Urban green land planning method and device based on green land accessibility evaluation model

Abstract

The invention relates to the technical field of urban planning, and discloses an urban green land planning method and device based on a green land accessibility evaluation model, and the method comprises the steps: constructing a network data set based on green land distribution data, road traffic network data and population distribution data; performing network analysis on the network data set to obtain a greenbelt service range; performing multi-scale kernel density analysis on the network data set to obtain a reachability index of coupling between the green land and the population demand and a reachability index of coupling between the green land and the public transport; constructing a green land accessibility evaluation model based on the green land service range, the green land and population demand coupled accessibility index and the green land and public transport coupled accessibility index; and performing quantitative evaluation on the accessibility of the urban green land by using the green land accessibility evaluation model, and establishing an urban green land planning strategy based on the accessibility evaluation result of the urban green land. According to the method, the reachability of the urban green land is accurately evaluated, and the use efficiency of the urban green land is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, and particularly relates to a city green land planning method and device based on a green land accessibility evaluation model. BACKGROUND

[0002] With the acceleration of urbanization, the contradiction between supply and demand of urban green infrastructure is increasingly prominent, and the related green land planning system excessively relies on static statistical parameters such as green land rate and green coverage rate, which has significant limitations: on the one hand, the mean accounting method based on administrative division is difficult to quantitatively evaluate the spatial differentiation characteristics of green land service, resulting in structural contradictions that residents in local areas cannot reach surrounding green land; on the other hand, the dynamic influence of the traffic network topology structure on the accessibility of green land is not fully considered, resulting in systematic deviation between the green land planning results and the actual recreational needs of residents. SUMMARY

[0003] Therefore, the present application provides a city green land planning method and device based on a green land accessibility evaluation model to solve the problems of low accessibility and use efficiency of green land.

[0004] In a first aspect, the present application provides a city green land planning method based on a green land accessibility evaluation model, which comprises: Collecting green land distribution data, road traffic network data and population distribution data of a target city, and constructing a network data set based on the green land distribution data, the road traffic network data and the population distribution data; Performing network analysis on the network data set to obtain a green land service range; Performing multi-scale kernel density analysis on the network data set to obtain an accessibility index of green land and population demand coupling and an accessibility index of green land and public transport coupling; Constructing a green land accessibility evaluation model based on the green land service range, the accessibility index of green land and population demand coupling and the accessibility index of green land and public transport coupling; Quantitatively evaluating the accessibility of city green land by using the green land accessibility evaluation model, and establishing a city green land planning strategy based on the accessibility evaluation results of city green land.

[0005] The city green land planning method based on the green land accessibility evaluation model provided in this embodiment realizes accurate evaluation of the accessibility of city green land by performing network analysis and multi-scale kernel density analysis on the network data set and integrating the network analysis results and the multi-scale kernel density analysis results, improves the use efficiency of city green land, and provides data support for the optimized layout of city green land.

[0006] In an optional implementation, constructing the network data set based on the green land distribution data, the road traffic network data and the population distribution data comprises: data preprocessing is performed on green land distribution data, road traffic network data and population distribution data; Based on the data preprocessing of green land distribution data, road traffic network data and population distribution data, the time speed resistance, turning restriction and path obstacle of each level road are calibrated, and the network data set is obtained.

[0007] The urban green land planning method based on the green land accessibility evaluation model provided in this embodiment ensures the accuracy and reliability of the data by performing data preprocessing on the green land distribution data, road traffic network data and population distribution data, and provides a solid foundation for subsequent green land accessibility evaluation by setting the time speed resistance, turning restriction and path obstacle of each level road.

[0008] In an optional implementation, network analysis is performed on the network data set to obtain the green land service range, including: determining the green land entrance, vertex and each traffic mode based on the network data set; Taking the green land entrance as the starting point, the vertex set that can be reached through each traffic mode within each time threshold is calculated to obtain the isochrone under multiple traffic modes; Based on the isochrone under multiple traffic modes, the number of green lands covered and the green land area range within each time threshold are determined, and the green land service range is determined based on the number of green lands covered and the green land area range within each time threshold.

[0009] The urban green land planning method based on the green land accessibility evaluation model provided in this embodiment realizes accurate calculation of the area that can be reached within different time thresholds by calculating the vertex set that can be reached through each traffic mode within each time threshold, and realizes precise calculation of the green land service range by calculating the number of green lands covered and the green land area range within each time threshold, thereby providing a solid foundation for subsequent green land accessibility evaluation.

[0010] In an optional implementation, multi-scale kernel density analysis is performed on the network data set to obtain the accessibility index of green land and population demand coupling and the accessibility index of green land and public transportation coupling, including: Kernel density analysis is performed on the green land distribution, population distribution and public transportation station distribution in the network data set to obtain the green land spatial distribution density, population spatial distribution density and public transportation spatial distribution density; Superimposed analysis is performed on the green land spatial distribution density and the population spatial distribution density to determine the accessibility index of green land and population demand coupling; Superimposed analysis is performed on the green land spatial distribution density and the public transportation spatial distribution density to determine the accessibility index of green land and public transportation coupling.

