CN112053076A - Livable city evaluation method and system based on big data - Google Patents

Livable city evaluation method and system based on big data Download PDF

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CN112053076A
CN112053076A CN202010960322.6A CN202010960322A CN112053076A CN 112053076 A CN112053076 A CN 112053076A CN 202010960322 A CN202010960322 A CN 202010960322A CN 112053076 A CN112053076 A CN 112053076A
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林灿星
王炜
郑晶
郑建生
刘孔杰
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Fujian Huizhengtong Information Technology Co ltd
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Abstract

The invention relates to the field of city evaluation, in particular to a livable city evaluation method and system based on big data. The livable city evaluation method based on big data comprises the following steps: acquiring sample data corresponding to different indexes for more than two consecutive years; calculating the growth proportion of the different indexes according to the sample data; calculating the weights of the different indexes by an entropy selection method; and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes. And multiple sample data with different indexes are obtained to evaluate the urban livability, so that a more accurate analysis result can be obtained.

Description

Livable city evaluation method and system based on big data
Technical Field
The invention relates to the field of city evaluation, in particular to a livable city evaluation method and system based on big data.
Background
Along with the development of science and technology and economy, more and more people are rushing to cities, and along with the progress of industrialization, the urban environment is polluted more and more. People have higher and higher livability requirements on a city, and a city with high livability also plays a key role in the attraction of talents in the future. At present, the common judgment on the livability of the city is to evaluate and analyze the livability city by adopting single data, and the accuracy of the analysis result is poor.
Disclosure of Invention
Therefore, a livable city evaluation method based on big data is needed to be provided for solving the problem that the evaluation and analysis of livable cities are performed by adopting single data and the accuracy of analysis results is poor in the prior art. The specific technical scheme is as follows:
a livable city evaluation method based on big data comprises the following steps:
acquiring sample data corresponding to different indexes for more than two consecutive years;
calculating the growth proportion of the different indexes according to the sample data;
calculating the weights of the different indexes by an entropy selection method;
and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes.
Further, the "calculating the growth proportions of the different indexes according to the sample data" further includes:
by the formula:
Figure BDA0002680301880000011
calculating the growth proportion of the different indexes, wherein: u shapeijData for the ith index at the j year, XijThe j-th index is the increase of the j-th index compared with the j-1 th index.
Further, before the "calculating the weights of the different indexes by the entropy selection method", the method further comprises the following steps:
and carrying out dimensionless operation on the sample data corresponding to different indexes to be within the interval of [0,1] by a maximum-minimum value method.
Further, the "calculating the weights of the different indexes by an entropy selection method" further includes the steps of:
calculating sample data X corresponding to different indicators after dimensionlessij' specific gravity Rij：
Figure BDA0002680301880000021
Calculating the entropy e of the jth indexjk：
Figure BDA0002680301880000022
Calculating the difference coefficient g of the j indexjk：
gjk＝1-ejk
Calculating the index Xij' weight wjk：
Figure BDA0002680301880000023
Calculating a weight set:
{wjk|j＝1,2,3,...；k＝1,2,3,...}。
further, the indexes comprise a first-level index, a second-level index is included under the first-level index, and a third-level index is included under the second-level index;
the first-level indexes are as follows: urban livability index;
the secondary indicators include one or more of: safety, health, convenience, comfort, economic development;
the safety index comprises one or more of the following three-level indexes: the public safety events are successfully processed and the safety education is carried out;
the health indicator comprises one or more of the following three levels: annual air standard daily rate, surface water, underground water, drinking water standard rate, urban regional daytime noise, urban main line daytime noise and garbage harmless treatment rate;
the convenience index includes one or more of the following three-level indices: urban traffic network transport capacity (ten thousand persons), highway density, number of health institutions (institute/ten thousand persons), personnel of the health institutions (people/ten thousand persons), number of beds of the health institutions (one/ten thousand persons), number of schools (institute) at each level, and number of supermarkets per square kilometer;
the comfort index comprises one or more of the following three levels: gas prevalence, cable television prevalence, tap water prevalence, daily average air temperature of 15-25 deg.C, urban greening coverage, per-capita park green area (square meter), per-capita fresh water volume (cubic meter), per-capita housing area;
the economic developability index comprises one or more of the following three-level indexes: the average GDP (yuan), urban resident domination income, urban unemployment rate, proportion of the third industry in all industries, third industry increase rate, resident consumption price total index (100 years), grain total output increase rate, oil material total output increase rate, import and export total output increase rate, natural population increase rate (mill), average house price per capita, average wage-house price ratio and industrial output value per capita (ten thousand yuan).
