KR102350252B1 - The method to build and use map library datasets - Google Patents

Abstract

The present invention relates to a method of constructing and utilizing a map library dataset which can effectively utilize grid-based spatial information by constructing a map library service-based dataset which can be usefully utilized in a typhoon situation. That is, the present invention is a system consisting of an operating computer with a built-in program (GIS-based integrated situation management system) for predicting storm and flood damage based on information related to natural disasters including wind and flood damage, and a simulation monitor connected to the operating computer.

Description

{The method to build and use map library datasets}

The present invention relates to a method of constructing and utilizing a map library data set that can effectively utilize grid-based spatial information by constructing a map library service-based data set that can be usefully utilized in a typhoon situation.

In general, the response process to a disaster situation consists of prevention, preparation, response, and recovery processes. A systematic disaster response system is required in order to effectively respond to emergency situations that change urgently from moment to moment, and various types of information are being used by each organization to build disaster response capabilities in each organization. The agency in charge of disaster management related to typhoons is the Ministry of Public Administration and Security, and the main response departments include the situation room, natural disaster response department, and recovery support department. In this study, in order to present a grid-based dataset for responding to typhoon situations, the typhoon management process was compared to the initial response process (prevention of typhoons and heavy rains) by referring to the National Disaster and Safety Research Institute's 「Task-based Disaster Information Analysis for Disaster Situation Recognition」 research report. , preparedness), Central Disaster and Safety Countermeasures Headquarters 1 and 2 processes (response), and recovery process (recovery) were identified.

The Central Disaster and Safety Situation Office shares the weather monitoring results when announcing a heavy rain advisory for three or more provinces and a heavy rain warning for one or more cities, and discusses whether to form a Central Disaster and Safety Countermeasures Headquarters. In the event of a localized heavy rain area where rainfall of 30 mm or more per hour is expected, an emergency process is declared to monitor the situation in the area. Emergency workers mainly perform tasks related to information collected and produced by other departments, Find out which areas are dangerous from typhoons in advance, identify issues related to typhoons, and reflect them in the response log.

In peacetime, the status of areas designated as disaster-prone areas, vulnerable facilities and areas are collected and utilized. For natural disaster response and disasters, a response plan report is established based on the results of a full-scale investigation of the current status of the area of concern and typhoon information, and then the disaster risk It reports the response status based on the local status, vulnerable facilities and local information, and supports the operation of systems used for typhoon situation management, such as the situation propagation system, disaster prevention and weather information system, and disaster image information integration and linkage system.

The Central Disaster and Safety Situation Office collects regional response status information based on the course of the typhoon, monitors the weather and typhoon status information on the typhoon at all times, and holds a situation judgment meeting based on the monitoring contents. In other words, it is necessary to quickly collect the current damage status, and to determine whether or not local countermeasures are being carried out.

Emergency workers start by disseminating the report form to collect information on the current status of damage by region and response status, and then identify the status of damage caused by heavy rain. The information collected by region includes on-site situation information, medical support status, and disaster relief-related information reported from the disaster site integrated support headquarters organized in local governments. As damage situation management should be focused on, rapid statistical processing of information received from local governments is required.

In addition, as a disaster situation occurs, information on areas at risk of being isolated is collected in advance to manage disaster site recovery situations. In the Natural Disaster Response Division, a situation judgment meeting is held by utilizing the weather situation information collected by the Central Disaster and Safety Situation Room and emergency workers, damage status information, recovery status information on disaster areas, and information reported from countermeasure meetings conducted by local governments. will be held Even in normal times, a response plan is established based on the experience of managing the typhoon situation and used in the event of a situation.

A response plan is established by using the damage information and response status information collected in real time from the central disaster safety situation room. Manage vulnerable areas so that damage can be reduced by predicting and analyzing the situation in the area by collecting information on existing disaster-prone areas and areas prone to flooding. In addition, it supports operation so that response departments, central and related organizations, and local governments can utilize NDMS (National Disaster Management System).