[0011] The embodiment provides a city green land planning method based on a green land accessibility evaluation model, the influence of traffic network complexity and resident travel mode diversity on city green land planning is comprehensively considered by performing superposition analysis on green land space distribution density and population space distribution density and superposition analysis on green land space distribution density and public transport space distribution density, so that the accessibility evaluation result of the city green land is more comprehensive and accurate, the actual accessibility situation can be reflected, and the accessibility and use efficiency of the green land are improved.

[0012] In an optional implementation, the accessibility of the city green land is quantitatively evaluated by using the green land accessibility evaluation model, and a city green land planning strategy is established based on the accessibility evaluation result of the city green land, and the city green land planning strategy comprises the following steps. The service capability of the city green land is evaluated by using the green land accessibility evaluation model, and the green land accessibility score of the target city is obtained. The population density of the target city is obtained, and the supply-demand imbalance degree of the target city is calculated based on the green land accessibility score of the target city and the population density of the target city. The city green land planning is optimized based on the supply-demand imbalance degree of the target city, and the city green land planning strategy is obtained.

[0013] The embodiment provides a city green land planning method based on a green land accessibility evaluation model, the spatio-temporal allocation efficiency of the green land resources is quantified by calculating the supply-demand imbalance degree of the target city, and then the city green land planning is optimized based on the supply-demand imbalance degree of the target city, so as to provide a reference for the design of the city public green land and the optimal layout of the city green land in city planning, and provide technical support for constructing a fair and efficient city green land ecological system.

[0014] In an optional implementation, the city green land planning is optimized based on the supply-demand imbalance degree of the target city, and the city green land planning strategy is obtained, and the city green land planning strategy comprises the following steps. The correction coefficient and the total length of the missing path are obtained, the length of the newly added walking path is determined based on the supply-demand imbalance degree of the target city, the correction coefficient and the total length of the missing path. The volume rate adjustment factor and the per capita green land standard data are obtained, and the newly added green land area is determined based on the supply-demand imbalance degree of the target city, the volume rate adjustment factor and the per capita green land standard data. The city green land planning is optimized based on the length of the newly added walking path and the newly added green land area, and the city green land planning strategy is obtained.

[0015] The embodiment provides a city green land planning method based on a green land accessibility evaluation model, the length of the newly added walking path and the newly added green land area are determined, the green land layout is accurately optimized, the city green land planning strategy is implemented, and the ecological value conversion efficiency and social service efficiency of the green land resources are significantly improved.

[0016] In a second aspect, the present application provides a device for urban green space planning based on a green space accessibility evaluation model, which comprises: a collection module configured to collect green space distribution data, road traffic network data and population distribution data of a target city, and to construct a network data set based on the green space distribution data, the road traffic network data and the population distribution data; a first analysis module configured to perform network analysis on the network data set to obtain a green space service range; a second analysis module configured to perform multi-scale kernel density analysis on the network data set to obtain a green space and population demand coupling accessibility index and a green space and public transport coupling accessibility index; a construction module configured to construct a green space accessibility evaluation model based on the green space service range, the green space and population demand coupling accessibility index and the green space and public transport coupling accessibility index; an evaluation module configured to quantitatively evaluate the accessibility of urban green space by using the green space accessibility evaluation model, and to establish an urban green space planning strategy based on the evaluation result of the accessibility of urban green space.

[0017] In a third aspect, the present application provides a computer device, which comprises a memory and a processor, the memory and the processor are communicatively connected with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for urban green space planning based on a green space accessibility evaluation model according to the first aspect or any one of the corresponding embodiments thereof.

[0018] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer execute the method for urban green space planning based on a green space accessibility evaluation model according to the first aspect or any one of the corresponding embodiments thereof.

[0019] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions, and the computer instructions are used to make a computer execute the method for urban green space planning based on a green space accessibility evaluation model according to the first aspect or any one of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the description of the specific embodiments or the prior art will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0021] Figure 1is a flowchart of a city green space planning method based on a green space accessibility evaluation model according to an embodiment of the present application; Figure 2 is a flowchart of another city green space planning method based on a green space accessibility evaluation model according to an embodiment of the present application; Figure 3 is a flowchart of yet another city green space planning method based on a green space accessibility evaluation model according to an embodiment of the present application; Figure 4 is a flowchart of still another city green space planning method based on a green space accessibility evaluation model according to an embodiment of the present application; Figure 5 is a flowchart of a city green space planning method based on a green space accessibility evaluation system according to an embodiment of the present application; Figure 6 is a structural block diagram of a city green space planning device based on a green space accessibility evaluation model according to an embodiment of the present application; Figure 7 is a hardware structure schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0023] The related city green space accessibility evaluation methods have the following problems: 1) Lack of green space accessibility evaluation considering traffic factors: mostly based on straight-line distance or simple buffer analysis, unable to accurately reflect the actual green space accessibility under different traffic conditions.

[0024] 2) Evaluation results are disconnected from city planning decisions: the green space accessibility evaluation results are not effectively integrated into the city planning decision-making process, leading to unreasonable allocation of green space resources.

