CN106897400B - Visualization method and system for seismic information in social networking media - Google Patents
Visualization method and system for seismic information in social networking media Download PDFInfo
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Abstract
The invention provides a visualization method and a visualization system for earthquake information in a social network media, which comprise the steps of establishing a keyword library respectively describing earthquake macroscopic abnormal information of a pre-earthquake time period, earthquake emotional information of a middle earthquake time period and rescue information of a post-earthquake time period; classifying the network social media data based on different time periods before and after the earthquake and the keyword library to obtain network social media data sets corresponding to different time periods; and setting respective unit observation periods for each social network media data set, acquiring the spatial gravity center position of each unit observation period based on the release time information, the positioning information and the unit observation periods, and displaying the spatial gravity center position on a map. The invention overcomes the defects that the coverage area of the seismic information is wide and scattered and no focus and accuracy are concerned in the prior art, and intensively displays the time-space transition of the seismic information.
Description
Technical Field
The invention relates to the field of seismic information display, in particular to a method and a system for visualizing seismic information in social networking media.
Background
When an earthquake occurs, the public often posts information about the earthquake disaster through a location-based social networking media such as a social networking media. A large amount of relevant data about earthquake disasters are transmitted through a position-based social networking media such as the social networking media, the data have the characteristics of interactivity, instantaneity, sociality and the like, and contain a large amount of valuable information, and in sudden natural disasters such as earthquakes, the transmission of the information plays a vital role in objective reflection of disaster situations, smooth disaster relief, dispersion of psychological pressure of the masses in disaster areas and social stability.
At present, network social media data are utilized to research and analyze earthquake disaster information, and disaster information spreading characteristics and public opinion monitoring are mainly researched from the perspective of propaganda science. With the wide application of the location-based service function by the social networking media, the earthquake disaster information propagation can be more accurately analyzed by combining the data mining and analysis of the geographic location information and the data information in the social networking media, and important information supplement is provided for the observation of earthquake disasters from the social public perspective.
In 2010, Sakaki et al used the geographic information in twitter to detect events such as earthquakes, typhoons, and the general areas of their occurrence. In 2015, a earthquake disaster situation classification table is established by using social media information of earthquake networks based on positions such as the Xujing sea, and disaster situation information analysis is performed on an earthquake disaster coverage area by using an inverse distance spatial interpolation method.
The inventor finds in the process of implementing the present invention that there are still some problems with mining and analysis of seismic data in location-based networked social media:
1. the earthquake information classification is divided only by means of keywords, and the result of the classification can be mixed with information of different time stages before and after the earthquake disaster;
2. the data mining analysis aims at information after earthquake disasters occur, and the reflection of macroscopic abnormal conditions in a period of time before the earthquake on a social network media is not brought into observation;
3. the seismic information of the network social media based on the positions is published into scattered point distribution, the coverage area is generally wide and scattered, obvious uneven distribution is often shown, accurate face-shaped results are difficult to obtain only by means of the seismic information of the network social media based on the positions through information point interpolation calculation, and particularly, the accuracy difference is large in areas with large information point density difference.
Disclosure of Invention
The present invention provides a system and method for visualizing seismic information in social networking media that overcomes, or at least partially solves, the above-mentioned problems.
According to one aspect of the invention, a method for visualizing seismic information in social networking media is provided, which comprises the following steps:
s1, respectively creating keyword libraries for describing earthquake macroscopic abnormal information of the pre-earthquake time period, earthquake situation information of the epicenter time period and rescue information of the post-earthquake time period;
s2, classifying the social networking media data based on different time periods before and after the earthquake and the keyword library to obtain social networking media data sets corresponding to different time periods;
s3, setting respective unit observation periods for each social network media data set, obtaining the spatial gravity center position of each unit observation period based on the release time information, the positioning information and the unit observation periods, and displaying the spatial gravity center position on a map;
the network social media data comprises seismic information, release time information and positioning information of a release person.
According to another aspect of the present invention, there is provided a system for visualizing seismic information in social networking media, comprising:
the keyword library module is used for creating keyword libraries which respectively describe the earthquake macro abnormal information of the pre-earthquake time period, the earthquake situation information of the middle earthquake time period and the rescue information of the post-earthquake time period;
the classification module is used for classifying the social network media data based on different time periods before and after the earthquake and the keyword library to obtain a social network media data set corresponding to the different time periods;
the display module is used for setting respective unit observation periods for each network social media data set, obtaining the spatial gravity center position of each unit observation period based on the release time information, the positioning information and the unit observation periods, and displaying the spatial gravity center position on a map;
the network social media data comprises seismic information, release time information and positioning information of a release person.
