Grid-based method and device for gross domestic product based on multi-source remote sensing data

Abstract

The application discloses a kind of based on multi-source remote sensing data's gross domestic product gridding method, according to the first industry gross domestic product of the region to be handled, the first industry gross domestic product of multiple first preset industries included in the first industry and land use type grid chart, obtain the first industry gross domestic product grid chart.According to impervious surface grid chart, population density grid chart, second industry gross domestic product and third industry gross domestic product, obtain the second industry gross domestic product grid chart and third industry gross domestic product grid chart.According to impervious surface grid chart, night light grid chart, preset category scenic spot buffer zone grid chart and tourism gross domestic product, obtain tourism gross domestic product grid chart.The first industry gross domestic product grid chart, the second industry gross domestic product grid chart, the third industry gross domestic product grid chart and tourism gross domestic product grid chart are added, and obtain the gross domestic product grid chart of the region to be handled.

Description

Technical Field

[0001] This invention relates to the field of economic statistics technology, and in particular to a method and apparatus for rasterizing GDP based on multi-source remote sensing data. Background Technology

[0002] Gross Domestic Product (GDP) is an important indicator for measuring the development level of various regions. GDP data can comprehensively reflect the economic characteristics, industrial structure, and living standards of residents in a specific region. Rasterizing GDP data allows for a more intuitive and effective assessment of the development status of various regions.

[0003] In existing technologies, the main approach to rasterizing GDP data is a spatially local linear framework centered on Geographically Weighted Regression (GWR). This method emphasizes locally linear solutions in the context of spatial heterogeneity. Its core principle is to estimate the locally linear coefficients for each observation unit using a spatial kernel function, while also focusing on the coupling between spatial heterogeneity and nonlinear mechanisms. Specifically, this method uses Random Forest (RF), Extreme Gradient Boosting (XGBoost), or Light Gradient Boosting Machine (LightGBM) as nonlinear base models, and then uses GWR to perform spatially local weighted fusion of the prediction surfaces of the base models, thereby achieving rasterization of GDP data. On the other hand, a spatial-nonlinear ensemble framework, represented by Geographically Weighted Stacking Ensemble (GWSE), can also achieve rasterization of GDP data. Specifically, GWSE emphasizes the dual coupling characteristics of spatial heterogeneity and nonlinear mechanisms, using Random Forest (RF), XGBoost, and LightGBM as nonlinear base models, and using Geographically Weighted Regression (GWR) to perform spatially local weighted fusion of the prediction results of the base models. In addition, the forecasting process also takes into account industry differentiation factors, that is, separate GWSE models are built for the primary, secondary and tertiary industries respectively, so as to achieve rasterization of GDP data.

[0004] However, existing technical solutions have obvious limitations: in scenarios where economic activities and multi-source big data interact in a complex manner, linear hypothesis modeling methods are often difficult to apply and have high requirements for the amount of data. In particular, in the process of rasterizing GDP data, the algorithm design is complex and difficult to implement. At the same time, it cannot meet the needs of fine rasterization processing of GDP data in small-scale regions (such as provincial, municipal, and district levels). Summary of the Invention

[0005] The purpose of this invention is to provide a rasterization method for GDP based on multi-source remote sensing data. This method addresses the challenge that existing technologies often struggle with linear modeling approaches in scenarios involving complex interactions between economic activities and multi-source big data, and also require substantial data volumes. Particularly in the GDP data rasterization process, the algorithm design is complex and implementation is difficult, while failing to meet the need for refined rasterization of GDP data at small scales (such as provincial, municipal, and district levels).

[0006] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a method for rasterizing GDP based on multi-source remote sensing data, the method comprising: Based on the GDP of the primary industry corresponding to the area to be processed, the GDP of multiple pre-defined primary industries included in the primary industry, and the land use type raster map, obtain the GDP raster map of the primary industry. Based on the impermeable surface raster map, population density raster map, GDP of the secondary industry and GDP of the tertiary industry corresponding to the area to be processed, obtain the GDP raster map of the secondary industry and the GDP raster map of the tertiary industry. Based on the impermeable surface raster map, the nighttime light raster map, the preset category scenic area buffer raster map, and the tourism GDP raster map corresponding to the area to be processed, obtain the tourism GDP raster map. The GDP raster maps of the first industry, the second industry, the tertiary industry, and the tourism industry are added together to obtain the GDP raster map corresponding to the region to be processed.

