A method and device for obtaining the spatial carbon emissions of residents

Abstract

The present invention provides a method and device for obtaining the spatial carbon emissions of residents, belonging to the technical field of environmental monitoring. First, obtain the POI data and night-time light remote sensing data of the target area, divide the target area into grids, determine the importance level of each land use type in each grid according to the POI data of each land use type in each grid, and then calculate the night-time pure light value of the residential land use type under each grid based on the obtained night-time light remote sensing data; determine the carbon emissions of each grid according to the total carbon emissions of residents in the target area and the proportion of the night-time pure light value of the residential land use type in each grid in the target area. Compared with the prior art, in the present invention, through the extracted night-time pure light value of the residential land use type, a more accurate spatial distribution pattern of residents' carbon emissions is obtained, the influence of night-time lights of other land use types is reduced, the accuracy of the spatial distribution result of residents' carbon emissions is improved, and a more accurate data source is provided for subsequent low-carbon research.

Description

Technical Field

[0001] The present invention relates to a method and device for obtaining the spatial carbon emissions of residents, belonging to the technical field of environmental monitoring. Background Art

[0002] The global warming caused by the increase in carbon emissions has a profound impact on people's lives and health. In order to cope with the serious consequences caused by global warming, countries around the world have started a series of actions to curb climate warming and reduce anthropogenic carbon dioxide emissions. Residential carbon emissions are an important source of carbon emissions. Studying the spatial distribution pattern of residential carbon emissions is of great significance for low-carbon development and low-carbon construction. Nighttime lights can objectively reflect the status of human social production and life activities, and nighttime light data has been widely used in the research of urban development, natural environment, and social and economic activities.

[0003] Currently, the main method for obtaining the spatial distribution pattern of residential carbon emissions is to combine nighttime light data with residential carbon emission statistical data; however, the obtained nighttime light data is a mixture of light data of various land types, and this nighttime light data includes data of residential, commercial, industrial, public service, etc. types. Directly using the nighttime light data and residential carbon emission statistical data to determine the spatial distribution of residential carbon emissions, the results obtained by this method are interfered by the nighttime light data of other land types, and it is impossible to accurately obtain the carbon emission distribution of residential land types in space, resulting in a low accuracy of the finally generated spatial distribution result of residential carbon emissions and being unable to provide accurate data for other related research (such as low-carbon research). Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for obtaining the spatial carbon emissions of residents to solve the problem that the accuracy of the spatial carbon emissions of residents obtained directly based on nighttime light data in the prior art is relatively low.

[0005] The present invention proposes a method for obtaining the spatial carbon emissions of residents, and the method includes the following steps:

[0006] 1) Obtain the POI data of each land type and nighttime light remote sensing data of the target area;

[0007] 2) Divide the target area into grids, calculate the POI density, dominance ratio, and total amount of POI of each land type under each grid; and determine the importance degree of each land type in each grid according to the POI density, dominance ratio, and total amount of POI of different land types in each grid;

[0008] 3) Calculate the nighttime pure light value of residential land types under each grid according to the obtained nighttime light remote sensing data and the importance degree of each land type in each grid;

[0009] 4) Obtain the total carbon emissions of residential land types in the target area, and calculate the proportion of the night pure light value of each grid's residential land type in the night pure light value of the residential land type in the target area; determine the carbon emissions of each grid's residential land type in the target area according to the proportion and the total carbon emissions of the residential land type in the target area.

[0010] The present invention also provides a device for obtaining the carbon emissions of residential space. The device includes a processor and a memory. The processor is configured to execute the computer program stored in the memory to implement the method for obtaining the carbon emissions of residential space as described above.

[0011] Through the importance levels of each land type obtained from the POI data of each land type, the present invention effectively extracts the night pure light value of the residential land type from the night light remote sensing data, and according to the total carbon emissions of the residential land type and the night pure light value of the residential land type in each grid extracted, obtains a relatively accurate spatial distribution pattern of residential carbon emissions. Compared with directly using night light remote sensing data to obtain residential carbon emissions in the prior art, it reduces the influence of the night lights of other land types, improves the accuracy of the spatial distribution result of residential carbon emissions, and provides a more accurate data source for subsequent low-carbon research.

