CN113421010A - Green road route selection planning design method based on big data - Google Patents
Green road route selection planning design method based on big data Download PDFInfo
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Abstract
The invention discloses a green track route selection planning design method based on big data, which comprises the following steps: initially planning line selection based on current construction conditions, acquiring natural conditions and human condition information of an area to be designed, and performing superposition analysis on the information to form final line selection; combining gis data for ecological network analysis including wild animal habitat analysis and animal migration corridor analysis; the analysis of the wild animal habitat is carried out by gis, selecting four single factors of macro habitat type, gradient, near-water distance and moving distance away from people, and carrying out superposition analysis by using an analytic hierarchy process to obtain the distribution condition of the main wild animal habitat; the animal migration corridor analysis is in the face of continuous habitat loss and isolation, the green corridor introduces high-quality connection between smashed, cut, perforated, contracted and lost habitat patches through the construction of a green corridor network and a control area, and the landscape connectivity is enhanced in the form of a wild animal migration corridor and a stepping stone, so that positive ecological benefits are generated.
Description
Technical Field
The invention belongs to the technical field of green road route selection planning, and particularly relates to a green road route selection planning design method based on big data.
Background
The green road is a linear green open space, is usually built along natural and artificial galleries such as riversides, brooks, ridges, scenic roads and the like, and is internally provided with a landscape rest route for pedestrians and riders to enter. The method has more successful practice in America, British, Germany, Singapore, domestic Zhujiang Delta and the like. In a broad sense, "greenway" refers to a general term for various linear open spaces for connection, including a habitat corridor from a community bicycle lane to guide seasonal migration of wildlife, a stream bank tree-shadow walk from a city waterside to a remote city, and the like.
The first work of the green road in the development of planning construction is to develop route selection planning on the green road, the route selection planning needs to be combined with a large number of elements of the field such as ecology, geography, humanity and the like to perform systematic and comprehensive analysis, the elements are complicated and mutually cause and effect, and if the elements are not superposed by scientific means to perform comprehensive analysis at the beginning of planning design, the reason analysis is lacked in the route selection layout of the green road, so that the layout of the green road is unreasonable, the use is inconvenient, and unnecessary construction waste is caused.
Disclosure of Invention
The invention aims to provide a green track route selection planning design method based on big data so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a green road route selection planning design method based on big data comprises the following steps:
step S01: line selection preliminary planning based on current construction conditions;
step S02: acquiring natural conditions and human condition information of an area to be designed;
step S03: combining gis ecological network analysis of data;
step S04: carrying out crowd use analysis based on the big data heat map;
step S05: and (5) performing information superposition analysis to form final line selection.
Preferably, the line selection preliminary planning based on the current construction conditions comprises site terrain analysis;
the green road route selection is mostly along the water side of the mountain, the slope is controlled within 5%, the mountain climbing section is controlled within 10%, and anti-skid treatment, safety guardrails and other measures are taken. And then, carrying out sight line analysis by combining terrain conditions, and setting landscape nodes.
Preferably, the acquiring of the natural condition and the human condition information of the area to be designed is based on the following principle: by analyzing the natural conditions and the humanistic conditions of the areas, summarizing the characteristics of the areas, grasping the interaction and the mutual influence among all elements, comparing, explaining and analyzing the differences and the connections among the areas, summarizing the reasons and the rules of the areas for differences, and summarizing the conditions and the modes of the area development; and the geographic objects and phenomena are recognized by means of tools such as maps and the like through comprehensive analysis of regions, regional comparison, change of cognitive modes such as analyzed spatial scales and the like and by taking correct spatial and regional viewpoints.
Preferably, the ecological network analysis combined with gis data comprises wild animal habitat analysis and animal migration corridor analysis;
the analysis of the wild animal habitat is carried out by gis, selecting four single factors of macro habitat type, gradient, near-water distance and moving distance away from people, and carrying out superposition analysis by using an analytic hierarchy process to obtain the distribution condition of the main wild animal habitat;
the animal migration corridor analysis is in the face of continuous habitat loss and isolation, the green corridor introduces high-quality connection between smashed, cut, perforated, contracted and lost habitat patches through the construction of a green corridor network and a control area, and the landscape connectivity is enhanced in the form of a wild animal migration corridor and a stepping stone, so that positive ecological benefits are generated.