[0007] In one embodiment, obtaining the GDP raster map of the first industry based on the GDP of the first industry corresponding to the area to be processed, the GDP of multiple first preset industries included in the first industry, and the land use type raster map includes: Based on the raster maps of GDP of each primary industry, GDP of the primary industry, and land use type, obtain the raster map of the economic share of the primary industry. Multiply the raster chart of the economic share of the first industry by the GDP of the first industry to obtain the raster chart of the GDP of the first industry.

[0008] In one embodiment, obtaining the economic share raster map of the primary industry based on the GDP of each first preset industry, the GDP of the primary industry, and the land use type raster map includes: Calculate the ratio of the GDP of each of the first pre-defined industries to the GDP of the primary industry; Based on multiple ratios and land use type raster maps, obtain a raster map of the proportion of the primary industry economy.

[0009] In one embodiment, obtaining the GDP raster map of the secondary industry and the GDP raster map of the tertiary industry based on the impermeable surface raster map, the population density raster map, the GDP of the secondary industry, and the GDP of the tertiary industry corresponding to the area to be processed includes: Multiply the impermeable surface raster map corresponding to the area to be processed with the population density raster map to obtain the impermeable surface population density raster map. Based on the impermeable surface population density raster map, the GDP of the secondary industry, and the GDP of the tertiary industry, obtain the GDP raster map of the secondary industry and the GDP raster map of the tertiary industry.

[0010] In one embodiment, obtaining the GDP raster map of the secondary industry and the GDP raster map of the tertiary industry based on the impermeable surface population density raster map, the GDP of the secondary industry, and the GDP of the tertiary industry includes: Based on the impermeable surface population density raster map and the secondary industry GDP, a secondary industry GDP raster map is obtained; Based on the impermeable surface population density raster map and the tertiary sector GDP, a tertiary sector GDP raster map is obtained.

[0011] In one embodiment, the preset category of scenic spots includes: multiple scenic spots of different preset levels. Before obtaining the tourism GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic spot buffer raster map, and tourism GDP of the area to be processed, the method further includes: In the tourism grid map corresponding to the area to be processed, determine the tourism grid maps corresponding to multiple tourist attractions of a preset level or above. Multiple tourism raster submaps are assigned values ​​using preset assignment rules to obtain the assigned tourism raster map. For the assigned tourism raster map, the preset category scenic area buffer raster map is obtained using ArcGIS software.

[0012] In one embodiment, obtaining a tourism GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer zone raster map, and tourism GDP of the area to be processed includes: Based on the impermeable surface raster map, the nighttime light raster map, and the preset category scenic area buffer zone raster map, a tourism economic distribution raster map is obtained; Based on the tourism economic distribution raster map and the tourism GDP, the tourism GDP raster map is obtained.

[0013] In one embodiment, obtaining a tourism economic distribution raster map based on the impermeable surface raster map, the nighttime light raster map, and the preset category scenic area buffer raster map includes: Multiply the impermeable surface grid map with the nighttime light grid map to obtain the nighttime light intensity grid map of the impermeable surface; The tourism economic distribution raster map is obtained by weighted summing of the nighttime light intensity raster map of the impermeable surface and the raster map of the preset category scenic area buffer zone.

[0014] In one embodiment, before performing a weighted summation of the impermeable surface nighttime light intensity raster map and the preset category scenic area buffer raster map to obtain the tourism economic distribution raster map, the method further includes: The nighttime light intensity raster map of the impermeable surface and the raster map of the preset category scenic area buffer zone are normalized.

[0015] Secondly, embodiments of the present invention provide a GDP rasterization device based on multi-source remote sensing data, the device comprising: The primary industry acquisition module is used to acquire a primary industry GDP raster map based on the primary industry GDP corresponding to the area to be processed, the GDP of multiple primary preset industries included in the primary industry, and the land use type raster map. The secondary and tertiary industry acquisition module is used to acquire the secondary industry GDP raster map and the tertiary industry GDP raster map based on the impermeable surface raster map, population density raster map, secondary industry GDP and tertiary industry GDP corresponding to the area to be processed. The tourism acquisition module is used to acquire a tourism GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer raster map, and tourism GDP corresponding to the area to be processed. The GDP raster acquisition module is used to add together the GDP raster maps of the first industry, the second industry, the tertiary industry, and the tourism industry to obtain the GDP raster map corresponding to the region to be processed.

