CN111415057B - Method and device for generating 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, wherein the method comprises the following steps: acquiring lean factor data of different types and the incidence rate of lean factors of each administrative village in a research area; performing space gridding treatment on the research area, and calculating the lean incidence rate of each cell in the space grid and the lean element space grid data of different types; calculating influence indexes of the poverty factors of different types on the poverty occurrence according to the poverty occurrence rate of each cell and the poverty factor space grid data; calculating the weight of the poverty factor of different types according to the influence index; according to the different types of lean element space grid data and the corresponding lean element weights, the lean index of each cell is calculated, and then a regional lean degree grading diagram is generated. According to the invention, the different types of poverty factors affecting poverty and the weight condition of the poverty factors affecting poverty are considered, so that the identification accuracy of poverty degree of different areas in a 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 significant problem and accurate identification of poverty areas is required. However, as the rural poverty of China is the result of the comprehensive actions of various factors such as geographical environment, resource endowment, infrastructure, human capital, social security and the like, the remarkable regional difference and spatial heterogeneity of poverty are revealed. Therefore, the regional poverty degree is known, and comprehensive judgment is performed from multiple angles, multiple layers and multiple elements.
In the multi-dimensional poverty research which has been developed in China at present, various factors are synthesized in consideration of poverty detection, but influence of a certain factor on poverty occurrence in different areas is ignored. For example, the chinese patent document with application publication number CN107944750a discloses a method and a system for analyzing poverty depth, wherein a village-level multidimensional poverty test index system relates to several aspects of geographic environment, administrative village characteristics, production and living conditions, labor conditions, medical health, social security, economic development and the like, but the corresponding index weight is fixed, and the corresponding index weight is not changed due to the change of research areas, so that the difference of poverty degrees in different areas is not favorable for objective recognition, and the poverty degree recognition result is inaccurate.
Disclosure of Invention
The invention aims to provide a method and a device for generating a regional poverty degree grading diagram, which are used for solving the problem of inaccurate poverty degree identification result in the prior art.
In order to solve the technical problems, the invention provides a method for generating a regional poverty degree grading diagram, which comprises the following steps:
acquiring the incidence rate of poverty of each administrative village in a research area and poverty factor data of different types, wherein the poverty factor data of different types comprises at least three of location condition data, geographical environment data, administrative village characteristic data and meteorological data;
performing space gridding treatment on a research area, and calculating the lean incidence of each cell in the space grid and the lean element space grid data of different types according to the acquired lean incidence and the lean element data of different types;
calculating influence indexes of the poverty factors of different types on poverty occurrence according to the poverty occurrence rate of each cell in the space grid and the space grid data of the poverty factors of different types;
calculating the weight of the poverty factors of different types according to the influence indexes of the poverty factors of different types on the poverty occurrence;
according to the acquired lean element space grid data of different types and the lean element weights of different types, calculating the lean index of each cell in the space grid;
and generating a regional poverty degree grading diagram according to the poverty index of each cell in the space grid.
In order to solve the technical problem, the invention also provides a device for generating the regional poverty degree grading diagram, which comprises a processor and a memory, wherein the processor is used for processing the instructions stored in the memory so as to realize the method for generating the regional poverty degree grading diagram.
The beneficial effects of the invention are as follows: through spatial gridding processing on a research area, the lean occurrence rate corresponding to each cell in the spatial grid and the lean element spatial grid data of different types are calculated, the lean element weights of different types are calculated through the lean element spatial grid data of different types, and finally the lean index of each cell in the spatial grid is determined, so that a regional lean degree grading diagram is generated. According to the invention, the occurrence rate of poverty of each administrative village and the poverty factors of different types affecting poverty are considered, and the weight condition of the poverty factors of different types affecting poverty is considered, so that the identification accuracy of poverty degree of different areas in a research area is improved.
As a further improvement of the method and the device, in order to obtain the lean element space grid data of different types corresponding to each cell, the lean element space grid data comprises at least three of location condition grid data, geographic environment grid data, administrative village characteristic grid data and meteorological grid data; the location condition grid data comprises: the distance of each cell to a road, river, county government premises and village and town government premises, respectively, within the research area; the geographic environment grid data includes: elevation, gradient and slope direction corresponding to each cell; the administrative village characteristic grid data includes: the area of each administrative village is equal to the area of each village; the weather grid data includes: the air temperature, precipitation and sunshine corresponding to each cell.
