CN115271549B - Construction method of urban park space quality comprehensive evaluation index system - Google Patents
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Abstract
The invention discloses a construction method of an urban park space quality comprehensive evaluation index system, which belongs to the technical field of municipal construction and comprises the following steps of; constructing an urban park substance environment space quality evaluation index system; constructing an urban park perceived environment space quality evaluation index system; determining the weight of each dimension and index in each dimension by adopting an analytic hierarchy process, and finally constructing a comprehensive evaluation index system of urban park space quality; the invention uses the comprehensive park as a key ecological background in the park city construction process, adopts new data formed by massive street view data and other development data sources and new technical environments formed by deep learning and the like in the artificial intelligence field to construct a park space quality comprehensive evaluation index system of two dimensions of park substance environments and perception environments and two dimensions of an external community and an internal community under each dimension, and realizes systematic evaluation combining quantification and qualitative.
Description
Technical Field
The invention relates to the technical field of municipal construction, in particular to a construction method of an urban park space quality comprehensive evaluation index system.
Background
Parks are important components of urban landscapes, and evaluation of park space quality has important value for urban planning management. At present, the park space quality evaluation method mainly comprises classical methods based on questionnaires and regression analysis, and also comprises a new technical method based on a public open space quality remote desktop evaluation tool. These methods are limited by time and manpower, and the manner of questionnaire and field survey is difficult to achieve fine full coverage of the entire city. Extensive and refined assessment of park space quality is a major challenge.
Disclosure of Invention
The invention aims to provide a construction method of an urban park space quality comprehensive evaluation index system, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a construction method of an urban park space quality comprehensive evaluation index system comprises the following steps of;
dividing the space quality of the urban park into two dimensions of a substance environment and a perception environment, and two dimensions of an external community and an internal community;
constructing an urban park substance environment space quality evaluation index system based on the two dimensions and the two dimensions;
constructing an urban park perceived environment space quality evaluation index system based on the two dimensions and the two dimensions;
and determining the weight of the index in each dimension by adopting an analytic hierarchy process, and finally constructing a comprehensive evaluation index system of the urban park space quality.
As a further technical scheme of the invention: the construction of the urban park substance environment space quality evaluation index system specifically comprises the following steps: the construction of the external community scale index in the park material environment and the construction of the internal scale index in the park material environment.
As a further technical scheme of the invention: the construction of the external community scale index in the park substance environment comprises the following steps: based on a literature consulting method and a Delphi method, selecting an external community scale evaluation index, wherein the external community scale evaluation index comprises the following steps: the selected indexes are quantized by adopting space syntax and GIS space analysis and statistics methods to obtain the external community scale indexes under the park substance environment.
As a further technical scheme of the invention: the construction of the internal scale index in the park substance environment comprises the following steps: based on a literature consulting method and a Delphi method, selecting an internal scale evaluation index, wherein the internal scale evaluation index comprises the following components: the method comprises the steps of normalizing angle integration degree, supporting service facility level, functional diversity and water cleanliness, and quantifying indexes by adopting a space syntax and field investigation method to obtain internal scale indexes in a park substance environment.
As a further technical scheme of the invention: the construction of the urban park perceived environment space quality evaluation index system comprises the following steps: the method comprises the steps of constructing an external community scale index in a park sensing environment and constructing an internal scale index in the park sensing environment.
As a further technical scheme of the invention: the construction of the external community scale index in the park perception environment comprises the following steps: based on a literature consulting method and a Delphi method, selecting an external community scale evaluation index, wherein the external community scale evaluation index comprises a green vision rate, sky openness, interface closure, motorized degree, sidewalk perception, building perception and people flow ratio, and quantifying the index through a google deep convolutional neural network technology and a cityscape evaluation data set based on deep learning in the artificial intelligence field to obtain the external community scale index in a park perception environment.
As a further technical scheme of the invention: the construction of the internal scale index in the park perception environment comprises the following steps: subdividing the park interior space into: 4 types of garden path space, water side space, square space and building small product space, constructing a park perception environment space quality evaluation index system more finely, selecting an internal scale evaluation index based on a literature reference method, a Delphi method, a landscape beauty evaluation method and a statistical analysis method, and comprising the following steps of: the method comprises the steps of quantifying indexes through questionnaire investigation, deep and Cityscapes technologies to obtain internal scale indexes under park perception environments, wherein the aesthetic degree evaluation indexes have subjectivity, but the aesthetic degree evaluation indexes are integrated into an evaluation index system through collecting sampling point street view photos and scoring to ensure the integrity of the sampling point street view photos.
