CN111415057A - Generation method and device of regional poverty degree grading diagram - Google Patents
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Abstract
The invention relates to a method and a device for generating a regional poverty degree grading diagram, which comprises the following steps: acquiring the incidence rate of poverty of each administrative village and poverty element data of different types in a research area; carrying out spatial gridding processing on the research area, and calculating the poverty incidence rate of each unit cell in the spatial grid and different types of poverty element spatial grid data; calculating the influence indexes of different types of poverty factors on poverty occurrence according to the poverty occurrence rate of each cell and the poverty factor space grid data; calculating weights of different types of poverty-stricken elements according to the influence indexes; and calculating the poverty index of each cell according to different types of poverty element space grid data and corresponding poverty element weights, and further generating a regional poverty degree grading diagram. According to the method, different types of poverty factors influencing poverty and the weight condition of the poverty influence of the different types of poverty factors are considered, and the identification accuracy of the poverty degrees of different areas in the research area is improved.
Description
Technical Field
The invention relates to a method and a device for generating a regional poverty degree grading diagram, and belongs to the technical field.
Background
Poverty is a global and significant problem requiring accurate identification of poverty areas. However, because the rural poverty in China is the result of the comprehensive effect of various factors such as geographic environment, resource endowment, infrastructure, human capital, social security and the like, the remarkable regional difference and spatial heterogeneity of poverty are revealed. Therefore, the cognition of the poor degree of the region needs to be comprehensively judged from multiple angles, multiple levels and multiple elements.
In the multi-dimensional poverty-stricken research developed in China at present, although various factors are integrated in consideration of poverty-stricken degree detection, the influence of a certain factor on poverty-stricken occurrence in different regions is neglected to be different. For example, chinese patent document with application publication No. CN107944750A discloses a poverty depth analysis method and system, wherein a village-level multidimensional poverty test index system relates to geographic environment, administrative village characteristics, production and living conditions, labor force conditions, medical health and social security, economic development and the like, but the corresponding index weight is fixed, and is not changed due to the change of research areas, and is not beneficial to objectively identify the difference of poverty degrees in different areas, which results in inaccurate poverty degree identification result.
Disclosure of Invention
The invention aims to provide a method and a device for generating a regional poverty degree grading graph, which are used for solving the problem of inaccurate identification result of poverty degree in the prior art.
In order to solve the technical problem, the invention provides a method for generating a regional poverty degree grading diagram, which comprises the following steps:
acquiring the incidence rate of poverty in each administrative village and poverty element data of different types in a research area, wherein the poverty element data of different types comprises at least three of regional condition data, geographic environment data, administrative village characteristic data and meteorological data;
carrying out spatial gridding processing on the research area, and calculating the poverty incidence rate of each unit cell in the spatial grid and different types of poverty element spatial grid data according to the obtained poverty incidence rate and different types of poverty element spatial grid data;
calculating the influence indexes of different types of poverty-lacking elements on poverty occurrence according to the poverty occurrence rate of each unit cell in the space grid and different types of poverty-lacking element space grid data;
calculating weights of different types of poverty-stricken elements according to the influence indexes of different types of poverty-stricken elements on poverty occurrence;
calculating the poverty index of each cell in the spatial grid according to the acquired different types of poverty element spatial grid data and different types of poverty element weights;
and generating a regional poverty degree grading graph according to the poverty index of each unit cell in the space grid.
In order to solve the above technical problem, the present invention further provides an apparatus for generating a regional poor degree ranking graph, which includes a processor and a memory, wherein the processor is configured to process instructions stored in the memory to implement the method for generating the regional poor degree ranking graph.
The invention has the beneficial effects that: the method comprises the steps of calculating poverty incidence rate corresponding to each unit cell in a spatial grid and poverty element spatial grid data of different types by carrying out spatial gridding processing on a research area, calculating poverty element weights of different types according to the poverty element spatial grid data of different types, finally determining poverty index of each unit cell in the spatial grid, and further generating a regional poverty degree grading graph. According to the method, because the incidence rate of poverty in each administrative village and different types of poverty factors influencing poverty are considered, and the weight condition of the poverty factors influencing the poverty is considered, the identification accuracy of the poverty degrees in different areas in the research area is improved.
