A method, apparatus, equipment, and medium for quantifying the uniformity of global heat island intensity distribution.

Abstract

This invention discloses a method, apparatus, device, and medium for quantifying the uniformity of global heat island intensity distribution. The method includes: generating corresponding surface temperature raster data; determining the heat island intensity value for each urban spatial unit globally; constructing an extended Lorenz curve based on the ranking, cumulative population ratio, and heat island intensity ratio of each urban spatial unit; calculating the improved Gini coefficient based on the extended Lorenz curve; fitting a first slope based on the sequence of improved Gini coefficients for each time period ranked by per capita GDP; fitting a second slope based on the sequence of improved Gini coefficients for each time period ranked by heat island intensity values; and determining the global trend interpretation result based on the first slope and its corresponding significance test results, and the second slope and its corresponding significance test results. This invention belongs to the field of heat island intensity quantification. This invention accurately identifies global heat island change trends.

Description

Technical Field

[0001] This invention relates to the field of heat island intensity quantification, and more particularly to a method, apparatus, equipment, and medium for quantifying the global heat island intensity distribution uniformity. Background Technology

[0002] With the intensification of urbanization and climate change, the urban heat island effect increasingly threatens residents' health, energy systems, and the ecological environment. Studies have found that the distribution of heat islands is not homogeneous and is often intertwined with socioeconomic factors such as income and population density, leading to unequal heat risks experienced by different groups. Therefore, quantifying the fairness of heat island distribution among populations and regions from an environmental justice perspective has significant scientific and policy implications.

[0003] Existing technologies have significant shortcomings in assessing the fairness of heat islands: traditional indicators such as the Gini coefficient cannot handle heat island intensity data containing both positive and negative values, which can easily lead to computational instability. Therefore, this invention provides a quantitative method for the equilibrium of global heat island intensity distribution. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for quantifying the uniformity of global heat island intensity distribution. It solves the technical problem in the prior art where traditional indicators such as the Gini coefficient cannot support reliable urban climate governance and environmental justice decisions because they cannot handle heat island intensity data containing positive and negative values. It achieves the technical effect of avoiding computational instability caused by the inability to handle heat island intensity data containing positive and negative values.

[0005] In a first aspect, the present invention provides a method for quantifying the uniformity of global heat island intensity distribution, comprising:

[0006] Acquire global remote sensing data streams, perform radiometric calibration and atmospheric correction on the remote sensing data streams, and generate corresponding surface temperature raster data;

[0007] By performing zonal statistical analysis on surface temperature raster data, the heat island intensity value of each urban spatial unit globally can be determined.

[0008] An extended Lorenz curve is constructed based on the ranking of urban spatial units, the cumulative population ratio, and the heat island intensity ratio. An improved Gini coefficient is calculated based on the extended Lorenz curve. The ranking of urban spatial units is related to per capita GDP or heat island intensity value. An improved Gini coefficient is calculated once for each ranking of urban spatial units. The ordered sequence of urban spatial units includes the corresponding heat island intensity value and the corresponding population data.

[0009] The first slope is obtained by fitting the improved Gini coefficient sequence of each time segment based on GDP per capita ranking, and the second slope is obtained by fitting the improved Gini coefficient sequence of each time segment based on heat island intensity value ranking. Based on the first slope and the corresponding significance test results, the second slope and the corresponding significance test results, the global trend interpretation results are determined. The trend interpretation results include overall deterioration, overall improvement, differentiated evolution, single-dimensional change, and overall stability.

[0010] Further, the cumulative population proportion is calculated, including:

[0011]

[0012] in, For the first The cumulative population ratio of each urban spatial unit For the first Population data for each urban spatial unit For the first Population data for each urban spatial unit The number of urban spatial units, For the first Each urban spatial unit, and Greater than or equal to And make , , , .

