CN117494859A - Urban carbon emission form partition construction method based on ground statistics - Google Patents
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Abstract
The invention belongs to the technical field of urban low carbon, and provides a method for optimizing urban morphology to reduce urban carbon dioxide emission. From the urban area scale, three types of urban form factor data representing urban external forms, internal features and development intensity are collected, main urban form factors influencing urban carbon dioxide emission are selected according to a geographic weighted regression method, and on the basis, urban carbon emission form partitions are constructed and inspected. The method mainly comprises factor detection, morphological partition construction and inspection of 3 parts. By combining the technical scheme with the urban functional area distribution, the low-carbon urban morphological characteristics of different types of urban functional areas can be analyzed, and the planning of urban sustainable development is facilitated.
Description
Technical Field
The invention belongs to the technical field of urban low carbon, and relates to a carbon emission form partition construction method which is based on priori knowledge and reference data, screens key characteristic factors by adopting a supervised learning method considering spatial information and combines the key characteristic factors.
Background
Urban low-carbon technology is a necessary choice for the current urban development path. Industrial construction and layout, energy conservation and utilization are the dominant route to reduce carbon dioxide emissions in cities, however as research goes deep, scholars will be more focused on optimizing city form features that can alter the selection and behavior of residents to produce less carbon dioxide emissions, and can also affect regional carbon dioxide emissions, changing energy consumption patterns, ultimately affecting carbon dioxide emissions and carbon dioxide emission rates. Urban tangible forms (hereinafter referred to as urban forms) are spatial configurations of human activities, and researches find that related indexes can explain about half of urban resident carbon dioxide emission, mainly by influencing traffic and residential energy use. In addition, socioeconomic factors, urban morphology and carbon dioxide emissions are interacted, and changes in carbon dioxide emissions can be reflected by changes in urban morphology. The research shows that the adjustment of the land utilization structure can reduce the total carbon dioxide emission of cities by 12 percent. Therefore, reducing carbon dioxide emissions by adjusting urban morphology through rational planning is one of the important routes for reducing urban carbon dioxide.
In recent years, main researches develop urban morphology characterization methods and models, explore linear direct relations between urban morphologies (such as fragmentation, compactness and land utilization mixing degree) and carbon dioxide emission of different areas under a multi-scale and multi-time sequence, analyze mediating factors influencing resident carbon dioxide emission, and put forward some action path assumptions. The spatial characteristics affecting carbon dioxide emission can be divided into three aspects of external space form, internal element space form and development strength, and also can be divided into three major categories of geometric and functional strength angles, or city pattern structure, functional organization type and building environment. The prior scholars design urban morphology measurement indexes from the aspects of external space morphology, internal element space morphology and development intensity. Common indexes of the urban external space structure can be divided into morphological characteristics which can be perceived directly, such as landscape pattern indexes, building heights and the like for representing the urban pattern structure; the internal element space morphology can use function mixed entropy, different land utilization type richness and the like from the viewpoint of urban function organization types; the development intensity index is morphological characteristics mainly brought by urban scale effect, and is carrier characteristics matched with economic activity development, such as road density, building density and the like. It is found that the city form related index can explain about half of city resident carbon dioxide emission, but the mediating factors of different cities have great difference, and the space form affects the city resident carbon dioxide emission by affecting the residence selection, travel characteristics, surrounding heat environment and other modes of the resident. Although many studies suggest that single-core city propagation, city morphology disruption and randomization will lead to more carbon dioxide emissions, there are partial studies that do not follow. This is because the mechanisms of influence of urban structure at different stages of urban development are varied and need to be studied at the same level of urban development. In addition, the contradictory results may also be related to climatic conditions, where rainfall affects people's travel options and air temperature affects heating and cooling energy consumption.