As a further technical scheme of the invention: the weight values of all indexes in the evaluation index system are obtained by adopting an AHP (advanced high performance) analytic hierarchy process and a Delphi method, particularly YAAHP software is used for constructing all index levels in the evaluation index system, the importance of the indexes is compared pairwise, corresponding values are given, and the average weight values of all indexes in the evaluation index system are obtained after 20 times of consistency test.
Compared with the prior art, the invention has the beneficial effects that: the invention uses the comprehensive park as a key ecological background in the park city construction process, adopts new data formed by massive street view data and other development data sources and new technical environments formed by deep learning and the like in the artificial intelligence field to construct a park space quality comprehensive evaluation index system of two dimensions of park substance environments and perception environments and two dimensions of an external community and an internal community under each dimension, thereby realizing systematic evaluation combining quantification and qualitative.
Drawings
FIG. 1 is a schematic diagram of a park substance environmental space quality evaluation index system.
FIG. 2 is a schematic diagram of a park-aware environmental spatial quality assessment index system.
FIG. 3 is a schematic diagram of a city park space quality comprehensive evaluation index system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
1-3, a construction method of an urban park space quality comprehensive evaluation index system comprises the following steps of;
firstly, dividing the space quality of the urban park into two dimensions of a material environment and a perception environment, and two dimensions of an external community and an internal community;
secondly, constructing an urban park substance environment space quality evaluation index system based on the two dimensions and the two dimensions;
thirdly, constructing an urban park perceived environment space quality evaluation index system based on the two dimensions and the two dimensions;
and finally, determining the weight of the index in each dimension by adopting an analytic hierarchy process, and finally constructing a comprehensive evaluation index system of the urban park space quality.
Example 2, on the basis of example 1,
the construction of the urban park substance environment space quality evaluation index system specifically comprises the following steps:
1) The content of the park external community scale evaluation index is as follows:
(1) normalized Angle Integration (NAIN): and importing the urban road network vector diagram with the data preprocessed into a Depthmap for line segment model analysis, and representing the accessibility condition of the park under study by calculating global NAIN.
(2) Bus stop density: and (3) calculating the bus stop ratio in the range of the buffer zone by using the GIS space analysis technology and taking the center of the land of the research park as a round point and the radius of 1000 meters as the buffer zone to obtain the density of the bus stops.
(3) Shortest walking distance between park entrance and subway station: and checking the shortest walking distance between each entrance and exit of the park and the subway station by using hundred-degree network map checking and ranging or GIS space analysis technology.
(4) Functional density: and (3) calculating the POI point occupation ratio in the range of the buffer zone by using the GIS space analysis technology and taking the center of the land of the research park as a round point and the radius of 1000 meters as the buffer zone to obtain the POI point density.
(5) Functional mix: through a GIS space analysis technology, the center of a land used in a research park is used as a round point, the radius of 1000 meters is used as a buffer area, and the information entropy of the occupation ratio of various POIs in the range of the buffer area is calculated respectively to obtain the POI point mixing degree.
(6) The land property of the surrounding land: the current land property of each land in the range is determined by taking the center of the land used in the research park as a round point and taking the radius of 1000 meters as a buffer zone, and if the land area ratio of the highest land property exceeds 60% of the land used in the buffer zone, the land property attribute is assigned to the research park to obtain the land property of the land surrounding the park.
(7) Peripheral land parcel development intensity: and calculating the building area corresponding to the building surface data through ArcGIS software, calculating the land area for the park by using the 1000m buffer area of the park, and dividing the total building area within the range of the park buffer area by the land area for the park buffer area to obtain the development intensity of the land block around the park.
2) The content of the community scale evaluation index in the park is as follows:
(1) normalized Angle Integration (NAIN) and normalized angle integration (NACH): the global NAIN and NACH were obtained by segment model analysis of the park internal garden under study using Depthmap.
(2) Level of ancillary services: through on-site survey, the index content related to the matched service facilities is collected as follows: various parking lots, public washrooms, kiosks (including self-help shopping machines), seats, garbage cans, tea buildings, explanation services (including voice explanation systems), guidance signs, alarming help-seeking facilities (including self-help alarming facilities), automatic external defibrillators and the like, and the scale scoring is carried out on the kiosks by adopting a dichotomy from the existence, quantity and quality of the facilities.