As a further improvement of the method and the device, according to the requirement of the geographic detector on type data of which the independent variable is required to be discretized, different types of lean element space grid data of each cell in the space grid are divided into different grades according to a natural breakpoint method, and according to the graded lean element space grid data, the influence indexes of the different types of lean elements on the lean occurrence are calculated.
As a further improvement of the method and the apparatus, in order to consider the influence indexes of the different types of the poverty factors on the occurrence of poverty, to improve the accuracy of the identification of the poverty degree, the process of calculating the influence indexes of the different types of the poverty factors on the occurrence of poverty includes:
and (3) inputting the lean occurrence rate corresponding to each cell in the space grid and the lean element space grid data of different types into a geographic detector, wherein the output value of the geographic detector is the influence index of the lean elements of different types on the lean occurrence.
As a further improvement of the method and the device, the calculation formula of the lean element weights of different types is as follows:
wherein W is i Weight of class i poverty factor, q i For i-th type of poverty factor to be a shadow of poverty occurrenceThe force index, n, is the number of poor elements.
As a further improvement of the method and apparatus, the formula for calculating the lean index of each cell in the spatial grid is:
wherein P is j Is the lean index of the jth cell, j=1, 2, … …, N is the number of cells in the study area, X ij Poor element space grid data of the ith class of the jth cell, W i The weight of the i-th type of poverty-relieving elements is that n is the number of poverty-relieving elements.
As a further improvement of the method and the device, in order to make the regional poverty degree grading diagram more visual, the poverty index of each cell in the space grid is graded into different grades according to a natural breakpoint method, and the poverty index of each cell in different grades is displayed by adopting different colors.
As a further improvement of the method and apparatus, arcgis10.2 software was used to display different colors for different levels of the poverty index for each cell.
Drawings
FIG. 1 is a flow chart of a method of generating a regional poverty grade map of the present invention;
FIGS. 2 (a) - (l) are schematic views of different types of space grids in county A of the present invention;
FIGS. 3 (a) - (l) are schematic views of different types of space grids in county B of the present invention;
FIG. 4 is a regional poverty grade chart of county A of the present invention;
fig. 5 is a regional poverty degree grading diagram of B county of 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 further described in detail with reference to the accompanying drawings and specific embodiments.
The embodiment of the method for generating the regional poverty degree grading diagram comprises the following steps:
the embodiment provides a method for generating a regional poverty degree grading diagram, and a corresponding flow chart is shown in fig. 1, and specifically includes the following steps:
(1) The incidence of poverty and the poverty factor data of different types for each administrative village within the study area are obtained.
Here, the occurrence rate of poverty in an administrative village refers to the ratio of the number of poverty in the administrative village to the total number of people in the administrative village. Different types of lean element data include:
location condition data: major roads, major rivers, county government residences, and village and town government residences within the research area;
geographical environment data: elevation, gradient and slope remote sensing images in a research area;
administrative village characteristic data: area of each village in administrative villages in the research area;
weather data: air temperature, precipitation, sun exposure in the study area.
Of course, as other embodiments, the acquired lean element data of different types in the study area may be any three of location condition data, geographical environment data, administrative village characteristic data, and meteorological data.
(2) And performing spatial gridding processing on the research area, and calculating the lean incidence of each cell in the spatial grid and the lean element spatial grid data of different types according to the acquired lean incidence and the lean element data of different types.
In this embodiment, the side length of the divided space grid is 1km. Of course, when the spatial gridding treatment is performed on the investigation region, the side length of the spatial grid needs to be adjusted according to the actual situation of the investigation region. The steps for calculating the incidence rate of poverty of each cell in the space grid and the corresponding poverty factor space grid data of different types are as follows:
2.1 The lean incidence grid data, the location condition grid data, the geographic environment grid data, the administrative village characteristic grid data and the meteorological grid data of each cell in the research area are calculated.