[0012] Further, the calculation formula for the carbon emissions of each grid is:

[0013] C g = S × Δ g

[0014]

[0015] In the formula, C g is the carbon emissions of the residential land type of each grid, S is the total carbon emissions of the residential land type in the target area based on statistical data, Δ g is the proportion of the night pure light value of the residential land type in the night pure light value of the residential land type in the target area, d g is the night pure light value of the residential land type of each grid, ∑d g is the night pure light value of the residential land type in the target area, g = 1, 2, …, m, and m is the total number of grids.

[0016] Through the above process, based on the proportion of the night light value of the residential land type in each grid in the total night light value of the residential land type in the target area where it is located and the total carbon emissions, determine the carbon emissions of each grid, and obtain the spatial distribution of residential carbon emissions.

[0017] Further, the calculation formula for the POI density of each land type is:

[0018]

[0019] In the formula, t represents the POI land type, nt Represents the quantity of POI land use type t in grid g, N t Represents the quantity of POI land use type t in the target area, N g Represents the quantity of all POIs of land use types in grid g, N represents the total quantity of POIs in the entire target area, g = 1, 2, …, m, where m is the total number of grids.

[0020] Furthermore, the calculation formula for the odds ratio is:

[0021]

[0022] In the formula, t represents the POI land use type, n t Represents the quantity of POI land use type t in grid g, N t Represents the quantity of POI land use type t in the target area, N g Represents the quantity of all POIs of land use types in grid g, N represents the total quantity of POIs in the target area, g = 1, 2, …, m, where m is the total number of grids.

[0023] Furthermore, the calculation formula for the importance degree of each land use type in each grid is:

[0024]

[0025] In the formula, O t,g Represents the odds ratio of land use type t under grid g, P t,g Represents the POI density of land use type t under grid g, n t,g Represents the total quantity of POIs of land use type t under grid g, S t,g Represents the importance degree of land use type t under grid g.

[0026] The POI density is determined through the above process to show the aggregation degree of any land use type under the grid where it is located. At the same time, since the POI densities of different land use types under the same grid may be similar, the odds ratio is calculated through the above process to evaluate which land use type has a more reliable POI density, and the importance degree of each land use type in each grid is determined based on the calculated POI density, odds ratio, and total quantity of POIs.

[0027] Furthermore, the calculation formula for the night-time pure light value of each land use type under each grid is:

[0028] Q g,t = A g × W gt

[0029] In the formula, Q g,t Is the night-time pure light value of land use type t in grid g, A g Is the total night-time light value in grid g, W gt Is the weight value of land use type t in grid g.

[0030] Further, the weight values of each land use type in the grid are normalized according to the importance of each land use type in each grid, and its calculation formula is as follows:

[0031]

[0032] In the formula, x' is the normalization result, x represents the original data, that is, the importance of different land use types under each grid, min represents the minimum value of the importance of the same land use type in all grids, and max represents the maximum value of the importance of the same land use type in all grids.

[0033] To facilitate the calculation of the night-time pure light value of each land use type under each grid, the importance of each land use type in each grid is normalized, and the normalized value is used as the weight value of a certain land use type under a certain grid. Based on this, the night-time pure light value of each land use type under each grid can be obtained more accurately.

[0034] Further, the division of the grid is determined according to the resolution of the night-time light remote sensing data. The higher the resolution of the night-time light remote sensing data, the denser the divided grid.

[0035] Through the above process, the grid is divided according to the resolution of the night-time light remote sensing data to achieve adaptive adjustment at different resolutions. The higher the resolution of the night-time light remote sensing data, the denser the divided grid, and the more accurate the obtained data.

[0036] Further, step 1) also includes preprocessing of the POI data, and this preprocessing includes data cleaning, duplicate checking, and coordinate transformation.

[0037] Since there may be duplicate or invalid data in the obtained POI data, it is necessary to clean and check the data, eliminate duplicate data and invalid data, reduce unnecessary calculations, ensure the accuracy of the data, and at the same time, it is also necessary to unify the coordinates of all data to ensure the smooth progress of the subsequent process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is the flow chart of obtaining the residential spatial carbon emissions of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0039] The following further describes the specific embodiments of the present invention with reference to the accompanying drawings.

