Land space planning implementation monitoring method based on livelihood public opinion big data

Abstract

The invention relates to a livelihood public opinion big data-based territorial space planning implementation monitoring method, which comprises the following steps of: constructing a multi-level space unit and a territorial space information lexicon according to territorial space multi-source data, and matching livelihood public opinion big data in the territorial space information lexicon to obtain a spatial position; matching the obtained spatial position with a multi-level spatial unit to obtain a spatial unit corresponding to the spatial position; identifying an emotion label for each record in the livelihood public opinion big data, and calculating a livelihood emotion index planned and implemented in the spatial range of each hierarchical spatial unit according to the emotion label; and processing the livelihood public opinion big data to obtain a territorial space livelihood keyword library, updating the territorial space livelihood keyword library, and generating each level of space unit planning and implementing livelihood public opinion keywords in combination with the territorial space information word library and the updated territorial space livelihood keyword library. The method can dynamically monitor subjective feelings of the public on planning implementation conditions and key problems of social livelihood attention.

Description

Technical Field

[0001] The present invention relates to the technical field of national land space planning, and in particular to a national land space planning implementation monitoring method based on people's livelihood public opinion big data. Background Art

[0002] The implementation monitoring of national land space planning is an important part of establishing a "smart national land space planning" that is perceptible, learnable, well governed, and adaptive. In the context of national land space planning in the new era, the technical path for the implementation monitoring of national land space planning is still in the exploratory stage.

[0003] At present, the spatial perception is mainly realized by "sky-ground" remote sensing technology, Internet of Things, big data and other technologies, and the status and change monitoring of natural resources and land space elements is realized by machine learning, spatial analysis, semantic extraction, model construction and other methods, supporting timely warning of problems such as lagging planning implementation progress, illegal use control behavior and deviation trend of urban spatial form, reflecting the effectiveness of spatial planning implementation and ensuring high-quality urban development. Among them, the use of big data means is often represented by data such as mobile phone signaling, location service information, traffic operation monitoring, and social media geographic check-in. Through quantitative analysis of urban spatial vitality, livability, and spatial quality, the implementation of urban and rural planning is reflected from the intensity of spatial activities. Although it has good dynamics and measurability, it lacks the reflection of residents' subjective feelings and livelihood emotions. In terms of the subjective evaluation of the current status of urban planning, construction and management, government services, and livelihood issues, the overall satisfaction of residents is mainly obtained through regular public disclosure, online and offline surveys and questionnaires, but this method has limitations in time, space, and samples in quantifying the subjective feelings and key issues of urban and rural residents, and lacks objective evaluation criteria. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a land space planning implementation monitoring method based on people's livelihood public opinion big data, which can dynamically monitor the public's subjective feelings on the implementation of the plan and key issues of social and people's livelihood.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a national land space planning implementation monitoring method based on people's livelihood public opinion big data, which is characterized by comprising the following steps:

[0006] Step 1: Obtaining big data on people's livelihood and public opinion and collecting multi-source data on land and space;

[0007] Step 2: Construct multi-level spatial units based on multi-source data of national land space;

[0008] Step 3: construct a national land space information word library based on the national land space multi-source data, match the people's livelihood public opinion big data in the national land space information word library, obtain the spatial position, and match the obtained spatial position with the multi-level spatial unit to obtain the spatial unit corresponding to the spatial position;

[0009] Step 4: Identify the sentiment label for each record in the livelihood public opinion big data, and calculate the livelihood sentiment index within the spatial scope of each level of spatial unit according to the sentiment label;

[0010] Step 5: Process the people's livelihood public opinion big data, obtain the land space people's livelihood keyword library, compare the land space information vocabulary with the land space people's livelihood keyword library, update the land space people's livelihood keyword library, and extract the people's livelihood public opinion big data within the spatial scope of each level of spatial units, and combine the land space information vocabulary and the updated land space people's livelihood keyword library to generate the people's livelihood public opinion keywords for the planning and implementation of each level of spatial units.

[0011] Preferably, the multi-source data of land space in step 1 includes the name, spatial range, spatial location and population distribution data of various types of land space.

[0012] Specifically, the multi-source data of land space in step 1 include administrative division data, spatial planning data, urban land space monitoring data, community village survey data, mobile phone signaling data and POI data, among which the administrative division data, spatial planning data, community village survey data, urban land space monitoring data and POI data are used to obtain the names and spatial ranges of various types of land spaces, and mobile phone signaling data is used to obtain spatial location and population distribution data.

[0013] The specific method for constructing the Chinese land space information word library in step 3 is:

[0014] Focus on the various human geographical elements required for the operation and development of the city, and extract the names and spatial ranges of various types of land spaces from the multi-source data of land space, that is, to realize the construction of the land space information vocabulary.