[0013] Further, the intensity ratio of the urban heat island is calculated, including:

[0014] The calculation of the total weighted heat island intensity includes:

[0015]

[0016] in, For the total weighted heat island intensity, For the first Heat island intensity values ​​for individual urban spatial units;

[0017] like Then, standardization is performed, including:

[0018]

[0019] in, To preset a positive threshold, For the first The heat island intensity ratio of each urban spatial unit For the first Heat island intensity values ​​for individual urban spatial units;

[0020] like Then, absolute value standardization is performed, including:

[0021]

[0022] Among them, let .

[0023] Furthermore, based on the extended Lorenz curve, the improved Gini coefficient is calculated, including:

[0024] Calculate the area between the perfectly parallel line and the extended Lorenz curve, including:

[0025]

[0026] in, The area between the perfectly parallel line and the extended Lorenz curve;

[0027] The improved Gini coefficient is determined based on the area between the absolute parity line and the extended Lorenz curve, including:

[0028]

[0029] in, To improve the Gini coefficient.

[0030] Furthermore, based on the sequence of improved Gini coefficients for each time slice ranked by GDP per capita, a first slope is fitted, and based on the sequence of improved Gini coefficients for each time slice ranked by urban heat island intensity, a second slope is fitted, including:

[0031]

[0032]

[0033] in, The second slope, For sorting based on heat island intensity values ​​and with time series as follows The sequence of improved Gini coefficients, The first slope, Based on GDP per capita ranking and with time series data The sequence of improved Gini coefficients;

[0034] Also includes:

[0035] right Perform a significance test and obtain the corresponding significance test results. ;

[0036] right Perform a significance test and obtain the corresponding significance test results. .

[0037] Furthermore, based on the first slope and its corresponding significance test results, and the second slope and its corresponding significance test results, the global trend interpretation results are determined, including:

[0038] when , , as well as At that time, the situation deteriorated across the board;

[0039] when , , as well as At that time, for comprehensive improvement;

[0040] when , , as well as At that time, it was a period of differentiation and evolution;

[0041] when , as well as At that time, it is a single-dimensional change;

[0042] when as well as At that time, the overall situation was stable.

[0043] Furthermore, the heat island intensity value for each urban spatial unit globally was determined, including:

[0044]

[0045] in, For the first Heat island intensity value of each urban spatial unit The first in the city's built-up area Temperature value of each pixel The first in the suburban background area of ​​the city Temperature value of each pixel For the first The area of ​​a pixel For the first The area of ​​a pixel The total number of effective pixels in the built-up area. This represents the total number of effective pixels in the suburban background area.

[0046] Secondly, the present invention provides a device for quantifying the uniformity of global heat island intensity distribution, comprising:

[0047] The data stream acquisition module is used to acquire global remote sensing data streams, perform radiometric calibration and atmospheric correction on the remote sensing data streams, and generate corresponding surface temperature raster data.

[0048] The heat island intensity estimation module is used to perform zonal statistics on surface temperature raster data to determine the heat island intensity value for each urban spatial unit globally.

[0049] An improved Gini coefficient calculation module is used to construct an extended Lorenz curve based on the ranking of urban spatial units, the cumulative population ratio, and the heat island intensity ratio, and to calculate the improved Gini coefficient based on the extended Lorenz curve. The ranking of urban spatial units is related to per capita GDP or heat island intensity value, and the improved Gini coefficient is calculated once for each ranking of urban spatial units. The ordered sequence of urban spatial units includes the corresponding heat island intensity value and the corresponding population data.

[0050] The trend interpretation results module is used to fit the improved Gini coefficient series of each time segment based on GDP per capita to obtain the first slope, and the improved Gini coefficient series of each time segment based on heat island intensity value to obtain the second slope. Based on the first slope and the corresponding significance test results, the second slope and the corresponding significance test results are used to determine the global trend interpretation results. The trend interpretation results include overall deterioration, overall improvement, differentiated evolution, single-dimensional change, and overall stability.

[0051] Thirdly, the present invention provides an electronic device, comprising:

[0052] processor;

[0053] Memory used to store processor-executable instructions;

[0054] The processor is configured to execute a quantification method for the uniformity of global heat island intensity distribution, as provided in the first aspect.

