CN111311173B - National county level unit economic arrangement and spatialization method - Google Patents
National county level unit economic arrangement and spatialization method Download PDFInfo
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Abstract
The invention provides a national county level unit economic arrangement and spatialization method, relating to the technical field of data arrangement statistics; the method utilizes the mode of network crawlers and manual auxiliary leak detection and deficiency repair, ensures the integrity and the reliability of data to the greatest extent, and simultaneously reduces errors caused by manual operation process to the greatest extent; establishing a regional name dictionary and a socioeconomic index name dictionary; carrying out automatic graphic merging and attribute calculation on the areas without data according to actual requirements; a set of space-time attribute labeling scheme is designed, and can be used for iterative updating and continuous perfection of social economic statistical data. And the automatic drawing visualization method and technology are utilized to carry out data quality inspection by combining with social and economic common knowledge. Through the above steps and methods, socioeconomic data for county-level and district-level administrative units of 2011-2015 are collected and spatially visualized.
Description
Technical Field
The invention relates to the field of economic statistics research, in particular to a national county level unit economic arrangement and spatialization method.
Background
In the related study of county level unit data, a plurality of scholars often analyze and analyze the evolution of economic space patterns of a certain provincial county domain to analyze the space mechanism and driving mechanism of economic difference evolution. There are also relationships between county unit population data and natural disaster association research new risk factors and traditional risk factors.
The data is the basis of all natural scientific researches, and the collected data is a fine and strict work, but the number of counties in the whole country is large, including 31 provinces, 344 district cities and direct jurisdictions, 2850 counties and huge data. The annual-bill type is various, administrative division boundary variation, administrative division name identical, various statistics annual-bill data inconsistency, various naming of index names in the annual-bill, non-unification of index data units of different annual-bill, no index data and other problems exist in the process of collecting and arranging data, and the downloading and arranging process is complicated and complex, and has large task quantity, so that the data analysis and arrangement are difficult.
Disclosure of Invention
The invention aims to provide a national county unit economic arrangement and spatialization method, so as to solve the problems in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a national county level unit economic arrangement and spatialization method comprises the following steps:
s1, collecting ground market data in a sorting way;
s2, collecting district and county level unit data;
s3, collecting data in a sorting mode, and carrying out spatialization on the data; spatialization, which is a process of placing data in an Excel form on a map or map space, i.e., finding a spatial position for each number;
s4, analyzing the spatial data.
Preferably, step S1 is preceded by: s0, analyzing the change of administrative division and name, specifically comprising:
a1, administrative division boundary variation;
a2, administrative division name change;
a3, administrative areas are the same in name;
a4, counting inconsistent annual-image data;
a5, inconsistent index names to be collected;
a6, index data are missing.
Preferably, step S2 specifically includes:
s21, collecting database data by adopting a crawler program;
s22, performing iterative search during statistics according to index names in an index name dictionary;
s23, supplementary iterative search supplementary data from other data networks;
s24, establishing EXCEL space-time labels for the collected data; the space-time labeling method is to insert a row and a column of information into an Excel file, and label the Excel file in a form of 'time_space_attribute';
s25, merging and unifying the ground city non-data areas;
s26, drawing the collected data in batches, and checking the accuracy and the completeness of the data.
Preferably, in step S24, the EXCEL space-time labeling identifies the EXCEL file by using labeling methods of time, space and attribute, including three labeling methods: time-fixed labeling, space-fixed labeling and attribute-fixed labeling.
Preferably, the step S25 includes the following cases:
b1, merging all non-data situations of the counties, and merging the counties to be represented by the district-level city data;
b2, partial county has no data, merging the county with no data, and subtracting the sum of the data areas from the district-level city data;
and B3, no data exists in the district level city to which the county belongs, and no processing is carried out on the county level unit boundary with the shortage value.
Preferably, the map prepared in step S26 includes at least one of a national grade unit GDP density map, a national grade unit resident population density map, a national grade unit town population density map, a national grade unit first, second, third industry increment value density map, a national grade unit end-of-year population, a country population map, and a corresponding national county unit map.