[0015] The specific method for obtaining the spatial position in step 3 is:

[0016] Segment each record in the people's livelihood public opinion big data, obtain multiple words in each record, and match the people's livelihood public opinion big data in the national land space information word library;

[0017] If there is only one word in a record of the people's livelihood public opinion big data that has a matching keyword in the national land and space information word library, then the corresponding spatial position of the word in the national land and space information word library is obtained;

[0018] If all the words in a record of the people's livelihood public opinion big data have no matching keywords in the national land space information vocabulary, the record will be matched according to the region, and the longitude and latitude of the region will be used as the spatial location;

[0019] If there is more than one word in a record of the people's livelihood public opinion big data that has matching keywords in the national land space information vocabulary, the spatial location will be matched according to the degree of priority, or the record will be matched according to the region.

[0020] The calculation formula for planning and implementing the livelihood sentiment index within the spatial scope of each level of spatial unit in step 4 is:

[0021]

[0022] Among them, E 正 / 中 / 负 is the positive / neutral / negative sentiment index, PV j is the number of replies and views of the jth post of the people’s livelihood public opinion big data, n is the total number of posts of the people’s livelihood public opinion big data, and P is the average population density of all spatial units during the monitoring period.

[0023] The specific method of constructing and updating the Chinese soil space livelihood keyword database in step 5 is:

[0024] Take at least part of the people's livelihood public opinion big data as samples, and after cleaning, word segmentation and heat processing in turn, sort them from large to small according to heat, and build the first N words into a national land space people's livelihood keyword library, and compare the constructed national land space people's livelihood keyword library with the national land space information vocabulary library, remove the repeated words in the national land space information vocabulary library, and obtain the updated national land space information vocabulary library.

[0025] The method for generating keywords for implementing livelihood public opinion in spatial unit planning at each level in step 5 is as follows:

[0026] The national land space information word library and the updated national land space livelihood keyword library are used as dictionaries to extract the livelihood public opinion big data within the spatial scope of each level of spatial unit. Each post in the livelihood public opinion big data is segmented by combining the dictionary, and the word heat value H is calculated for each word after segmentation. The calculation formula of H is:

[0027]

[0028] Among them, f j is the frequency of the word appearing in the jth post in the people's livelihood public opinion big data;

[0029] The words corresponding to the largest word heat value in the people's livelihood public opinion big data within the spatial scope of each level of spatial units are used as the keywords for planning and implementing people's livelihood public opinion in each level of spatial units.

[0030] Compared with the prior art, the advantages of the present invention are: by obtaining big data of people's livelihood public opinion and multi-source data of land and space, on the one hand, the big data of people's livelihood public opinion and multi-source data of land and space are used to dynamically monitor the public's subjective feelings on the implementation of the plan and the key issues of social livelihood, and provide a set of intelligent, scientific and efficient urban and rural resident evaluation solutions for land planning implementation monitoring; on the other hand, by constructing a land and space information vocabulary and a land and space people's livelihood keyword library, the efficiency of natural language keyword extraction for planning implementation of people's livelihood public opinion is improved, and spatial information of people's livelihood public opinion is given without obtaining network IP location information, thereby avoiding network security risks; in addition, by constructing multi-level spatial units, multi-scale spatial coupling of planning implementation of people's livelihood public opinion is realized, and spatial measurement of people's livelihood public opinion is provided for planning evaluation, adjustment and compilation, and classified and quantitative measurement of people's livelihood public opinion is realized through planning implementation of people's livelihood sentiment index within the spatial range of spatial units at each level. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of a method for monitoring the implementation of land space planning based on people’s livelihood public opinion big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention is further described in detail below with reference to the accompanying drawings.

[0033] like Figure 1 As shown, the national land space planning implementation monitoring method based on people's livelihood public opinion big data in this embodiment includes the following steps:

[0034] Step 1: Obtaining big data on people's livelihood and public opinion and collecting multi-source data on land and space;

[0035] The livelihood public opinion big data in this embodiment is mainly obtained by regularly crawling the mass message posting information released on the network livelihood service platform during the monitoring period, and obtaining the mass message posting time, netizen name, type, region, topic, reply and viewing number and other information;

[0036] The multi-source data of national land space includes the names, spatial scopes, spatial locations and population distribution data of various types of national land spaces; in this embodiment, the multi-source data of national land space includes administrative division data, spatial planning data, urban national land space monitoring data, community village survey data, mobile phone signaling data and POI data, wherein the administrative division data, spatial planning data, community village survey data, urban national land space monitoring data and POI data are used to obtain the names and spatial scopes of various types of national land spaces, and the mobile phone signaling data is used to obtain the spatial location and population distribution data;