[0016] The technical solution provided by the embodiments of the present invention has the following advantages compared with the prior art: This invention provides a method for rasterizing GDP based on multi-source remote sensing data. The method obtains a GDP raster map of the primary industry based on the GDP of the primary industry corresponding to the area to be processed, the GDP of multiple preset industries included in the primary industry, and a land use type raster map. It then obtains GDP raster maps of the secondary and tertiary industries based on the impermeable surface raster map, population density raster map, GDP of the secondary industry, and GDP of the tertiary industry corresponding to the area to be processed. Finally, it obtains a GDP raster map of the tourism industry based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer zone raster map, and tourism industry GDP corresponding to the area to be processed. Finally, it adds the GDP raster maps of the primary, secondary, and tertiary industries together to obtain the GDP raster map corresponding to the area to be processed. In this way, the complexity of GDP rasterization algorithm design can be simplified, solving the problem that linear assumption modeling methods are often difficult to apply in scenarios where there are complex interactions between economic activities and multi-source big data, and have high requirements for data volume. In particular, the algorithm design is complex and difficult to implement in the process of GDP data rasterization. Specifically, it obtains the corresponding GDP raster map based on multi-source remote sensing data corresponding to the small-scale area to be processed, avoiding the problem that existing technologies cannot meet the needs of fine rasterization processing of GDP data in small-scale areas (such as provincial, municipal, and district levels). Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating a method for rasterizing GDP based on multi-source remote sensing data, provided in an embodiment of the present invention; Figure 2 A schematic diagram illustrating comparative experimental results provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a GDP rasterization device based on multi-source remote sensing data, provided as an embodiment of the present invention. Detailed Implementation

[0018] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0019] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0020] In this invention, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between the associated objects, indicating that three relationships can exist.

[0021] like Figure 1 As shown, Figure 1 A flowchart illustrating a method for rasterizing GDP based on multi-source remote sensing data, provided in this embodiment of the invention, specifically includes the following steps: S10: Obtain the GDP raster map of the primary industry based on the GDP of the primary industry corresponding to the area to be processed, the GDP of multiple primary preset industries included in the primary industry, and the land use type raster map.

[0022] The area to be processed refers to a small-scale area relative to the global area or the national area, such as a provincial area, a municipal area, or a district area, but is not limited thereto. This invention does not specifically limit the scope, and those skilled in the art can set it according to the actual situation.

[0023] The first presupposed industry refers to the agricultural industry, forestry industry, animal husbandry industry, and fishery industry included in the primary industry.

[0024] A land use type raster map refers to land resource units with the same land use patterns. It is a basic geographical unit divided according to regional differences in land use, used to reflect the land's use, nature, and distribution patterns. These units, during the process of transforming and utilizing land for production and construction, form various land use categories with different utilization directions and characteristics. Based on this, the land use type raster map includes: multiple preset type raster maps corresponding to a first preset industry, namely, a cultivated land type raster map corresponding to the agricultural industry, a forest land type raster map corresponding to the forestry industry, a grassland type raster map corresponding to the animal husbandry industry, and a water body type raster map corresponding to the fishery industry. However, it is not limited to these; this invention does not specifically limit it, and those skilled in the art can set it according to actual conditions.

[0025] Specifically, the GDP of the primary industry corresponding to the area to be processed, the GDP of multiple first-preset industries included in the primary industry, and the land use type raster map are obtained. Based on the GDP of the primary industry corresponding to the area to be processed, the GDP of multiple first-preset industries included in the primary industry, and the land use type raster map, the GDP of the primary industry corresponding to the area to be processed is obtained.

[0026] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S10 may be: S101: Obtain the economic proportion raster map of the primary industry based on the GDP of each primary preset industry, the GDP of the primary industry, and the land use type raster map.

[0027] The primary industry economic share grid chart is used to reflect the economic share of each predefined primary industry in the primary industry.

[0028] Specifically, based on the GDP of each of the primary preset industries included in the primary industry, the GDP of the primary industry, and the land use type raster map, a raster map of the economic proportion of the primary industry corresponding to the region to be processed is obtained.