Preferably, the step of checking the accuracy and integrity of the data in step S26 includes:
c1, finding out the distribution range of the maximum value, the minimum value or certain low values in the prepared graph, and checking whether the maximum value and the minimum value are abnormal or not;
c2, quickly looking up the geographic position of the data missing value to obtain a non-data area;
and C3, judging whether the data is abnormal, and judging that the data is abnormal when the data is low value data suddenly appearing near a high value or high value data suddenly appearing near a low value area.
Preferably, the analysis of the data in step S4 includes a spatial autocorrelation analysis and a centralized analysis.
Preferably, the step of spatial autocorrelation analysis is:
d1, analyzing the space difference degree between each county level area and the peripheral area by adopting Local Moran's I statistic;
wherein x is i Representing the observed value at the i-th spatial position,is the average value of x, w ij Is a space binary weight matrix w n×n Element S of (2) 0 Is the sum of all elements of the spatial weight matrix w;
d2, drawing a spatial Moran's I graph according to the formula in the step D1.
Preferably, the centralized analysis comprises the steps of:
e1, solving a coefficient of the foundation of the area to be counted;
e2, calculating the coefficient of the foundation of a certain index element according to the following formula:
Q＝(A1/A2)/(A3/A4)
wherein Q is a coefficient of Kerning; a1 is a certain county index value; a2 is the total index value of each county of the whole country; a3 is the total area of the land in a certain county; a4 is the total area of land in the whole country;
e3, sequentially listing the land area occupation ratio and the index data occupation ratio of each county of the whole country according to the order of the coefficient of the foundation from large to small, calculating the accumulated percentage, and manufacturing a Lorentzian curve for each county which is arranged and dividing the Lorentzian curve into six sections;
and E4, representing areas with the same area by the same reference sign on the value of the coefficient of the foundation, and producing a space lorentz diagram.
The beneficial effects of the invention are as follows:
the invention provides a national county level unit economic arrangement and spatialization method, which utilizes a mode of network crawlers and manual auxiliary leak detection and deficiency repair to ensure the complete accuracy and reliability of data to the greatest extent and simultaneously reduce errors caused by manual operation process to the greatest extent; establishing a regional name dictionary and a socioeconomic index name dictionary; carrying out automatic graphic merging and attribute calculation on the areas without data according to actual requirements; a set of space-time attribute labeling scheme is designed, and can be used for iterative updating and continuous perfection of social economic statistical data. And the automatic drawing visualization method and technology are utilized to carry out data quality inspection by combining with social and economic common knowledge. Through the above steps and methods, socioeconomic data for county-level and district-level administrative units of 2011-2015 are collected and spatially visualized.
Drawings
FIG. 1 is a schematic flow diagram of a national county level unit economic collation and spatialization method;
FIG. 2 is a flow chart of a method of gathering data;
FIG. 3 is a schematic diagram of a process for reading annotation data using excel space-time annotation.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the invention.
Examples
The embodiment provides a method for socioeconomic arrangement and spatialization of county-level units in China, which is shown in a flow chart in FIG. 1 and comprises the following steps:
s0, analyzing the changes of administrative division and names of each district level to be counted;
since the zone names may be renamed or changed, the total consideration in the process of formally counting administrative zones is required to be included
A1, administrative division boundary variation; such as two or more administrative regions, the Xuanwu and Chong Chinese regions of Beijing are merged into the Western and east urban regions, respectively; an administrative area is divided into two separate areas that are merged into two different ground markets, such as: splitting the Anhui Chaohu city; a division of administrative areas exists in two places, such as: the city of Hubei, free-form, was split into a free-form and free-form.
A2, the administrative division names change, and the administrative division names change due to the change of local geographic features, topography, humanity histories and other factors, so that the problem of the administrative division name change needs to be considered in actual collection work.
A3, administrative areas are the same in name; there are 2850 county level units in the country, the number is large, the problem of duplicate names is unavoidable, there are 73 county level units in different district level cities corresponding to 30 administrative division names, for example, there are the problem of duplicate names of regions in the south mountain area in the crane city, the deep mountain city, the SQL sentence is utilized to search and count the same name county, 73 duplicate name regions corresponding to 30 administrative division names, and we adopt to add the last level name to the same name to distinguish, for example, the south mountain area is changed into the south mountain area in the crane city and the south mountain area in the deep mountain city.