[0037] Among them, administrative division data, spatial planning data and community village survey data are mainly used to obtain information such as names and spatial scopes of various areas such as districts (counties, cities), administrative divisions at different levels, key development areas, planning zones, planning units, communities, administrative villages, etc.; mobile phone signaling data is collected by mobile phone operators to determine the user's spatial location through the interaction of information between mobile phone users and base stations, and mainly obtains the real-time population quantity and distribution during the monitoring period; urban land space monitoring data is mainly used to provide the names and spatial locations of various types of urban public service facilities, transportation facilities, commercial complexes, roads, waters and other current urban facilities; POI data is points of interest, which are mainly used to extract and supplement the names and spatial locations of urban communities;

[0038] The above-mentioned multi-source data of national land space are conventional data related to national land space in the prior art. This application only involves the collection and data processing of multi-source data of national land space in the prior art.

[0039] Step 2: Construct multi-level spatial units based on multi-source data of national land space;

[0040] In this embodiment, a multi-level spatial unit for corresponding planning implementation monitoring is constructed according to the national land space planning system, with the administrative boundaries of cities, districts and counties (cities), and towns (streets) as the basic monitoring units. According to the scope, content, and scale of various planning of the national land space master plan, detailed plan, and special plan, the planning monitoring units required for the corresponding monitoring are supplemented and connected to support the transmission and aggregation of public opinion indicators for monitoring people's livelihood at all levels and across regions and plans; the spatial unit includes spatial location information and attribute information, and the spatial location information is consistent with the sources of administrative division data, spatial planning data, community village survey data, urban land space monitoring data, and POI data. The attribute information includes the name, source, area, and average population density of each spatial unit. The number of permanent residents in each spatial unit during the monitoring period is obtained according to the mobile phone signaling data, and the number of permanent residents in each spatial unit during the monitoring period is divided by the area of ​​each spatial unit to obtain the average population density of each spatial unit during the monitoring period;

[0041] Step 3: construct a national land space information word library based on the national land space multi-source data, match the people's livelihood public opinion big data in the national land space information word library, obtain the spatial position, and match the obtained spatial position with the multi-level spatial unit to obtain the spatial unit corresponding to the spatial position;

[0042] The specific construction method of the national land space information word library in this embodiment is:

[0043] Focus on the various human geography elements required for the operation and development of the city, and extract the names and spatial ranges of various land spaces from the multi-source data of land space, that is, to build a land space information thesaurus;

[0044] The construction of the national land space information word library can improve the processing efficiency and accuracy of natural language segmentation, and provide the index required for the association matching for the subsequent reverse acquisition of the spatial position of the semantic words. The composition and examples of the national land space information word library are shown in Table 1 below. The division of the main types and subtypes of the national land space information word library in Table 1 adopts the national land space division law in the existing technology, follows the laws of nature, and is widely suitable for the national land space in different regions;

[0045] Table 1 Composition and examples of national land space information word library

[0046]

[0047] The specific method for obtaining the spatial position is:

[0048] Segment each record in the people's livelihood public opinion big data, obtain multiple words in each record, and match the people's livelihood public opinion big data in the national land space information word library;

[0049] If there is only one word in a record of the people's livelihood public opinion big data that has a matching keyword in the national land and space information word library, then the corresponding spatial position of the word in the national land and space information word library is obtained;

[0050] If all the words in a record of the people's livelihood public opinion big data have no matching keywords in the national land space information vocabulary, the record will be matched according to the region, and the longitude and latitude of the region will be used as the spatial location;

[0051] If more than one word in a record of the people's livelihood public opinion big data has a matching keyword in the national land space information word library, the spatial location is matched according to the degree of priority or the record is matched according to the region;

[0052] The specific method of matching spatial positions according to the degree of importance is as follows: first, the spatial positions corresponding to all words in the national land space information word library are obtained, and the importance of each spatial position is judged to select the most important spatial position; the importance of each spatial position can be confirmed based on experience, and will not be traced back here;

[0053] Step 4: Identify the sentiment label for each record in the livelihood public opinion big data, and calculate the livelihood sentiment index within the spatial scope of each level of spatial unit according to the sentiment label;

[0054] According to the "post type" attribute of the public opinion data, the sentiment label is marked for each record. The specific mapping is shown in Table 2 below:

[0055] Table 2 Post type corresponding to emotional labels

[0056] Emotional Tags Post Type front Like neutral Consultation and suggestions Negative Complaints, requests for help, inquiries