For the poverty incidence, spatially interpolating the poverty incidence of each administrative village in the research area according to the interval of 1km, namely calculating poverty incidence grid data corresponding to each cell;
for the location condition, calculating the distance between the center of each cell in the research area and the main road, the main river, the county government residence and the village and town government residence, thereby obtaining location condition grid data;
for the geographic environment, the elevation data are obtained by directly extracting each pixel value from a DEM remote sensing image with 1km resolution, and gradient and slope direction data are obtained according to the obtained elevation grid data in ArcGIS10.2 software through a gradient and slope direction calculation tool, so that the geographic environment grid data are obtained;
for the characteristics of the administrative villages, spatially interpolating the area of the administrative village average cultivated areas of the administrative villages in the research area according to the interval of 1km, namely calculating the area of the administrative village average cultivated areas of the administrative villages corresponding to each cell, so as to obtain the characteristic grid data of the administrative villages;
for weather, the air temperature, precipitation and sunshine of each weather observation station in the research area are spatially interpolated according to 1km intervals, namely the air temperature, precipitation and sunshine corresponding to each cell are calculated.
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 cell in the research area into different grades according to a natural breakpoint method, wherein the grid data of different grades of each cell is the poor element space grid data of each cell.
In this embodiment, different types of grid data corresponding to each cell in the spatial grid are converted into 1-7, so as to obtain different types of poor factor spatial grid data corresponding to each cell in the spatial grid. Of course, the number of different types of mesh data division levels may be set as needed.
When grid division is performed on the space grid data of the poor factors of different types, the uniformity of the directivity is ensured. The administrative village characteristics (the area of the cultivated land of village people) are positive indexes; the location condition (distance from roads, rivers, county governments and village and town governments) is a negative index; the geographical environment (elevation, gradient and slope direction) is a negative index; the weather (air temperature, sunlight and precipitation) data have small changes in different years, but the research case areas are mostly poor areas, and the agricultural production takes an important role, so that the weather can be regarded as a forward index, namely, the higher annual average air temperature, sunlight and precipitation conditions are favorable for the agricultural production.
In this embodiment, taking county a and county B as examples, after grading, the space grid data of the lean factors corresponding to each cell in the space grid are shown in fig. 2 and 3 respectively. Wherein, fig. 2 (a) -2 (l) are the distance to main road, the distance to river, the distance to county government, the distance to rural government, elevation, slope, village average cultivated area, air temperature, insolation, precipitation, lean incidence (lean incidence does not need to be graded) of county, respectively; fig. 3 (a) -3 (l) are distance to main road, distance to river, distance to county government, distance to rural government, elevation, slope, area of village people plowing, air temperature, sun, precipitation, incidence of poverty (incidence of poverty does not need to be graded), respectively.
(3) And calculating the influence indexes of the poverty factors of different types on the poverty occurrence according to the poverty occurrence rate grid data and the poverty factor space grid data of each cell after grading.
Specifically, geographic detection is performed on the lean occurrence rate grid data and the space grid data of the classified lean elements of different types, so that influence indexes of the lean elements of different types on lean occurrence are obtained. That is, the spatial grid data of the incidence of poverty and the different types of poverty elements after grading obtained in the step (2) are input into a geographic detector, so that an influence index q of the different types of poverty elements on the incidence of poverty is obtained. The influence index q is the explanatory power of the independent variable to the dependent variable in the geographic detector, and the closer the q value is to 1, the stronger the explanatory power of the independent variable to the dependent variable is, and the dominant factor of the occurrence of poverty can be identified according to the q value. In this embodiment, the geographic probe model used is:
where h=1, 2 …, L is the division of the different types of poverty-stricken elements, in this embodiment, according to the villages and towns of each county and the street division, the a county divides 17 divisions, and the B county divides 9 divisions; n is the number of units in the area, namely the 1km spacing grid number in the county; n (N) h The number of units in each layer is 1km spacing grid number in each partition; sigma (sigma) 2 h Sum sigma 2 Lean incidence variance for layer h and full region, respectively; SSW is the sum of intra-layer variances; SST is the total variance of the region. If the partition is generated by a lean element, a larger q value indicates a stronger interpretation of the lean element of the occurrence of a lean and, conversely, a weaker interpretation.
The magnitude of the impact of different types of poverty factors on the occurrence of poverty may vary depending on the objective differences in different types of poverty factors in different areas. Taking county A and county B as examples, as shown in table 1, the influence of the different types of poverty factors of county A on poverty occurrence is ranked 5 times before, and the factors are respectively precipitation, air temperature, distance from county government, sunshine and area of village people average cultivated land from large to small; as shown in Table 2, the influence of different types of poverty factors in county B on poverty occurrence is ranked 5 times top, and from large to small, the distance from county government, precipitation, sunshine, the distance from village and town government and the air temperature are respectively. Therefore, besides the remarkable influence of precipitation on the A county poverty in the meteorological and the remarkable influence of the distance between the regional condition and the county government on the B county poverty in the regional condition, the influence index value and the ranking mean difference of other poverty factors are remarkable.