A4, counting inconsistent annual-image data; because the sources of the data of the statistical yearbooks are different, the index data in different statistical yearbooks have the problem of inconsistency, and the problem includes inconsistency of units and inconsistency of numerical values.
A5, inconsistent index names to be collected; since in different statistical yearbooks, for the index data we want to collect, different index names will be corresponded, for example: the regional production total can be expressed by GDP, the town population can be expressed by town total population, the town total population can be expressed by town total population, and the first industry increment value can be calculated by using the first industry increment value to account for the GDP proportion index. In the actual index data searching process, the index data cannot be searched by only a single index name.
A6, in the process of actually collecting data, the condition of index data deficiency can occur, and in the process of collecting data, different methods are adopted according to different conditions.
Establishing a regional name dictionary for the regional market to be counted, preparing administrative division boundaries of past years, counting administrative division with repeated names and establishing a related index dictionary; in the actual collection work, some data are missing, statistics of the statistics annual certificates are not counted, and different methods are adopted according to specific situations in the collection process.
S1, collecting data of a ground market in a sorting way;
s2, collecting district and county level unit data;
s21, in the process of collecting data, a database crawler program is adopted, and in the example of a stone house, the search index name is: GDP; the search index time range is: from 2011 to 2015; the search results obtained are shown in table 1,
TABLE 1
In the same region, the same index value appears in a plurality of yearbooks, and the value with the largest appearance frequency of the same value is taken; 2) Marking the condition that the previous yearbook is inconsistent, recording in a log file, wherein the recorded information comprises 'regional name-field name-year'.
For example, the GDP of a city has a plurality of data values, the same year and region, the same index value appears in a plurality of yearbooks, and the value with the largest appearance frequency of the same value is taken;
s22, taking into account the deviation of index names, searching is performed by using the index name dictionary in Table 2,
table 2 index name dictionary
S23, supplementary iterative search of supplementary data from other data networks
S24, establishing space-time labels for the collected excel data
Each statistic number in the statistic yearbook corresponds to a time (time), space (space) and attribute (attribute) field name, so that the program can automatically read the excel file, collect and import the excel file into a corresponding database as long as the identification information is made in the excel file. For the data of our current need to divide the administrative division units of the district level, the administrative division units of the county level, so we build two databases: the place-level database mcity is used for storing a place-level unit database and the county-level unit database county is used for storing a county-level unit database.
The space_time_attribute or space_time_attribute_t is adopted, t represents a labeling method of transposition (in practice, underlining does not need to be input) to identify an excel file, then a program is designed to automatically acquire the excel data, space area names are mc and co represent regional and county level statistical units respectively, and index names, year names and area names of the excel established by us are labeled and are in one-to-one correspondence with index names, year names and area names in a shp table of collected data. And the imported data is convenient for later programming reading.
The method comprises the following specific situations:
1. time fixed marking method
For example 2013 data space-2013-attribute
Table 3 time fixed label name value table
Pretreatment example: the labels are listed as space names, the labels are named as each name of behavior attribute, and the labels are mc2011at
Table 4 statistics and labeling table for total production values of various markets in 2011 of Shandong province
The time in the table is fixed, and the gray cells are marked by us.
Space fixing labeling method
Such as Beijing city data for the ground city: mc Beijing city-year-attribute
Table 5 space fixed label name value table
Pretreatment example: the column of marked location is the attribute name, the behavior time name of marked location is marked as the yc Shaoxing city yrat (last t represents time and attribute transposition)
Table 6 Shaoxing city, main year, last year, village, town demographic annotation table
The space in the upper table is fixed, and the gray cells are marked by us.
Attribute fixed labeling method
A specific index is determined to include specific index data for a plurality of regions of a plurality of years, such as, for example, end-of-year population data:
table 7 fixed name-labeling value table for attributes
Pretreatment example: the label location is listed as the area name and the action time name of the label location is labeled mcyrCPOP as shown in table 8.
TABLE 8 labeling table of demographics (2010-2016) of final-year household in each city (state) of the calendar year of Sichuan province
The attributes in the table above are fixed, and the gray cells are marked by us.