[0057] The emotion tags corresponding to the post types in Table 2 above are conventional tags in the prior art;

[0058] The planning and implementation of the people's livelihood sentiment index is divided into positive sentiment index, neutral sentiment index and negative sentiment index according to the type, and there is no positive and negative offset between the various indexes; the calculation formula for the planning and implementation of the people's livelihood sentiment index within the spatial scope of each level of spatial unit is:

[0059]

[0060] Among them, E 正 / 中 / 负 is the positive / neutral / negative sentiment index, PV j is the number of replies and views of the jth post of the people's livelihood public opinion big data, n is the total number of posts of the people's livelihood public opinion big data, and P is the average population density of all spatial units during the monitoring period;

[0061] Step 5: Process the people's livelihood public opinion big data, obtain the land space people's livelihood keyword library, compare the land space information vocabulary with the land space people's livelihood keyword library, update the land space people's livelihood keyword library, and extract the people's livelihood public opinion big data within the spatial scope of each level of spatial units, and combine the land space information vocabulary and the updated land space people's livelihood keyword library to generate the people's livelihood public opinion keywords for the planning and implementation of each level of spatial units.

[0062] The specific methods for constructing and updating the keyword database of national land space and people's livelihood are as follows:

[0063] At least part of the livelihood public opinion big data is used as a sample, and after cleaning, word segmentation and heat processing in sequence, it is sorted from large to small according to heat, and the first N words are constructed into a land space livelihood keyword library, and the constructed land space livelihood keyword library is compared with the land space information word library, and the words repeated in the land space information word library and the land space information word library are removed, so as to obtain an updated land space information word library;

[0064] N in this embodiment is a value confirmed based on the processing of the people's livelihood public opinion big data. The constructed land space people's livelihood keyword library is shown in Table 3 below, wherein the main types of the land space people's livelihood keyword library in Table 3 adopt the land space people's livelihood keyword division law in the prior art, follow the laws of nature, and are widely suitable for land space people's livelihood keywords in different regions;

[0065] Table 3 Composition and examples of the keyword database of national land space and people's livelihood

[0066]

[0067] The method for generating keywords for the implementation of people's livelihood public opinion in spatial unit planning at each level is as follows:

[0068] The national land space information word library and the updated national land space livelihood keyword library are used as dictionaries to extract the livelihood public opinion big data within the spatial scope of each level of spatial unit. Each post in the livelihood public opinion big data is segmented by combining the dictionary, and the word heat value H is calculated for each word after segmentation. The calculation formula of H is:

[0069]

[0070] Among them, f j is the frequency of the word appearing in the jth post in the people's livelihood public opinion big data;

[0071] The words corresponding to the largest word heat value in the people's livelihood public opinion big data within the spatial scope of each level of spatial units are used as the keywords for planning and implementing people's livelihood public opinion in each level of spatial units.

[0072] The specific process of segmenting each post in the livelihood public opinion big data by combining the dictionary is as follows:

[0073] After obtaining the public opinion data of the people's livelihood each time, preliminary data cleaning is required to remove abnormal problem data such as duplication, blank, garbled, missing information, etc. After cleaning, the data is stored in a standardized manner and assigned a unique code. The data structure is shown in Table 4 below. The attribute names and the notes corresponding to each attribute in the network public opinion data storage structure of Table 4 are all prior art. This application involves the data processing of conventional public opinion data of the people's livelihood, rather than the formulation of rules;

[0074] Table 4 Data storage structure of online public opinion data

[0075]

[0076] Using Jieba Chinese word segmentation component, the land and space information word library and the land and space livelihood keyword library are initialized and loaded as dictionaries, and the posting content in the livelihood public opinion big data after preliminary cleaning is segmented. According to the regular segmentation of Chinese characters, phrase segmentation is performed, and a directed acyclic graph DAG (Directed Acyclic Graph) of all possible word segmentation situations is constructed through string matching. The maximum path probability of each node is calculated, and the end position is recorded. The word segmentation result is obtained according to the node path. For the unregistered words in the dictionary, the HMM (Hidden Markov Model) model Viterbi algorithm in Jieba is used to obtain the word segmentation data; the above-mentioned directed acyclic graph DAG and HMM (Hidden Markov Model) model Viterbi algorithm are both existing technologies and will not be elaborated here. The data after word segmentation is secondary cleaned, including full-width and half-width conversion, uppercase and lowercase conversion, removal of symbols, conjunctions, pronouns and other non-sentence stem content, and finally the word heat value of all words after the secondary cleaning is calculated.