[0029] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S101 may be: S1011: Calculate the ratio of the GDP of each of the first preset industries to the GDP of the primary industry.

[0030] Specifically, for each primary pre-defined industry, the ratio of the GDP of each primary pre-defined industry to the GDP of the primary industry is calculated.

[0031] S1012: Obtain a raster map of the economic proportion of the primary industry based on multiple ratios and land use type raster maps.

[0032] Specifically, after obtaining the ratio of GDP of each primary preset industry to GDP of the primary industry, the land use type raster map is assigned values ​​based on multiple ratios to obtain the primary industry economic proportion raster map corresponding to the area to be processed.

[0033] Optionally, based on the above embodiments, the land use type raster map includes: multiple preset type raster sub-maps corresponding to the first preset industries, namely, a cultivated land type raster map corresponding to the agricultural industry, a forest land type raster map corresponding to the forestry industry, a grassland type raster map corresponding to the animal husbandry industry, and a water body type raster map corresponding to the fishery industry. Based on this, in some embodiments of the present invention, one implementation of S1011 may be: calculating the sum of multiple raster data included in the preset type raster map corresponding to each first preset industry, dividing the ratio corresponding to each first preset industry by the sum, and assigning the quotient to the preset type raster map corresponding to each first preset industry, thereby obtaining the first industry economic proportion raster map corresponding to the area to be processed.

[0034] S102: Multiply the raster chart of the economic share of the primary industry with the GDP of the primary industry to obtain the raster chart of the GDP of the primary industry.

[0035] Specifically, after obtaining the raster map of the economic share of the primary industry, the raster map of the economic share of the primary industry is multiplied by the GDP of the primary industry to obtain the raster map of the GDP of the primary industry for the region to be processed.

[0036] S11: Based on the impermeable surface raster map, population density raster map, secondary industry GDP and tertiary industry GDP corresponding to the area to be processed, obtain the secondary industry GDP raster map and the tertiary industry GDP raster map.

[0037] Among them, impermeable surface raster maps refer to raster maps obtained by high-precision data rasterization processing of multi-source remote sensing data, such as optical, radar and topographic data, combined with machine learning models, such as random forest classifiers.

[0038] Population density refers to the number of people per unit area of ​​land, usually measured in people per square kilometer or people per hectare. It is an important indicator for measuring the population distribution of a country or region. The land area used to calculate population density refers to the land area and inland waters within the territory, excluding territorial waters. Because population density assumes that the population is evenly distributed within a certain area, the smaller the area calculated, the more accurately it reflects the actual population distribution; a larger area only reveals the general trend of population distribution. A population density raster map refers to storing population density in a raster format.

[0039] Specifically, obtain the impermeable surface raster map, population density raster map, secondary industry GDP, and tertiary industry GDP corresponding to the area to be treated. Based on the impermeable surface raster map, population density raster map, secondary industry GDP, and tertiary industry GDP corresponding to the area to be treated, obtain the secondary industry GDP raster map and the tertiary industry GDP raster map corresponding to the area to be treated.

[0040] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S11 may be: S111: Multiply the impermeable surface raster map corresponding to the area to be processed with the population density raster map to obtain the impermeable surface population density raster map.

[0041] Specifically, the impermeable surface raster map corresponding to the area to be processed is multiplied with the population density raster map corresponding to the area to be processed to obtain the impermeable surface population density raster map corresponding to the area to be processed.

[0042] S112: Based on the impermeable surface population density raster map, the GDP of the secondary industry, and the GDP of the tertiary industry, obtain the GDP raster map of the secondary industry and the GDP raster map of the tertiary industry.

[0043] Specifically, after obtaining the impermeable surface population density raster map corresponding to the area to be processed, based on the impermeable surface population density raster map, the GDP of the secondary industry and the GDP of the tertiary industry, a raster map of the GDP of the secondary industry corresponding to the GDP of the tertiary industry is obtained.

[0044] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S112 may be: S1121: Based on the impermeable surface population density raster map and the secondary industry GDP, obtain the secondary industry GDP raster map.

[0045] Specifically, based on the impermeable surface population density raster map corresponding to the area to be treated and the secondary industry GDP corresponding to the area to be treated, a secondary industry GDP raster map corresponding to the secondary industry GDP is obtained.