In addition to the above three labeling methods, some special situations may occur, such as time, space, and fixed attributes, where a method is adopted to determine a fixed amount first, and the rest is used as an unfixed amount, and then labeling is performed by using the above methods, and specific examples are as follows:
pretreatment example: and determining the behavior time name of Cangzhou city, marking the column of Cangzhou city as an attribute name, and marking the behavior time name of Cangzhou city as mc.
Table 9 statistical labeling of urban domestic pollution emissions and treatment conditions in 2011 of Cangzhou city
| mc Cangzhou urban yrat | 2011 | ||
| Index name | Unit (B) | Practical in the present year | |
| Urban population | CPOP | Universal person | 308.82 |
| Total amount of coal consumption | Wan Dun | 1322.6 | |
| Consumption of domestic coal | Wan Dun | 68.69 | |
| Consumption of natural gas | Ten thousand cubic meters | 6884.02 | |
| Average sulfur content of domestic coal | ％ | 0.9 |
The above table is considered to be spatially fixed, and the gray cells are what we label.
After the Excel is marked, each Excel has a time attribute space identifier, when the program automatically reads the Excel, firstly, the Excel mark is read, firstly, the first two characters mc or co are read to determine the corresponding provincial level and county level database, and then, according to the mark, the judgment of the following three conditions is determined, and the process is shown in fig. 3:
1) Program reads labeling information, if the labeling contains year nameAnd judging the first condition, fixing the time, marking the name of the region, and marking the name of the behavior index. V (V) ij The method comprises the steps of representing values corresponding to an excel row and column, representing a row and a column, reading tag information in the excel row by a program to obtain values of i and j, traversing the values in the current row and column to obtain names of all areas, and obtaining corresponding V by index names corresponding to the values of i and j ij And replace the corresponding value of the same index name with the same area name in the shp file of the corresponding year
2) The program reads the labeling information, if the labeling contains the regional name information, the labeling is judged to be the second case, the regional name is fixed, the column where the labeling is located is the index name, and the behavior time name where the labeling is located is marked. V (V) ij The method comprises the steps of representing values corresponding to an excel row and column, wherein i represents a row, j represents a column, reading marked information in the excel row by a program to obtain values of i and j, traversing the marked information in the current row and column to obtain time information, and obtaining corresponding V by index names corresponding to the values of i and j ij And replace the corresponding value of the same index name with the same area name in the shp file of the corresponding year
3) The program reads the labeling information, if the labeling contains the index name, the labeling is judged to be the third case, the index name is fixed, the column where the labeling is located is the area name, and the behavior time name where the labeling is located is marked. V (V) ij The method comprises the steps of representing values corresponding to an excel row and column, representing a row and a column, reading marked information in the excel row by a program to obtain values of i and j, traversing the excel row and column at present to obtain time information, and obtaining corresponding V by corresponding region names to the values of i and j ij And replacing corresponding values of the same index names in the same area names in the shp file of the corresponding year.
S3, collecting data in a sorting mode, and carrying out spatialization on the data, wherein the data are combined with data areas in the unified city at first, and the three conditions are as follows:
b1, merging all non-data situations of the counties, and merging the counties to be represented by the district-level city data;
b2, partial county has no data, merging the county with no data, and subtracting the sum of the data areas from the district-level city data;
and B3, no data exists in the district level city to which the county belongs, and no processing is carried out on the county level unit boundary with the shortage value.
Spatially mapping the collected data, including batch production of national grade unit GDP density profiles, national grade unit resident population density profiles, national grade unit town population density profiles, national grade unit first, second, third industry increment value density profiles, national grade unit end-of-year population, country population profiles, etc., and corresponding national county grade unit profiles.
S4, analyzing the spatial data, including spatial autocorrelation analysis and centralized analysis.
Spatial autocorrelation analysis
Spatial autocorrelation reflects the degree to which a certain spatial phenomenon or attribute value on one region unit correlates with the same phenomenon or attribute value on a neighboring region unit. When the attribute value is independent of position, the spatial autocorrelation is 0; positive spatial autocorrelation exists when the properties of observation units that are similar in location tend to be similar; negative spatial autocorrelation exists when the properties of closely spaced observation units tend to be more dissimilar than more distant properties.