[0025] 3) Focus on single-dimensional analysis, without considering factors such as traffic network complexity and resident travel mode diversity in the evaluation process, resulting in inaccurate evaluation results and difficulty in reflecting the actual accessibility situation.

[0026] 4) Lack of optimization suggestions for evaluation results, unable to effectively guide city planning.

[0027] In order to more scientifically guide urban green space planning, improve the accessibility and use efficiency of green space, a green space planning method based on green space accessibility evaluation model is needed.

[0028] The embodiment of the present application provides a green space planning method based on a green space accessibility evaluation model, which breaks through the related urban planning paradigm by integrating the ArcGIS network analysis extension module and the multi-scale kernel density analysis method, establishing a multi-objective optimization decision green space accessibility evaluation model through traffic network impedance analysis (time cost, distance decay, transfer convenience) and population demand superposition analysis, and realizing the paradigm shift from "index orientation" to "efficiency orientation", thereby providing a reference for the design of urban public green space and the optimal layout of the urban public green space in urban planning, and providing technical support for building a fair and efficient urban green space ecological system.

[0029] The embodiment of the present application provides a green space planning method based on a green space accessibility evaluation model, and it should be noted that the green space planning method based on the green space accessibility evaluation model provided in the embodiment of the present application can be a green space planning device based on the green space accessibility evaluation model, which can be realized as part or all of an electronic device in the form of software, hardware or a combination of software and hardware, wherein the electronic device can be a server or a terminal, wherein the server in the embodiment of the present application can be a server or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, a wearable device, a smart robot and other smart hardware devices. In the following method embodiment, the execution subject is taken as an example to be described.

[0030] According to the embodiment of the present application, a green space planning method based on a green space accessibility evaluation model is provided, and it should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In the embodiment, a green space planning method based on a green space accessibility evaluation model is provided, which can be used in the above-mentioned electronic device, Figure 1 is a flowchart of a green space planning method based on a green space accessibility evaluation model according to the embodiment of the present application, as Figure 1 shown, the flow includes the following steps: Step S101, collect green space distribution data, road traffic network data and population distribution data of the target city, and construct a network data set based on the green space distribution data, the road traffic network data and the population distribution data.

[0032] Specifically, the basic data such as city green space distribution map data, road traffic network data (including trunk roads, secondary roads, branch roads, sidewalks, etc.) and population distribution data are obtained by satellite remote sensing, point of interest (POI) data grabbing and the like.

[0033] Further, the green space distribution data includes spatial distribution data of target area city parks, country parks and community green spaces, including park boundaries, entrance locations, areas and the like, wherein the green space distribution data is in SHP format (a file storage method).

[0034] Further, the population distribution data is obtained by integrating the population density data of each region published by the target area government statistics office, and is used for analyzing the green space demand distribution.

[0035] Further, the road traffic network data is obtained by collecting the location information of subway stations and bus stops, and is used for analyzing the influence of public transportation on the accessibility of green spaces.

[0036] Further, a network dataset is created in ArcGIS (a comprehensive geographic spatial platform for professionals and organizations).

[0037] In step S102, network analysis is performed on the network dataset to obtain the green space service range.

[0038] In step S103, multi-scale kernel density analysis is performed on the network dataset to obtain the accessibility index of the coupling of green space and population demand and the accessibility index of the coupling of green space and public transportation.

[0039] In step S104, a green space accessibility evaluation model is constructed based on the green space service range, the accessibility index of the coupling of green space and population demand and the accessibility index of the coupling of green space and public transportation.

[0040] In step S105, the green space accessibility evaluation model is used to quantitatively evaluate the accessibility of city green spaces, and city green space planning strategies are established based on the evaluation results of the accessibility of city green spaces.

[0041] The city green space planning method based on the green space accessibility evaluation model provided in this embodiment realizes accurate evaluation of the accessibility of city green spaces by performing network analysis and multi-scale kernel density analysis on the network dataset and integrating the network analysis results and the multi-scale kernel density analysis results, improves the use efficiency of city green spaces, and provides data support for the optimal layout of city green spaces.

[0042] In this embodiment, a city green space planning method based on a green space accessibility evaluation model is provided, which can be used in the electronic device described above, Figure 2is a flowchart of a city green space planning method based on a green space accessibility evaluation model according to an embodiment of the present application, as shown in Figure 2 The flowchart includes the following steps: Step S201, collect green space distribution data, road traffic network data and population distribution data of a target city, and construct a network data set based on the green space distribution data, the road traffic network data and the population distribution data.

[0043] Specifically, the above step S201 includes: Step S2011, data preprocessing is performed on the green space distribution data, the road traffic network data and the population distribution data.

[0044] Specifically, the data preprocessing includes data cleaning, topology checking and coordinate system unification, wherein the data cleaning includes checking the connectivity of the road network data, repairing broken road segments, removing redundant data, and ensuring the accuracy of network analysis; the topology checking includes checking and correcting logical errors (such as hanging nodes, overlapping line segments, etc.) of the road network data using the topology tool (Topology Checker) in ArcGIS; the coordinate system unification includes format conversion, coordinate unification, etc. on the collected data.

[0045] Further, distributed computing technology is used to improve data processing efficiency and ensure that the data can meet the needs of subsequent analysis.