As a further improvement of the method and the device, in order to obtain different types of poor element space grid data corresponding to each cell, the poor element space grid data comprises at least three of location condition grid data, geographic environment grid data, administrative village feature grid data and meteorological grid data; the location conditional grid data comprises: the distance from each cell to a road, a river, a county government station and a village and town government station in the research area; the geographic environment grid data includes: the elevation, the gradient and the slope direction corresponding to each cell; the administrative village characteristic grid data comprises: the village per capita cultivation area of the administrative village corresponding to each cell; the meteorological grid data includes: the temperature, precipitation and sunshine corresponding to each cell.
According to the requirement of the geographic detector on the type data that the independent variable must be discretized, different grades of poor element space grid data of different types of each unit grid in the space grid are divided into different grades according to a natural breakpoint method, and influence indexes of the poor elements of different types on poor occurrence are calculated according to the poor element space grid data after grading.
As a further improvement of the method and the apparatus, in order to consider the influence indexes of different types of poverty-deficient elements on poverty-deficient occurrence and improve the accuracy of poverty-deficient degree identification, the process of calculating the influence indexes of different types of poverty-deficient elements on poverty-deficient occurrence includes:
and inputting the poverty incidence rate corresponding to each unit cell in the spatial grid and different types of poverty element spatial grid data into a geographic detector, wherein the output value of the geographic detector is the influence index of different types of poverty elements on poverty incidence.
As a further improvement of the method and the device, the calculation formula of the weights of the different types of poverty-stricken elements is as follows:
wherein, WiAs a class i poverty element weight, qiThe index of the influence of the ith type poverty-lacking elements on poverty-lacking occurrence is shown, and n is the number of the poverty-lacking elements.
As a further improvement of the method and apparatus, the poverty-stricken index of each cell in the spatial grid is calculated by the formula:
wherein, PjThe poverty index of the jth cell, j is1, 2, … …, N is the number of cells in the research area, XijPoor element space grid data of class i for the jth cell, WiThe weight of the ith poor element and the number of the n poor elements.
As a further improvement of the method and the device, in order to enable the regional poor degree grading diagram to be more visual, the poor indexes of each unit cell in the space grid are divided into different grades according to a natural breakpoint method, and the poor indexes of the different grades of each unit cell are displayed by adopting different colors.
As a further improvement of the method and the device, the different levels of poverty-related indexes of each cell are displayed by using different colors by using ArcGIS10.2 software.
Drawings
FIG. 1 is a flow chart of a method of generating a regional poverty level ranking graph of the present invention;
FIGS. 2(a) - (l) are schematic diagrams of different types of space grids in county A of the present invention;
FIGS. 3(a) - (l) are schematic diagrams of different types of spatial grids in prefecture B of the present invention;
FIG. 4 is a graph of the regional poverty level rating of county A of the present invention;
fig. 5 is a graph showing the regional poverty degree classification in prefecture B according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The embodiment of the generation method of the regional poverty degree grading graph comprises the following steps:
the embodiment provides a method for generating a regional poverty degree ranking graph, and a corresponding flowchart is shown in fig. 1, and specifically includes the following steps:
(1) and acquiring the incidence rate of poverty of each administrative village and different types of poverty element data in the research area.
The incidence rate of poverty in the administrative village is the ratio of the number of poverty in the administrative village to the total number of the population in the administrative village. Different types of poverty element data include:
location condition data: major roads, major rivers, county government residences, village and town government residences in the research district;
geographic environmental data: remote sensing images of elevation, gradient and slope in a research area;
administrative village characteristic data: the village per capita cultivated land area of an administrative village in a research area;
meteorological data: the temperature, precipitation and sunshine in the research area.
Of course, as other embodiments, the obtained different types of poverty essential data in the research area can be any three of location condition data, geographic environment data, administrative village characteristic data and meteorological data.