The Central Disaster and Safety Situation Office reviews the establishment of a recovery plan (draft) reported by local governments based on damage information collected during the typhoon period. This is to establish a recovery plan using statistically processed information on the damage information by region. Emergency workers perform a series of processes to collect, manage, and report damage information as they operate the emergency recovery headquarters. It supports the establishment of a recovery plan through processing of regional statistical processing information for damage information. The Natural Disaster Response Division supports the establishment of a recovery plan by collecting damage information, recovery information, and resource information uploaded to the NDMS and analyzing the current situation.

Information whether the disaster is information that supports the related work to respond to each situation, process and analyze past and current disaster situation can be divided into observation, history, a total of five kinds of status, foundation, analysis, as shown in the diagram below . If the disaster situation awareness information is clearly defined, it is possible to support not only the person in charge of disaster-related tasks but also the residents of the disaster area to respond to their own situation based on the same information. can share the same information.

A large amount of information helps to improve decision-making and performance, but it can be difficult to derive meaningful information due to the flood of information. The diagram below shows the connection between the disaster response work of the Natural Disaster Response Division and situational awareness information. In order to respond to a typhoon situation, it is important to first recognize the current typhoon situation and to predict and analyze the typhoon situation in the near future. In a typhoon situation, not only historical information but also various real-time information is produced, so integrated monitoring is required.

However, even when a variety of information is monitored, the analysis results vary greatly depending on the person who interprets it. The decision maker, who is currently in charge of disaster response, is a rotating public official, so it changes every time, and each person in charge has different strategies and knowledge to use in response according to his or her previous experience. It is difficult and each person in charge has difficulty in deriving the desired analysis result from the selected information, and there are also technical problems in the process of expressing various information on the digital map space.

The information required for understanding a disaster situation is diverse, but due to the nature of the digital map space, when a lot of information is displayed on the map at the same time, each information is superimposed on one map screen, making it difficult for the map user to interpret the information. In addition, spatial information is composed of various vector data such as points, lines, and planes, and when a lot of vector data is visualized in the map space, it takes a lot of time to process the information, which makes it difficult to utilize.

Patent Document 1: Korean Patent Publication No. 2013-0043422 Patent Document 2: Korean Patent Publication No. 2010-0112304

In order to solve the above problem, the present invention has a first object to enable consistent analysis and response by preventing the analysis results of various disaster information from being different for each person.

Although the information required to understand a disaster situation is diverse, by selecting a dataset in advance by combining the risk factors required for each disaster process differently, the map user can interpret the information even if a lot of information is overlapped and displayed in the digital map space. so you don't have any trouble at all,

By converging multiple pieces of information to provide one piece of information, it solves the difficulty of interpreting various information, assists each disaster manager with map interpretation ability above a certain level, and system loading that can occur by visualizing multiple pieces of information on a map The second purpose is to reduce the

In the method of constructing and utilizing the map library dataset of the present invention for achieving the above object;

In a system consisting of a large number of information related to natural disasters, a program for predicting storm and flood damage based on the information, and a monitor for simulating storm and flood damage, the system is configured as follows. characterized in that

1) When a lot of information related to the natural disaster is introduced, the process of classifying it into various indicators of the disaster risk assessment system and building a database for disaster situation recognition information;

2) The process of constructing a database by combining the indicators of the disaster risk assessment system differently for each disaster situation response process;

3) Standardization implementation process of deriving standardized indices for each indicator so that comparison and calculation between the indicators is possible;

4) The process of deriving evaluation results by risk, exposure, and vulnerability by applying weights to the standardized index derived in step 3);

5) The process of finally deriving the current and future disaster risk assessment results by summing the evaluation results for each detailed element.

In the process 1), various indicators of the disaster risk assessment system are composed of hazard, exposure, and vulnerability. or condition, exposure relates to persons, property, systems, or other elements located in a hazardous area that could be damaged, and vulnerability relates to an organization, system, It concerns the nature and circumstances of the property.