In order to solve the technical problems, the method also provides a livable city evaluation system based on big data, and the specific technical scheme is as follows:
a livable city evaluation system based on big data comprises: the system comprises a sample data acquisition module, a growth proportion calculation module, a weight calculation module and an evaluation model establishment module;
the sample data acquisition module is used for: acquiring sample data corresponding to different indexes for more than two consecutive years;
the growth proportion calculation module is used for: calculating the growth proportion of the different indexes according to the sample data;
the weight calculation module is configured to: calculating the weights of the different indexes by an entropy selection method;
the evaluation model building module is used for: and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes.
Further, the increase proportion calculation module is further configured to:
by the formula:
Figure BDA0002680301880000041
calculating the growth proportion of the different indexes, wherein: u shapeijData for the ith index at the j year, XijThe j-th index is the increase of the j-th index compared with the j-1 th index.
Further, the weight calculation module is further configured to: and carrying out dimensionless operation on the sample data corresponding to different indexes to be within the interval of [0,1] by a maximum-minimum value method.
Further, the weight calculation module is further configured to:
calculating sample data X corresponding to different indicators after dimensionlessij' specific gravity Rij：
Figure BDA0002680301880000042
Calculating the entropy e of the jth indexjk：
Figure BDA0002680301880000043
Calculating the difference coefficient g of the j indexjk：
gjk＝1-ejk
Calculating the index Xij' weight wjk：
Figure BDA0002680301880000044
Calculating a weight set:
{wjk|j＝1,2,3,...；k＝1,2,3,...}。
further, the indexes comprise a first-level index, a second-level index is included under the first-level index, and a third-level index is included under the second-level index;
the first-level indexes are as follows: urban livability index;
the secondary indicators include one or more of: safety, health, convenience, comfort, economic development;
the safety index comprises one or more of the following three-level indexes: the public safety events are successfully processed and the safety education is carried out;
the health indicator comprises one or more of the following three levels: annual air standard daily rate, surface water, underground water, drinking water standard rate, urban regional daytime noise, urban main line daytime noise and garbage harmless treatment rate;
the convenience index includes one or more of the following three-level indices: urban traffic network transport capacity (ten thousand persons), highway density, number of health institutions (institute/ten thousand persons), personnel of the health institutions (people/ten thousand persons), number of beds of the health institutions (one/ten thousand persons), number of schools (institute) at each level, and number of supermarkets per square kilometer;
the comfort index comprises one or more of the following three levels: gas prevalence, cable television prevalence, tap water prevalence, daily average air temperature of 15-25 deg.C, urban greening coverage, per-capita park green area (square meter), per-capita fresh water volume (cubic meter), per-capita housing area;
the economic developability index comprises one or more of the following three-level indexes: the average GDP (yuan), urban resident domination income, urban unemployment rate, proportion of the third industry in all industries, third industry increase rate, resident consumption price total index (100 years), grain total output increase rate, oil material total output increase rate, import and export total output increase rate, natural population increase rate (mill), average house price per capita, average wage-house price ratio and industrial output value per capita (ten thousand yuan).
The invention has the beneficial effects that: obtaining sample data corresponding to different indexes for more than two consecutive years; calculating the growth proportion of the different indexes according to the sample data; calculating the weights of the different indexes by an entropy selection method; and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes. And multiple sample data with different indexes are obtained to evaluate the urban livability, so that a more accurate analysis result can be obtained.
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FIG. 1 is a flow chart of a method for evaluating a livable city based on big data according to an embodiment;
fig. 2 is a schematic block diagram of a livable city evaluation system based on big data according to an embodiment.
Description of reference numerals:
200. a livable city evaluation system based on big data,
201. a sample data acquisition module for acquiring the sample data,
202. a growth proportion calculation module for calculating the growth proportion of the plant,
203. a weight calculation module for calculating the weight of the target,
204. and an evaluation model establishing module.