The application provides a method and a system for visualizing seismic information in a social network media, which take the social network media as a carrier and public perception as a sensor, and bring the reflection of macroscopic abnormal conditions on the social network media in a period of time before an earthquake into earthquake observation so as to provide an important data source of macroscopic abnormal big data for earthquake disaster prevention and reduction research; earthquake information is classified by combining different time periods and keywords, the meaning of the classification result is definite, and the information of earthquake disasters at different time periods is better eliminated; the spatial gravity center position of each type of seismic information in a corresponding unit observation period is calculated by applying a spatial gravity center model, the spatial-temporal evolution process of the seismic information is displayed on a map, and the gravity center position of a network social media user group of different types of seismic information in a seismic disaster area and the evolution of the gravity center position along with time are reflected.
Drawings
FIG. 1 is a schematic flow chart of a method for visualizing seismic information in social networking media according to an embodiment of the invention;
FIG. 2 is a spatial centroid distribution trace diagram of pre-earthquake time seismic information for the Atalana Sichuan earthquake obtained in accordance with an embodiment of the present invention;
fig. 3 is a spatial gravity center distribution trace diagram of seismic information of pre-earthquake time of a yunnan yi-nationality earthquake obtained according to an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The invention provides a visualization method of seismic information in a social network media, aiming at overcoming the problems that seismic data mining and information displaying are inaccurate because seismic information classification is divided only by means of key words and information attention key shift of different time periods before and after a seismic disaster occurs is ignored in the prior art.
Fig. 1 shows a flow chart of a method for visualizing seismic information in social networking media according to an embodiment of the present invention, and as can be seen from fig. 1, the method includes:
and S1, creating keyword libraries which respectively describe the earthquake macroscopic abnormal information of the pre-earthquake time period, the earthquake situation information of the epicenter time period and the rescue information of the post-earthquake time period.
And S2, classifying the social network media data based on different time periods before and after the earthquake and the keyword library to obtain a social network media data set corresponding to different time periods.
S3, setting respective unit observation periods for each social network media data set, obtaining the spatial gravity center position of each unit observation period based on the release time information, the positioning information and the unit observation periods, and displaying the spatial gravity center position on a map;
the network social media data comprises seismic information, release time information and positioning information of a release person.
According to the method, the spatial gravity center position of the social networking media at different time is calculated, the defects that the coverage area of the seismic information is wide and scattered, the emphasis and the accuracy are not concerned in the prior art are overcome, the time-space transition of the seismic information is intensively displayed, and the method is clear at a glance.
The network social media comprise media such as microblogs, posts and people networks, a user can publish new messages on the network social media at any time, and the network social media data are data contents of the network social media, are composed of characters and punctuations and have maximum character limitation, so that each network social media needs to describe complete and important information by a language which is as short as possible, and the method is very suitable for big data analysis.
The earthquake information is that the social media of the network contains a keyword "earthquake", for example: the content of a certain social media of the network is "earthquake occurred today", or "just earthquake, people feel how? "and the like, all belong to seismic information.
Each piece of network social media contains the publishing time information, but not each piece of network social media contains the positioning information of the publisher, and the positioning information refers to information which shows the current position and the content of the network social media when the publisher sends the network social media.
In one embodiment, the step S1 further includes:
and respectively setting a pre-earthquake time period, a middle earthquake time period and a post-earthquake time period based on the occurrence time of the earthquake.
The method is characterized in that a macroscopic abnormal condition usually occurs before an earthquake occurs, and a network social media is possibly generated when a person finds the macroscopic abnormal condition, so that the setting of the pre-earthquake time period is favorable for the centralized analysis of pre-earthquake information in the future, the midsummer time period represents a time period when people are nervous and unconscious when the earthquake just occurs, the network social media sent by the people in the time period are likely to have more adjectives and verbs and excited emotion and are completely different from the pre-earthquake mental state, the post-earthquake time period represents a time period when the people start to accept the fact and rescue the earthquake-stricken area, and in the time period, more network social media are positive information reflecting the rescue.
The invention defines the macroscopic earthquake abnormity as various natural abnormity phenomena related to the induction and occurrence of the earthquake which can be directly sensed by human senses or measured by simple measuring tools.