In the process 1), various indicators of the disaster risk assessment system are composed of a pressure index, a status index, and a response index. The pressure index is a hydrometeorological factor, a regional characteristic factor, and a city It relates to the causes of disasters, such as economic factors, the status quo relates to the current state of damage caused by the disaster, and the response index relates to the ability to prevent or mitigate the disaster.

Risk is a natural phenomenon that directly causes damage, and is used as information to predict the size of typhoon risk and the extent of the area where damage occurs. It is used as information that can predict the degree of exposure to damage and the extent of the exposed area. Vulnerability is a factor that can intensify the typhoon risk damage. used as available information.

Rainfall and wind speed are the main causes of typhoon damage, such as flooding and collapse, so they are selected as risk-related indicators. Roads, bridges, railroads, rivers, and reservoirs are selected as exposure indicators because they are major infrastructure. , basic livelihood recipients, and vulnerable populations are vulnerable to typhoon response and can intensify typhoon damage. In the case of steep slopes, strong winds and heavy rains can exacerbate damage such as landslides, so it can be selected as a vulnerability index.

In the process 2) above, when the disaster response process is normal, an index corresponding to exposure and an index corresponding to vulnerability are combined to derive a typhoon response plan area, and when a typhoon occurs, the risk is Information on areas exposed to typhoon impact can be created by combining the relevant index and the index corresponding to exposure, and the area of deep damage due to typhoon is derived by combining the index corresponding to exposure and index corresponding to vulnerability.

In the process 4), the Z-Score indicating the distance from the average was used for the index values to standardize the indexes having each other size and unit, and the weight can be determined as follows.

z _i = Z-Score value, X _i = index value, X _mean = index mean value, X _std = standard deviation of index values

In the case of Z-Score, there is a disadvantage in that values smaller than the average are displayed as negative numbers. To compensate for this, T-Score can be applied as follows.

In the process 4) above, weights were given to the detailed indicators constituting the risk to derive the sum, and the formula is as follows.

H ₁ to H _n = indicators constituting the risk, w _n = weights, n = number of indicators

In the process 4), weights were given to the detailed indicators constituting the exposure to derive the sum, and the formula is as follows.

E ₁ ~E _n = index constituting exposure, w _n = weight, n = index coefficient

In the process 4) above, weights were given to the detailed indicators constituting the vulnerability to derive the sum, and the formula is as follows.

V ₁ to V _n = indicator constituting vulnerability, w _n = weight, n = indicator coefficient

In the process 4) above, the degree of risk was derived by adding risk, exposure, and vulnerability factors, and the formula is as follows.

In the process 4), weights for each indicator are selected based on the AHP method to generate grid-type typhoon risk area convergence information later in the process. After creating a decision layer by dividing into large, medium, and small classification, weights are derived by constructing a comparison matrix, and the degree of consistency of answers is evaluated through CR (Consistency Ratio). and the final weight is derived from the results less than 0.2.

A grid transformation process of the collected data is added between steps 2) and 3) to construct a grid-based typhoon risk information dataset. You can choose between the direct use method, ② the method of converting the collected source data into a grid and using it, and ③ the method of processing the collected source data into new information and converting it into a grid.

When the disaster situation recognition information has the same national branch number-based grid geometry property as the grid system, ① Using a method that uses the source data directly without spatial conversion to grid, without spatial conversion to grid Source data can be used.

When the disaster situation recognition information is provided in text format, spatial join is performed with the administrative boundary information of the city, county, and gu to be processed into spatial information, and each information is used as a grid-based typhoon risk information dataset using the area weighting method. is transformed into a grid by

The present invention has the effect of enabling consistent analysis and coping by preventing the analysis results of various disaster information from being different for each person.