(3) Functional diversity: through on-site investigation, the content of the relevant indexes about the functional diversity is collected as follows: the children entertainment facilities, the middle-aged and young people activities and the old people activities are scored by a dichotomy from the functions, the quantity and the quality.
(4) Water body cleanliness: the scale is scored by a dichotomy from the presence or absence of water scenes, the quantity and the quality by on-site stepping.
Embodiment 3, on the basis of embodiment 2, the establishment of the urban park perceived environmental space quality evaluation index system specifically comprises the following steps:
1) And (5) evaluating index content of the park external community scale.
(1) Green viewing rate: the vegetation ratio is taken as the green vision rate. The street view photo data is obtained through calculation, and the calculation formula is as follows:
L=P vegetation /PWherein L is the green vision rate, P Vegetation The total pixel value of the green vegetation in each photo, and P is the total pixel value of each photo.
(2) Sky width: sky opening degree was measured in terms of sky area ratio. The street view photo data is obtained through calculation, and the calculation formula is as follows:
T=P sky Wherein T is the sky width, P is the total pixel value of sky in each photo, and P is the total pixel value of each photo.
(3) Interface closure degree: taking the sum of the building, the enclosing fence, the wall, the rod, the indication board and the vegetation as the boundary enclosing degree. The street view photo data is obtained through calculation, and the calculation formula is as follows:
J interface(s) =P Interface(s) P type middle J Interface(s) For the degree of interfacial closure, P Interface(s) For the total pixel value of the building, fence, wall, pole, sign and vegetation in each photograph, P is the total pixel value of each photograph.
(4) Degree of motorization: the degree of motorization is measured as the sum of the road and the vehicle duty cycle. The street view photo data is obtained through calculation, and the calculation formula is as follows:
J motorized device =P Motorized device P type middle J Motorized device For degree of motorization, P Interface(s) For the total pixel value of the road and the car in each photo, P is the total pixel value of each photo.
(5) Pavement perception degree: the sidewalk duty ratio is used as the sidewalk perception degree. The street view photo data is obtained through calculation, and the calculation formula is as follows:
R sidewalk =P Sidewalk R in P type Sidewalk For the perception degree of the sidewalk, P Sidewalk For the total pixel value of the sidewalk in each photo, P is the total pixel value of each photo.
(6) Building perceptibility: building perceptibility is measured in terms of building ratio. The street view photo data is obtained through calculation, and the calculation formula is as follows:
B building construction =P Building construction B in P type Building construction Is a buildingPerception degree, P Sidewalk For the total pixel value of the building in each photo, P is the total pixel value of each photo.
(7) The ratio of the flow of people: the ratio of the number of people is taken as the ratio of the flow of people. The street view photo data is obtained through calculation, and the calculation formula is as follows:
R human body =P Human body R in P type Human body For human flow, P Human body The total pixel value of the flow of people in each photo is given, and P is the total pixel value of each photo.
2) Park internal scale evaluation index content:
(1) based on the walking reachable range, taking an average of 10 meters as 1 sampling point, using a GPS tool to acquire longitude and latitude coordinate values of the point, and taking 180-degree street view photos at the human visual angle, wherein two photos are taken at each 1 sampling point, so that 360-degree panoramic photos of each sampling point are obtained.
(2) According to the characteristics of the environment of the park, the internal space of the park is subdivided into 4 different types of spaces, namely a park road space, a water side space, a square space and a building small-article space.
The garden path space is a greening space in various roads and surrounding environments of the built-up environment in the park, and also comprises a landscape bridge with a walking function and an overhead trestle.
The water space is a greening space in a water scene for people to visit and the surrounding environment of the water scene with a certain scale in the built environment of the park, and also comprises a park path reaching the vicinity of the water scene.
The square space refers to various scenery theme squares, park entrance and exit gathering and distributing squares, children and old people playing squares, sports fitness squares, open-air playing squares and other paving squares with certain scale for people to use in the park, and also comprises greening spaces in the surrounding environment.
The small building space is a greening space in a small building facility or a pavilion, a corridor, a rack, a champs and other landscape facilities with a certain function in an environment built inside a park and the surrounding environment, and also comprises a garden path reaching the vicinity of the small building.