[0046] Step S2012, based on the green space distribution data, the road traffic network data and the population distribution data after data preprocessing, the time speed impedance, the turning restriction and the path obstacle of each grade road are calibrated to obtain the network data set.

[0047] Specifically, the time speed impedance of each grade road is to take time (minute) as the main impedance, and set different speeds according to the traffic mode (such as walking, cycling, public transportation); wherein the total travel time corresponding to the traffic mode in each grade road is taken as the time speed impedance of each grade road, and the composite impedance (i.e. time speed impedance) of walking, cycling and public transportation (including transfer time) is constructed, and the calculation formula is as follows: (1) In the above formula, is the total travel time (min), is the length of the road segment (km), is the traffic mode speed (km / h) of the road segment, is the transfer penalty coefficient, the transfer penalty coefficient of public transportation is 0.5, is the transfer time, such as the walking waiting time (min) of taking subway to bus.

[0048] Further, the turning restriction considers special paths such as overpasses, tunnels, and pedestrian crossings, and avoids generating unreasonable detour routes.

[0049] Further, the path obstacle is a marker for an impassable area (such as water, mountains, etc.), ensuring that the path planning conforms to the actual situation.

[0050] Step S202, network analysis is performed on the network data set to obtain the green space service range. For details, please refer to Figure 1 Step S102 of the embodiment shown in the figure will not be repeated here.

[0051] Step S203, multi-scale kernel density analysis is performed on the network data set to obtain the accessibility index of the coupling of green space and population demand and the accessibility index of the coupling of green space and public transportation. For details, please refer to Figure 1 Step S103 of the embodiment shown in the figure will not be repeated here.

[0052] Step S204, a green space accessibility evaluation model is constructed based on the green space service range, the accessibility index of the coupling of green space and population demand, and the accessibility index of the coupling of green space and public transportation. For details, please refer to Figure 1 Step S104 of the embodiment shown in the figure will not be repeated here.

[0053] Step S205, the accessibility of the urban green space is quantitatively evaluated using the green space accessibility evaluation model, and the urban green space planning strategy is established based on the accessibility evaluation result of the urban green space. For details, please refer to Figure 1 Step S105 of the embodiment shown in the figure will not be repeated here.

[0054] The urban green space planning method based on the green space accessibility evaluation model provided in this embodiment ensures the accuracy and reliability of the data by performing data preprocessing on the green space distribution data, road traffic network data, and population distribution data, and provides a solid foundation for subsequent green space accessibility evaluation by setting the speed impedance of each level of road, the turning restriction, and the path obstacle.

[0055] In this embodiment, an urban green space planning method based on a green space accessibility evaluation model is provided, which can be used in the electronic device described above, Figure 3 is a flowchart of an urban green space planning method based on a green space accessibility evaluation model according to an embodiment of the present application, as shown in the figure, the flow includes the following steps: Figure 3 The flow includes the following steps: Step S301, collect the green space distribution data, road traffic network data, and population distribution data of the target city, and construct a network data set based on the green space distribution data, road traffic network data, and population distribution data. For details, please refer to Figure 2 Step S201 of the embodiment shown in the figure will not be repeated here.

[0056] Step S302, network analysis is performed on the network dataset to obtain the green space service range.

[0057] Specifically, the above step S302 includes: Step S3021, determining the green space entrances, vertices and traffic modes based on the network dataset.

[0058] Specifically, the green space entrances, vertices and traffic modes are determined based on the network dataset. (ensuring that at least 1-2 main entrances are set for each green space), vertices (including intersections, bus stops, subway stations, shared bicycle parking points, etc.) and traffic modes (including various transportation modes), and based on the network dataset (including speed impedance, turning restrictions, etc.), the speed for each traffic mode is set.

[0059] Step S3022, taking the green space entrances as the starting point, calculating the vertex set that can be reached by each traffic mode within each time threshold to obtain the isochrone under multi-traffic modes.

[0060] Specifically, taking the green space (such as a park) entrance as the facility point, different time thresholds (such as 5, 8, 11,..., 29 minutes) are set according to the "15-minute life circle" regulation.

[0061] Further, the path search is performed using the service area analysis tool of ArcGIS: taking the green space entrance as the starting point, calculating all vertices (intersections, bus stops, etc.) that can be reached by the traffic mode within the time , wherein the calculation formula of the path time is as follows: (2) (3) In the above formula, is the time of the path, is the total time cost required to pass through a road segment e in the traffic network, is the actual physical length of the road segment e, is the time lost for waiting for public transportation to arrive.

[0062] Further, all vertices that satisfy are merged to form a polygon service area .

[0063] Further, for the same time threshold , the service area of all traffic modes , the isochrone under multi-traffic mode can be expressed as: (4) Where, the isochrone under multi-traffic mode shows the area that can be reached within different time thresholds.

[0064] Further, for each time threshold , collect all that satisfy the following conditions: (5) In the above formula, represents the set of all vertices (or locations) that can be reached within the time threshold from the green space entry point by different traffic network modes (walking, cycling, bus, etc.).