Detailed Description
To explain technical contents, structural features, and objects and effects of the technical solutions in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.
Referring to fig. 1, in the present embodiment, a livable city evaluation method based on big data can be applied to a storage device, which includes but is not limited to: personal computers, servers, general purpose computers, special purpose computers, network devices, embedded devices, programmable devices, intelligent mobile terminals, etc. The specific implementation is as follows:
step S101: and acquiring sample data corresponding to different indexes for more than two consecutive years.
Step S102: and calculating the growth proportion of the different indexes according to the sample data.
Step S103: and calculating the weights of the different indexes by an entropy selection method.
Step S104: and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes.
Obtaining sample data corresponding to different indexes for more than two consecutive years; calculating the growth proportion of the different indexes according to the sample data; calculating the weights of the different indexes by an entropy selection method; and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes. And multiple sample data with different indexes are obtained to evaluate the urban livability, so that a more accurate analysis result can be obtained.
In this embodiment, the index includes a first-level index, a second-level index is included below the first-level index, and a third-level index is included below the second-level index;
the first-level indexes are as follows: urban livability index;
the secondary indicators include one or more of: safety, health, convenience, comfort, economic development;
the safety index comprises one or more of the following three-level indexes: the public safety events are successfully processed and the safety education is carried out;
the health indicator comprises one or more of the following three levels: annual air standard daily rate, surface water, underground water, drinking water standard rate, urban regional daytime noise, urban main line daytime noise and garbage harmless treatment rate;
the convenience index includes one or more of the following three-level indices: urban traffic network transport capacity (ten thousand persons), highway density, number of health institutions (institute/ten thousand persons), personnel of the health institutions (people/ten thousand persons), number of beds of the health institutions (one/ten thousand persons), number of schools (institute) at each level, and number of supermarkets per square kilometer;
the comfort index comprises one or more of the following three levels: gas prevalence, cable television prevalence, tap water prevalence, daily average air temperature of 15-25 deg.C, urban greening coverage, per-capita park green area (square meter), per-capita fresh water volume (cubic meter), per-capita housing area;
the economic developability index comprises one or more of the following three-level indexes: the average GDP (yuan), urban resident domination income, urban unemployment rate, proportion of the third industry in all industries, third industry increase rate, resident consumption price total index (100 years), grain total output increase rate, oil material total output increase rate, import and export total output increase rate, natural population increase rate (mill), average house price per capita, average wage-house price ratio and industrial output value per capita (ten thousand yuan).
In step S101, sample data needs to be acquired from data source channels such as the national city statistical yearbook, the local statistical yearbook, and the government affairs data, and at least sample data of more than two consecutive years needs to be collected. In this embodiment, sample data is preferably collected for two consecutive years, such as from 2016-.
After collecting the data, step S102 further includes the steps of: by the formula:
Figure BDA0002680301880000081
calculating the growth proportion of the different indexes, wherein: u shapeijData for the ith index at the j year, XijThe j-th index is the increase of the j-th index compared with the j-1 th index. The growth rate can more directly reflect the development of urban livability.
In other embodiments, to reduce the amount of computation, only a portion of the representative index increase rate may be selected.
Furthermore, due to differences of respective measurement units and orders of magnitude, there is incommercity between the evaluation indexes. In order to eliminate the influence of different variable dimensions, most statistical models require non-dimensionalization of data. In this embodiment, before the "calculating the weights of the different indexes by the entropy selection method", the method further includes: and carrying out dimensionless operation on the sample data corresponding to different indexes to be within the interval of [0,1] by a maximum-minimum value method. Such as:
Figure BDA0002680301880000082
wherein: fi' is data after each index is dimensionless, Fimax，FiminThe minimum and maximum values in the sample data of the index are respectively.