[0055] Fourthly, the present invention provides a non-transitory computer-readable storage medium, wherein when instructions in the non-transitory computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to execute a quantification method for achieving the global heat island intensity distribution equilibrium as provided in the first aspect.

[0056] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0057] This invention introduces a dynamic standardization judgment mechanism, which uses a calculation unit to detect in real time whether the weighted total strength is close to zero and adaptively switches between conventional standardization and absolute value standardization strategies. This fundamentally ensures the numerical stability and mathematical rationality of the improved Gini coefficient calculation and solves the key technical bottleneck of quantifying the fairness of signed environmental indicators.

[0058] This invention breaks through the limitations of traditional single-dimensional analysis. By using cyclical calculations to improve the Gini coefficient, and employing per capita GDP and the intensity of the urban heat island as ranking benchmarks, it achieves multi-dimensional comparative monitoring of socioeconomic equity and natural exposure equity, effectively separating the natural attributes and social driving factors of urban heat island distribution. Combined with time-series slope fitting and significance testing, the system can automatically identify five types of global trends, including overall deterioration and differentiated evolution, generating conclusions with clear policy implications. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating a method for quantifying the uniformity of global heat island intensity distribution provided by the present invention. Detailed Implementation

[0061] This invention provides a quantitative method for the distribution equilibrium of global heat island intensity, solving the technical problem that traditional indicators such as the Gini coefficient cannot support reliable urban climate governance and environmental justice decision-making because they cannot handle heat island intensity data containing positive and negative values.

[0062] The technical solution of this invention is to solve the above-mentioned technical problems, and the overall idea is as follows:

[0063] A method for quantifying the global heat island intensity distribution equilibrium includes: acquiring global remote sensing data streams and performing radiometric calibration and atmospheric correction on the remote sensing data streams to generate corresponding land surface temperature raster data; performing zonal statistical analysis on the land surface temperature raster data to determine the heat island intensity value for each urban spatial unit globally; constructing an extended Lorenz curve based on the ranking, cumulative population proportion, and heat island intensity proportion of each urban spatial unit; and calculating the improved Gini coefficient based on the extended Lorenz curve, wherein the ranking of each urban spatial unit is related to per capita GDP or heat island intensity value, and the coefficient is calculated each time the urban spatial unit is ranked. The improved Gini coefficient is calculated once. The ordered sequence of each urban spatial unit includes the corresponding heat island intensity value and the corresponding population data. Based on the improved Gini coefficient sequence of each time piece ranked by GDP per capita, the first slope is fitted, and based on the improved Gini coefficient sequence of each time piece ranked by heat island intensity value, the second slope is fitted. Based on the first slope and the corresponding significance test results, the second slope and the corresponding significance test results, the global trend interpretation results are determined. The trend interpretation results include overall deterioration, overall improvement, differentiated evolution, single-dimensional change, and overall stability.

[0064] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0065] First, it should be clarified that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0066] This invention provides, for example Figure 1 The method shown includes steps S11-S14 for quantifying the global heat island intensity distribution equilibrium:

[0067] Step S11: Acquire global remote sensing data streams, perform radiometric calibration and atmospheric correction on the remote sensing data streams, and generate corresponding surface temperature raster data.

[0068] It can refer to acquiring global remote sensing data streams (usually digital values ​​recorded by sensors, i.e., DN values) from remote sensing platforms such as satellites and drones.

[0069] After acquiring the remote sensing data stream, the DN values ​​are converted into physically meaningful radiance or apparent brightness temperature through radiometric calibration; then, atmospheric correction is performed to eliminate the interference of atmospheric composition on the thermal infrared signal, thereby retrieving thermal radiation information that is closer to the actual surface radiation; finally, surface temperature raster data with pixels as the unit and a unified spatial reference and temperature unit are generated.

[0070] Step S12: Perform regional statistics on the surface temperature raster data to determine the heat island intensity value for each urban spatial unit globally.