[0046] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S1121 may be: calculating the sum of multiple impermeable surface population density raster data included in the impermeable surface population density raster map, dividing each impermeable surface population density raster data by the sum, and multiplying it by the GDP of the secondary industry to obtain the GDP of the secondary industry raster map.

[0047] S1122: Based on the impermeable surface population density raster map and the tertiary sector GDP, obtain the tertiary sector GDP raster map.

[0048] Specifically, based on the impermeable surface population density raster map corresponding to the area to be treated and the tertiary industry GDP corresponding to the area to be treated, a tertiary industry GDP raster map is obtained.

[0049] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S1122 may be: calculating the sum of multiple impermeable surface population density raster data included in the impermeable surface population density raster map, dividing each impermeable surface population density raster data by the sum, and multiplying it by the GDP of the tertiary industry to obtain the GDP of the tertiary industry raster map.

[0050] S12: Based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer raster map, and tourism GDP raster map corresponding to the area to be processed, obtain the tourism GDP raster map.

[0051] The preset category of scenic spots refers to the scenic spots within the area to be processed, which are categorized and determined accordingly. For example, a preset category scenic spot could be a Category A scenic spot. However, this invention is not limited to this; those skilled in the art can set the category according to the actual situation.

[0052] Specifically, the process involves obtaining the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer zone raster map, and tourism GDP corresponding to the area to be processed. Based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer zone raster map, and tourism GDP corresponding to the area to be processed, the process also involves obtaining the tourism GDP raster map corresponding to the area to be processed.

[0053] Optionally, based on the above embodiments, the preset category of scenic spots includes: multiple scenic spots with different preset levels. For example, following the above embodiments, the preset category of scenic spots can be, for example, category A scenic spots. Category A scenic spots can be further subdivided into levels, including: level A scenic spots, level 2A scenic spots, level 3A scenic spots, level 4A scenic spots, and level 5A scenic spots. However, it is not limited to this. The present invention is not specifically limited, and those skilled in the art can set it according to the actual situation.

[0054] Based on this, and building upon the above embodiments, in some embodiments of the present invention, before executing S12, the following is further included: S20: In the tourism raster map corresponding to the area to be processed, determine the tourism raster maps corresponding to multiple tourist attractions at or above the preset level.

[0055] Specifically, for the tourism raster map corresponding to the area to be processed, tourism sub-maps corresponding to multiple tourist attractions at or above a preset level are determined within the tourism raster map.

[0056] S21: Assign values ​​to multiple tourism raster sub-maps using preset assignment rules to obtain the assigned tourism raster map.

[0057] The preset assignment rules are assignment rules formulated for multiple tourist attractions at different preset levels. For example, the corresponding assignment size of multiple tourist attractions at different preset levels can be determined according to the income of tourist attractions at different preset levels. This invention is not specifically limited, and those skilled in the art can set it according to the actual situation.

[0058] S22: For the assigned tourism raster map, obtain the preset category scenic area buffer raster map using ArcGIS software.

[0059] Specifically, based on pre-set assignment rules, multiple tourism raster sub-maps are assigned values ​​to obtain the assigned tourism raster map. After obtaining the assigned tourism raster map, the corresponding preset category scenic area buffer raster map of the area to be processed can be obtained through ArcGIS software.

[0060] Optionally, based on the above embodiments, in some embodiments of the present invention, S12 may be implemented as follows: S121: Based on the impermeable surface raster map, the nighttime light raster map, and the preset category scenic area buffer zone raster map, a tourism economic distribution raster map is obtained.

[0061] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S121 may be: S1211: Multiply the impermeable surface raster map with the nighttime light raster map to obtain the impermeable surface nighttime light intensity raster map.

[0062] Specifically, the impermeable surface raster map corresponding to the area to be processed is multiplied with the nighttime light raster map to obtain the nighttime light intensity raster map of the impermeable surface corresponding to the area to be processed.

[0063] S1212: The nighttime light intensity raster map of impermeable surfaces is weighted and summed with the raster map of the preset category scenic area buffer zone to obtain the tourism economic distribution raster map.