TABLE 1
TABLE 2
(4) According to the influence indexes of the different types of poverty factors on poverty occurrence, calculating the weights of the different types of poverty factors, wherein the corresponding calculation formula is as follows:
wherein W is i Weight of class i poverty factor, q i The index of influence of the i-th type of poverty factors on poverty occurrence is shown, and n is the number of poverty factors.
The weight of the different types of poverty factors will be different due to the difference in influence of the different types of poverty factors on the occurrence of poverty in different regions, as shown in tables 1 and 2.
(5) Based on the lean element weights of different types, the lean index of each cell in the space grid is calculated, and the corresponding calculation formula is as follows:
wherein P is j The depletion index of the jth cell, j=1, 2, … …, N is the number of cells in the study area, X ij Poor element space grid data of the ith class of the jth cell, W i Is the class i lean element weight.
(6) And generating a regional poverty degree grading diagram according to the poverty index of each cell in the space grid.
Specifically, the lean index of each cell in the space grid obtained in the step (5) is divided into different grades according to a natural breakpoint method, and the number of grades can be set according to the needs. The different levels of the poverty index for each cell are displayed in different colors using existing software, such as ArcGIS10.2 software (geographic information system software), to generate different types of poverty grading diagrams. In this example, the poverty index is divided into four grades of mild poverty, moderate poverty, severe poverty and deep poverty, and the poverty degree classification chart of the county a is shown in fig. 4, and the poverty degree classification chart of the county B is shown in fig. 5.
An embodiment of the device for generating a regional poverty degree grading diagram:
the embodiment provides a device for generating a regional poverty degree grading diagram, which comprises a processor and a memory, wherein the processor is used for processing instructions stored in the memory to realize the method for generating the regional poverty degree grading diagram in the embodiment of the method for generating the regional poverty degree grading diagram. Because the method is described in detail in the embodiment of the method for generating the regional poverty degree grading diagram, the description is omitted here.
Finally, it should be noted that the foregoing embodiments are merely for illustrating the technical solution of the present invention and not for limiting the scope of protection thereof, and although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the application after reading the present application, and these changes, modifications or equivalents are within the scope of protection of the claims of the present invention.
Claims (7)
1. The method for generating the regional poverty degree grading diagram is characterized by comprising the following steps:
acquiring the incidence rate of poverty of each administrative village in a research area and poverty factor data of different types, wherein the poverty factor data of different types comprises at least three of location condition data, geographical environment data, administrative village characteristic data and meteorological data;
performing space gridding treatment on a research area, and calculating the lean incidence of each cell in the space grid and the lean element space grid data of different types according to the acquired lean incidence and the lean element data of different types;
calculating influence indexes of the poverty factors of different types on poverty occurrence according to the poverty occurrence rate of each cell in the space grid and the space grid data of the poverty factors of different types;
calculating the weight of the poverty factors of different types according to the influence indexes of the poverty factors of different types on the poverty occurrence;
the calculation formula of the weight of the lean factors of different types is as follows:
wherein W is i Weight of class i poverty factor, q i The influence index of the i-th type of poverty factors on poverty occurrence is given, and n is the number of poverty factors;
according to the acquired lean element space grid data of different types and the lean element weights of different types, calculating the lean index of each cell in the space grid;
the formula for calculating the poverty index of each cell in the spatial grid is:
wherein P is j Is the lean index of the jth cell, j=1, 2, … …, N is the number of cells in the study area, X ij Poor element space grid data of the ith class of the jth cell, W i The weight of the i-th type of poverty-relieving elements is that n is the number of poverty-relieving elements;
and generating a regional poverty degree grading diagram according to the poverty index of each cell in the space grid.
2. The method of generating a regional poverty degree ranking map of claim 1, wherein the poverty factor space grid data includes at least three of location condition grid data, geographic environment grid data, administrative village characteristic grid data, and meteorological grid data; the location condition grid data comprises: the distance of each cell to a road, river, county government premises and village and town government premises, respectively, within the research area; the geographic environment grid data includes: elevation, gradient and slope direction corresponding to each cell; the administrative village characteristic grid data includes: the area of each administrative village is equal to the area of each village; the weather grid data includes: the air temperature, precipitation and sunshine corresponding to each cell.