[0071] Zonal statistics of land surface temperature raster data refers to using the spatial analysis function of a geographic information system to divide continuous land surface temperature raster data according to predefined geographic boundaries (such as urban administrative boundaries, built-up areas and suburban areas, etc. vector layers), and to perform statistical calculations on temperature pixel values ​​within each spatial zone.

[0072] In this invention, the average surface temperature of each urban built-up area and the corresponding suburban background area is calculated separately, and then used to quantify the heat island intensity, thereby realizing the conversion from pixel-scale temperature data to urban-scale thermal environment indicators.

[0073] Determine the heat island intensity value for each urban spatial unit globally, including:

[0074]

[0075] in, For the first Heat island intensity value of each urban spatial unit The first in the city's built-up area Temperature value of each pixel The first in the suburban background area of ​​the city Temperature value of each pixel For the first The area of ​​a pixel For the first The area of ​​a pixel The total number of effective pixels in the built-up area. This represents the total number of effective pixels in the suburban background area.

[0076] In this invention, the urban spatial unit can also be interpreted as a city.

[0077] Step S13: Construct an extended Lorenz curve based on the ranking of each urban spatial unit, the cumulative population ratio, and the heat island intensity ratio. Calculate the improved Gini coefficient based on the extended Lorenz curve. The ranking of each urban spatial unit is related to per capita GDP or heat island intensity value. The improved Gini coefficient is calculated once for each ranking of urban spatial units. The ordered sequence of each urban spatial unit includes the corresponding heat island intensity value and the corresponding population data.

[0078] Specifically, when the spatial units of cities are arranged in ascending order based on GDP per capita, a sequence can be obtained: ;

[0079] When the urban spatial units are sorted in ascending order by their heat island intensity values, another sequence can be obtained: .

[0080] When sorting urban spatial units in ascending order by GDP per capita, the "Calculate Cumulative Population Ratio - Calculate Improved Gini Coefficient" function is executed once. Similarly, when sorting urban spatial units in ascending order by heat island intensity value, the "Calculate Cumulative Population Ratio - Calculate Improved Gini Coefficient" function is executed once.

[0081] It is understandable that each calculation of the improved Gini coefficient will only yield the improved Gini coefficient corresponding to that specific time slice. For example, when the time slice is 2024-2025, only the improved Gini coefficient for the 2024-2025 period will be obtained. The improved Gini coefficient means that, under the original calculation logic of the Gini coefficient, the original parameters are replaced with the urban heat island intensity value and population data.

[0082] Calculating the cumulative population proportion includes:

[0083]

[0084] in, For the first The cumulative population ratio of each urban spatial unit For the first Population data for each urban spatial unit For the first Population data for each urban spatial unit The number of urban spatial units, For the first Each urban spatial unit, and Greater than or equal to And make , , , .

[0085] Calculating the heat island intensity ratio includes:

[0086] The calculation of the total weighted heat island intensity includes:

[0087]

[0088] in, For the total weighted heat island intensity, For the first Heat island intensity values ​​for individual urban spatial units;

[0089] like Then, standardization is performed, including:

[0090]

[0091] in, To preset a positive threshold, For the first The heat island intensity ratio of each urban spatial unit For the first Heat island intensity values ​​for individual urban spatial units;

[0092] like Then, absolute value standardization is performed, including:

[0093]

[0094] Among them, let .

[0095] The improved Gini coefficient is calculated based on the extended Lorenz curve, including:

[0096] Calculate the area between the perfectly parallel line and the extended Lorenz curve, including:

[0097]

[0098] in, The area between the perfectly parallel line and the extended Lorenz curve;

[0099] The improved Gini coefficient is determined based on the area between the absolute parity line and the extended Lorenz curve, including:

[0100]

[0101] in, To improve the Gini coefficient.

[0102] When sorting urban spatial units in ascending order by GDP per capita, the process of "calculating cumulative population ratio - calculating improved Gini coefficient" can be performed according to... The symbol and size are used to automatically generate a preliminary judgment on the fairness level.