[0065] Step S3023, determine the number of green spaces and the range of green space area that can be covered within each time threshold based on the isochrone under multi-traffic mode, and determine the green space service range based on the number of green spaces and the range of green space area that can be covered within each time threshold.

[0066] Specifically, use spatial join (Spatial Join) to superimpose with other green space boundary layers, and count the number of green spaces contained in the service area. The calculation formula of the number of green spaces that can be covered is as follows: (6) Further, use intersect analysis (Intersect) to calculate the overlapping area of and green space polygons, and aggregate to obtain the total coverage area. The calculation formula of the range of green space area that can be covered is as follows: (7) Further, determine the green space service range according to the number or area range of green spaces that can be covered within different time thresholds.

[0067] Step S303, perform multi-scale kernel density analysis on the network dataset to obtain the accessibility index of green space coupled with population demand and the accessibility index of green space coupled with public transportation.

[0068] Specifically, the above step S303 includes: ​Step S3031: Perform kernel density analysis on the distribution of green space, population, and public transportation stations in the network dataset to obtain the spatial distribution density of green space, population, and public transportation.

[0069] Specifically, kernel density analysis is used to calculate the spatial clustering of green space distribution: input the center point or boundary of the green space (depending on the analysis requirements); set the search radius (Bandwidth): which can be adjusted according to urban density (e.g., 500 meters); set the output cell size (Cell Size), such as 30 meters (for high-precision analysis); the granularity of raster data within the study area is adopted. , computing facility point density at The calculation formula is as follows: (8) In the above formula, This refers to the bandwidth (i.e., the search radius). The number of sample points existing within the bandwidth range. For facility points To the The Euclidean distance between the green spaces This is the Gaussian kernel function.

[0070] Among them, bandwidth The calculation formula is: (9) In the above formula, sd is the standard distance. This is the median distance.

[0071] facility points To the Euclidean distance of the green space The calculation formula is: (10) Gaussian kernel function The calculation formula is: (11) Furthermore, based on facility points density at A heat map of green space density is generated, with high-value areas indicating dense parks and low-value areas indicating a lack of green space.

[0072] Furthermore, kernel density analysis was performed on population distribution and public transportation stations (such as subway stations and bus stops) to obtain the spatial distribution density of population and public transportation. The kernel density analysis method is the same as that used for green space distribution analysis.

[0073] Step S3032: Overlay analysis of green space spatial distribution density and population spatial distribution density to determine the accessibility index of green space coupled with population demand.

[0074] Specifically, the green space density and population density layers are overlaid (with 30% transparency) to analyze their spatial matching relationship: the spatial distribution density of green space and population is calculated in ArcGIS, and then reclassified using the Reclassify function in ArcGIS; the reclassification results are converted from raster data to vector data, and the Intersect function is used to perform surface intersection; the formula for calculating the accessibility index of green space and population demand coupling is as follows: (12) If the accessibility index is greater than 1, then the green space is relatively abundant and the accessibility index is high; if the accessibility index is less than 1, then the green space is relatively insufficient and the accessibility index is low.

[0075] Step S3033: Overlay analysis of green space spatial distribution density and public transportation spatial distribution density to determine the accessibility index of green space and public transportation coupling.

[0076] Specifically, the green space density and public transport density layers are overlaid (with transparency set to 30%) to analyze their spatial matching relationship.

[0077] Furthermore, define public transportation stations green space Contribution : (13) In the above formula, For public transportation stations To the green space Walking distance (meters) The attenuation coefficient is 0.001.

[0078] Among them, when In this case, public transportation stops overlap with green spaces. At this time, the contribution is the greatest; when ,but At this point, the contribution is halved.

[0079] Step S304: Construct a green space accessibility evaluation model based on the green space service area, the accessibility index of green space coupled with population demand, and the accessibility index of green space coupled with public transportation. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0080] Step S305, the accessibility of the urban green land is quantitatively evaluated by using the green land accessibility evaluation model, and urban green land planning strategies are established based on the accessibility evaluation results of the urban green land. For details, please refer to Figure 2 Step S205 of the embodiment shown will not be repeated here.

[0081] The urban green land planning method based on the green land accessibility evaluation model provided in this embodiment realizes accurate calculation of the areas that can be reached within different time thresholds by calculating the vertex set that can be reached within each time threshold through each traffic mode, and realizes accurate calculation of the service range of the green land by calculating the number and area range of the green land that can be covered within each time threshold, thereby providing a solid foundation for subsequent green land accessibility evaluation. Secondly, by superimposing and analyzing the green land spatial distribution density and the population spatial distribution density, as well as the green land spatial distribution density and the public transportation spatial distribution density, the influence of the complexity of the traffic network and the diversity of the travel mode of the residents on the urban green land planning is comprehensively considered, so that the accessibility evaluation results of the urban green land are more comprehensive and accurate, and can reflect the actual accessibility situation, thereby improving the accessibility and use efficiency of the green land.

[0082] In this embodiment, an urban green land planning method based on a green land accessibility evaluation model is provided, which can be used in the electronic device described above, Figure 4 is a flowchart of an urban green land planning method based on a green land accessibility evaluation model according to an embodiment of the present application, as shown in the figure, the flowchart includes the following steps: Figure 4 Step S401, the green land distribution data, road traffic network data and population distribution data of the target city are collected, and a network data set is constructed based on the green land distribution data, road traffic network data and population distribution data. For details, please refer to Figure 3 Step S301 of the embodiment shown will not be repeated here.