On the other hand, although the information required for understanding a disaster situation is diverse, by selecting a dataset in advance by combining the risk factors required for each disaster process differently, even if a lot of information is overlapped and expressed in the digital map space, the map user can use the information so that there is no difficulty in interpreting

By converging multiple pieces of information to provide one piece of information, it solves the difficulty of interpreting various information, assists each disaster manager with map interpretation ability above a certain level, and system loading that can occur by visualizing multiple pieces of information on a map has the effect of reducing

1 is a basic system configuration diagram for performing a map library dataset construction and utilization method according to the present invention;
2 is an exemplary diagram of convergence information combining information and tasks required for each disaster response process among disaster information in the present invention;
3 is an exemplary diagram of typhoon risk area convergence information (typhoon hazard factors, typhoon exposure factors, typhoon damage deepening factors) for constructing a map library dataset according to the present invention;
4 is a flowchart showing a process of deriving a typhoon risk area convergence information dataset according to the present invention;
5 is an exemplary diagram of a dataset finally derived according to the present invention;
6 is an exemplary diagram of a fusion information dataset derived according to the present invention;
7 is a flowchart illustrating a process of deriving weights through AHP analysis according to the present invention;
8 is an exemplary view showing a weight derivation result through AHP analysis according to the present invention;
9 is an exemplary diagram in which the source data grid is used as it is without spatial transformation into the grid according to the present invention;
10 is an exemplary diagram in which the source data of polygon information is converted into a grid according to the present invention and utilized;
11 is an exemplary diagram in which textual source data is processed and then converted into a grid according to the present invention;
12 is another exemplary view in which the source data in text form is processed and then converted into a grid according to the present invention.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. However, it goes without saying that the scope of the present invention is not limited thereto.

In the present specification, the present embodiment is provided so that the disclosure of the present invention is complete, and is provided to completely inform those of ordinary skill in the art to which the present invention belongs, the scope of the invention, the scope of the present invention is only It is only defined by the claims. Accordingly, in some embodiments, well-known components, well-known operations, and well-known techniques have not been specifically described in order to avoid obscuring the present invention.

The terms used herein are for the purpose of describing the embodiments and are in no way intended to limit the present invention. In this specification, the singular also includes the plural unless otherwise specified in the phrase. In addition, elements and operations referred to as 'include (or include)' do not exclude the presence or addition of one or more other elements and operations.

In general, the basic task of each disaster response process is monitoring of the disaster situation, and identification of emergency situations and hazardous areas is very important. As shown in Figure 2, more than 10 types of information are needed for situation prediction and analysis in the Central Disaster and Safety Countermeasures Headquarters 1 and 2 processes in the Natural Disaster Response Division. The same level of insight can be expected. Therefore, in the present invention, in order to monitor the typhoon situation in the process of responding to the typhoon situation, a grid-based typhoon risk information that can intuitively understand the typhoon situation and risk level was constructed as a dataset.

Due to the impact of climate change, the types and damages of disasters are diversifying, and in order to determine the risk area for disasters, it is caused by the interrelationship of various factors. Research that converges a large number of information to derive the risk level for disasters has been continuously conducted.

Among the evaluation models for deriving disaster risk, the universally used methods are the United Nations Ofice for Disaster Reduction (UNISDR), the climate change risk assessment theory presented by the Intergovernment Panel on Climate Change (IPCC), and the Organization for Economic Co- (OECD). There are PSR (Pressure-State-Response) structural system of operation and development), and the research related to risk assessment using these models is shown in the table below.

UNISDR (UN International Strategy for Disaster Reduction) defines risk mitigation as 'structural/nonstructural measures taken to reduce the adverse effects of natural disasters, environmental degradation, and technological disasters', and management and diagnostic methods to measure it The concept of vulnerability, risk, etc. was presented, stating the need for research.

The three components of disaster risk presented by international organizations such as UNISDR and IPCC are hazard, exposure, and vulnerability. Disaster risk, according to this definition, refers to the likelihood that adverse effects will occur in the future. Disaster risk arises from the interaction of social and environmental processes, i.e. the combination of physical hazards and the vulnerability of exposed elements.