[0103] Specifically, when This indicates that the heat island effect is concentrated in lower-ranked groups (such as affluent areas). This indicates that the heat island effect is concentrated in the top-ranked groups (such as impoverished areas). The larger the value, the more uneven the distribution.

[0104] When the urban spatial units are sorted in ascending order by per capita GDP and the "calculation of cumulative population ratio - calculation of improved Gini coefficient" is executed, this invention can transform the heat island effect from a physical phenomenon into a quantifiable social equity indicator.

[0105] Specifically, this invention ranks cities based on socioeconomic variables to construct a cumulative distribution relationship between population and heat island burden; then, it uses a dynamic standardization strategy to address the numerical instability problem when the total heat island intensity approaches zero; finally, it calculates a signed improved Gini coefficient based on the trapezoidal method, which not only reflects the degree of uneven distribution of heat islands but also reveals the direction of injustice through the sign.

[0106] In addition, when you get as well as After that, you can also To make a comparison, if The absolute value is significantly greater than This indicates that the distribution of heat island intensity is highly correlated with economic level.

[0107] Step S14: Based on the sequence of improved Gini coefficients for each time slice ranked by GDP per capita, a first slope is fitted; and based on the sequence of improved Gini coefficients for each time slice ranked by heat island intensity, a second slope is fitted. Based on the first slope and the corresponding significance test results, and the second slope and the corresponding significance test results, the global trend interpretation results are determined. The trend interpretation results include overall deterioration, overall improvement, differentiation and evolution, single-dimensional change, and overall stability.

[0108] Generally speaking, the improved Gini coefficient can be calculated once a year, or the number of calculations can be determined based on the actual situation.

[0109] For example, this invention statistically analyzes the improved Gini coefficient over a 10-year period, calculated annually, meaning the sequence of improved Gini coefficients contains 10 improved Gini coefficients. Specifically, the sequence of improved Gini coefficients for each time period ranked by GDP per capita contains 10 improved Gini coefficients, and the sequence of improved Gini coefficients for each time period ranked by urban heat island intensity also contains 10 improved Gini coefficients.

[0110] Based on the sequence of improved Gini coefficients for each time slice ranked by GDP per capita, a first slope was fitted; and based on the sequence of improved Gini coefficients for each time slice ranked by urban heat island intensity, a second slope was fitted, including:

[0111]

[0112]

[0113] in, The second slope, For sorting based on heat island intensity values ​​and with time series as follows The sequence of improved Gini coefficients, The first slope, Based on GDP per capita ranking and with time series data The sequence of improved Gini coefficients;

[0114] Also includes:

[0115] right Perform a significance test and obtain the corresponding significance test results. ;

[0116] right Perform a significance test and obtain the corresponding significance test results. .

[0117] This invention mainly uses the T-test for significance testing. Significance testing is a conventional technique in this field, so it will not be elaborated in this invention. For details, please refer to relevant literature, such as the paper "Equivalence Analysis of Three Significance Tests for Univariate Linear Regression Equations".

[0118] Further analysis was conducted on the first slope, the second slope, and their corresponding significance test results:

[0119] Population exposure equity trends ( ): And it passed the significance test ( This indicates that the natural spatial distribution of heat island intensity is becoming more concentrated and uneven, and the unfairness of heat island effect exposure at the population level is intensifying. Furthermore, the significance test indicates that the spatial distribution of heat island intensity is becoming more dispersed and balanced, and the unfairness of the heat island effect at the population level is being alleviated or is improving. The absolute value represents the rate of change; if it fails the significance test ( If the value is 0, it means that during the observation period, the spatial distribution of heat island intensity did not show a significant linear trend, and the differences in heat island exposure among different population groups remained relatively stable.

[0120] Economic development is linked to the trend of fairness ( ): And it passed the significance test ( This indicates a significantly strengthened correlation between heat island intensity and economic development level, meaning the disparity in thermal environment exposure between affluent and relatively poor regions continues to widen, and environmental inequality caused by socioeconomic factors is worsening; if Furthermore, the significance test indicates that the correlation between heat island intensity and economic development level has significantly weakened, and environmental inequality caused by socioeconomic factors is improving. The absolute value represents the rate of change; if it fails the significance test ( If the value is 0, it indicates that the correlation between the heat island intensity distribution and the socioeconomic level did not show a significant linear trend during the observation period.