After carrying out dimensionless transformation on sample data corresponding to different indexes, the step of calculating the weights of the different indexes by an entropy selection method further comprises the following steps:
calculating sample data X corresponding to different indicators after dimensionlessij' specific gravity Rij：
Figure BDA0002680301880000091
Wherein n is the total number of different indicators;
calculating the entropy e of the jth indexjk：
Figure BDA0002680301880000092
Calculating the difference coefficient g of the j indexjk：
gjk＝1-ejk
Calculating the index Xij' weight wjk：
Figure BDA0002680301880000093
Calculate weight set (in this embodiment, calculate weight set by Matlab programming):
{wjk|j＝1,2,3,...；k＝1,2,3,...}。
after the growth ratios of the different indexes and the weights of the different indexes are calculated, the evaluation model established in step S104 is as follows:
Figure BDA0002680301880000094
wherein wjkIs a weight representing the j-th index, XjiIs the growth rate of the ith year representing the jth index.
In another embodiment, based on the entropy weight method, weights of 45 indexes can be obtained respectively, so that a multi-index comprehensive model for 3 samples of urban livings between 2016 and 2018 can be established as follows:
Figure BDA0002680301880000101
wherein e isjScore normalized for the j-th index, wjAnd obtaining the weight of the j index by an entropy weight method.
Referring to fig. 2, in the present embodiment, a livable city evaluation system 200 based on big data is implemented as follows:
a livable city evaluation system 200 based on big data, comprising: a sample data acquisition module 201, a growth ratio calculation module 202, a weight calculation module 203 and an evaluation model establishment module 204;
the sample data acquiring module 201 is configured to: acquiring sample data corresponding to different indexes for more than two consecutive years;
the increase proportion calculation module 202 is configured to: calculating the growth proportion of the different indexes according to the sample data;
the weight calculation module 203 is configured to: calculating the weights of the different indexes by an entropy selection method;
the evaluation model building module 204 is configured to: and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes.
Further, the increase ratio calculation module 202 is further configured to:
by the formula:
Figure BDA0002680301880000102
calculating the growth proportion of the different indexes, wherein: u shapeijData for the ith index at the j year, XijThe j-th index is the increase of the j-th index compared with the j-1 th index.
Further, the weight calculation module 203 is further configured to: and carrying out dimensionless operation on the sample data corresponding to different indexes to be within the interval of [0,1] by a maximum-minimum value method.
Further, the weight calculation module 203 is further configured to:
calculating sample data X corresponding to different indicators after dimensionlessij' specific gravity Rij：
Figure BDA0002680301880000103
Calculating the entropy e of the jth indexjk：
Figure BDA0002680301880000111
Calculating the difference coefficient g of the j indexjk：
gjk＝1-ejk
Calculating the index Xij' weight wjk：
Figure BDA0002680301880000112
Calculating a weight set:
{wjk|j＝1,2,3,...；k＝1,2,3,...}。
after the growth proportions of different indexes and the weights of different indexes are calculated, an evaluation model is established as follows:
Figure BDA0002680301880000113
wherein wjkIs a weight representing the j-th index, XjiIs the growth rate of the ith year representing the jth index.
In another embodiment, based on the entropy weight method, weights of 45 indexes can be obtained respectively, so that a multi-index comprehensive model for 3 samples of urban livings between 2016 and 2018 can be established as follows:
Figure BDA0002680301880000114
wherein e isjScore normalized for the j-th index, wjAnd obtaining the weight of the j index by an entropy weight method.
Further, the indexes comprise a first-level index, a second-level index is included under the first-level index, and a third-level index is included under the second-level index;
the first-level indexes are as follows: urban livability index;
the secondary indicators include one or more of: safety, health, convenience, comfort, economic development;
the safety index comprises one or more of the following three-level indexes: the public safety events are successfully processed and the safety education is carried out;
the health indicator comprises one or more of the following three levels: annual air standard daily rate, surface water, underground water, drinking water standard rate, urban regional daytime noise, urban main line daytime noise and garbage harmless treatment rate;
the convenience index includes one or more of the following three-level indices: urban traffic network transport capacity (ten thousand persons), highway density, number of health institutions (institute/ten thousand persons), personnel of the health institutions (people/ten thousand persons), number of beds of the health institutions (one/ten thousand persons), number of schools (institute) at each level, and number of supermarkets per square kilometer;
the comfort index comprises one or more of the following three levels: gas prevalence, cable television prevalence, tap water prevalence, daily average air temperature of 15-25 deg.C, urban greening coverage, per-capita park green area (square meter), per-capita fresh water volume (cubic meter), per-capita housing area;
the economic developability index comprises one or more of the following three-level indexes: the average GDP (yuan), urban resident domination income, urban unemployment rate, proportion of the third industry in all industries, third industry increase rate, resident consumption price total index (100 years), grain total output increase rate, oil material total output increase rate, import and export total output increase rate, natural population increase rate (mill), average house price per capita, average wage-house price ratio and industrial output value per capita (ten thousand yuan).