In some studies, a total of four components that added capacity for risk reduction to risk, exposure, and vulnerability defined by UNISDR and IPCC were used. The table below shows the definition of each component in the study that evaluated the disaster risk using this.

The PSR structural system is an evaluation system of a causal relationship approach, and it derives disaster risk through three elements: pressure, status, and response. The table below shows the definition of each component in the study that evaluated the disaster risk through this.

In the present invention, a dataset of typhoon risk area convergence information (typhoon hazard factors, typhoon exposure factors, and typhoon damage deepening factors) was generated using the risk assessment models of UNISDR and IPCC. It is the definition of information.

Typhoon-related risk, exposure, and vulnerability information used to generate typhoon risk information has great meaning only with individual information. It is used as information that can predict the range. Exposure is information that is subject to damage due to typhoon information, and is used as information to predict the extent of exposure to damage and the extent of the exposed area according to typhoon hazard factors. Vulnerability is a factor that can intensify typhoon risk damage, and is used as information to predict the extent to which damage can be aggravated according to typhoon hazard factors and the extent of the Sihwa area.

In the present invention, the procedure and method for deriving a dataset are as follows.

First, in order to select the typhoon risk area convergence information dataset, previous research cases and literature related to typhoon risk information were referred to. It is desirable to actively use well-organized indicators for risk assessment and evaluation indicators, and it is important to present developmental indicators through the review of previous research cases. Indices used in previous studies and related items are selected as primary indicator candidates.

Second, the primary indicator candidate group is categorized and representative indicators for each type are selected. In previous studies, the detailed dataset (indicator) for risk assessment is slightly different depending on the purpose, subject, information level, and research method of risk assessment, but a significant number of indicators consist of the same or similar information. Therefore, the same or similar information is categorized, has representativeness among these information, and an appropriate representative indicator is selected as the secondary indicator candidate group.

Third, among the secondary indicator candidates, indicators are selected according to the appropriateness of the indicators and the ease of data acquisition and construction. The environment in which information can be produced and used in connection with each subject of previous research is different, and the appropriate indicator group for our research may be different depending on the purpose and target of risk assessment. Finally, the validity of the dataset was secured through expert opinion gathering, and weights for each indicator were derived through the AHP (Analytic Hierarchy Process) method. In this regard, Figure 3.5 shows the flow chart of the typhoon risk area convergence information dataset derivation.

According to the above research procedure and method, five directions of detailed index setting were defined as shown in the chart below. In particular, to generate dense grid-based convergence information, source data was used as more specific information than criminal boundaries as much as possible, and indicators that could be used immediately for disaster response were selected to match the purpose of this study.

In the present invention, tasks performed in typhoon and fire disaster situations are classified and analyzed for each process, and information used in each process is identified and then defined as disaster situation awareness information. Through this, the corresponding responder can recognize the information needed and used for each situation before a disaster occurs. In the present invention, a total of 38 typhoon situation recognition information was used as shown in the chart below to derive indicators that can be used in real-time typhoon situation response.

Based on the direction of index setting defined in the present invention, 2 risk-related indicators, 10 exposure indicators, 8 vulnerability indicators, and a total of 20 final indicators were derived as shown in FIG. 5 . Rainfall and wind speed are selected as risk-related indicators because they are the main causes of typhoon damage such as flooding and collapse. In the case of population, it was selected as an exposure index because it is the subject of major casualties due to typhoons, the official land, farmland, harbor, and fishing port are subject to major property damage, and roads, bridges, railroads, rivers, and reservoirs are major infrastructure.

Semi-subterranean households, impervious surfaces, low-lying areas, and river recovery rates are factors that can exacerbate damage from heavy rains. In the case of land, it was selected as a vulnerability index because it is a factor that can intensify damage such as landslides due to strong winds and heavy rain.