[0121] Based on the above description, the global trend interpretation results can be determined according to the first slope and its corresponding significance test results, and the second slope and its corresponding significance test results, including:

[0122] when , , as well as At that time, the situation deteriorated across the board; that is, during the observation period, the environmental inequities caused by the heat island effect showed a significant increasing trend in both the dimensions of population exposure and economic development.

[0123] when , , as well as At that time, the overall improvement was achieved; that is, during the observation period, the fairness of the distribution of heat island intensity was significantly improved in both the population and economic dimensions.

[0124] when , , as well as At that time, the spatial distribution of heat island intensity tended to be more balanced during the observation period, but its correlation with economic level increased significantly, indicating that socioeconomic factors are becoming a new driving force for inequality.

[0125] when , as well as At that time, the change was unidimensional; that is, during the observation period, the fairness of the distribution of heat island intensity was mainly reflected in the significant weakening of its correlation with economic level, while the balance of its population spatial distribution did not change significantly.

[0126] when as well as During the observation period, the overall situation was stable. This means that no significant linear trend was detected in the distribution of heat island intensity across both population exposure and economic linkages, indicating a relatively stable pattern of regional thermal environmental equity.

[0127] In addition, the applicant also provides the corresponding system, including:

[0128] Remote sensing data receiving module: Configured to receive surface temperature remote sensing data streams from a satellite remote sensing platform. This module includes a satellite signal receiving antenna and a network interface, and has a physical structure for real-time or near-real-time data acquisition.

[0129] Data processing server cluster: A dedicated hardware system consisting of multiple computing nodes connected via a high-speed network, configured to execute the core computing and control logic of this invention. It includes:

[0130] Data preprocessing unit: used to perform radiometric calibration, atmospheric correction and geometric fine correction on the received raw remote sensing data to generate standardized land surface temperature raster data.

[0131] Heat Island Intensity Calculation Unit: Used to execute spatial analysis commands, based on urban boundary vector data, to calculate the temperature difference between the built-up area and the suburban background area of ​​each urban unit, i.e., the heat island intensity value.

[0132] Fairness Quantification Unit: Used to load socioeconomic data, perform extended Lorenz curve modeling, and improve Gini coefficient calculation.

[0133] Spatiotemporal evolution analysis unit: used to execute spatiotemporal clustering algorithms, identify hot and cold regions in the heat island intensity distribution, and calculate their spatiotemporal variation trends.

[0134] Visualization terminals, such as large displays or graphics workstations, are controlled by a cluster of data processing servers and are used to dynamically display generated Lorenz curves, improved Gini coefficients, spatial distribution maps, and comprehensive evaluation reports.

[0135] Connections and functions:

[0136] The remote sensing data receiving module is connected to the data processing server cluster via a network communication link, and is responsible for inputting the raw data. Within the data processing server cluster, each unit exchanges data and coordinates instructions with memory via the system bus, completing the entire process from data preprocessing to feature quantization. Finally, the data processing server cluster controls a visualization terminal through a graphical output interface, outputting the calculation results in a visual format.

[0137] In summary, this invention introduces a dynamic standardization judgment mechanism, which uses a calculation unit to detect in real time whether the weighted total strength is close to zero and adaptively switches between conventional standardization and absolute value standardization strategies. This fundamentally ensures the numerical stability and mathematical rationality of the improved Gini coefficient calculation and solves the key technical bottleneck of quantifying the fairness of signed environmental indicators.

[0138] This invention breaks through the limitations of traditional single-dimensional analysis. By using cyclical calculations to improve the Gini coefficient, and employing per capita GDP and the intensity of the urban heat island as ranking benchmarks, it achieves multi-dimensional comparative monitoring of socioeconomic equity and natural exposure equity, effectively separating the natural attributes and social driving factors of urban heat island distribution. Combined with time-series slope fitting and significance testing, the system can automatically identify five types of global trends, including overall deterioration and differentiated evolution, generating conclusions with clear policy implications.