(2) And carrying out spatial gridding processing on the research area, and calculating the poverty incidence rate of each unit cell in the spatial grid and different types of poverty element spatial grid data according to the obtained poverty incidence rate and different types of poverty element spatial grid data.
In this embodiment, the side length of the divided spatial grid is1 km. Of course, when the spatial grid processing is performed on the research area, the side length of the spatial grid needs to be adjusted according to the actual situation of the research area. The steps of calculating the poverty incidence rate of each unit cell in the spatial grid and corresponding to the poverty element spatial grid data of different types are as follows:
and 2.1) calculating poverty incidence grid data, regional condition grid data, geographic environment grid data, administrative village feature grid data and meteorological grid data of each unit grid in the research area.
For the poverty incidence rate, interpolating the poverty incidence rates of all administrative villages in the research area according to a 1km interval space, namely calculating poverty incidence rate grid data corresponding to each cell;
for the regional conditions, calculating the distance between the center of each unit qualification in the research region and a main road, a main river, a county government station and a village and town government station, thereby obtaining regional condition grid data;
for the geographic environment, elevation data are obtained by directly extracting pixel values from a DEM remote sensing image with the resolution of 1km, and slope direction data are obtained in ArcGIS10.2 software through a slope and slope direction calculation tool according to the obtained elevation grid data, so that grid data of the geographic environment are obtained;
for the characteristics of the administrative village, interpolating the village per capita cultivated land area of the administrative village in the research area according to 1km interval space, namely calculating the village per capita cultivated land area of the administrative village corresponding to each cell, thereby obtaining characteristic grid data of the administrative village;
for meteorology, the air temperature, precipitation and sunshine of each meteorological observation station in the research area are interpolated according to a space of 1km, and the air temperature, precipitation and sunshine corresponding to each cell are calculated.
And 2.2) dividing the regional condition grid data, the geographic environment grid data, the administrative village characteristic grid data and the meteorological grid data of each unit grid in the research area into different levels according to a natural breakpoint method, wherein the grid data of each unit grid in different levels are poverty-stricken element space grid data of each unit grid.
In the embodiment, different types of grid data corresponding to each unit cell in the spatial grid are all converted into 1 to 7, so that different types of poor element spatial grid data corresponding to each unit cell in the spatial grid are obtained. Of course, the number of different types of mesh data division levels may be set as needed.
It should be noted that, when grid division is performed on grid data of different types of poor element space, consistency in directionality is to be ensured. The characteristics of the administrative village (village per capita cultivated land area) are forward indexes; the position condition (distance from road, river, county government and village and town government) is a negative indicator; the geographical environment (elevation, gradient and slope direction) is a negative indicator; the meteorological data (temperature, sunlight and rainfall) change little in different years, but the research case area is mostly poor area, and the agricultural production is important, so the meteorological data can be regarded as a forward index, namely, higher annual average temperature, sunlight and rainfall conditions, and the agricultural production is facilitated.
In this embodiment, taking prefecture a and prefecture B as an example, after different types of poor element spatial grid data corresponding to each cell in the spatial grid are subjected to level division, corresponding schematic diagrams are respectively shown in fig. 2 and fig. 3. Wherein, fig. 2(a) -2(l) are respectively the distance from prefecture A to the main road, the distance from river, the distance from prefecture government, the distance from county government, elevation, gradient, slope direction, village per capita cultivated land area, air temperature, sunlight, precipitation and poverty incidence (poverty incidence does not need to be graded); fig. 3(a) -3(l) are distance to main road, distance to river, distance to county government, distance to country government, elevation, gradient, slope direction, village per capita cultivated land area, air temperature, sunlight, precipitation, and incidence of poverty (poverty incidence does not need to be graded), respectively, in prefecture B.
(3) And calculating the influence indexes of different types of poverty factors on poverty occurrence according to the poverty occurrence rate grid data and the graded poverty factor space grid data of each cell.
