coordinates of the residential community nodes and facility nodes in the community basic information graph and the public service facility graph, as well as the road geometric vectors in the basic database; Analyze the relevance between the community points and facility points in the geographic information system, calculate and identify the relevance threshold through the DBSCAN algorithm, and then set the extraction rules respectively to extract the associated facilities around the residential community to form the initial community facility group; integrate the community basic information graph and the public service facility graph, and add the association relationship to the community points and facility points in the same initial community facility group in the graph to form the community public service graph; Step 5: Division of the community living circle Extract the function grade classification and geographical coordinate attributes of the facility nodes in the community public service graph, and identify the spatially associated facility clusters through the community detection algorithm; Extract the community population attribute of the residential community nodes and the reachable time attribute of the facility nodes in the community public service graph, calculate the service demand degree of the community, and extract the facility points that meet the corresponding needs to form the community living facility group; integrate the community living facility group and the related spatially associated facility clusters through the K-means algorithm, divide the community living circle by its spatial boundary, and match the spatial boundary according to the facility classification respectively to form a multi-level and multi-functional community living circle system; Step 6: Visual integration display Integrate and display the community basic information graph, the public service facility graph, and the community public service graph through a digital projector, and overlay the visual information of the interactive community living circle system. At the same time, display their real spatial relationships in the geographic information system.

2. The community living circle recognition method based on a public service map according to claim 1, wherein In Step 3, dividing the service function types and service levels of the facility points according to the facility type and quantity means dividing various facilities into five function types: commercial entertainment, cultural and sports activities, education and medical care, road traffic, and administrative office according to the facility type information. The division rules are shown in the following table: Dividing the service levels of facility points according to the number of facilities means obtaining the number of various facilities under each functional type. Through the natural break classification method, the facilities are divided into three facility levels: high-level facilities, medium-level facilities, and low-level facilities according to the ascending order of the number of facilities.

3. A method for identifying a community living circle based on a public service map according to claim 2, characterized in that, The analysis of the association between community points and facility points in step 4 refers to calculating the reachable time and azimuth between the community points and the facility points; wherein the reachable time T refers to the shortest travel time from the community point to the facility point calculated based on the road network topology; the azimuth θ refers to the geographical direction from the community point to the facility point; first, construct a community point set C = {c1, c2, ..., c m } and the facility point set F={f1,f2,…,f n }; for each community point c i Calculate its distance to all facilities f i The reachable time T ij and azimuth angle θ ij , forming a data point set D = {(T ij ,θ ij )}.

4. The community living circle recognition method based on a public service map according to claim 3, characterized in that, In step 4, calculating and identifying the relevance threshold means setting the weight coefficient of the reachable time T as w T , and the weight coefficient of the azimuth angle θ as w θ , calculating the relevance between the community point and the facility point, that is, the weighted distance d(c i , f i ), and constructing a distance matrix; d(c i ,f i ) = w T ·|T ij -T kl | + w θ ·sin(|θ ij -θ kl |, 2π - |θ ij -θ kl ) w T +w θ = 1 Among them, c i and f i refer to the community point and the facility point respectively; T ij refers to the accessible time between the community point c i and the facility point f i ; T kl refers to the accessible time between the community point c k and the facility point f l ; θ ij refers to the azimuth angle between the community point c i and the facility point f i ; θ kl refers to the azimuth angle between the community point c k and the facility point f l .

5. The community living circle recognition method based on a public service map according to claim 4, wherein In the fourth step, extraction rules are respectively set to extract the associated facilities around the residential community, which means setting the average accessible time as the accessible time threshold T threshold , setting the standard deviation of the average azimuth as the azimuth uniformity threshold θ uniformity , calculating the weighted distance threshold d threshold , that is, the relevance threshold: d threshold = w T · T threshold + w θ · θ uniformity If community point c i and facility point f i 's weighted distance d(c i , f i ) < d threshold , then the facility has a strong association with the community, meets the extraction conditions, and belongs to the associated facilities of community c i .