[0040] Method Embodiment

[0041] The present invention provides a method for obtaining the residential spatial carbon emissions. The specific process is as Figure 1As shown in the figure. First, obtain the POI data and nighttime light remote sensing data of the target area, divide the target area into grids, determine the importance level of each land use type in each grid according to the POI data of each land use type in each grid, and then calculate the nighttime pure light value of each land use type under each grid based on the obtained nighttime light remote sensing data; determine the carbon emissions of each grid according to the total carbon emissions of residents in the target area and the proportion of the nighttime pure light value of the residential land use type in each grid in the target area.

[0042] Step 1. Obtain data and preprocess it

[0043] The present invention first obtains the POI data and nighttime light remote sensing data in the target area, and classifies the POI data in the target area. In this embodiment, the POI data is divided into public service type, commercial service type, residential type, and industrial type. Among them, the POI data can be obtained from existing geographical databases. At the same time, in order to ensure the accuracy of the final result, the acquisition times of the two types of data should not be too different, and the nighttime light remote sensing data with the closest time to the acquisition time of the POI data can be selected according to the acquisition time of the POI data. As other implementation manners, the specific classification categories of the POI data can be determined according to actual requirements.

[0044] At the same time, it is also necessary to preprocess the obtained POI data, including data duplicate checking, cleaning, and coordinate transformation. Since there may be duplicate or invalid data in the obtained POI data, it is necessary to clean and check the data, remove duplicate data and invalid data, reduce unnecessary calculations, and ensure the accuracy of the data. At the same time, considering the diversity of the databases from which the data is obtained, and the possible different coordinate systems of the POI data and the nighttime light remote sensing data, in order to ensure the smooth progress of the subsequent process and the accuracy of the final calculation result, it is necessary to ensure that the coordinate systems of all data are unified, and all data can be unified under the coordinate system where the obtained nighttime light remote sensing data is located (such as WGS-84, CGCS2000).

[0045] Step 2. Divide the target area into grids

[0046] According to the resolution of the nighttime light remote sensing data, divide the target area into grids, and divide the target area into grids with the same size as the resolution of the nighttime light remote sensing data, that is, the size of each grid is the same as the resolution of the nighttime light remote sensing data. For example, if the resolution of the nighttime light remote sensing data is 1KM, then each grid in the target area is 1KM×1KM; if the resolution of the nighttime light remote sensing data is 500m, then each grid in the target area is 500m×500m; when the resolution is higher, the divided grids are denser.

[0047] Step 3. Calculate the importance level of different land use types in each grid

[0048] For the grids divided in Step 2, count the POI data of each land use type under each grid, and thus determine the POI density, odds ratio and total amount of POI for each land use type under each grid. Calculate the importance degree of each land use type under each grid according to the POI density, odds ratio and total amount of POI. Among them, the POI density represents the degree of aggregation of this land use type in this grid; since the POI densities of different land use types in different grids may be similar, the odds ratio is used to evaluate which land use type is more reliable; the total amount of POI refers to the quantity of each land use type in each grid.

[0049] Among them, the calculation formula of the POI density is as follows:

[0050]

[0051] In the formula, t represents the POI land use type, and n t represents the quantity of the POI land use type t in the grid g; N t represents the quantity of the POI land use type t in the target area; N g represents the quantity of all land use type POIs in the grid g; N represents the total amount of POIs in the entire target area, g = 1, 2, …, m, and m is the total number of grids. In this embodiment, the POI data is divided into 4 categories. Therefore, t = 1, 2, 3, 4, representing public service, commercial service, residential, and industrial categories respectively.

[0052] The calculation formula of the odds ratio is as follows:

[0053]

[0054] In the formula, t represents the POI land use type, and n t represents the quantity of the POI land use type t in the grid g; N t represents the quantity of the POI land use type t in the target area; N g represents the quantity of all land use type POIs in the grid g; N represents the total amount of POIs in the target area, g = 1, 2, …, m, and m is the total number of grids.

[0055] Therefore, the calculation formula of the importance degree of different land use types under each grid is as follows:

[0056]

[0057] In the formula, Q g,t is the night-time pure light value of the land use type t in the grid g; A g is the total night-time light value in the grid g; W gt is the weight value of the land use type t in the grid g.

