Urban community child public service facility configuration efficiency evaluation method

Abstract

The invention discloses a configuration efficiency evaluation method for urban community child public service facilities, and the method comprises the steps: obtaining POI data points of the community child public service facilities, a community range, a road network and community child population data, and importing the data into an ArcGIS, and enabling the data to serve as a community base map; according to the community base map, dotted processing is carried out on the community child population data and the community child public service facility data, and spatial density analysis is carried out on the community child population data and the community child public service facility data; the method comprises the following steps: dividing community child public service facilities into six types, and performing spatial assignment on POI points of various types of facilities in ArcGIS to complete classification processing; spatial layout features of the community children public service facilities are obtained through a kernel density analysis method, and reachability analysis is carried out on POI data of the community children public service facilities in combination with a community road network; density center comparison is carried out according to the community child population space density data and the community child public service facility space density data, correlation analysis is carried out on reachability data of the community child population space density data and the community child public service facility space density data through a Gini coefficient, and the community child public service facility configuration efficiency evaluation grade is determined.

Description

Technical Field

[0001] The invention relates to a method for evaluating the configuration efficiency of public service facilities for children in urban communities. Background Art

[0002] As the smallest unit of urban space, the community is also the space where children's daily activities are most frequent, and it plays an important role in the transformation and construction of child-friendly cities. The layout of children's public service facilities is particularly important in the construction of child-friendly communities. How to ensure the efficiency of facility configuration within a limited community space is not only conducive to the high-quality use of space, but also enables more children to meet their growth needs more conveniently, ensuring that every child in the community can enjoy the rights and interests of development.

[0003] At present, the research and construction of community children's public service facilities are still in the early stages of exploration. Existing research on children's public service facilities is mostly discussed at the urban scale, and the facility attribute data has not been sorted out and differentiated from the micro scale to clarify the spatial demand point distribution of children's public service facilities. In addition, the research methods for facility configuration issues are mostly focused on quantitative research on the spatial layout characteristics and accessibility of facility construction. There are few studies that combine the distribution characteristics of the child population to evaluate the configuration efficiency of facilities, thereby proposing corresponding research methods and conducting configuration efficiency analysis of community children's public service facilities based on the different distribution characteristics of children in the community. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art and to provide a method for evaluating the configuration efficiency of public service facilities for children in urban communities.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for evaluating the configuration efficiency of public service facilities for children in urban communities, characterized by comprising the following steps:

[0007] Step S1: crawl the network to obtain the POI data points of community children's public service facilities, the basic geographic information data of the community scope and road network, obtain the community children's population data, and import the community vector data into ArcGIS as the community base map;

[0008] Step S2: Based on the community base map, the community child population data and community child public service facility data are processed into point form, and the spatial density analysis of the community child population and community child public service facilities is performed;

[0009] Step S3: Divide public child service facilities into six categories: childcare service facilities, educational facilities, medical and health facilities, child welfare facilities, cultural reading facilities, and comprehensive service facilities. Perform spatial assignment on the POI points of community public child service facilities in ArcGIS to complete the classification process.

[0010] Step S4: The spatial layout characteristics of community children's public service facilities are obtained by kernel density analysis, and the POI data of community children's public service facilities are analyzed for 5 minutes, 10 minutes, and 15 minutes accessibility in combination with the community road network;

[0011] Step S5: Compare the density centers based on the spatial density data of community child population and the spatial density data of community child public service facilities, use the Gini coefficient to analyze the correlation between the community child population and the accessibility data of community child public service facilities, and determine the configuration efficiency evaluation level of community child public service facilities based on the calculated Gini coefficient G.

[0012] Furthermore, the specific steps of step S1 are:

[0013] Step S11: Obtain the AutoNavi interface key code from the AutoNavi open platform, use Anaconda combined with the key code, and obtain the POI data points of community children's public service facilities after selecting the range on the AutoNavi open platform;

[0014] Step S12: using the Tiandi Map geographic information public service platform, searching for and analyzing community groups of the plot, and crawling vector data in the developer tool window;

[0015] Step S13: In OpenMapStreet, search for the corresponding local area network and export the oms data file;

[0016] Step S14: query the number of students enrolled in kindergartens in each community within the community in the past 14 years to determine the population data of children in the community;

[0017] Step S15: Import the community vector data into ArcGIS as the community base map, pull the POI data points of the community children's public service facilities into the community base map, convert the road network data into shp format and load it into the base map.