(3) According to the collection of the large sample size of the street view photos of 4 different types of spaces in the central urban park, 6 evaluation indexes of the perceived quality of the various types of spaces and the quality which can be influenced by the quality are respectively realized, namely, the green vision rate, the sky openness, the road square perception, the construction perception, the interface closure degree and the traffic ratio.
(4) Based on the analysis result, selecting common evaluation indexes applicable to 4 different types of spaces, namely, scenery color beauty degree, floor pavement beauty degree, plant type or scenery element richness, and carrying out statistical correlation analysis and regression analysis on 3 evaluation indexes of park space quality perceived by artificial judgment of park street view photos of each sampling point and possibly influencing the quality.
(5) And (3) based on the evaluation indexes obtained in the steps (3) and (4), respectively, the common evaluation indexes applicable to various types of spaces and applicable to 4 types of spaces, and finally, the evaluation index content of 4 different types of spaces (garden path space, water side space, square space and building small-scale space) with internal dimensions in park perception environment space quality is formed.
The garden path space quality evaluation index content comprises the following steps:
green viewing rate: the vegetation ratio is taken as the green vision rate. The street view photo data is obtained through calculation, and the calculation formula is as follows:
L=P vegetation Wherein L is green vision rate, P Vegetation The total pixel value of the green vegetation in each photo, and P is the total pixel value of each photo.
Sky width: sky opening degree was measured in terms of sky area ratio. The street view photo data is obtained through calculation, and the calculation formula is as follows:
T=P sky Wherein T is the sky width, P is the total pixel value of sky in each photo, and P is the total pixel value of each photo.
And the road square perceptibility is that the road square duty ratio is used as the road square perceptibility. The street view photo data is obtained through calculation, and the calculation formula is as follows:
R=P road square R is road square perception degree, P Road square And P is the total pixel value of each photo.
Building perceptibility is measured in terms of building ratio. The street view photo data is obtained through calculation, and the calculation formula is as follows:
B building construction =P Building construction B in P type Building construction For building perception, P Sidewalk For the total pixel value of the building in each photo, P is the total pixel value of each photo.
And the interfacial enclosure degree is defined as the total ratio of the building, the fence, the wall, the rod, the indication board and the vegetation. The street view photo data is obtained through calculation, and the calculation formula is as follows:
J interface(s) =P Interface(s) P type middle J Interface(s) For the degree of interfacial closure, P Interface(s) For the total pixel value of the building, fence, wall, pole, sign and vegetation in each photograph, P is the total pixel value of each photograph.
Landscape color aesthetic degree: refers to the psychological change caused by the stimulation of the visual system of people by landscape colors, and the color factors are considered from the four seasons of landscape transformation.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Color and aesthetic degree scoring standard of garden path space landscape: the color of the garden path is 0.5 minute, the color of the garden path is 1 minute, and the other conditions of the garden path are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(2) Color and aesthetic degree scoring standard of the water space landscape: the water body is black and 0.5 minute, the turbidity of the water body is grey and 1 minute, and the water body is clear and colorless and transparent and 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(3) Square space landscape color aesthetic degree scoring standard: the single color of the square is 0.5 minute, the color of the square is equal to 1 minute, and the other cases of the square are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(4) Building small-scale space landscape color aesthetic degree scoring standard: the single color of the building small product is 0.5 minute, the color of the building small product is equal to two kinds of the building small products and the rest of the building small products are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
The floor pavement aesthetic feeling is mainly judged from the visual effects of the floor pavement such as lines, splicing, layering and the like.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Road space floor pavement aesthetic degree scoring standard: the single material used in the garden is 1 minute, the two materials used in the garden are 1.5 minutes, and the rest of the garden is 3 minutes.
(2) Grading standard of the beauty degree of the land pavement in the water space: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
(3) Grading standard for the beauty degree of the ground pavement in the square space: the single material used in the square is 1 minute, the two materials used in the square are 1.5 minutes, and the rest of the square is 3 minutes.
(4) Building small-scale space floor pavement aesthetic feeling scoring standard: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
Plant species or landscape element richness is measured by whether the plant species is diverse or whether the landscape element is rich. The street view photo data is obtained through calculation, and the calculation formula is as follows:
V plants and methods of making the same =P Plants and methods of making the same P type medium V Plants and methods of making the same Is rich in plant species or landscape elements, P Plants and methods of making the same For the total pixel value of the plant species (or landscape element) in each photograph, P is the total pixel value of each photograph.