[0083] Step S402, network analysis is performed on the network data set to obtain the service range of the green land. For details, please refer to Figure 3 Step S302 of the embodiment shown will not be repeated here.

[0084] Step S403, multi-scale kernel density analysis is performed on the network data set to obtain the accessibility index of the coupling of the green land and population demand and the accessibility index of the coupling of the green land and public transportation. For details, please refer to Figure 3 Step S303 of the embodiment shown will not be repeated here.

[0085] Step S404, a green land accessibility evaluation model is constructed based on the service range of the green land, the accessibility index of the coupling of the green land and population demand, and the accessibility index of the coupling of the green land and public transportation.

[0086] ​Specifically, the weights of the multi-modal transportation and the weights of the time threshold are defined based on the speed impedance in the network dataset, and the service range of the green space, the accessibility index of the coupling of the green space and the population demand, and the accessibility index of the coupling of the green space and the public transportation are used to determine the number of population / POIs that can be covered by each transportation mode within the time threshold from the facility point , and the number of demand points that can be covered by each transportation mode within the time threshold .

[0087] Further, the service capacity of the urban green space is evaluated by comprehensively considering multi-transportation modes (walking, bus, subway, cycling, etc.), and the weighted accessibility score under the multi-modal transportation (i.e., the green space accessibility evaluation model) can be expressed as: (14) In the above formula, is the comprehensive accessibility score (which can be normalized to [0, 1]) of the facility point ; is the weight of the transportation mode , which can be set based on the transportation efficiency and the sharing rate, and the weight is different for different cities, for example, the weight of cycling is significantly reduced in mountainous cities compared to plain cities; is the weight of the time threshold (e.g., the weight of 5 minutes is 1.0, and the weight of 15 minutes is 0.5); is the balance coefficient of the population and the demand point (default is 0.5); is the number of population / POIs that can be covered by the transportation mode from the facility point within the time threshold (i.e., the proportion of the population living within the service range of the green space); is the number of demand points (such as green space and park) that can be covered by the transportation mode within the time threshold .

[0088] Step S405, the accessibility of the urban green space is quantitatively evaluated by using the green space accessibility evaluation model, and the urban green space planning strategy is established based on the accessibility evaluation result of the urban green space.

[0089] Specifically, the above step S405 includes: Step S4051, the service capacity of the urban green space is evaluated by using the green space accessibility evaluation model, and the green space accessibility score of the target city is obtained.

[0090] Further, the green space accessibility score of the target city is calculated by using the above formula (14), and the green space accessibility score of the target city is normalized, which can be expressed as:​ (15) In the above formula, denotes the normalized facility point comprehensive accessibility score, min(A) denotes the minimum green space accessibility score, and max(A) denotes the maximum green space accessibility score.

[0091] Further, The closer to 1, the better the green space accessibility of the city.

[0092] Step S4052, obtaining the population density of the target city, and calculating the supply-demand imbalance degree of the target city based on the green space accessibility score of the target city and the population density of the target city.

[0093] Specifically, superimpose the population density data to identify high-demand low-supply areas (such as densely populated residential areas with insufficient green space), and prioritize planning for new green space; wherein the population density data can be directly obtained, or can be determined according to the spatial matching relationship between the green space density and the population density.

[0094] Further, the calculation formula of the supply-demand imbalance degree of the target city / region is: (16) In the above formula, is the population density of the target city / region , and is the green space accessibility score (full score 1.0) of the target city / region .

[0095] Further, if , the demand is higher than the supply (which needs to be optimized first); if , the supply is sufficient or the demand is low.

[0096] Step S4053, optimizing the city green space planning based on the supply-demand imbalance degree of the target city to obtain the city green space planning strategy.

[0097] In some optional embodiments, the above step S4053 comprises: Step a1, obtaining a correction coefficient and a total length of missing paths, and determining the length of the new sidewalk based on the supply-demand imbalance degree of the target city, the correction coefficient and the total length of the missing paths.

[0098] Specifically, the length of the new sidewalk needs to meet: (17) In the above formula, is the correction coefficient (valued according to government reports), Total length of broken link / missing path (m) calculated by the "Missing Links" tool in ArcGIS network analysis.

[0099] Step a2, obtain the plot ratio adjustment factor and the per capita green space standard data, and determine the newly added green space area based on the supply-demand imbalance degree of the target city, the plot ratio adjustment factor and the per capita green space standard data.

[0100] Specifically, the newly added green space area According to the demand allocation: (18) In the above formula, is the plot ratio adjustment factor.

[0101] Step a3, based on the newly added sidewalk length and the newly added green space area, optimize the city green space planning, and obtain the city green space planning strategy.

[0102] The city green space planning method based on the green space accessibility evaluation model provided in this embodiment quantifies the spatio-temporal allocation efficiency of green space resources by calculating the supply-demand imbalance degree of the target city, and then optimizes the city green space planning based on the supply-demand imbalance degree of the target city, thereby providing a reference for the design of city public green space and the optimal layout of city public green space in city planning, and providing technical support for building a fair and efficient city green space ecological system.