An effective association is not established between the conclusion of the research result of the city morphology influence mechanism and the city planning practice tool. The research result taking the whole city administrative boundary as an analysis unit can only provide guidance opinion for the overall strategic planning, and is difficult to guide practice development in detailed planning of a specific region. However, the advent of carbon dioxide emissions spatial grid data provides the basis for further research. The grid pixels are used as an analysis unit, key morphological indexes and the value range thereof are summarized according to typical morphological differences affecting urban carbon dioxide emission, and the relation between research results and planning practices can be effectively established. Similar ideas apply in planning practice as traffic cells (TAZ, traffic Analysis Zone) divided for reasonable distribution of traffic demands and local climate zones (LCZ, local Climate Zone) divided for reduction of urban heat island effects. The partition dividing technical thought mainly comprises a clustering dividing method (spatial feature clustering and mobile phone traffic clustering) based on data driving and a feature dividing method according to key influence factors (such as building height and underlying surface type). TAZ partitioning is mostly based on a data-driven clustering partitioning method, has the advantages of large inter-group difference and small intra-group difference, but requires large input data, and typical characteristics of grouping data are difficult to interpret. The LCZ partition is simpler to operate according to the characteristic segmentation of the key influencing factors, but is not suitable for the condition of excessive key factors because the relation between the number of the key factors and the grouping types is exponentially increased. Since urban morphology (such as configuration and underlying surface characteristics) affects microclimate, and thus building energy consumption and carbon dioxide emission, there are studies on analyzing the relationship between urban carbon dioxide emission and urban morphology based on LCZ. Studies have shown that the degree of mixing of land use type and function has a significant impact on urban carbon dioxide emissions, however LCZ zoning does not take such factors into account. Therefore, it is necessary to construct a partition (LCEZ) representing different typical urban carbon emission patterns based on key factors affecting urban carbon dioxide emission, improve the accuracy of results of analysis of urban patterns on carbon dioxide emission, and connect research results with planning practice applications.
Disclosure of Invention
The invention aims to: the key form factors affecting urban carbon dioxide emissions are resolved using a geographically weighted regression model and combined to form different carbon emission form partitions. By dividing different typical city morphological modes, different points of main carbon emission morphological partition types of different population densities or city functional areas are compared, key parameters and values of low-carbon city morphological are distinguished, and control parameters and ranges thereof are provided for regional scale city morphological planning.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the urban carbon emission form partition construction method based on the ground statistics is characterized by comprising the following steps of:
step 1, data preparation, including preparing a global city boundary data set (GUB), a global carbon grid v1.0 data set of a global infrastructure emissions database (GID), and data for calculating city form factors. First, carbon dioxide distribution vector data of urban residential buildings and traffic emissions is calculated from GID data. Meanwhile, using geographic information system software, respectively calculating according to a formula to obtain the spatial distribution of various form factor indexes, wherein the used data comprise: building contour number with spatial attribute information, road network data, and point of interest data. The calculable city exterior morphology index includes: building height, building shape index, volume rate, crowding degree, sky opening factor; the internal characteristic index includes: functional area mixed entropy, intersection density, bus stop density and landscape accessibility; the development strength index comprises: road density, building density, public facility density;
step 2, dividing urban central areas and suburban areas according to a global urban boundary data set (GUB);
and step 3, selecting urban form factors with stronger influence by using a geographic weighted regression method in a urban center area. Taking the spatial distribution of urban carbon dioxide emission as a dependent variable, taking three types of urban form factors as independent variables, removing multiple collinearity independent variables by using a variance expansion factor method, and carrying out normalization treatment on the independent variables which are selected and returned finally;
step 4, using the regression result obtained in the step 3, selecting three or more factors with the maximum absolute value of the average regression coefficient to construct urban carbon emission form partitions;
step 5, based on the city form factor (n is more than or equal to 3) obtained in the step 4, dividing the city form factor into three types of high, medium and low values by adopting a Natural break point method (Natural break) method, and combining all screened factors in a region for city construction to form k 1 A plurality of different modes of carbon emission morphology partitioning; wherein the method comprises the steps of
Step 6, in the coverage area of the urban non-construction land, respectively combining the screened urban internal characteristic index (m) with vegetation coverage, bare land and water body type areas; when m is greater than or equal to 1, form k 2 A plurality of different modes of carbon emission morphology partitioning; wherein the method comprises the steps ofWhen m=0, no carbon emission morphology partition is built;
step 7, according to the partition distribution characteristics of the carbon emission morphology, if the distribution characteristics do not accord with normal distribution, verifying the difference between the carbon emission morphology partitions by using a non-parameter test method, otherwise, checking whether the difference between the different carbon emission morphology partitions is obvious or not by using a single-factor analysis of variance according to the standard that the P value is less than 0.1;
and 8, verifying the representativeness of the carbon emission morphology partition obtained in the step 4 and the step 5 by using the q value of the factor detection module in the geographic detector, wherein the q value is closer to 1, so that the representativeness is better.