[0139] Based on the same inventive concept, this invention provides a device for quantifying the uniformity of global heat island intensity distribution, comprising:

[0140] The data stream acquisition module is used to acquire global remote sensing data streams, perform radiometric calibration and atmospheric correction on the remote sensing data streams, and generate corresponding surface temperature raster data.

[0141] The heat island intensity estimation module is used to perform zonal statistics on surface temperature raster data to determine the heat island intensity value for each urban spatial unit globally.

[0142] An improved Gini coefficient calculation module is used to construct an extended Lorenz curve based on the ranking of urban spatial units, the cumulative population ratio, and the heat island intensity ratio, and to calculate the improved Gini coefficient based on the extended Lorenz curve. The ranking of urban spatial units is related to per capita GDP or heat island intensity value, and the improved Gini coefficient is calculated once for each ranking of urban spatial units. The ordered sequence of urban spatial units includes the corresponding heat island intensity value and the corresponding population data.

[0143] The trend interpretation results module is used to fit the improved Gini coefficient series of each time segment based on GDP per capita to obtain the first slope, and the improved Gini coefficient series of each time segment based on heat island intensity value to obtain the second slope. Based on the first slope and the corresponding significance test results, the second slope and the corresponding significance test results are used to determine the global trend interpretation results. The trend interpretation results include overall deterioration, overall improvement, differentiated evolution, single-dimensional change, and overall stability.

[0144] Based on the same inventive concept, the present invention also provides an electronic device, comprising:

[0145] processor;

[0146] Memory used to store processor-executable instructions;

[0147] The processor is configured to execute a quantification method for the uniformity of global heat island intensity distribution as described above.

[0148] Based on the same inventive concept, the present invention also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to execute a quantification method for the uniformity of global heat island intensity distribution as described above.

[0149] Since the electronic device described in this embodiment is an electronic device used to implement the information processing method in the embodiments of the present invention, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the information processing method described in the embodiments of the present invention. Therefore, how the electronic device implements the method in the embodiments of the present invention will not be described in detail here. Any electronic device used by those skilled in the art to implement the information processing method in the embodiments of the present invention falls within the scope of protection of the present invention.

[0150] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0154] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0155] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for quantifying the global heat island intensity distribution uniformity, characterized in that, The method comprises the following steps: acquiring a global remote sensing data stream, and performing radiation calibration and atmospheric correction on the remote sensing data stream to generate corresponding land surface temperature grid data; performing zonal statistics on the land surface temperature grid data to determine the heat island intensity value of each urban spatial unit in the world; constructing an extended Lorenz curve according to the ordering of each urban spatial unit, the cumulative population proportion, and the heat island intensity proportion, and calculating an improved Gini coefficient according to the extended Lorenz curve, wherein the ordering of each urban spatial unit is related to the per capita GDP or the heat island intensity value, and the improved Gini coefficient is calculated once for each ordering of the urban spatial unit, and the ordered sequence of each urban spatial unit includes the corresponding heat island intensity value and the corresponding population data; fitting a first slope based on the sequence of the improved Gini coefficient of each time slice under the per capita GDP ordering, and fitting a second slope based on the sequence of the improved Gini coefficient of each time slice under the heat island intensity value ordering, and determining a trend interpretation result of the world according to the first slope and the corresponding significance test result, the second slope and the corresponding significance test result, wherein the trend interpretation result includes comprehensive deterioration, comprehensive improvement, differentiation evolution, single-dimensional change, and overall stability; wherein the cumulative population proportion is calculated by: wherein, is the cumulative population proportion of the first city space unit, is the population data of the first city space unit, is the population data of the first city space unit, is the number of city space units, is the first city space unit, and is greater than or equal to and let , , , ; wherein the heat island intensity proportion is calculated by: the total weighted heat island intensity is calculated by: wherein, is the total weighted heat island intensity, is the heat island intensity value of the th urban spatial unit; If then the regular standardization is performed, including: wherein, is a preset positive threshold value, is a heat island intensity ratio of the th urban space unit, is a heat island intensity value of the th urban space unit; If then absolute value normalization is performed, including: wherein let .