[0103] The specific steps of the city green space planning method based on the green space accessibility evaluation model will be described below through a specific embodiment.

[0104] Embodiment 1: As Figure 5 shown, the specific steps of the city green space planning method based on the green space accessibility evaluation model include: 1) Data acquisition module: integrate remote sensing images, traffic big data, population heat map and POI (Point of Interest) data, and obtain and collect basic data such as city green space distribution data, road traffic network data and population distribution data.

[0105] 2) Network analysis module: build a road network dataset based on the ArcGIS platform, which contains a comprehensive traffic network dataset of four travel modes: walking, cycling, bus / subway and private car, accurately calibrate the speed impedance of roads of different grades, and calculate the green space service area with time impedance as the index.

[0106] 3) Kernel density analysis module: calculate the spatial distribution density of green space and public transportation sites, and analyze the spatial correlation between the two.

[0107] 4) Accessibility comprehensive evaluation module: integrate the results of network analysis and kernel density analysis, build a green space accessibility evaluation system model, and quantitatively evaluate the accessibility of city green space.

[0108] 5) Planning Optimization Suggestion Module: Based on the accessibility evaluation results, suggestions for traffic improvement and optimization of green space layout are proposed.

[0109] In the above embodiment 1, by quantifying the spatiotemporal allocation efficiency of green space resources, the pain point of traditional planning "emphasizing scale over efficiency" is innovatively solved, providing precise decision support for building a fair, efficient and resilient urban green space system, and significantly improving the ecological value transformation efficiency and social service efficiency of green space resources.

[0110] This embodiment also provides an urban green space planning device based on a green space accessibility evaluation model. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0111] This embodiment provides an urban green space planning device based on a green space accessibility evaluation model, such as... Figure 6 As shown, it includes: The data acquisition module 601 is used to collect green space distribution data, road traffic network data, and population distribution data of the target city, and to construct a network dataset based on the green space distribution data, road traffic network data, and population distribution data. The first analysis module 602 is used to perform network analysis on the network dataset to obtain the service area of ​​the green space. The second analysis module 603 is used to perform multi-scale kernel density analysis on the network dataset to obtain the accessibility index of green space coupled with population demand and the accessibility index of green space coupled with public transportation. Module 604 is used to construct a green space accessibility evaluation model based on the green space service area, the accessibility index of green space coupled with population demand, and the accessibility index of green space coupled with public transportation. Evaluation module 605 is used to quantitatively evaluate the accessibility of urban green spaces using a green space accessibility evaluation model, and to establish urban green space planning strategies based on the evaluation results.

[0112] In some alternative implementations, the acquisition module 601 includes: The preprocessing unit is used to preprocess data on green space distribution, road traffic network, and population distribution. The calibration unit is used to calibrate the speed impedance, steering restrictions, and path obstacles of roads of various levels based on the preprocessed green space distribution data, road traffic network data, and population distribution data, thereby obtaining a network dataset.

[0113] In some alternative implementations, the first analysis module 602 includes: The first determining unit is used to determine the green space entrance, vertices, and various traffic modes based on the network dataset; The first calculation unit is used to take the green space entrance as the starting point, calculate the set of vertices that can be reached by each traffic mode within each time threshold, and obtain the isochronous circle under multiple traffic modes. The second determining unit is used to determine the number of green spaces and the range of green space area that can be covered within each time threshold based on the isochronous circles under multiple traffic modes, and to determine the green space service area based on the number of green spaces and the range of green space area that can be covered within each time threshold.

[0114] In some alternative implementations, the second analysis module 603 includes: The kernel density analysis unit is used to perform kernel density analysis on the distribution of green space, population, and public transportation stations in the network dataset, respectively, to obtain the spatial distribution density of green space, population, and public transportation. The first overlay analysis unit is used to overlay the spatial distribution density of green space and the spatial distribution density of population to determine the accessibility index of the coupling between green space and population demand. The second overlay analysis unit is used to overlay the spatial distribution density of green space and the spatial distribution density of public transportation to determine the accessibility index of the coupling between green space and public transportation.

[0115] In some alternative implementations, the evaluation module 605 includes: The assessment unit is used to evaluate the service capacity of urban green spaces using a green space accessibility assessment model, and to obtain a green space accessibility score for the target city. The second calculation unit is used to obtain the population density of the target city and calculate the supply-demand imbalance of the target city based on the green space accessibility score and the population density of the target city. The optimization unit is used to optimize urban green space planning based on the supply and demand imbalance of the target city, and to obtain urban green space planning strategies.

[0116] In some optional implementations, the optimization unit includes: The first determined sub-unit is used to obtain the correction coefficient and the total length of missing paths, and to determine the length of the new pedestrian path based on the supply and demand imbalance of the target city, the correction coefficient and the total length of missing paths; The second determination sub-unit is used to obtain the plot ratio adjustment factor and per capita green space standard data, and to determine the area of ​​newly added green space based on the supply and demand imbalance, plot ratio adjustment factor and per capita green space standard data of the target city. The optimization sub-unit is used to optimize urban green space planning based on the length of newly added pedestrian walkways and the area of ​​newly added green space, so as to obtain urban green space planning strategies.