[0018] Furthermore, the specific steps of step S2 are:

[0019] Step S21: Use Anju.com to crawl community addresses, create a csv file, and add child population density data of each community to the file;

[0020] Step S22: converting the community address field in the csv file into longitude and latitude, with the coordinate system being the WGS1984 geographic coordinate system;

[0021] Step S23: Add the csv file processed in step S22 into ArcGIS and convert the data into a point layer;

[0022] Step S24: exporting the community base map layer as the community child population density layer, using ArcGIS to create a fishing net based on the community child population density layer to form a community child population density fishing net layer;

[0023] Step S25: connect the community child population density fishnet layer and the csv file layer together;

[0024] Step S26: In the community child population density fishing net layer, color classification is performed based on the community child population field to form a gridded community child population density analysis map.

[0025] Furthermore, the specific steps of step S4 are:

[0026] Step S41: Calculate the kernel density of community children's public service facilities and analyze the spatial agglomeration degree of community children's public service facilities;

[0027] Step S42: using Gaussian two-step moving search method to measure the accessibility of community children's public service facilities;

[0028] Step S43: Taking the facility point i as the center, 5 minutes, 10 minutes and 15 minutes are respectively selected as the time thresholds of the search range to calculate the supply-demand ratio Ri of point i;

[0029] Step S44: Taking the time threshold as the search range, calculate the accessibility value Ak of ​​point k in the residential community.

[0030] Furthermore, the specific steps of step S5 are:

[0031] Step S51: combining the community children population grid density analysis map and the community children public service facilities kernel density analysis map, comparing whether the density center positions of the two maps are close to each other;

[0032] Step S52: Based on the number of children in the community and the analysis results of the accessibility of public service facilities for children in the community within 5 minutes, 10 minutes and 15 minutes, a matching analysis is performed using the Gini coefficient.

[0033] Furthermore, the accessibility measurement of community children's public service facilities is analyzed with the community center point determined by the population density dot map as the demand point.

[0034] The beneficial effects of the present invention are as follows: (1) In view of the fact that the scale of previous studies on the efficiency of the configuration of public child service facilities is relatively broad, the present invention focuses on the community level and, through a scientific analysis model, realizes targeted configuration research on public child service facilities in micro-unit communities, thereby improving the reliability and rationality of the research results. (2) Considering that most related studies draw conclusions through one-way supply-side analysis, the present invention is based on huge geographic information data and road network data, combined with screened child population data, and uses an information processing software platform to conduct connection analysis on both the supply and demand sides, thereby improving the objectivity and scientificity of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with the embodiments. Figure 1 shown.

[0037] Step S1: crawl the POI data points of community children's public service facilities on Amap, as well as basic geographic information data such as community scope and road network data, obtain population data from the community party affairs center, and import them into ArcGIS;

[0038] Step S11: Obtain the AutoNavi interface key code in the AutoNavi open platform. Use Anaconda combined with the key code to obtain the POI data points of community children's public service facilities after selecting the range in the AutoNavi open platform.

[0039] Step S12: Use the Tiandi Map geographic information public service platform, select the classic application, search and analyze the community groups of the plot, and then crawl the vector data in the developer tool window of the web page.

[0040] Step S13: In OpenMapStreet, search for the corresponding local area network and export the oms data file.

[0041] Step S14: Confirm the child population data of the community by querying the number of students enrolled in kindergartens in various communities within the community in the past 14 years.

[0042] Step S15: Import the community vector data into ArcGIS as a community map. Pull the POI data points of the community children's public service facilities directly into the base map. Use the Load OSM Flie tool in the ArcGIS toolbox to convert the road network data into shp. format and load them onto the base map.

[0043] Step S2: Combined with the community base map, the community child population data is processed into point form, and the layout density analysis of the community child population is performed through gridding data;

[0044] Step S21: using Anju.com, combined with the xpath syntax in python to parse HTML, loading the code in the Pycharm project, crawling the addresses of each community in the community, making a csv file, and adding fields to increase the child population density of each community in the file;

[0045] Step S22: using the MapLocation address converter, convert the community address field in the Anju.com dataset into longitude and latitude, and the coordinate system is the WGS1984 geographic coordinate system;

[0046] Step S23: Add the processed csv file into ArcGIS, use the projection conversion tool to convert the coordinate system to match the community base map coordinates, and right-click the layer and select Display XY Data to convert the data into a point layer;

[0047] Step S24: export the community base map layer and save it as a population density layer, use the fishing net tool in the ArcGIS toolbox to create a fishing net based on the population density layer to form a population density fishing net layer;

[0048] Step S25: right-click the fishing net layer, select the connection function, and connect the fishing net layer and the csv file layer together through the data of another layer based on the spatial position;

[0049] Step S26: In the fishing net layer properties, the symbol properties are used to perform color classification based on the population field to form a grid population density analysis map. The darker the color, the higher the degree of facility concentration.