Specifically, the grid data of the poverty occurrence rate and the spatial grid data of different types of poverty elements after classification are geographically detected, and the influence indexes of the different types of poverty elements on the poverty occurrence are obtained, that is, the grid data of the poverty occurrence rate obtained in the step (2) and the spatial grid data of the different types of poverty elements after classification are input into the geographic detector, so as to obtain the influence indexes q of the different types of poverty elements on the poverty occurrence, where the influence index q is the explanatory power of the independent variable to the dependent variable in the geographic detector, and q ∈ [0,1], where the closer the q value is to 1, the stronger the explanatory power of the independent variable to the dependent variable is, the more dominant factor of the poverty occurrence can be identified according to the magnitude of the q value, in this embodiment, the geographic detector model adopted is:
wherein h is1, 2 …, L is different types of districts of poverty-poor elements, in this embodiment, 17 districts are divided in county A and 9 districts are divided in county B according to county villages, towns and streets, N is the number of units in the region, namely the number of grids at intervals of 1km in county A, and N is the number of units in the regionhThe number of units in each layer is 1km interval grid number in each partition; sigma2 hAnd σ2Poverty incidence variances of layer h and the whole area, respectively; SSW is the sum of the in-layer variances; SST is the regional total variance. If a partition is generated from a poor element, a larger q value indicates that the poor element is more powerful in explaining the occurrence of poor, and conversely, is weaker.
The magnitude of the impact of different types of poverty deficient elements on poverty occurrence will vary depending on the objective differences of different types of poverty deficient elements in different areas. Taking counties A and B as examples, as shown in Table 1, the influence of different types of poverty factors in county A on poverty occurrence ranks 5 top, and from large to small, the influence is rainfall, air temperature, distance from counties and governments, sunshine and rural area per capita respectively; as shown in table 2, the influence of different types of poverty factors in B county on poverty occurrence ranks 5 top, and from large to small, the distance from county government, precipitation, sunshine, distance from village and town government, and temperature are respectively. Therefore, the influence of precipitation in the meteorological phenomena on the occurrence of poverty in county A is very obvious, and the influence of the distance from county government in the regional conditions on the occurrence of poverty in county B is very obvious, and the influence index values of other poverty-deficient factors are different from the ranking values.
TABLE 1
TABLE 2
(4) Calculating the weights of the poor factors of different types according to the influence indexes of the poor factors of different types on the poor occurrence, wherein the corresponding calculation formula is as follows:
wherein, WiAs a class i poverty element weight, qiThe index of the influence of the ith type poverty-lacking elements on poverty-lacking occurrence is shown, and n is the number of the poverty-lacking elements.
The weights of different types of poverty-deficient elements are different depending on the difference in influence of different types of poverty-deficient elements on poverty occurrence in different regions, as shown in tables 1 and 2.
(5) Based on different types of poverty element weights, calculating poverty indexes of each cell in the space grid, wherein the corresponding calculation formula is as follows:
wherein, PjThe poverty index of the jth cell, j is1, 2, … …, N, N is the number of cells in the research area, XijPoor element space grid data of class i for the jth cell, WiAnd weighting the ith poor element.
(6) And generating a regional poverty degree grading graph according to the poverty index of each unit cell in the space grid.
Specifically, the poverty-stricken index of each cell in the spatial grid obtained in the step (5) is divided into different levels according to a natural breakpoint method, and the number of the levels can be set as required. The poverty indexes of different levels of each cell are displayed in different colors by using the existing software, such as ArcGIS10.2 software (geographic information system software), so as to generate different types of poverty degree grading maps. In this embodiment, the poverty index is divided into four levels of mild poverty, moderate poverty, severe poverty, and deep poverty, where the poverty degree ranking graph of prefecture a is shown in fig. 4, and the poverty degree ranking graph of prefecture B is shown in fig. 5.