[0064] Based on this, and building upon the above embodiments, since the data scales stored in the impermeable surface nighttime light intensity raster map and the preset category scenic area buffer raster map are relatively large, in some embodiments of the present invention, before executing S1212, the following is further included: The nighttime light intensity raster map of impermeable surfaces and the raster map of the preset category scenic area buffer zone are normalized.

[0065] Specifically, after obtaining the nighttime light intensity raster map of the impermeable surface corresponding to the area to be processed, the nighttime light intensity raster map of the impermeable surface is normalized to the raster map of the preset category scenic area buffer zone.

[0066] Thus, this embodiment solves the problem of relatively large data scales in the nighttime light intensity raster map of impermeable surfaces and the raster map of preset category scenic area buffer zones by normalizing the two raster maps, which facilitates subsequent calculations.

[0067] Specifically, after obtaining the raster map of the nighttime light intensity of the impermeable surface corresponding to the area to be processed, the raster map of the nighttime light intensity of the impermeable surface and the raster map of the preset category scenic area buffer zone are normalized. The normalized raster map of the nighttime light intensity of the impermeable surface and the raster map of the preset category scenic area buffer zone are then weighted and summed to obtain the tourism economic distribution raster map corresponding to the area to be processed.

[0068] S122: Based on the tourism economic distribution raster map and the tourism GDP, a tourism GDP raster map is obtained.

[0069] Specifically, after obtaining the tourism economic distribution raster map corresponding to the area to be processed, a tourism GDP raster map is obtained based on the tourism economic distribution raster map and the tourism GDP.

[0070] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S122 may be: calculating the sum of multiple tourism economic distribution grid data included in the tourism economic distribution grid map, dividing each tourism economic distribution grid data by the sum, and multiplying it by the tourism GDP to obtain the tourism GDP grid map.

[0071] S13: Add the raster maps of GDP of the primary industry, secondary industry, tertiary industry, and tourism industry to obtain the raster map of GDP corresponding to the region to be processed.

[0072] Specifically, after obtaining the raster maps of GDP for the primary industry, secondary industry, tertiary industry, and tourism industry corresponding to the region to be processed, the raster maps of GDP for the primary industry, secondary industry, tertiary industry, and tourism industry are added together to obtain the GDP raster map corresponding to the region to be processed.

[0073] Thus, the GDP rasterization method based on multi-source remote sensing data provided in this embodiment obtains a primary industry GDP raster map by using the primary industry GDP corresponding to the area to be processed, the GDP of multiple first-preset industries included in the primary industry, and a land use type raster map. It then obtains secondary industry GDP raster maps and tertiary industry GDP raster maps based on the impermeable surface raster map, population density raster map, secondary industry GDP, and tertiary industry GDP corresponding to the area to be processed. Finally, it obtains a tourism industry GDP raster map by adding the primary industry GDP raster map, the secondary industry GDP raster map, the tertiary industry GDP raster map, and the tourism industry GDP raster map to obtain the GDP raster map corresponding to the area to be processed. This approach simplifies the design complexity of GDP rasterization algorithms, addressing the challenges of existing technologies where linear modeling methods are often inapplicable in scenarios involving complex interactions between economic activities and multi-source big data. These methods also require substantial data volumes, particularly in the complex design and implementation of GDP data rasterization. Specifically, this approach obtains the corresponding GDP raster map from multi-source remote sensing data corresponding to the small-scale region to be processed, avoiding the limitations of existing technologies in meeting the need for refined rasterization of GDP data at small scales (such as provincial, municipal, and district levels).

[0074] Optionally, based on the above embodiments, in some embodiments of the present invention, in order to verify that the method of the present invention can solve the problem that linear hypothesis modeling methods are often difficult to apply in scenarios where there are complex interactions between economic activities and multi-source big data, and have high requirements for data volume, especially in the process of GDP data rasterization, where algorithm design is complex and implementation is difficult, the corresponding GDP raster map is obtained based on multi-source remote sensing data corresponding to the small-scale area to be processed. This avoids the problem that the existing technology cannot meet the need for refined rasterization processing of GDP data of small-scale areas (such as provincial, municipal, and district levels). Experiments are conducted to compare the method proposed in this embodiment with the published national GDP raster data. Figure 2 As shown. Figure 2 (a) is the raster data result for the national gross domestic product. Figure 2 (b) shows the raster data results of GDP corresponding to this invention. From Figure 2 (a) It can be seen that the spatial distribution of the national GDP raster data results is rather general. Figure 2(b) It can be seen that the GDP raster data results corresponding to the present invention can effectively obtain GDP raster data results for small-scale regions (such as provincial, municipal, and district levels).