[0050] Step S3: Based on children's needs, children's public service facilities are divided into six categories: childcare service facilities, educational facilities, medical and health facilities, children's welfare facilities, cultural reading facilities, and comprehensive service facilities. The POI points of community children's public service facilities are spatially assigned in ArcGIS according to the evaluation matrix results of the AHP hierarchical analysis method to complete the classification process;

[0051] Step S31: In the attribute table of different major categories of POI facility layers, the attribute selection function is used to select the six major categories of children's public service facilities, as shown in Table 1, and the corresponding facility keywords are selected respectively, and the original POI facility data are classified according to the six categories of community children's public service facilities according to the formula of "name" LIKE'% keyword 1%' OR "name" LIKE'% keyword 2%' OR "name" LIKE'% keyword 3%'... OR "name" LIKE'% keyword n%'.

[0052] Table 1 Classification of public service facilities for children

[0053]

[0054] Step S31: Combine local detailed normative documents with development characteristics and expert opinions to obtain the evaluation matrix results in the AHP hierarchical analysis method. In the attribute table of the facility layer, add fields to perform facility classification and value assignment processing based on the results.

[0055] Step S4: The spatial layout characteristics of community children's public service facilities are obtained through the kernel density analysis method. Combined with the community road network, the spatial POI data of community children's public service facilities are analyzed for 5-minute, 10-minute, and 15-minute accessibility through a two-step mobile search method.

[0056] Step S41: The kernel density expression formula is:

[0057]

[0058] Among them, ∫(s) represents the kernel density function, h is the community range, k is the kernel function, n is the number of all facility POI points involved in the kernel density calculation, and a s -a i is the facility sample point a s To sample point a i The spatial distance of the community children's public service facilities was analyzed by the kernel density tool in the ArcGIS toolbox. The darker the color, the higher the degree of facility aggregation.

[0059] Step S42: Use the Gaussian two-step moving search method to measure the accessibility of community children's public service facilities. In this method, the center point of the residential area (population density dot map) should be used as the demand point for analysis;

[0060] Step S43: Taking the facility point i as the center, according to the guidance of the Technical Guidelines for Community Living Circle Planning, 5 minutes, 10 minutes and 15 minutes are substituted into d0 as the time threshold of the search range to calculate the supply-demand ratio R of point i. i The formula is:

[0061]

[0062] Among them, S i Represents the service scale of the facility, D k Represents the size of the community child population within the search range, d ki represents the actual travel time between residential point k and facility point i, g(d ki ,d0) is the population size weight of the residential area k. If the result is larger, it means the utilization rate is lower.

[0063] Step S44: Calculate the accessibility value A of the k points in the residential area according to the determined time threshold as the search rangek The expression formula is:

[0064]

[0065] Among them, R i represents the supply-demand ratio of facility i within the spatial demand range with d0 as the time threshold, d ka represents the actual time required for the residential area center point k to reach the facility point i, g(d ii ,d0) is the weight of the service capacity of facility i. k The larger it is, the better the accessibility of the service at this point is and the stronger the service capacity of the facility is.

[0066] Step S5: Compare the density centers based on the spatial density data of community child population and the spatial density data of community child public service facilities, and use the Gini coefficient to conduct a correlation analysis between the community child population and the three-level community child public service facility accessibility data.

[0067] Step S51: Combine the population grid density analysis diagram and the facility core density analysis diagram to compare whether the density center positions of the two are close to each other.

[0068] Step S52: Based on the number of children in the community, and the results of the accessibility analysis of public service facilities for children in the community within 5 minutes, 10 minutes, and 15 minutes, a matching degree analysis is performed using the Gini coefficient. The formula is as follows:

[0069]

[0070] Among them, p i is the number of children in community i, i is the unit number of each community in the community, S i is the accessibility variable of i spatial facilities, and n is the number of communities in the community. According to the United Nations Development Program, the Gini coefficient is divided into five levels: G≤0.2 is highly average, 0.2<G≤0.3 is relatively average, 0.3<G≤0.4 is relatively reasonable, 0.4<G≤0.6 is a large gap, and 0.6<G is a huge gap.