Preferably, the big data thermal map is adopted for crowd use analysis based on the big data thermal map, and the thermal map based on the real-time crowd distribution, density and variation trend of the hundred-degree big data is used for observing the daily travel and distribution rules of residents in the area to be built on weekdays and weekends in the morning and evening, reasonably arranging the directions and the positions of the entrances and exits of green roads, facilitating the travel of residents and improving the green road utilization rate.
Preferably, the final route selection formed by the information superposition analysis needs to be performed according to the following route selection key points of the green lane: connecting a forest/rural park, a main reservoir and a water body; the method comprises the following steps of (1) passing through an entrance and an exit of a forest/suburban park which are built mature under the current situation and an entrance and exit area with conditions in the future; under the condition that the site conditions are met, the route selection needs to go deep into the interior of the mountain and the reservoir bank, and the considerable swimming property of the green road is guaranteed.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, the method comprises the following steps: acquiring natural conditions and human condition information of an area to be designed; the green channel design is more natural and humanistic in fit, and the ecological network can be prevented from being influenced.
2. In the invention, the method comprises the following steps: combining gis ecological network analysis of data; so that the distribution of the main wild animal habitats can be obtained and positive ecological benefits can be generated.
3. In the invention, the method comprises the following steps: carrying out crowd use analysis based on the big data heat map; the convenience is brought to the travel of residents, and the utilization rate of the green road is improved.
4. In the invention, the method comprises the following steps: information superposition analysis is carried out to form final line selection; so that the considerable swimming ability of the green lane is ensured.
Drawings
FIG. 1 is a planning flow chart of a green track route selection planning design method based on big data according to the present invention;
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Referring to fig. 1, the present invention provides the following technical solutions: a green road route selection planning design method based on big data comprises the following steps:
step S01: line selection preliminary planning based on current construction conditions;
step S02: acquiring natural conditions and human condition information of an area to be designed;
step S03: combining gis ecological network analysis of data;
step S04: carrying out crowd use analysis based on the big data heat map;
step S05: and (5) performing information superposition analysis to form final line selection.
Preferably, the line selection preliminary planning based on the current construction conditions comprises site terrain analysis;
the green road route selection is mostly along the water side of the mountain, the slope is controlled within 5%, the mountain climbing section is controlled within 10%, and anti-skid treatment, safety guardrails and other measures are taken. And then, sight line analysis is carried out by combining with terrain conditions, and landscape nodes are set, so that safety planning and preliminary analysis of site and terrain can be carried out in advance.
Preferably, the natural condition and the human condition information of the area to be designed are acquired according to the following principle: by analyzing the natural conditions and the humanistic conditions of the areas, summarizing the characteristics of the areas, grasping the interaction and the mutual influence among all elements, comparing, explaining and analyzing the differences and the connections among the areas, summarizing the reasons and the rules of the areas for differences, and summarizing the conditions and the modes of the area development; and by means of a map and other tools, through comprehensive analysis of regions, regional comparison, change of cognitive modes such as analyzed spatial scales and the like, correct spatial and regional viewpoints are inherited to know geographic objects and phenomena, and through the method, the green track design is more fit with naturalness and humanity, and influence on an ecological network can be avoided.
Preferably, the analysis of the ecological network combined with the gis data includes wild animal habitat analysis and animal migration corridor analysis;
the analysis of the wild animal habitat is carried out by gis, four single factors of macro habitat type, gradient, near-water distance and moving distance away from people are selected, and the superposition analysis is carried out by utilizing an analytic hierarchy process, so that the distribution condition of the main wild animal habitat can be obtained by the method;
animal migration corridor analysis is faced with persistent habitat loss and isolation, the greenhouses introduce high quality connections between fragmented, cut, perforated, contracted and worn habitat patches through greenhouse network and control area construction, landscape connectivity is enhanced in the form of wild animal migration corridors and stepping stones, and positive ecological benefits are enabled by such methods.
Preferably, the big data thermal map is adopted for crowd use analysis based on the big data thermal map, and the thermal map based on the real-time crowd distribution, density and variation trend of hundred-degree big data is used for observing daily travel and distribution rules of residents in a to-be-constructed area on working days and weekends, morning and evening, and reasonably arranging the directions and the positions of the entrances and exits of green roads.