[0058] Step 4. Determine the weight values of different land use types under each grid

[0059] According to Formulas (1)-(3), the importance levels of different land types under each grid can be calculated. The importance levels of each land type in each grid are normalized, and the specific calculation method is as follows:

[0060]

[0061] In the formula, x′ represents the normalization result, x represents the original data, that is, the importance levels of each land type under each grid calculated, min represents the minimum value of the importance level of the same land type in all grids, and max represents the maximum value of the importance level of the same land type in all grids. Through this formula, the normalization results of each land type in each grid can be determined, and the importance levels of each land type in each grid are converted to between 0 and 1, and this result is used as the weight value of each land type in each grid.

[0062] Step 5. Calculate the nighttime pure light value of the residential land type under each grid

[0063] According to the location (latitude and longitude) of each grid, find the nighttime light value at the location corresponding to the grid in the nighttime light remote sensing data, and use the weights of different land types calculated above to calculate the nighttime pure light value of different land types under each grid. The formula is as follows:

[0064] Q g,t =A g ×W gt (5)

[0065] In the formula, Q g,t is the nighttime pure light value of land type t in grid g; A g is the total nighttime light value in grid g; W gt is the weight value of land type t in grid g, g = 1, 2, …, m, where m is the total number of grids; through this formula, the nighttime pure light value of the residential land type can be calculated.

[0066] Step 6. Determine the carbon emissions of each grid

[0067] Since the carbon emission statistical data in a certain area has a certain periodicity, and the nighttime light remote sensing data has a certain acquisition frequency, it cannot be guaranteed that the two data are completely synchronized. Therefore, when obtaining the carbon emission statistical data, according to the acquisition time of the nighttime light remote sensing data, select the carbon emission statistical data with the same or the closest time.

[0068] Then, according to the calculated nighttime pure light value of the residential land type in each grid, calculate the total nighttime pure light value of the residential land type in the target area, and calculate the proportion of the nighttime pure light value of the residential land type in each grid to the total nighttime pure light value of the residential land type. Its calculation method is shown in Formula (6):

[0069]

[0070] In the formula, Δ g is the proportion of the nighttime pure light value of the residential land type in the target area to the nighttime pure light value of the residential land type in the target area, d g is the nighttime pure light value of the residential land type in each grid, and ∑d g is the nighttime pure light value of the residential land type in the target area. In this embodiment, the target area may refer to an area such as a district, a city, or a province.

[0071] According to the proportion calculated by formula (6) and the statistically obtained data of the residential carbon emissions in the target area, the carbon emissions of each grid are determined. The calculation formula is as follows:

[0072] C g = S×Δ g (7)

[0073] In the formula, C g is the carbon emissions of the residential land type in each grid, S is the total carbon emissions of the residential land type in the target area based on statistical data, and Δ g is the proportion of the nighttime pure light value of the residential land type in the target area to the nighttime pure light value of the residential land type in the target area, where g = 1, 2,..., m, and m is the total number of grids.

[0074] According to the above process, the distribution of the residential spatial carbon emissions in the target area can be determined. In addition, when the scope of the target area is larger than the smallest unit of the obtained carbon emission statistical data, the target area can be split into different regions, and the carbon emissions of each grid in each region are calculated. For example, if the obtained carbon emission statistical data is in units of districts (counties), and the target area is a municipal area, the entire target area can be divided into regions according to districts (counties) at this time. The carbon emissions of each grid in each region are calculated through formula (6) to determine the distribution of the residential spatial carbon emissions in different regions, so as to determine the distribution of the residential spatial carbon emissions in the target area.

[0075] Device embodiment

[0076] The present invention also proposes a device for obtaining residential spatial carbon emissions. The system includes a processor and a memory. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the method of the above method embodiment is implemented. That is to say, the method in the above method embodiment should be understood as a process for obtaining the method of residential spatial carbon emissions that can be implemented by computer program instructions. These computer program instructions can be provided to the processor, so that by executing these instructions by the processor, the functions specified by the above method process are generated.

[0077] The processor referred to in this embodiment refers to a processing device such as a microprocessor MCU or a field-programmable gate array FPGA; the memory referred to in this embodiment includes a physical device for storing information, usually storing the information after digitization and then using media such as electricity, magnetism, or optics. For example: various memories that store information using electrical energy, such as RAM, ROM, etc.; various memories that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, bubble memories, USB flash drives; various memories that store information using optical methods, such as CDs or DVDs. Of course, there are also other types of memories, such as quantum memories, graphene memories, etc.