For our collection of nationwide county level unit economic data, the spatial correlation Local area index (Local Indicators of Spatial Association, LISA) significance level is calculated by analysis, and the Local Moran's I statistic is adopted to analyze the spatial difference degree between each county level region and the surrounding region.
By assigning a binary weight matrix w n×n To express the spatial proximity of n target units. According to the adjacency criterion, when object i and object j are adjacency, element w of the spatial weight matrix ij 1, otherwise 0, and the values of all diagonal elements are set to 0. A second order adjacency, or a higher order adjacency, may also be defined if two elements are not directly adjacency. For planar targets, an identification point, such as the centroid of the face, can be used for representing the planar target;
the calculation formula of Local Moran's I is:
x i representing the observed value at the i-th spatial position,is the average value of x, w ij Is a space binary weight matrix w n×n Element S of (2) 0 Is the sum of all elements of the spatial weight matrix w.
A spatial Local Moran's I map of GDP was made from the above formula in 2015.
Centralized analysis
In order to measure the concentration (or dispersion) degree of each index in a research area, the concentration analysis of each index can be carried out by utilizing a spatial lorenz, firstly, the coefficient of the foundation of a certain area of the area is calculated, then, the land area occupation ratio and the index data occupation ratio of each county of the whole country are sequentially listed according to the order of the coefficient of the foundation from large to small, and the cumulative percentage is calculated, a lorentz curve is manufactured for each county of the arrangement, and is divided into six sections, for example, when a space distribution map of the GDP lorentz curve is manufactured in 2015, the first 50% of the area with the highest GDP density only occupies 2.3% of the land area, the 50% -60% of the area with the highest GDP density only occupies 1.8% of the land area, and similarly, the area occupied by the 10% GDP with the higher GDP density sequentially reduced density can be sequentially seen to be 3.1%,5.2%,9,6% and 78% of the land area occupied by the GDP; and the regions with values between the same segments are represented by the same color, so that a Lorenz curve space distribution diagram of the last 2015 population and a Lorenz curve space distribution diagram of the 2015 GDP are produced.
The coefficient of the kunity is mainly used for measuring the spatial distribution condition of a certain area element. The coefficient of the index element refers to the ratio of the index value of a county to the total value of the index of each county in the whole country to the total area of the land of the county. The formula is:
Q＝(A1/A2)/(A3/A4)
wherein Q is a coefficient of Kerning; a1 is a certain county index value; a2 is the total index value of each county of the whole country; a3 is the total area of the land in a certain county; a4 is the total area of the land in the whole country.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
1. the method has the advantages that the complete accuracy and reliability of data are ensured to the greatest extent by utilizing the mode of network crawlers and manual auxiliary leak detection and deficiency repair, and meanwhile, errors caused by manual operation processes are reduced to the greatest extent;
2. establishing a regional name dictionary and a socioeconomic index name dictionary;
3. carrying out automatic graphic merging and attribute calculation on the areas without data according to actual requirements;
4. a set of space-time attribute labeling scheme is designed, and can be used for iterative updating and continuous perfection of social economic statistical data.
5. And the automatic drawing visualization method and technology are utilized to carry out data quality inspection by combining with social and economic common knowledge. The method can analyze the stress of the social economic development of China on the ecological environment and the driving force analysis of the ecological environment, establish a supplementary and perfect index noun dictionary, a regional name dictionary and a historical administrative division boundary database, and arrange and visualize the social economic data of the county level unit of 2010-2015 in space, and perform the associated analysis research on the aspects of sustainable development, environmental pollution, geological disasters, remote sensing big data and the like; in addition, the socioeconomic data of the district level unit are automatically extracted by utilizing an NLP (natural language processing) technology and a deep learning method. The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which is also intended to be covered by the present invention.