[0071] The above contents are only used to illustrate the technical solution of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention made by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for evaluating the configuration efficiency of public service facilities for children in urban communities, characterized by: The following steps are involved: Step S1: crawl the network to obtain the POI data points of community children's public service facilities, the basic geographic information data of the community scope and road network, obtain the community children's population data, and import the community vector data into ArcGIS as the community base map; Step S2: Based on the community base map, the community child population data and community child public service facility data are processed into point form, and the spatial density analysis of the community child population and community child public service facilities is performed; Step S3: Divide public child service facilities into six categories: childcare service facilities, educational facilities, medical and health facilities, child welfare facilities, cultural reading facilities, and comprehensive service facilities. Perform spatial assignment on the POI points of community public child service facilities in ArcGIS to complete the classification process. Step S4: The spatial layout characteristics of community children's public service facilities are obtained by kernel density analysis, and the POI data of community children's public service facilities are analyzed for 5 minutes, 10 minutes, and 15 minutes accessibility in combination with the community road network; Step S5: Compare the density centers based on the spatial density data of community child population and the spatial density data of community child public service facilities, use the Gini coefficient to analyze the correlation between the community child population and the accessibility data of community child public service facilities, and determine the configuration efficiency evaluation level of community child public service facilities based on the calculated Gini coefficient G.

2. The method for evaluating the configuration efficiency of public service facilities for children in urban communities according to claim 1 is characterized by: The specific steps of step S1 are: Step S11: Obtain the AutoNavi interface key code from the AutoNavi open platform, use Anaconda combined with the key code, and obtain the POI data points of community children's public service facilities after selecting the range on the AutoNavi open platform; Step S12: using the Tiandi Map geographic information public service platform, searching for and analyzing community groups of the plot, and crawling vector data in the developer tool window; Step S13: In OpenMapStreet, search for the corresponding local area network and export the oms data file; Step S14: query the number of students enrolled in kindergartens in each community within the community in the past 14 years to determine the population data of children in the community; Step S15: Import the community vector data into ArcGIS as the community base map, pull the POI data points of the community children's public service facilities into the community base map, convert the road network data into shp format and load it into the base map.

3. The method for evaluating the configuration efficiency of public service facilities for children in urban communities according to claim 1 is characterized by: The specific steps of step S2 are: Step S21: Use Anju.com to crawl community addresses, create a csv file, and add child population density data of each community to the file; Step S22: converting the community address field in the csv file into longitude and latitude, with the coordinate system being the WGS1984 geographic coordinate system; Step S23: Add the csv file processed in step S22 into ArcGIS and convert the data into a point layer; Step S24: exporting the community base map layer as the community child population density layer, using ArcGIS to create a fishing net based on the community child population density layer to form a community child population density fishing net layer; Step S25: connect the community child population density fishnet layer and the csv file layer together; Step S26: In the community child population density fishing net layer, color classification is performed based on the community child population field to form a gridded community child population density analysis map.

4. The method for evaluating the configuration efficiency of public service facilities for children in urban communities according to claim 1 is characterized by: The specific steps of step S4 are: Step S41: Calculate the kernel density of community children's public service facilities and analyze the spatial agglomeration degree of community children's public service facilities; Step S42: using Gaussian two-step moving search method to measure the accessibility of community children's public service facilities; Step S43: Taking the facility point i as the center, 5 minutes, 10 minutes and 15 minutes are respectively selected as the time thresholds of the search range to calculate the supply-demand ratio Ri of point i; Step S44: Taking the time threshold as the search range, calculate the accessibility value Ak of ​​point k in the residential community.

5. The method for evaluating the configuration efficiency of public service facilities for children in urban communities according to claim 1 is characterized by: The specific steps of step S5 are: Step S51: combining the community children population grid density analysis map and the community children public service facilities kernel density analysis map, comparing whether the density center positions of the two maps are close to each other; Step S52: Based on the number of children in the community and the analysis results of the accessibility of public service facilities for children in the community within 5 minutes, 10 minutes and 15 minutes, a matching analysis is performed using the Gini coefficient.

6. The method for evaluating the configuration efficiency of public service facilities for children in urban communities according to claim 5 is characterized by: The accessibility measurement of community children's public service facilities is analyzed with the community center point determined by the population density dot map as the demand point.

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