[0078] The device composed of the above-mentioned memory, processor, and computer program is implemented in a computer by the processor executing corresponding program instructions. The processor can be equipped with various operating systems, such as the Windows operating system, Linux system, Android, iOS system, etc. As another implementation, the system may further include a display for displaying the results of the carbon emissions in the residential space for the reference of the staff.

Claims

1. A method for obtaining the spatial carbon emissions of residents, characterized in that, The method includes the following steps: 1) Obtain the POI data of each land use type in the target area and the night light remote sensing data; 2) Divide the target area into grids, calculate the POI density, odds ratio and total POI of each land use type under each grid; and determine the importance degree of each land use type in each grid according to the POI density, odds ratio and total POI of different land use types in each grid; the calculation formula of the odds ratio is: Wherein, t represents the POI land type, and n t represents the quantity of the POI land type t in the grid g, and N t represents the quantity of the POI land type t in the target area, and N g represents the quantity of all land type POIs in the grid g. N represents the total quantity of POIs in the target area, g = 1, 2, …, m, and m is the total number of grids; The calculation formula for the importance degree of each land type in each grid is as follows: In the formula, O t,g represents the odds ratio of land use type t under grid g, P t,g represents the POI density of land use type t under grid g, n t,g represents the total amount of POIs of land use type t under grid g, S t,g represents the importance degree of land use type t under grid g; 3) Calculate the night pure light value of the residential land use type under each grid according to the obtained night light remote sensing data and the importance degree of each land use type in each grid; the calculation formula of the night pure light value of each land use type under each grid is: Q g,t = A g × W gt Where Q g,t is the night-time pure light value of land use type t in grid g, A g is the total night-time light value in grid g, and W gt is the weight value of land use type t in grid g; the weight values of land use types in the grid are normalized according to the importance of land use types in each grid; 4) Obtain the total carbon emissions of the residential land use type in the target area, and calculate the proportion of the night pure light value of the residential land use type in each grid in the night pure light value of the residential land use type in the target area; determine the carbon emissions of the residential land use type in each grid in the target area according to the proportion and the total carbon emissions of the residential land use type in the target area.

2. The method for obtaining the spatial carbon emissions of residents according to claim 1, characterized in that, The calculation formula of the carbon emissions of each grid is: C g = S × Δ g where C g is the carbon emission of the residential area type in the g-th grid, S is the total carbon emission of the residential area type in the target area based on statistical data, and Δ g is the proportion of the pure night light value of the residential area type in the g-th grid to the pure night light value of the residential area type in the target area, d g is the pure night light value of the residential area type in the g-th grid, ∑d g is the pure night light value of the residential area type in the target area, g = 1, 2, …, m, and m is the total number of grids.

3. The method for obtaining the spatial carbon emissions of residents according to claim 1, characterized in that The calculation formula of the POI density of each land use type is: where t represents the POI land use type, n t represents the quantity of POI land use type t in grid g, N t represents the quantity of POI land use type t in the target area, N g represents the quantity of all POI land use types in grid g, N represents the total quantity of POIs in the entire target area, g = 1, 2, …, m, and m is the total number of grids.

4. The method for obtaining the spatial carbon emissions of residents according to claim 1, characterized in that, The calculation formula used when obtaining the weight value of each land use type in the grid by normalizing the importance degree of each land use type in each grid is as follows: In the formula, x′ is the normalization result, x represents the original data, that is, the importance degree of different land use types under each grid, min represents the minimum value of the importance degree of the same land use type in all grids, and max represents the maximum value of the importance degree of the same land use type in all grids.

5. The method for obtaining the spatial carbon emissions of residents according to claim 1, wherein The division of the grid is determined according to the resolution of the night light remote sensing data. The higher the resolution of the night light remote sensing data, the denser the divided grid.

6. The method for obtaining the spatial carbon emissions of residents according to claim 1, wherein The step 1) also includes the preprocessing of the POI data, and the preprocessing includes data cleaning, duplicate checking and coordinate transformation.

7. An apparatus for obtaining the spatial carbon emissions of residents, characterized in that, The device includes a processor and a memory. The processor is used to execute the computer program stored in the memory to implement the method for obtaining the carbon emissions of the residential space as described in any one of claims 1-6 above.

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