3. The method of generating a regional poverty degree classification map as claimed in claim 2, further comprising: and dividing the space grid data of the poor factors of different types of each cell in the space grid into different grades according to a natural breakpoint method, and calculating the influence indexes of the poor factors of different types on the occurrence of the poor according to the classified space grid data of the poor factors.
4. A method of generating a regional poverty degree ranking map in accordance with any one of claims 1 to 3, wherein the process of calculating an impact index of poverty factors of different types on the occurrence of poverty comprises:
and (3) inputting the lean occurrence rate corresponding to each cell in the space grid and the lean element space grid data of different types into a geographic detector, wherein the output value of the geographic detector is the influence index of the lean elements of different types on the lean occurrence.
5. A method of generating a regional poverty degree grading diagram according to any of claims 1-3, wherein the poverty index of each cell in the spatial grid is graded according to a natural break point method, and the poverty index of each cell in different grades is displayed in different colors.
6. The method of claim 5, wherein the arcgis10.2 software is used to display different levels of the poverty index for each cell in different colors.
7. An apparatus for generating a regional lean degree ranking map, comprising a processor and a memory, the processor configured to process instructions stored in the memory to implement the method for generating a regional lean degree ranking map according to any one of claims 1-6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218517A (en) * | 2013-03-22 | 2013-07-24 | 南京信息工程大学 | GIS (Geographic Information System)-based region-meshed spatial population density computing method |
| CN107944750A (en) * | 2017-12-12 | 2018-04-20 | 中国石油大学(华东) | A kind of poverty depth analysis method and system |
| CN107945079A (en) * | 2016-10-12 | 2018-04-20 | 普天信息技术有限公司 | A kind of poverty alleviation object selection method and device |
| CN108805396A (en) * | 2018-04-23 | 2018-11-13 | 中国农业大学 | The poor coupled relation evaluation method with natural calamity in area based on GIS |
| CN109447383A (en) * | 2018-08-24 | 2019-03-08 | 中国科学院遥感与数字地球研究所 | Poverty Analysis method and Poverty Analysis system |
| CN109886103A (en) * | 2019-01-14 | 2019-06-14 | 中山大学 | Urban poverty measure of spread method |
| CN109948737A (en) * | 2019-04-08 | 2019-06-28 | 河南大学 | Poor spatial classification recognition methods and device based on big data and machine learning |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120123682A1 (en) * | 2010-02-25 | 2012-05-17 | Jai Ho Oh | Method and system for producing climate crisis index |
| US20150019395A1 (en) * | 2013-07-11 | 2015-01-15 | Lumesis, Inc. | Geographic score model and service |
-
2019
- 2019-12-04 CN CN201911228309.5A patent/CN111415057B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218517A (en) * | 2013-03-22 | 2013-07-24 | 南京信息工程大学 | GIS (Geographic Information System)-based region-meshed spatial population density computing method |
| CN107945079A (en) * | 2016-10-12 | 2018-04-20 | 普天信息技术有限公司 | A kind of poverty alleviation object selection method and device |
| CN107944750A (en) * | 2017-12-12 | 2018-04-20 | 中国石油大学(华东) | A kind of poverty depth analysis method and system |
| CN108805396A (en) * | 2018-04-23 | 2018-11-13 | 中国农业大学 | The poor coupled relation evaluation method with natural calamity in area based on GIS |
| CN109447383A (en) * | 2018-08-24 | 2019-03-08 | 中国科学院遥感与数字地球研究所 | Poverty Analysis method and Poverty Analysis system |
| CN109886103A (en) * | 2019-01-14 | 2019-06-14 | 中山大学 | Urban poverty measure of spread method |
| CN109948737A (en) * | 2019-04-08 | 2019-06-28 | 河南大学 | Poor spatial classification recognition methods and device based on big data and machine learning |
Non-Patent Citations (4)
| Title |
|---|
| Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies;Yansui Liu;Journal of Rural Studies;第52卷;第66-75页 * |
| 中国县域农村贫困化分异机制的地理探测与优化决策;刘彦随;《地理学报》;20170115;第72卷(第1期);参见第163-168页 * |
| 兰州城市贫困住区空间分异特征及其影响因素;张新红;《中国科学院大学学报》;20170515(第03期);全文 * |
| 县域农村贫困化空间分异及其影响因素――以陕西山阳县为例;武鹏等;《地理研究》;20180328(第03期);全文 * |
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