Sample data corresponding to different indexes for more than two consecutive years is acquired through a sample data acquisition module 201; the growth proportion calculation module 202 calculates the growth proportions of the different indexes according to the sample data; the weight calculation module 203 calculates the weights of the different indexes by an entropy selection method; the evaluation model establishing module 204 establishes an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes. And multiple sample data with different indexes are obtained to evaluate the urban livability, so that a more accurate analysis result can be obtained.
It should be noted that, although the above embodiments have been described herein, the invention is not limited thereto. Therefore, based on the innovative concepts of the present invention, the technical solutions of the present invention can be directly or indirectly applied to other related technical fields by making changes and modifications to the embodiments described herein, or by using equivalent structures or equivalent processes performed in the content of the present specification and the attached drawings, which are included in the scope of the present invention.

Claims (10)

1. A livable city evaluation method based on big data is characterized by comprising the following steps:
acquiring sample data corresponding to different indexes for more than two consecutive years;
calculating the growth proportion of the different indexes according to the sample data;
calculating the weights of the different indexes by an entropy selection method;
and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes.
2. The method for evaluating a livable city based on big data according to claim 1, wherein the step of calculating growth proportions of the different indexes according to the sample data further comprises the steps of:
by the formula:
Figure FDA0002680301870000011
calculating the growth proportion of the different indexes, wherein: u shapeijData for the ith index at the j year, XijThe j-th index is the increase of the j-th index compared with the j-1 th index.
3. The livingchiness evaluation method based on big data as claimed in claim 1, wherein before said "calculating the weight of said different indexes by entropy selection", further comprising the steps of:
and carrying out dimensionless operation on the sample data corresponding to different indexes to be within the interval of [0,1] by a maximum-minimum value method.
4. The livingchiness evaluation method based on big data as claimed in claim 3, wherein said "calculating the weight of said different indexes by entropy selection" further comprises the steps of:
calculating sample data X corresponding to different indicators after dimensionlessij' specific gravity Rij：
Figure FDA0002680301870000012
Calculating the entropy e of the jth indexjk：
Figure FDA0002680301870000013
Calculating the difference coefficient g of the j indexjk：
gjk＝1-ejk
Calculating the index Xij' weight wjk：
Figure FDA0002680301870000021
Calculating a weight set:
{wjk|j＝1,2,3,...；k＝1,2,3,...}。
5. the livable city evaluation method based on big data according to claim 4,
the indexes comprise first-level indexes, second-level indexes are included under the first-level indexes, and third-level indexes are included under the second-level indexes;
the first-level indexes are as follows: urban livability index;
the secondary indicators include one or more of: safety, health, convenience, comfort, economic development;
the safety index comprises one or more of the following three-level indexes: the public safety events are successfully processed and the safety education is carried out;
the health indicator comprises one or more of the following three levels: annual air standard daily rate, surface water, underground water, drinking water standard rate, urban regional daytime noise, urban main line daytime noise and garbage harmless treatment rate;
the convenience index includes one or more of the following three-level indices: urban traffic network transport capacity (ten thousand persons), highway density, number of health institutions (institute/ten thousand persons), personnel of the health institutions (people/ten thousand persons), number of beds of the health institutions (one/ten thousand persons), number of schools (institute) at each level, and number of supermarkets per square kilometer;
the comfort index comprises one or more of the following three levels: gas prevalence, cable television prevalence, tap water prevalence, daily average air temperature of 15-25 deg.C, urban greening coverage, per-capita park green area (square meter), per-capita fresh water volume (cubic meter), per-capita housing area;
the economic developability index comprises one or more of the following three-level indexes: the average GDP (yuan), urban resident domination income, urban unemployment rate, proportion of the third industry in all industries, third industry increase rate, resident consumption price total index (100 years), grain total output increase rate, oil material total output increase rate, import and export total output increase rate, natural population increase rate (mill), average house price per capita, average wage-house price ratio and industrial output value per capita (ten thousand yuan).