In one embodiment, the present invention creates keywords that reflect the pre-earthquake anomaly information, the epicenter earthquake information, and the post-earthquake rescue information, respectively.
In one embodiment, the step S2 includes:
s2.1, for any piece of network social media data, retrieving the matching condition of the entry contained in the piece of network social media data and each keyword library and the matching condition of the release time information and each time period; and
and S2.2, attributing the network social media data to a network social media data set which simultaneously matches the time period and the keyword library of the time period.
In one embodiment, the step S3 includes:
s3.1, setting respective unit observation periods for different network social media data sets, wherein the time length ratio of the unit observation periods to the corresponding time periods is an integer.
Assuming that the pre-earthquake time period is L days before the earthquake occurs and the same day as the earthquake occurs, the mid-earthquake time period is M hours after the earthquake occurs, the post-earthquake time period is M-N hours after the earthquake occurs, and the unit observation period of the pre-earthquake time period is set as p days respectively; the unit observation period of the epicenter time period is q hours; the unit observation period of the post-earthquake time period is r hours, and the conditions that L/p, M/q, (M-N)/r are positive integers are met, so that the time window is just evenly divided by the unit observation period.
And S3.2, regarding any social networking media data set, taking the social networking media data of which the release time information belongs to the current unit observation period as an observation object, and obtaining the spatial gravity center position of the unit observation period based on the positioning information of the observation object.
And S3.3, based on the step S2.2, obtaining the spatial gravity center position of all the network social media data sets in each unit observation period.
In one embodiment, the pre-earthquake time period in step S1.1 is from the day of earthquake occurrence to 30 days before earthquake occurrence, the epicenter time period is from the earthquake occurrence time to 2 hours after earthquake occurrence, and the earthquake relief golden period is 72 hours, and the propagation of earthquake relief information is concentrated in this time period, so 2 hours to 72 hours after earthquake occurrence is selected as the post-earthquake time period.
In one embodiment, the keywords reflecting the pre-earthquake anomaly information are shown in table 1 and include: one or more of an animal anomaly, a groundwater anomaly, a weather anomaly, a ground sound anomaly, a plant anomaly, a ground anomaly, an electromagnetic anomaly, and a seismic cloud anomaly.
TABLE 1 keywords for pre-earthquake anomaly information
The earthquake condition refers to the condition of earthquake activity and earthquake influence, and comprises the time, the place, the magnitude, the earthquake condition, the sensible range, the reaction of people and the like of the earthquake.
In general, in the social media data information related to earthquake emotion, nouns are used for expressing descriptions of earthquake magnitude and earthquake feeling, adjectives are used for expressing emotional attitudes of people on earthquake emotion, such as 'fear', 'fright', and the like, and verbs are used for expressing feelings of earthquake by social media users in earthquake, such as 'shake', 'collapse', and the like.
In one embodiment, the keywords of the epicenter information are shown in table 2, and include: one or more of a tremolo, a magnitude of tremolo, a middle of tremolo, a source of hypocenter, an adjective that expresses a mood, and a verb that expresses a tremolo.
TABLE 2 keywords of the epicenter information
Table 3 shows keywords of the rescue information after earthquake.
TABLE 3 keywords for post-earthquake rescue information
In one embodiment, said step S2.2 comprises:
s2.2.1, annotating and segmenting any one piece of network social media data, and labeling the part of speech of each vocabulary;
s2.2.2, obtaining the matching condition of the social network media data and the keyword libraries based on whether the participles in the social network media data are matched with the keyword libraries; and
s2.2.3, obtaining the matching condition of the social networking media data and the time periods based on whether the publishing time information in the social networking media data is matched with each time period.
If there are traditional words in the data information, it will be difficult to parse the semantic word segmentation, and an error result will be caused, so a complicated operation must be performed before parsing the semantic word segmentation, and in an embodiment, the step S2.2.1 further includes: and converting the network social media data containing traditional Chinese into the network social media data containing simplified Chinese.
In one embodiment, the invention also performs annotation segmentation on special data content: if the user name and the punctuation mark are marked, the URL and Email are automatically marked; and automatically segmenting and marking the forwarded content on the social media.