2. The method of quantifying global heat island intensity distribution uniformity according to claim 1, wherein, the improved Gini coefficient is calculated according to the extended Lorenz curve by: the area between the absolute equality line and the extended Lorenz curve is calculated by: wherein, is the area between the absolute line of equality and the extended Lorenz curve; the improved Gini coefficient is determined according to the area between the absolute equality line and the extended Lorenz curve by: wherein, to improve the Gini coefficient.

3. The method of quantifying global heat island intensity distribution uniformity according to claim 1, wherein, the first slope is fitted based on the sequence of the improved Gini coefficient of each time slice under the per capita GDP ordering, and the second slope is fitted based on the sequence of the improved Gini coefficient of each time slice under the heat island intensity value ordering by: in, The second slope, For sorting based on heat island intensity values ​​and with time series as follows The sequence of improved Gini coefficients, The first slope, Based on GDP per capita ranking and with time series data The sequence of improved Gini coefficients; further comprising: right Perform a significance test and obtain the corresponding significance test results. ; performing a significance test to obtain a corresponding significance test result .​ 4. The method of quantifying global heat island intensity distribution uniformity according to claim 3, wherein, the trend interpretation result of the world is determined according to the first slope and the corresponding significance test result, the second slope and the corresponding significance test result by: When , , and , it is a general deterioration; When , , and , for overall improvement; When , , and differentiation evolves. When , and , it is a single dimension change; When and is overall stable.

5. The method of quantifying global heat island intensity distribution uniformity according to claim 1, wherein, the heat island intensity value of each urban spatial unit in the world is determined by: wherein, is the heat island intensity value of the th urban spatial unit, is the temperature value of the th pixel in the built-up area of the city, is the temperature value of the th pixel in the suburban background area of the city, is the area of the th pixel, is the area of the th pixel, is the total number of valid pixels in the built-up area, is the total number of valid pixels in the suburban background area.

6. A device for quantifying the uniformity of global heat island intensity distribution, characterized in that, the method for quantifying the balance of global heat island intensity distribution according to any one of claims 1-5 comprises: a data stream acquisition module for acquiring a global remote sensing data stream, and performing radiation calibration and atmospheric correction on the remote sensing data stream to generate corresponding land surface temperature grid data; a heat island intensity estimation module for performing zonal statistics on the land surface temperature grid data to determine the heat island intensity value of each urban spatial unit in the world; an improved Gini coefficient calculation module for constructing an extended Lorenz curve according to the ordering of each urban spatial unit, the cumulative population proportion, and the heat island intensity proportion, and calculating an improved Gini coefficient according to the extended Lorenz curve, wherein the ordering of each urban spatial unit is related to the per capita GDP or the heat island intensity value, and the improved Gini coefficient is calculated once for each ordering of the urban spatial unit, and the ordered sequence of each urban spatial unit includes the corresponding heat island intensity value and the corresponding population data; The trend interpretation result module is configured to fit a first slope based on a sequence of the improved Gini coefficients of each time slice in the GDP per capita ranking, fit a second slope based on a sequence of the improved Gini coefficients of each time slice in the heat island intensity value ranking, and determine a global trend interpretation result according to the first slope and a corresponding significance test result, the second slope and a corresponding significance test result, wherein the trend interpretation result includes overall deterioration, overall improvement, differentiation evolution, single-dimensional change, and overall stability.

7. An electronic device, comprising: Comprise: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute to implement a global heat island intensity distribution balance quantification method as claimed in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium, comprising: When the instructions in the non-transitory computer-readable storage medium are executed by the processor of the electronic device, the electronic device is enabled to implement a global heat island intensity distribution balance quantification method as claimed in any one of claims 1 to 5.

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