6. The community living circle recognition method based on a public service map according to claim 5, wherein In step five, identifying the spatially associated facility clusters means classifying different types of facilities based on the facility proximity calculation method and the K-Means clustering algorithm. First, for each community C, use the formula to calculate the average neighborhood distance between different types of facilities f i and f j within its scope to quantify the spatial association degree between different facility types. Among them, F i , F j respectively represent the facility sets of facility types i and j, and d(p, q) is the Euclidean distance between facility points p and q. Second, since different types of facilities have different service capabilities for the community, it is necessary to construct a facility proximity weight matrix according to the calculated proximity mean where γ is a regulation parameter used to control the influence of distance on the weight; adjust the influence factor during facility clustering through proximity weights so that spatial distance and functional category are balanced when identifying facility clusters. After obtaining the weights, construct a feature vector v i , y i ) and facility function type φ i v i =(x i , y i , φ i ), and use the K-Means clustering method to identify spatially associated facility clusters with spatial compactness and service function similarity.

7. A method for identifying a community living circle based on a public service map according to claim 6, characterized in that, In step 5, calculate the service demand degree of the community, and extract the facility points that meet the corresponding requirements to form a community living facility group, which means extracting facility points by considering the ratio of the population quantity to the facility quantity and comprehensively considering the facility quantity and accessibility, so as to measure the service demand degree of the community and judge the quantity of various types of facilities required; in terms of the facility quantity, for different types of facilities f, define the demand degree of each type of facility Among them, is the total quantity of facility type f in community C; when exceeds a certain threshold , it indicates that this type of facility is relatively scarce and needs to be given priority to supplement this type of facility during the identification process of the community living circle; further calculate the accessibility A C,f , and the shortest path time t(C, f) from facility point f to community center C is calculated by the formula A C,f = e -αt(C,f) ; calculate the comprehensive applicability score of the facility point through the formula , where λ1 and λ2 are weight parameters that control the influence of demand degree and accessibility on facility screening; according to the sorting of S f , extract the facility points with higher scores, and screen out the facility points that meet the community needs by comprehensively considering the quantity and accessibility of the facilities to form a community living facility group.

8. A method for identifying a community living circle based on a public service map according to claim 7, characterized in that, In step five, integrating the community living facility group with the related spatial association facility clusters means that in spatial association analysis, if it is judged through the formula That is, the community living facility group F L and a certain spatial association facility cluster F S have a spatial intersection. Among them, d(f i , f j ) represents the Euclidean distance between facilities f i and f j . ∈ is the set merging threshold. Then, the facility cluster F S is merged into the community living facility group of this community to form an extended community living facility group F′ L = F L ∪F S ; To further clarify the boundary of the community living circle and the ownership relationship of facilities, add living circle information to the community public service atlas, and enhance the atlas through the link of attribution attributes and connection relationships; specifically, for the living circle L k within the community C, add the living circle ownership attribute to each facility node f in the atlas At the same time, introduce a new living circle connection relationship between facility nodes i<j k ij (||p i - p j || - d ij ) 2 to calculate and minimize E to achieve a reasonable layout of the facility clusters. Among them, p i and p j are the coordinates of facilities i and j in the visualization space respectively, d ij is the distance between the two in the original space, and k ij is the elastic coefficient used to control the stretching force of the edge; in addition, further adopt a hierarchical layout to optimize the organizational structure of the community living circle, arrange facilities at different levels in layers to ensure that the hierarchical relationship of the community living circle is clearly visible, and finally obtain the hierarchical and distribution characteristics of the community living circle clusters.

9. A method for identifying a community living circle based on a public service map according to claim 8, characterized in that In the multi-level and multi-functional community living circle system in step five, multi-level refers to three living circle levels: high-level living circle, medium-level living circle, and low-level living circle. Multi-functional means that each level includes five different public service function living circles: commercial and entertainment living circle, cultural and sports activity living circle, education and medical living circle, road traffic living circle, and administrative office living circle. Thus, a community living circle system composed of 1 overall community living circle, 3 multi-level living circles, and 15 multi-functional living circles is formed.

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