The quality evaluation index content of the water space is as follows:
green viewing rate: the vegetation ratio is taken as the green vision rate. The street view photo data is obtained through calculation, and the calculation formula is as follows:
L=P vegetation /PWherein L is the green vision rate, P Vegetation The total pixel value of the green vegetation in each photo, and P is the total pixel value of each photo.
Sky width: sky opening degree was measured in terms of sky area ratio. The street view photo data is obtained through calculation, and the calculation formula is as follows:
T=P sky Wherein T is the sky width, P is the total pixel value of sky in each photo, and P is the total pixel value of each photo.
Building perceptibility is measured in terms of building ratio. The street view photo data is obtained through calculation, and the calculation formula is as follows:
B building construction =P Building construction B in P type Building construction For building perception, P Sidewalk For the total pixel value of the building in each photo, P is the total pixel value of each photo.
And the interfacial enclosure degree is defined as the total ratio of the building, the fence, the wall, the rod, the indication board and the vegetation. The street view photo data is obtained through calculation, and the calculation formula is as follows:
J interface(s) =P Interface(s) P type middle J Interface(s) For the degree of interfacial closure, P Interface(s) For the total pixel value of the building, fence, wall, pole, sign and vegetation in each photograph, P is the total pixel value of each photograph.
Landscape color aesthetic degree: refers to the psychological change caused by the stimulation of the visual system of people by landscape colors, and the color factors are considered from the four seasons of landscape transformation.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Color and aesthetic degree scoring standard of garden path space landscape: the color of the garden path is 0.5 minute, the color of the garden path is 1 minute, and the other conditions of the garden path are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(2) Color and aesthetic degree scoring standard of the water space landscape: the water body is black and 0.5 minute, the turbidity of the water body is grey and 1 minute, and the water body is clear and colorless and transparent and 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(3) Square space landscape color aesthetic degree scoring standard: the single color of the square is 0.5 minute, the color of the square is equal to 1 minute, and the other cases of the square are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(4) Building small-scale space landscape color aesthetic degree scoring standard: the single color of the building small product is 0.5 minute, the color of the building small product is equal to two kinds of the building small products and the rest of the building small products are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
The floor pavement aesthetic feeling is mainly judged from the visual effects of the floor pavement such as lines, splicing, layering and the like.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Road space floor pavement aesthetic degree scoring standard: the single material used in the garden is 1 minute, the two materials used in the garden are 1.5 minutes, and the rest of the garden is 3 minutes.
(2) Grading standard of the beauty degree of the land pavement in the water space: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
(3) Grading standard for the beauty degree of the ground pavement in the square space: the single material used in the square is 1 minute, the two materials used in the square are 1.5 minutes, and the rest of the square is 3 minutes.
(4) Building small-scale space floor pavement aesthetic feeling scoring standard: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
Plant species or landscape element richness is measured by whether the plant species is diverse or whether the landscape element is rich. The street view photo data is obtained through calculation, and the calculation formula is as follows:
V plants and methods of making the same =P Plants and methods of making the same P type medium V Plants and methods of making the same Is rich in plant species or landscape elements, P Plants and methods of making the same For the total pixel value of the plant species (or landscape element) in each photograph, P is the total pixel value of each photograph.
The square space quality evaluation index content:
green viewing rate: the vegetation ratio is taken as the green vision rate. The street view photo data is obtained through calculation, and the calculation formula is as follows:
L=P vegetation /PWherein L is the green vision rate, P Vegetation The total pixel value of the green vegetation in each photo, and P is the total pixel value of each photo.
Building perceptibility is measured in terms of building ratio. The street view photo data is obtained through calculation, and the calculation formula is as follows:
B building construction =P Building construction B in P type Building construction For building perception, P Sidewalk For the total pixel value of the building in each photo, P is the total pixel value of each photo.
And the interfacial enclosure degree is defined as the total ratio of the building, the fence, the wall, the rod, the indication board and the vegetation. The street view photo data is obtained through calculation, and the calculation formula is as follows:
J interface(s) =P Interface(s) P type middle J Interface(s) For the degree of interfacial closure, P Interface(s) For the total pixel value of the building, fence, wall, pole, sign and vegetation in each photograph, P is the total pixel value of each photograph.