[0075] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0076] In one embodiment, such as Figure 3 As shown, Figure 3 A schematic diagram of a GDP rasterization device based on multi-source remote sensing data provided in an embodiment of the present invention includes: a first industry acquisition module 10, a second and tertiary industry acquisition module 11, a tourism industry acquisition module 12, and a GDP raster map acquisition module 13.

[0077] The primary industry acquisition module 10 is used to acquire a primary industry GDP raster map based on the primary industry GDP corresponding to the area to be processed, the GDP of multiple first preset industries included in the primary industry, and the land use type raster map.

[0078] The secondary and tertiary industry acquisition module 11 is used to acquire the secondary industry GDP raster map and the tertiary industry GDP raster map based on the impermeable surface raster map, population density raster map, secondary industry GDP and tertiary industry GDP corresponding to the area to be processed.

[0079] The tourism acquisition module 12 is used to obtain a tourism GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer raster map, and tourism GDP corresponding to the area to be processed.

[0080] The GDP raster acquisition module 13 is used to add together the GDP raster maps of the primary industry, the secondary industry, the tertiary industry, and the tourism industry to obtain the GDP raster map corresponding to the region to be processed.

[0081] Thus, the GDP rasterization device based on multi-source remote sensing data provided in this embodiment obtains a GDP raster map of the first industry through the first industry acquisition module, based on the GDP of the first industry corresponding to the area to be processed, the GDP of multiple first preset industries included in the first industry, and a land use type raster map. The second and third industry acquisition modules obtain GDP raster maps of the second and third industries based on the impermeable surface raster map, population density raster map, GDP of the second industry, and GDP of the tertiary industry corresponding to the area to be processed. The tourism industry acquisition module obtains a tourism industry GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer zone raster map, and tourism industry GDP corresponding to the area to be processed. The GDP raster map acquisition module adds the GDP raster maps of the first, second, and tertiary industries and the tourism industry to obtain the GDP raster map corresponding to the area to be processed. In this way, the complexity of GDP rasterization algorithm design can be simplified, solving the problem that linear assumption modeling methods are often difficult to apply in scenarios where there are complex interactions between economic activities and multi-source big data, and have high requirements for data volume. In particular, the algorithm design is complex and difficult to implement in the process of GDP data rasterization. Specifically, it obtains the corresponding GDP raster map based on multi-source remote sensing data corresponding to the small-scale area to be processed, avoiding the problem that existing technologies cannot meet the needs of fine rasterization processing of GDP data in small-scale areas (such as provincial, municipal, and district levels).

[0082] Specific limitations regarding the GDP rasterization device based on multi-source remote sensing data can be found in the limitations of the GDP rasterization method based on multi-source remote sensing data mentioned above, and will not be repeated here. Each module in the aforementioned server can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the operations corresponding to each module.

[0083] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM), etc.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for rasterizing GDP based on multi-source remote sensing data, characterized in that, The method includes: Based on the GDP of the primary industry corresponding to the area to be processed, the GDP of multiple pre-defined primary industries included in the primary industry, and the land use type raster map, obtain the GDP raster map of the primary industry. Based on the impermeable surface raster map, population density raster map, GDP of the secondary industry and GDP of the tertiary industry corresponding to the area to be processed, obtain the GDP raster map of the secondary industry and the GDP raster map of the tertiary industry. Based on the impermeable surface raster map, the nighttime light raster map, the preset category scenic area buffer raster map, and the tourism GDP raster map corresponding to the area to be processed, obtain the tourism GDP raster map. The GDP raster maps of the first industry, the second industry, the tertiary industry, and the tourism industry are added together to obtain the GDP raster map corresponding to the region to be processed.

2. The method according to claim 1, characterized in that, The step of obtaining the GDP raster map of the primary industry based on the GDP of the primary industry corresponding to the area to be processed, the GDP of multiple first-preset industries included in the primary industry, and the land use type raster map includes: Based on the raster maps of GDP of each primary industry, GDP of the primary industry, and land use type, obtain the raster map of the economic share of the primary industry. Multiply the raster chart of the economic share of the first industry by the GDP of the first industry to obtain the raster chart of the GDP of the first industry.