6. A livable city evaluation system based on big data is characterized by comprising: the system comprises a sample data acquisition module, a growth proportion calculation module, a weight calculation module and an evaluation model establishment module;
the sample data acquisition module is used for: acquiring sample data corresponding to different indexes for more than two consecutive years;
the growth proportion calculation module is used for: calculating the growth proportion of the different indexes according to the sample data;
the weight calculation module is configured to: calculating the weights of the different indexes by an entropy selection method;
the evaluation model building module is used for: and establishing an evaluation model according to the growth proportion of the different indexes and the weights of the different indexes.
7. The livable city evaluation system based on big data according to claim 6,
the growth proportion calculation module is further configured to:
by the formula:
Figure FDA0002680301870000031
calculating the growth proportion of the different indexes, wherein: u shapeijData for the ith index at the j year, XijThe j-th index is the increase of the j-th index compared with the j-1 th index.
8. The livable city evaluation system based on big data according to claim 6,
the weight calculation module is further configured to: and carrying out dimensionless operation on the sample data corresponding to different indexes to be within the interval of [0,1] by a maximum-minimum value method.
9. The big-data-based livable city evaluation system according to claim 8,
the weight calculation module is further configured to:
calculating sample data X corresponding to different indicators after dimensionlessij' specific gravity Rij：
Figure FDA0002680301870000041
Calculating the entropy e of the jth indexjk：
Figure FDA0002680301870000042
Calculating the difference coefficient g of the j indexjk：
gjk＝1-ejk
Calculating the index Xij' weight wjk：
Figure FDA0002680301870000043
Calculating a weight set:
{wjk|j＝1,2,3,...；k＝1,2,3,...}。
10. the livable city evaluation system based on big data according to claim 6,
the indexes comprise first-level indexes, second-level indexes are included under the first-level indexes, and third-level indexes are included under the second-level indexes;
the first-level indexes are as follows: urban livability index;
the secondary indicators include one or more of: safety, health, convenience, comfort, economic development;
the safety index comprises one or more of the following three-level indexes: the public safety events are successfully processed and the safety education is carried out;
the health indicator comprises one or more of the following three levels: annual air standard daily rate, surface water, underground water, drinking water standard rate, urban regional daytime noise, urban main line daytime noise and garbage harmless treatment rate;
the convenience index includes one or more of the following three-level indices: urban traffic network transport capacity (ten thousand persons), highway density, number of health institutions (institute/ten thousand persons), personnel of the health institutions (people/ten thousand persons), number of beds of the health institutions (one/ten thousand persons), number of schools (institute) at each level, and number of supermarkets per square kilometer;
the comfort index comprises one or more of the following three levels: gas prevalence, cable television prevalence, tap water prevalence, daily average air temperature of 15-25 deg.C, urban greening coverage, per-capita park green area (square meter), per-capita fresh water volume (cubic meter), per-capita housing area;
the economic developability index comprises one or more of the following three-level indexes: the average GDP (yuan), urban resident domination income, urban unemployment rate, proportion of the third industry in all industries, third industry increase rate, resident consumption price total index (100 years), grain total output increase rate, oil material total output increase rate, import and export total output increase rate, natural population increase rate (mill), average house price per capita, average wage-house price ratio and industrial output value per capita (ten thousand yuan).
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109615243A (en) * 2018-12-14 2019-04-12 辽宁工程技术大学 A kind of novel level of urbanization evaluation method based on improved entropy method
CN113505999A (en) * 2021-07-15 2021-10-15 中国科学院生态环境研究中心 Index calculation method applied to urban space quality assessment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109615243A (en) * 2018-12-14 2019-04-12 辽宁工程技术大学 A kind of novel level of urbanization evaluation method based on improved entropy method
CN113505999A (en) * 2021-07-15 2021-10-15 中国科学院生态环境研究中心 Index calculation method applied to urban space quality assessment

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