Landscape color aesthetic degree: refers to the psychological change caused by the stimulation of the visual system of people by landscape colors, and the color factors are considered from the four seasons of landscape transformation.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Color and aesthetic degree scoring standard of garden path space landscape: the color of the garden path is 0.5 minute, the color of the garden path is 1 minute, and the other conditions of the garden path are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(2) Color and aesthetic degree scoring standard of the water space landscape: the water body is black and 0.5 minute, the turbidity of the water body is grey and 1 minute, and the water body is clear and colorless and transparent and 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(3) Square space landscape color aesthetic degree scoring standard: the single color of the square is 0.5 minute, the color of the square is equal to 1 minute, and the other cases of the square are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(4) Building small-scale space landscape color aesthetic degree scoring standard: the single color of the building small product is 0.5 minute, the color of the building small product is equal to two kinds of the building small products and the rest of the building small products are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
The floor pavement aesthetic feeling is mainly judged from the visual effects of the floor pavement such as lines, splicing, layering and the like.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Road space floor pavement aesthetic degree scoring standard: the single material used in the garden is 1 minute, the two materials used in the garden are 1.5 minutes, and the rest of the garden is 3 minutes.
(2) Grading standard of the beauty degree of the land pavement in the water space: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
(3) Grading standard for the beauty degree of the ground pavement in the square space: the single material used in the square is 1 minute, the two materials used in the square are 1.5 minutes, and the rest of the square is 3 minutes.
(4) Building small-scale space floor pavement aesthetic feeling scoring standard: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
Plant species or landscape element richness is measured by whether the plant species is diverse or whether the landscape element is rich. The street view photo data is obtained through calculation, and the calculation formula is as follows:
V plants and methods of making the same =P Plants and methods of making the same P type medium V Plants and methods of making the same Is rich in plant species or landscape elements, P Plants and methods of making the same For the total pixel value of the plant species (or landscape element) in each photograph, P is the total pixel value of each photograph.
The content of the space quality evaluation index of the building small product is as follows:
green viewing rate: the vegetation ratio is taken as the green vision rate. The street view photo data is obtained through calculation, and the calculation formula is as follows:
L=P vegetation Wherein L is green vision rate, P Vegetation The total pixel value of the green vegetation in each photo, and P is the total pixel value of each photo.
Landscape color aesthetic degree: refers to the psychological change caused by the stimulation of the visual system of people by landscape colors, and the color factors are considered from the four seasons of landscape transformation.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Color and aesthetic degree scoring standard of garden path space landscape: the color of the garden path is 0.5 minute, the color of the garden path is 1 minute, and the other conditions of the garden path are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(2) Color and aesthetic degree scoring standard of the water space landscape: the water body is black and 0.5 minute, the turbidity of the water body is grey and 1 minute, and the water body is clear and colorless and transparent and 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(3) Square space landscape color aesthetic degree scoring standard: the single color of the square is 0.5 minute, the color of the square is equal to 1 minute, and the other cases of the square are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
(4) Building small-scale space landscape color aesthetic degree scoring standard: the single color of the building small product is 0.5 minute, the color of the building small product is equal to two kinds of the building small products and the rest of the building small products are 1.5 minutes; the greening color is 0.5 minute singly, the greening color is equal to 1 minute two, and the rest greening conditions are 1.5 minutes.
The floor pavement aesthetic feeling is mainly judged from the visual effects of the floor pavement such as lines, splicing, layering and the like.
Through street view data, deep learning technology in the artificial intelligence field is adopted, scoring staff with professional backgrounds firstly score part of park street view data according to four standards (specific standards are attached), the score is 1-3, the score is 1 is low, the score is 2, the score is general, the score is 3, the park street view data after being assigned is taken as sample data, the park street view data and corresponding assigned values are called in batches by using Python programming language in a second generation open source artificial intelligence learning system TensorFlow developed by google, nonlinear complex feature selection is obtained through the deep learning technology, and therefore automatic batch scoring capability is achieved for the rest of park street view data, namely, autonomous batch scoring of the rest of park street view data is achieved through deep learning model training on the rest of park street view data.
Evaluation criteria:
(1) Road space floor pavement aesthetic degree scoring standard: the single material used in the garden is 1 minute, the two materials used in the garden are 1.5 minutes, and the rest of the garden is 3 minutes.
(2) Grading standard of the beauty degree of the land pavement in the water space: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
(3) Grading standard for the beauty degree of the ground pavement in the square space: the single material used in the square is 1 minute, the two materials used in the square are 1.5 minutes, and the rest of the square is 3 minutes.