3. The method according to claim 2, characterized in that, The step of obtaining the economic share raster map of the primary industry based on the GDP of each first preset industry, the GDP of the primary industry, and the land use type raster map includes: Calculate the ratio of the GDP of each of the first pre-defined industries to the GDP of the primary industry; Based on multiple ratios and land use type raster maps, obtain a raster map of the proportion of the primary industry economy.

4. The method according to claim 1, characterized in that, The step of obtaining the GDP raster map for the secondary industry and the GDP raster map for the tertiary industry based on the impermeable surface raster map, population density raster map, GDP of the secondary industry, and GDP of the tertiary industry corresponding to the area to be processed includes: Multiply the impermeable surface raster map corresponding to the area to be processed with the population density raster map to obtain the impermeable surface population density raster map. Based on the impermeable surface population density raster map, the GDP of the secondary industry, and the GDP of the tertiary industry, obtain the GDP raster map of the secondary industry and the GDP raster map of the tertiary industry.

5. The method according to claim 4, characterized in that, The step of obtaining the GDP raster map for the secondary industry and the GDP raster map for the tertiary industry based on the impermeable surface population density raster map, the GDP of the secondary industry, and the GDP of the tertiary industry includes: Based on the impermeable surface population density raster map and the secondary industry GDP, a secondary industry GDP raster map is obtained; Based on the impermeable surface population density raster map and the tertiary sector GDP, a tertiary sector GDP raster map is obtained.

6. The method according to claim 1, characterized in that, The preset category of scenic spots includes: multiple scenic spots of different preset levels. Before obtaining the tourism GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic spot buffer zone raster map, and tourism GDP of the area to be processed, the process also includes: In the tourism grid map corresponding to the area to be processed, determine the tourism grid maps corresponding to multiple tourist attractions of a preset level or above. Multiple tourism raster submaps are assigned values ​​using preset assignment rules to obtain the assigned tourism raster map. For the assigned tourism raster map, the preset category scenic area buffer raster map is obtained using ArcGIS software.

7. The method according to claim 6, characterized in that, The step of obtaining a tourism GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer zone raster map, and tourism GDP of the area to be processed includes: Based on the impermeable surface raster map, the nighttime light raster map, and the preset category scenic area buffer zone raster map, a tourism economic distribution raster map is obtained; Based on the tourism economic distribution raster map and the tourism GDP, the tourism GDP raster map is obtained.

8. The method according to claim 7, characterized in that, The process of obtaining a tourism economic distribution raster map based on the impermeable surface raster map, the nighttime light raster map, and the preset category scenic area buffer zone raster map includes: Multiply the impermeable surface grid map with the nighttime light grid map to obtain the nighttime light intensity grid map of the impermeable surface; The tourism economic distribution raster map is obtained by weighted summing of the nighttime light intensity raster map of the impermeable surface and the raster map of the preset category scenic area buffer zone.

9. The method according to claim 8, characterized in that, Before performing a weighted summation of the impermeable surface nighttime light intensity raster map and the preset category scenic area buffer raster map to obtain the tourism economic distribution raster map, the method further includes: The nighttime light intensity raster map of the impermeable surface and the raster map of the preset category scenic area buffer zone are normalized.

10. A GDP rasterization device based on multi-source remote sensing data, characterized in that, The device includes: The primary industry acquisition module is used to acquire a primary industry GDP raster map based on the primary industry GDP corresponding to the area to be processed, the GDP of multiple primary preset industries included in the primary industry, and the land use type raster map. The secondary and tertiary industry acquisition module is used to acquire the secondary industry GDP raster map and the tertiary industry GDP raster map based on the impermeable surface raster map, population density raster map, secondary industry GDP and tertiary industry GDP corresponding to the area to be processed. The tourism acquisition module is used to acquire a tourism GDP raster map based on the impermeable surface raster map, nighttime light raster map, preset category scenic area buffer raster map, and tourism GDP corresponding to the area to be processed. The GDP raster acquisition module is used to add together the GDP raster maps of the first industry, the second industry, the tertiary industry, and the tourism industry to obtain the GDP raster map corresponding to the region to be processed.

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