Drawings
FIG. 1 is a schematic flow chart of creating carbon emission morphology zones according to an embodiment of the present invention. Step 1, using Luojia-01 satellite data as covariates to enable the spatial resolution of GID data to be suitable for urban scale analysis, and then selecting suitable key factors by using a geographic weighting model; step 2, dividing the key form factors into three types of high, medium and low values and combining the three types, wherein white dots represent points of interest (POIs) for calculating the mixed entropy of the functional area; step 3 represents verifying the divided morphology partition.
Detailed Description
The following examples further illustrate the invention but are not to be construed as limiting the invention.
Example 1: and (3) constructing a carbon emission form partition of Shanghai city, and preparing data. First, carbon dioxide distribution vector data of urban residential buildings and traffic emissions is calculated from GID data. Simultaneously, using geographic information system software to respectively calculate the spatial distribution of various form factor indexes, wherein the data comprises: building contour number and electricity of interest data from the high-altitude map, and road network data from the hundred-degree map.
The exterior shape index selects building height, shape index, volume rate, crowding degree and sky opening factor, the building height is represented by floor information owned by building outline data, and the shape index, volume rate, crowding degree and sky opening factor are calculated by space vector data of the outline data. The internal characteristic index selection function area mixed entropy, the intersection density, the bus stop density and the landscape accessibility can be obtained by calculating the interest point data comprising longitude and latitude, names and category information. The development intensity index selects road density, building density and public facility density, the road density is calculated by road network data, the building density data is obtained by building contour data, and the public facility density is calculated by interest point data.
And 2, dividing urban central areas and suburban areas according to a global urban boundary data set (GUB).
And 3, removing multiple collinearity independent variables by using a variance expansion factor method to obtain 6 indexes of building height, shape index, functional area mixed entropy, intersection density, road density and building density. Subsequently, normalization processing is performed on the 6 independent variables.
And 4, the average regression coefficient absolute values (average value plus or minus standard deviation) of 6 independent variables of building height, shape index, functional area mixed entropy, intersection density, road density and building density in the geographical weighted regression result are respectively 0.027 plus or minus 0.13, 0.0025 plus or minus 0.063, 0.032 plus or minus 0.072, 0.0046 plus or minus 0.058, 0.025 plus or minus 0.054 and 0.00021 plus or minus 0.027. And selecting 3 factors of building height with the maximum absolute value of the average regression coefficient, mixing entropy of the functional area and road density to construct the urban carbon emission form partition.
And 5, dividing the urban form factors into three types of high, medium and low values by adopting a Natural break point method (Natural break) based on the 3 urban form factors obtained in the step 4, and combining all the screened factors in the urban construction area to form 27 carbon emission form areas with different modes.
And 6, respectively combining the screened urban internal characteristic index (functional area mixed entropy) with vegetation coverage, bare land and water body type areas in the urban non-construction land coverage area to form 9 carbon emission form subareas in different modes.