[0077] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for monitoring the implementation of national land space planning based on people's livelihood public opinion big data, characterized by The steps include: Step 1: Obtaining big data on people's livelihood and public opinion and collecting multi-source data on land and space; Step 2: Construct multi-level spatial units based on multi-source data of national land space; Step 3: construct a national land space information word library based on the national land space multi-source data, match the people's livelihood public opinion big data in the national land space information word library, obtain the spatial position, and match the obtained spatial position with the multi-level spatial unit to obtain the spatial unit corresponding to the spatial position; Step 4: Identify the sentiment label for each record in the livelihood public opinion big data, and calculate the livelihood sentiment index within the spatial scope of each level of spatial unit according to the sentiment label; Step 5: Process the people's livelihood public opinion big data, obtain the land space people's livelihood keyword library, compare the land space information vocabulary with the land space people's livelihood keyword library, update the land space people's livelihood keyword library, and extract the people's livelihood public opinion big data within the spatial scope of each level of spatial units, and combine the land space information vocabulary and the updated land space people's livelihood keyword library to generate the people's livelihood public opinion keywords for the planning and implementation of each level of spatial units.

2. The method for monitoring the implementation of national land space planning according to claim 1, characterized in that: The multi-source data of land space in step 1 include the name, spatial range, spatial location and population distribution data of various types of land space.

3. The method for monitoring the implementation of national land space planning according to claim 2, characterized in that: The multi-source data of land space in step 1 include administrative division data, spatial planning data, urban land space monitoring data, community village survey data, mobile phone signaling data and POI data, among which the administrative division data, spatial planning data, community village survey data, urban land space monitoring data and POI data are used to obtain the names and spatial scopes of various types of land spaces, and mobile phone signaling data is used to obtain spatial location and population distribution data.

4. The method for monitoring the implementation of national land space planning according to claim 2, characterized in that: The specific method for constructing the Chinese land space information word library in step 3 is: Focus on the various human geographical elements required for the operation and development of the city, and extract the names and spatial ranges of various types of land spaces from the multi-source data of land space, that is, to realize the construction of the land space information vocabulary.

5. The method for monitoring the implementation of national land space planning according to claim 4, characterized in that: The specific method for obtaining the spatial position in step 3 is: Segment each record in the people's livelihood public opinion big data, obtain multiple words in each record, and match the people's livelihood public opinion big data in the national land space information word library; If there is only one word in a record of the people's livelihood public opinion big data that has a matching keyword in the national land and space information word library, then the corresponding spatial position of the word in the national land and space information word library is obtained; If all the words in a record of the people's livelihood public opinion big data have no matching keywords in the national land space information vocabulary, the record will be matched according to the region, and the longitude and latitude of the region will be used as the spatial location; If there is more than one word in a record of the people's livelihood public opinion big data that has matching keywords in the national land space information vocabulary, the spatial location will be matched according to the degree of priority, or the record will be matched according to the region.

6. The method for monitoring the implementation of national land space planning according to claim 5, characterized in that: The calculation formula for planning and implementing the livelihood sentiment index within the spatial scope of each level of spatial unit in step 4 is: Among them, E 正 / 中 / 负 is the positive / neutral / negative sentiment index, PV j is the number of replies and views of the jth post of the people’s livelihood public opinion big data, n is the total number of posts of the people’s livelihood public opinion big data, and P is the average population density of all spatial units during the monitoring period.

7. The method for monitoring the implementation of national land space planning according to claim 6, characterized in that: The specific method of constructing and updating the Chinese soil space livelihood keyword database in step 5 is: Take at least part of the people's livelihood public opinion big data as samples, and after cleaning, word segmentation and heat processing in turn, sort them from large to small according to heat, and build the first N words into a national land space people's livelihood keyword library, and compare the constructed national land space people's livelihood keyword library with the national land space information vocabulary library, remove the repeated words in the national land space information vocabulary library, and obtain the updated national land space information vocabulary library.

8. The method for monitoring the implementation of national land space planning according to claim 7, characterized in that: The method for generating keywords for implementing livelihood public opinion in spatial unit planning at each level in step 5 is as follows: The national land space information word library and the updated national land space livelihood keyword library are used as dictionaries to extract the livelihood public opinion big data within the spatial scope of each level of spatial unit. Each post in the livelihood public opinion big data is segmented by combining the dictionary, and the word heat value H is calculated for each word after segmentation. The calculation formula of H is: Among them, f j is the frequency of the word appearing in the jth post in the people's livelihood public opinion big data; The words corresponding to the largest word heat value in the people's livelihood public opinion big data within the spatial scope of each level of spatial units are used as the people's livelihood public opinion keywords for planning and implementing spatial units at each level.

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