Preferably, the final route selection formed by information superposition analysis needs to be performed according to the following route selection key points of the green track: connecting a forest/rural park, a main reservoir and a water body; the method comprises the following steps of (1) passing through an entrance and an exit of a forest/suburban park which are built mature under the current situation and an entrance and exit area with conditions in the future; under the condition that the site conditions are met, the route selection needs to go deep into the interior of the mountain and the water bank of the reservoir, and the considerable swimming property of the green road is ensured by the method.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A green road route selection planning design method based on big data is characterized in that: the method comprises the following steps:
step S01: line selection preliminary planning based on current construction conditions;
step S02: acquiring natural conditions and human condition information of an area to be designed;
step S03: combining gis ecological network analysis of data;
step S04: carrying out crowd use analysis based on the big data heat map;
step S05: and (5) performing information superposition analysis to form final line selection.
2. The big data-based green track route selection planning and design method according to claim 1, wherein: the line selection preliminary planning based on the current construction conditions comprises site terrain analysis;
the green road route selection is mostly along the water side of the mountain, the slope is controlled within 5%, the mountain climbing section is controlled within 10%, and anti-skid treatment, safety guardrails and other measures are taken. And then, carrying out sight line analysis by combining terrain conditions, and setting landscape nodes.
3. The big data-based green track route selection planning and design method according to claim 1, wherein: the natural condition and the human condition information of the area to be designed are acquired according to the following principle: by analyzing the natural conditions and the humanistic conditions of the areas, summarizing the characteristics of the areas, grasping the interaction and the mutual influence among all elements, comparing, explaining and analyzing the differences and the connections among the areas, summarizing the reasons and the rules of the areas for differences, and summarizing the conditions and the modes of the area development; and the geographic objects and phenomena are recognized by means of tools such as maps and the like through comprehensive analysis of regions, regional comparison, change of cognitive modes such as analyzed spatial scales and the like and by taking correct spatial and regional viewpoints.
4. The big data-based green track route selection planning and design method according to claim 1, wherein: the ecological network analysis combined with gis data includes wild animal habitat analysis and animal migration corridor analysis;
the analysis of the wild animal habitat is carried out by gis, selecting four single factors of macro habitat type, gradient, near-water distance and moving distance away from people, and carrying out superposition analysis by using an analytic hierarchy process to obtain the distribution condition of the main wild animal habitat;
the animal migration corridor analysis is in the face of continuous habitat loss and isolation, the green corridor introduces high-quality connection between smashed, cut, perforated, contracted and lost habitat patches through the construction of a green corridor network and a control area, and the landscape connectivity is enhanced in the form of a wild animal migration corridor and a stepping stone, so that positive ecological benefits are generated.
5. The big data-based green track route selection planning and design method according to claim 1, wherein: the big data thermal map is adopted for crowd use analysis based on the big data thermal map, and the thermal map based on the real-time crowd distribution, density and variation trend of hundred-degree big data is used for observing daily trips and distribution rules of residents in a to-be-constructed area on weekdays and weekends, morning and evening, reasonably arranging green road directions and access positions, facilitating the trips of residents and improving the green road utilization rate.
6. The big data-based green track route selection planning and design method according to claim 1, wherein: the final line selection formed by the information superposition analysis needs to be performed according to the following line selection key points of the green track: connecting a forest/rural park, a main reservoir and a water body; the method comprises the following steps of (1) passing through an entrance and an exit of a forest/suburban park which are built mature under the current situation and an entrance and exit area with conditions in the future; under the condition that the site conditions are met, the route selection needs to go deep into the interior of the mountain and the reservoir bank, and the considerable swimming property of the green road is guaranteed.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114139875A (en) * | 2021-11-05 | 2022-03-04 | 北京中森国际工程咨询有限责任公司 | Participatory ecological identification system planning and designing method applied to natural education |
| CN117763450A (en) * | 2024-02-22 | 2024-03-26 | 交通运输部天津水运工程科学研究所 | Road network blocking effect index calculation method and system for wild animals |
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Application publication date: 20210921 |