(4) Building small-scale space floor pavement aesthetic feeling scoring standard: the single ground material is 1 minute, the two ground materials are 1.5 minutes, and the rest ground materials are 3 minutes.
Plant species or landscape element richness is measured by whether the plant species is diverse or whether the landscape element is rich. The street view photo data is obtained through calculation, and the calculation formula is as follows:
V plants and methods of making the same =P Plants and methods of making the same P type medium V Plants and methods of making the same Is rich in plant species or landscape elements, P Plants and methods of making the same For the total pixel value of the plant species (or landscape element) in each photograph, P is the total pixel value of each photograph.
The weight values of all indexes in the evaluation index system are obtained by adopting an AHP (advanced high performance) analytic hierarchy process and a Delphi method, particularly YAAHP software is used for constructing all index levels in the evaluation index system, the importance of the indexes is compared pairwise, corresponding values are given, and the average weight values of all indexes in the evaluation index system are obtained after consistency test is repeated for 20 times.
The respective evaluation index systems and weights are shown in the following table:
table 1: a city park space quality comprehensive evaluation index system and a weight table;
example 4, on the basis of example 3, a city park space quality comprehensive evaluation index system was constructed specifically as follows: the urban park space quality comprehensive evaluation index system is finally formed through the construction of the park substance environment space quality evaluation index system and the construction of the park perception environment space quality evaluation index system, and the weight value of each index is determined by adopting a hierarchical analysis method.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (2)
1. The construction method of the urban park space quality comprehensive evaluation index system is characterized by comprising the following steps of;
dividing the space quality of the urban park into two dimensions of a substance environment and a perception environment, and two dimensions of an external community and an internal community;
based on the two dimensions and the two dimensions, constructing an urban park substance environment space quality evaluation index system, wherein the construction of the urban park substance environment space quality evaluation index system specifically comprises the following steps: constructing an external community scale index in a park substance environment and constructing an internal scale index in the park substance environment;
the construction of the external community scale index in the park substance environment comprises the following steps: selecting an external community scale evaluation index, wherein the external community scale evaluation index comprises: the selected indexes are quantized to obtain external community scale indexes under park substance environment, wherein the standard angle integration degree, the bus station density, the distance from subway entrance, the function density, the function mixing degree, the land property of surrounding land parcels and the development strength of surrounding land parcels are standardized;
the construction of the internal scale index in the park substance environment comprises the following steps: selecting an internal scale evaluation index, wherein the internal scale evaluation index comprises: the method comprises the steps of normalizing angle integration degree, supporting service facility level, functional diversity and water cleanliness, and quantifying indexes by adopting a space syntax and an on-site investigation method to obtain internal scale indexes in a park substance environment;
based on the two dimensions and the two dimensions, constructing an urban park perceived environment space quality evaluation index system comprises: building an external community scale index in a park sensing environment and building an internal scale index in the park sensing environment;
determining the weight of each dimension and index in each dimension by adopting an analytic hierarchy process, and finally constructing a comprehensive evaluation index system of urban park space quality;
the construction of the external community scale index in the park perception environment comprises the following steps: selecting an external community scale evaluation index, wherein the external community scale evaluation index comprises a green vision rate, sky opening degree, interface enclosing degree, motorized degree, sidewalk perception degree, building perception degree and people flow rate ratio, and obtaining an external community scale index under a park perception environment;
the construction of the internal park scale index in the park perception environment comprises the following steps: subdividing the park interior space into: 4 types of garden path space, water side space, square space and building small product space, constructing a park perception environment space quality evaluation index system more finely, selecting an internal park scale evaluation index based on a literature consulting method, a Delphi method, a landscape beauty evaluation method and a statistical analysis method, and comprising the following steps of: landscape color beauty, ground pavement beauty, plant types or landscape element richness, green vision rate, sky opening degree, road square perception, building perception and people flow ratio, and indexes are quantized through questionnaire investigation, deep and Cityscapes technologies to obtain internal park scale indexes under park perception environments;
and evaluating each index weight value in the index system by adopting an AHP analytic hierarchy process and a Delphi method.
2. The method for constructing the urban park space quality comprehensive evaluation index system according to claim 1, wherein YAAHP software is used for constructing each index level in the evaluation index system, corresponding values are given through comparing index importance pairwise, and the average weight value of each index in the evaluation index system is obtained after 20 times of repetition of consistency test.
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