Claims (7)
1. The national county level unit economic arrangement and spatialization method is characterized by comprising the following steps:
s1, collecting ground market data in a sorting way;
s2, collecting district and county level unit data;
s3, collecting data in a sorting mode, and carrying out spatial processing on the data;
s4, analyzing the space data;
the step S2 specifically comprises the following steps:
s21, collecting database data by adopting a crawler program;
s22, performing iterative search in statistics according to index names in the index name dictionary;
s23, supplementary iterative search supplementary data from other data networks;
s24, establishing EXCEL space-time labels for the collected data;
s25, merging and unifying the ground city non-data areas;
s26, drawing the collected data in batches, and checking the accuracy and the completeness of the data;
the batch drawing in step S26 includes at least one of a national grade unit GDP density profile, a national grade unit resident population density profile, a national grade unit town population density profile, a national grade unit first, second, third industry increment value density profile, a national grade unit year end population, a country population profile, and a corresponding national county grade unit profile;
the step S1 further includes: s0, analyzing and judging the change of administrative division and name, specifically comprising:
a1, administrative division boundary variation, including two or more administrative division combinations, one administrative division being divided into two and combined into two different ground cities and one administrative division being divided into two and existing;
a2, administrative division name change;
a3, administrative areas are the same in name;
a4, counting inconsistent annual-image data, wherein the inconsistent annual-image data comprises inconsistent annual-image data sources and inconsistent index data in different statistical annual-images, and the inconsistent index data comprises inconsistent units and inconsistent numerical values;
a5, inconsistent index names to be collected;
a6, index data deletion, including establishing a regional name dictionary for the regional market to be counted, preparing administrative division boundaries of past years, counting administrative division with repeated names and establishing a related index dictionary.
2. The national county level unit economic arrangement and spatialization method according to claim 1, wherein the EXCEL space-time labeling in step S24 uses labeling methods of time, space and attribute to identify the EXCEL file, comprising three labeling methods: time-fixed labeling, space-fixed labeling and attribute-fixed labeling.
3. The county-level unit economic arrangement and spatialization method according to claim 1, wherein step S25 comprises the following cases:
b1, merging all non-data situations of the counties, and merging the counties to be represented by the district-level city data;
b2, partial county has no data, merging the county with no data, and subtracting the sum of the data areas from the district-level city data;
and B3, no data exists in the district level city to which the county belongs, and no processing is carried out on the county level unit boundary with the shortage value.
4. The county-level unit economic organization and spatialization method according to claim 1, wherein the step of checking the accuracy and integrity of the data in step S26 comprises:
c1, finding out the distribution range of the maximum value, the minimum value or certain low values in the prepared graph, and checking whether the maximum value and the minimum value are abnormal or not;
c2, quickly looking up the geographic position of the data missing value to obtain a non-data area;
and C3, judging whether the data is abnormal according to common knowledge, wherein the abnormal situation comprises low-value data suddenly appearing near a high value and high-value data suddenly appearing near a low value area.
5. The county-wide unit economic arrangement and spatialization method according to claim 1, wherein the analysis of the data in step S4 comprises a spatial autocorrelation analysis and a centralization analysis.
6. The county-level unit economic arrangement and spatialization method according to claim 5, wherein the step of spatial autocorrelation analysis is:
d1, analyzing the space difference degree between each county level area and the peripheral area by adopting Local Moran's I statistic;
wherein x is i Representing the observed value at the i-th spatial position,is the average value of x, w ij Is a space binary weight matrix w n×n Element S of (2) 0 Is the sum of all elements of the spatial weight matrix w;
d2, drawing a spatial Moran's I graph according to the formula in the step D1.
7. The county-level unit economic arrangement and spatialization method of claim 5, wherein said centralized analysis comprises the steps of:
e1, solving a coefficient of the foundation of the area to be counted;
e2, calculating the coefficient of the foundation of a certain index element according to the following formula:
Q＝(A1/A2)/(A3/A4)
wherein Q is a coefficient of Kerning; a1 is a certain county index value; a2 is the total index value of each county of the whole country; a3 is the total area of the land in a certain county; a4 is the total area of land in the whole country;
e3, sequentially listing the land area occupation ratio and the index data occupation ratio of each county of the whole country according to the order of the coefficient of the foundation from large to small, calculating the accumulated percentage, and manufacturing a Lorentzian curve space distribution diagram for each county which is arranged, and dividing the space distribution diagram into six sections;
and E4, representing areas with the same area by the same reference sign on the value of the coefficient of the foundation, and producing a space lorentz diagram.
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