In one embodiment, the word segmentation of the data is realized by labeling and segmenting special text contents such as user names, forwarding contents, punctuation marks, URLs (uniform resource locators) and emails through an ICTCCLAS (information communication technology Class Chinese word segmentation system), and a word segmentation word bank in the system comprises special nouns and network expressions in the earthquake emergency field, such as Chinese international rescue team, Chinese earthquake bureau and basin friends, besides word bank contents already disclosed in the prior art, so that the words can be divided into one word instead of being disassembled during word segmentation, and the word segmentation accuracy is improved.
In one embodiment, the spatial center of gravity, i.e., the concept of an average center. For a plurality of dispersed geospatial objects, it is represented by the average of the geometric coordinates of a series of spatial objects. The basic calculation formula is
Wherein N is the total number of spatial objects, which is the number of social media data in the network, XiAnd YiIs the coordinate value of the ith spatial object,
namely the coordinate value of the center of gravity.
The invention also provides a visualization system of seismic information in the social networking media, which comprises the following steps:
the keyword library module is used for creating keyword libraries which respectively describe the earthquake macro abnormal information of the pre-earthquake time period, the earthquake situation information of the middle earthquake time period and the rescue information of the post-earthquake time period;
the classification module is used for classifying the social network media data based on different time periods before and after the earthquake and the keyword library to obtain a social network media data set corresponding to the different time periods;
the display module is used for setting respective unit observation periods for each network social media data set, obtaining the spatial gravity center position of each unit observation period based on the release time information, the positioning information and the unit observation periods, and displaying the spatial gravity center position on a map;
the network social media data comprises seismic information, release time information and positioning information of a release person.
In one embodiment, the method is used for obtaining the spatial gravity center position of the social networking media data before and after the earthquake of the Ataland Sichuan, performing spatial visualization by using geographic information system software, marking the gravity center points on a map, and connecting the gravity centers one by one to obtain a moving track path.
FIG. 2 is a diagram showing the distribution trace of the spatial center of gravity of the earthquake information of the earthquake of the time before earthquake of Skawa Yaan, which is obtained according to the embodiment of the invention, and as can be seen from FIG. 2, the movement trace of the center of gravity of the macroscopic abnormality of the earthquake is approximately in an inverted 8 shape and is shifted to the direction of the epicenter, and is distributed around the provincial origin. As can be seen from the distribution of the centers of gravity of the population in the figure, the population distribution phenomenon in Sichuan province is obviously uneven because the west is mainly high mountains and plateaus, the smoke of people is rare, the east is mainly hills, the east is a region with dense population, and the population distribution has the characteristic of being dense in the east, secret and sparse in the west. The economic center of gravity is located in the Yangyang city and deviates to the west from the population center of gravity, which shows that the GDP of the western region of Sichuan is generally higher than that of the east region, and the economic development level is high in the west and low in the east.
The gravity centers of the macroscopic abnormalities are mostly distributed in the gravity centers of the GDPs and the population in the west, which shows that the distribution of the attention of the network social media to the macroscopic abnormalities is not mainly the east with a large population, and the attention of people to the macroscopic abnormalities is high although the population in the west is rare.
Combining with the Seattan intensity chart of Sichuan, the intensity level is from IX-VI, and it can be seen that most of the gravity centers of the macroscopic anomalies fall in the area with the seismic intensity less than VI, namely the sensitive area, and are distributed in parallel with the long axis direction of the intensity chart.
The gravity center distribution of the macroscopic anomaly is more concentrated in an earthquake sensitive area and is not overlapped with the gravity center of the GDP and the gravity center of the population, the gravity centers of the GDP and the population are distributed in the west, the gravity centers of the GDP and the population are changed along with the change of time and are shifted to the epicenter direction, the economic developed area is formed, the population adsorption area is large, the population number is large, the attention to the earthquake macroscopic anomaly is high, the gravity centers are distributed near the metropolis, and the metropolis are in the sensitive area range.
Fig. 3 shows a spatial gravity center distribution trace diagram of seismic information of time before earthquake of yunan yi-nations good earthquake obtained according to an embodiment of the invention, and as can be seen from fig. 3, a gravity center movement trace of a macroscopic earthquake anomaly is approximately in an inverted 8 shape and is distributed in province-meeting Kunming cities. As can be seen from the distribution of the centers of gravity of the population in the figure, the population distribution phenomenon in Yunnan province is obviously uneven, and the population distribution has the characteristic of being sparse in east, west and east. The economic center of gravity almost coincides with the center of gravity of population, namely the GDP of the average person in the western region of Yunnan is generally lower than that in the east region, and the economic development level is high in east and low in west. The gravity center distribution of the network social media concerned about macroscopic anomalies mainly occurs in areas with large population and developed economy, the Kunming city is a provincial city, the economy is developed and is a human mouth absorption place, the gravity center distribution is deviated towards the epicenter direction, and the gravity center distribution is mainly distributed on the extension line of the major axis of the earthquake intensity graph.
Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A visualization method for seismic information in social networking media is characterized by comprising the following steps:
s1, creating keyword libraries for respectively describing earthquake macroscopic abnormal information of the pre-earthquake time period, earthquake situation information of the middle earthquake time period and rescue information of the post-earthquake time period;
s2, classifying the social networking media data based on different time periods before and after the earthquake and the keyword library to obtain social networking media data sets corresponding to different time periods;
s3, setting respective unit observation periods for each social network media data set, obtaining the spatial gravity center position of each unit observation period based on the release time information, the positioning information and the unit observation periods, and displaying the spatial gravity center position on a map;
the network social media data comprises seismic information, release time information and positioning information of a release person.
2. A visualization method as recited in claim 1, wherein the step S1 further comprises:
and respectively setting the pre-earthquake time period, the middle earthquake time period and the post-earthquake time period based on the occurrence time of the earthquake.
3. A visualization method as recited in claim 1, wherein the keyword library describing the earthquake macro anomaly information in the step S1 includes words expressing animal anomalies, groundwater anomalies, weather anomalies, earth sound anomalies, plant anomalies, ground anomalies, electromagnetic anomalies, and earthquake cloud anomalies.
4. A visualization method as recited in claim 1, wherein the keyword library describing earthquake emotion information in step S1 includes adjectives expressing a sense of earthquake, a magnitude of earthquake, a middle of earthquake, a source of earthquake, expressing a human emotion, and verbs expressing a sense of earthquake.
5. A visualization method as recited in claim 1, wherein the step S2 comprises the steps of:
s2.1, for any piece of network social media data, retrieving the matching condition of the entry contained in the piece of network social media data and each keyword library and the matching condition of the release time information and each time period; and
and S2.2, attributing the network social media data to a network social media data set which simultaneously matches the time period and the keyword library of the time period.
6. Visualization method according to claim 5, wherein said step S3 comprises the steps of:
s3.1, setting respective unit observation periods for different network social media data sets, wherein the time length ratio of the unit observation periods to the corresponding time periods is an integer;
s3.2, regarding any social networking media data set, taking the social networking media data of which the release time information belongs to the current unit observation period as an observation object, and obtaining the spatial gravity center position of the unit observation period based on the positioning information of the observation object; and
and S3.3, based on the step S2.2, obtaining the spatial gravity center position of all the network social media data sets in each unit observation period.
7. A visualization method as recited in claim 2, wherein the pre-earthquake time period in step S1 is from the day of earthquake occurrence to 30 days before the earthquake occurrence, the mid-earthquake time period is from the time of earthquake occurrence to 2 hours after the earthquake occurrence, and the post-earthquake time period is from 2 hours after the earthquake occurrence to 72 hours after the earthquake occurrence.
8. A visualization method as recited in claim 5, wherein said step S2.2 comprises the steps of:
s2.2.1, annotating and segmenting any one piece of network social media data, and labeling the part of speech of each vocabulary;
s2.2.2, obtaining the matching condition of the social network media data and the keyword libraries based on whether the participles in the social network media data are matched with the keyword libraries; and
s2.2.3, obtaining the matching condition of the network social media data and the time periods based on whether the publishing time information in the network social media data is matched with each time period.
9. A visualization method as recited in claim 8, wherein said step S2.2.1 is preceded by: and converting the network social media data containing traditional Chinese into the network social media data containing simplified Chinese.
10. A system for visualizing seismic information in social networking media, comprising:
the keyword library module is used for creating keyword libraries which respectively describe the earthquake macro abnormal information of the pre-earthquake time period, the earthquake situation information of the middle earthquake time period and the rescue information of the post-earthquake time period;
the classification module is used for classifying the social network media data based on different time periods before and after the earthquake and the keyword library to obtain a social network media data set corresponding to the different time periods;
the display module is used for setting respective unit observation periods for each network social media data set, obtaining the spatial gravity center position of each unit observation period based on the release time information, the positioning information and the unit observation periods, and displaying the spatial gravity center position on a map;
the network social media data comprises seismic information, release time information and positioning information of a release person.
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