[0117] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0118] In this embodiment, the urban green space planning device based on the green space accessibility evaluation model is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0119] This invention also provides a computer device having the above-described features. Figure 6 The urban green space planning device shown is based on a green space accessibility evaluation model.

[0120] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0121] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0122] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0123] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0125] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0126] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0127] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0128] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0129] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for urban green space planning based on a green space accessibility evaluation model, characterized in that, The method includes: Collect green space distribution data, road traffic network data, and population distribution data of the target city, and construct a network dataset based on the green space distribution data, road traffic network data, and population distribution data; Network analysis is performed on the network dataset to obtain the service area of ​​the green space; Multi-scale kernel density analysis was performed on the network dataset to obtain the accessibility index of green space coupled with population demand and the accessibility index of green space coupled with public transportation. A green space accessibility evaluation model is constructed based on the green space service area, the accessibility index of green space coupled with population demand, and the accessibility index of green space coupled with public transportation. The accessibility evaluation model of green space is used to quantitatively evaluate the accessibility of urban green space, and urban green space planning strategies are established based on the evaluation results.

2. The method according to claim 1, characterized in that, The construction of the network dataset based on the green space distribution data, the road traffic network data, and the population distribution data includes: Data preprocessing is performed on the green space distribution data, the road traffic network data, and the population distribution data; Based on the preprocessed green space distribution data, road traffic network data, and population distribution data, the speed impedance, steering restrictions, and path obstacles of roads of each level are calibrated to obtain the network dataset.

3. The method according to claim 1, characterized in that, The process of performing network analysis on the network dataset to obtain the green space service area includes: Based on the aforementioned network dataset, the green space entrance, vertices, and various traffic modes are determined; Taking the green space entrance as the starting point, calculate the set of vertices that can be reached by each traffic mode within each time threshold to obtain the isochronous circle under multiple traffic modes. Based on the isochronous circles under the multi-traffic modes, the number of green spaces and the range of green space area that can be covered within each time threshold are determined, and the green space service area is determined based on the number of green spaces and the range of green space area that can be covered within each time threshold.

4. The method according to claim 1, characterized in that, The multi-scale kernel density analysis of the network dataset yields the accessibility index of green space coupled with population demand and the accessibility index of green space coupled with public transportation, including: Kernel density analysis was performed on the distribution of green space, population, and public transportation stops in the network dataset to obtain the spatial distribution density of green space, population, and public transportation. By overlaying the spatial distribution density of green space and the spatial distribution density of population, an accessibility index for the coupling between green space and population demand is determined. By overlaying the spatial distribution density of green space and the spatial distribution density of public transportation, the accessibility index of the coupling between green space and public transportation is determined.

5. The method according to claim 1, characterized in that, The process of using the green space accessibility evaluation model to quantitatively evaluate the accessibility of urban green spaces, and establishing urban green space planning strategies based on the evaluation results, includes: The service capacity of urban green spaces is evaluated using the green space accessibility evaluation model to obtain the green space accessibility score of the target city. Obtain the population density of the target city, and calculate the supply-demand imbalance of the target city based on the green space accessibility score and the population density of the target city; The urban green space planning is optimized based on the supply and demand imbalance of the target city to obtain the urban green space planning strategy.

6. The method according to claim 5, characterized in that, The optimization of urban green space planning based on the supply-demand imbalance of the target city yields the urban green space planning strategy, including: Obtain the correction coefficient and the total length of missing paths, and determine the length of the new pedestrian path based on the supply and demand imbalance of the target city, the correction coefficient, and the total length of missing paths; Obtain plot ratio adjustment factor and per capita green space standard data, and determine the area of ​​newly added green space based on the supply and demand imbalance of the target city, the plot ratio adjustment factor and per capita green space standard data; The urban green space planning is optimized based on the length of the newly added pedestrian walkway and the area of ​​the newly added green space to obtain the urban green space planning strategy.

7. An urban green space planning device based on a green space accessibility evaluation model, characterized in that, The device includes: The data acquisition module is used to collect green space distribution data, road traffic network data, and population distribution data of the target city, and to construct a network dataset based on the green space distribution data, the road traffic network data, and the population distribution data. The first analysis module is used to perform network analysis on the network dataset to obtain the green space service area; The second analysis module is used to perform multi-scale kernel density analysis on the network dataset to obtain the accessibility index of green space coupled with population demand and the accessibility index of green space coupled with public transportation. A construction module is used to construct a green space accessibility evaluation model based on the green space service area, the accessibility index of the green space coupled with population demand, and the accessibility index of the green space coupled with public transportation. The evaluation module is used to quantitatively evaluate the accessibility of urban green spaces using the green space accessibility evaluation model, and to establish urban green space planning strategies based on the urban green space accessibility evaluation results.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the urban green space planning method based on the green space accessibility evaluation model as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the urban green space planning method based on the green space accessibility evaluation model as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the urban green space planning method based on the green space accessibility evaluation model as described in any one of claims 1 to 6.

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