The embodiment of the generation device of the classification graph of the regional poverty degree comprises the following steps:
the embodiment provides a generation device of a region poor degree ranking graph, which comprises a processor and a memory, wherein the processor is used for processing instructions stored in the memory so as to realize the generation method of the region poor degree ranking graph in the generation method embodiment of the region poor degree ranking graph. Since the method has been described in detail in the embodiment of the method for generating the regional poverty degree ranking map, no further description is given here.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope thereof, and although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present application, various changes, modifications or equivalents of the embodiments of the present application can be made, and these changes, modifications or equivalents are within the protection scope of the claims of the present invention.
Claims (9)
1. A generation method of a regional poverty degree grading graph is characterized by comprising the following steps:
acquiring the incidence rate of poverty in each administrative village and poverty element data of different types in a research area, wherein the poverty element data of different types comprises at least three of regional condition data, geographic environment data, administrative village characteristic data and meteorological data;
carrying out spatial gridding processing on the research area, and calculating the poverty incidence rate of each unit cell in the spatial grid and different types of poverty element spatial grid data according to the obtained poverty incidence rate and different types of poverty element spatial grid data;
calculating the influence indexes of different types of poverty-lacking elements on poverty occurrence according to the poverty occurrence rate of each unit cell in the space grid and different types of poverty-lacking element space grid data;
calculating weights of different types of poverty-stricken elements according to the influence indexes of different types of poverty-stricken elements on poverty occurrence;
calculating the poverty index of each cell in the spatial grid according to the acquired different types of poverty element spatial grid data and different types of poverty element weights;
and generating a regional poverty degree grading graph according to the poverty index of each unit cell in the space grid.
2. The method for generating the regional poverty-stricken degree map according to claim 1, wherein the poverty-stricken element spatial grid data includes at least three of location condition grid data, geographic environment grid data, administrative village feature grid data and meteorological grid data; the location conditional grid data comprises: the distance from each cell to a road, a river, a county government station and a village and town government station in the research area; the geographic environment grid data includes: the elevation, the gradient and the slope direction corresponding to each cell; the administrative village characteristic grid data comprises: the village per capita cultivation area of the administrative village corresponding to each cell; the meteorological grid data includes: the temperature, precipitation and sunshine corresponding to each cell.
3. The method for generating the regional poverty degree ranking map according to claim 2, further comprising: dividing different types of poor element space grid data of each unit grid in the space grid into different levels according to a natural breakpoint method, and calculating influence indexes of the different types of poor elements on poor occurrence according to the poor element space grid data after the levels are divided.
4. The method for generating the regional poverty-related grading map according to any of claims 1-3, wherein the step of calculating the index of the influence of different types of poverty-related elements on poverty occurrence comprises:
and inputting the poverty incidence rate corresponding to each unit cell in the spatial grid and different types of poverty element spatial grid data into a geographic detector, wherein the output value of the geographic detector is the influence index of different types of poverty elements on poverty incidence.
5. The method for generating the regional poverty degree ranking map according to any of claims 1-3, characterized in that the formula for calculating the weights of different types of poverty elements is:
wherein, WiAs a class i poverty element weight, qiThe index of the influence of the ith type poverty-lacking elements on poverty-lacking occurrence is shown, and n is the number of the poverty-lacking elements.
6. The method for generating the regional poverty degree ranking map according to any of claims 1-3, characterized in that the poverty index of each cell in the spatial grid is calculated by the formula:
wherein, PjThe poverty index of the jth cell, j is1, 2, … …, N is the number of cells in the research area, XijPoor element space grid data of class i for the jth cell, WiThe weight of the ith poor element and the number of the n poor elements.
7. The generation method of the regional poverty poor degree grading graph according to any one of claims 1-3, characterized in that the poverty poor index of each cell in the spatial grid is divided into different grades according to a natural breakpoint method, and the poverty poor index of each cell in different grades is displayed in different colors.
8. The method for generating the regional poverty degree grading map according to claim 7, wherein the poverty indexes of different levels of each cell are displayed in different colors by using ArcGISI 10.2 software.
9. An apparatus for generating a regional poor level ranking graph, comprising a processor and a memory, wherein the processor is configured to process instructions stored in the memory to implement the method for generating the regional poor level ranking graph according to any of claims 1 to 8.
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