And 7, according to the partition distribution characteristics of the carbon emission forms, finding that the carbon emission forms do not accord with normal distribution, verifying the difference between the carbon emission form partitions of the urban construction surface and the non-construction surface by using a Kruskal-Wallis non-parameter inspection method, and finding that the progressive significance H is smaller than 0.01, wherein the difference is obvious.
Step 8, verifying the representativeness of the carbon emission morphology partition by using the q value of the factor detection module in the geographic detector, and finding that the similarity (q=0.121) of the carbon emission morphology partition of the urban construction surface is higher than the similarity (q=0.051) of the carbon emission morphology partition covered by the non-construction land. The carbon emission morphology zone similarity of 3 factors (building height, functional zone mixed entropy and road density) is higher in the high value zone than in the low value zone, and much higher than in the median zone.
And 9, using cold and hot point analysis to ascertain high-value and low-value aggregation areas of carbon dioxide emission in Shanghai city, counting typical carbon emission form partition proportion of the low-value aggregation areas, and finding out that the combination type of low-road network density in low-mixed entropy and low-building height is a main combination type, wherein the main city form characteristics of the partitions are that the building density and the road density change trend are opposite. It is therefore recommended that morphological feature values of the compact low-level low-road network (1-1-1), the more compact low-level medium-road network (1-1-2), the compact medium-level low-road network (1-2-1), the more compact medium-level medium-road network (1-2-2) are used as control thresholds when low mixing (function area mixing entropy is less than or equal to 0.0029) is recommended in the detailed planning of the neighborhood scale.
Claims (2)
1. The urban carbon emission form partition construction method based on the ground statistics is characterized by comprising the following steps of:
step 1, data preparation, which comprises preparing urban land utilization type, urban carbon dioxide emission space distribution and urban form factor data; wherein, the urban carbon dioxide emission spatial distribution data comprises: carbon dioxide emission distribution data for residential homes and transportation trips; the city form factor data includes: three types of city form factor data of city exterior form, interior feature and development intensity;
step 2, dividing the city into a city center area and a suburban area according to the construction land area proportion;
step 3, selecting urban form factors with stronger influence by using a geographic weighted regression method in a urban center according to the result of the step 2; taking urban carbon dioxide emission space distribution as a dependent variable, taking three types of urban form factors as independent variables, and removing factors with variance expansion factor values larger than 5 on the basis of normalized independent variable factors and multiple collinearity analysis;
step 4, using the regression result obtained in the step 3, selecting three or more factors with the maximum absolute value of the average regression coefficient to construct urban carbon emission form partitions;
step 5, obtaining the product in step 4The obtained city form factor (n is more than or equal to 3) is taken as a basis, and is divided into three types of high, medium and low values by adopting a Natural break point method (Natural break) method, and all screened factors are combined in a city construction area to form k 1 A plurality of different modes of carbon emission morphology partitioning; wherein the method comprises the steps of；
Step 6, in the coverage area of the urban non-construction land, respectively combining the screened urban internal characteristic index (m) with vegetation coverage, bare land and water body type areas; when m is greater than or equal to 1, form k 2 A plurality of different modes of carbon emission morphology partitioning; wherein the method comprises the steps ofThe method comprises the steps of carrying out a first treatment on the surface of the When m=0, no carbon emission morphology partition is built;
step 7, verifying the difference between the carbon emission morphology partitions obtained in the step 5 and the step 6 by using a statistical test method;
and 8, verifying the representativeness of the carbon emission morphology partition obtained in the step 5 and the step 6 by using a factor detection module in the geographic detector.
2. The method according to claim 1, wherein step 1) the city exterior comprises: building height, building shape index, volume rate, crowding degree, sky opening factor; the internal features include: functional area mixed entropy, intersection density, bus stop density and landscape accessibility; the development strength comprises: road density, building density, public facility density.
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| CN117911880B (en) * | 2024-03-20 | 2024-05-31 | 浙江大学 | Urban carbon emission space-time distribution simulation method and system based on remote sensing image |
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