In the present invention, a “map library” service-based dataset was constructed using the indicators constituting the typhoon risk information (convergence grid), basic information, and typhoon information. This can be usefully utilized not only in the normal typhoon prevention process as shown in FIG. 6 but also in the typhoon occurrence situation. In normal times, by combining an index corresponding to exposure and an index corresponding to vulnerability, an area for preparing typhoon countermeasures can be derived. In the event of a typhoon, information on areas exposed to typhoon impact can be created by combining an indicator corresponding to risk and an indicator corresponding to exposure, and by combining an indicator corresponding to exposure and an indicator corresponding to vulnerability, regions with severe damage from typhoons are derived. can do

The disaster risk assessment procedure can be broadly divided into DB construction by index, standardization, weighting, assessment by detailed elements, and disaster risk assessment. Establish a DB for each indicator using statistical data and spatial information, and standardize each indicator to enable comparison and calculation between indicators. Weights are applied to the derived standardized indices, and evaluation results for each risk, exposure, and vulnerability are derived. Finally, the present and future disaster risk assessment results are derived by summing the evaluation results for each detailed element.

For weighting, the Z-Score, which indicates the distance from the mean, was used to standardize the index values between indices having different sizes and units, and the formula is as follows.

z _i = Z-Score value, X _i = index value, X _mean = index mean value, X _std = standard deviation of index values

In the case of Z-Score, values smaller than the average are displayed as negative numbers. To compensate for this, T-Score was applied as follows.

The total was derived by assigning weights to the detailed indicators constituting the risk, and the formula is as follows.

H ₁ to H _n = indicators constituting the risk, w _n = weights, n = number of indicators

The sum was derived by assigning weights to the detailed indicators constituting the exposure, and the formula is as follows.

E ₁ ~E _n = index constituting exposure, w _n = weight, n = index coefficient

The sum was derived by assigning weights to the detailed indicators constituting the vulnerability, and the formula is as follows.

V ₁ to V _n = indicator constituting vulnerability, w _n = weight, n = indicator coefficient

Risk was derived by adding risk, exposure, and vulnerability factors, and the formula is as follows.

In order to generate grid-type typhoon risk area convergence information in the future, weights for each indicator were selected based on the AHP method. AHP is a stratified analysis process and is an approach for quantifying the weight of decision criteria. Using the experience of individual experts, we evaluate the relative importance of each indicator through pairwise comparisons. In the present invention, AHP analysis was performed through the process of FIG. 7 by conducting a survey targeting experts in the field of disaster and geographic information systems. A decision-making hierarchy was created by dividing each index into large, medium, and small classifications, and based on this, survey items were prepared and expert surveys were conducted. As a result of the survey, weights were derived by constructing a comparison matrix, and the consistency of answers was evaluated through CR (Consistency Ratio). Cases with a CR value of 0.2 or higher were excluded from the weight calculation, and final weights were derived from results less than 0.2. The final weights for each of the large classification, the medium classification, and the small classification are shown in FIG. 8 .

In the present invention, a grid transformation process of collected data is required to construct a grid-based typhoon risk information dataset. In this case, the procedure for grid transformation for each source data can be broadly classified into three types. First, it is a method that uses the source data directly without spatial transformation into a grid. Source data can be used as it is without spatial transformation for information based on country branch numbers using a spatial grid as in the present invention. Second, the collected source data is converted into a grid and used. This method is used when the source data is not a national branch number-based grid, but the meaning of the source data is the same as the meaning of the dataset of typhoon risk information. Third, it is a method of converting the collected source data into new information by processing it into a grid. This is a case where the source data is stored in text form such as an address, and a geocoding procedure is required, and the data processing is required because the source data does not have the same meaning as a dataset of typhoon risk information.

For the resident population grid and the official land price grid, the total number of population and the official land price were collected from the national land statistics map service. This information has the same national branch number-based grid geometry as the grid system of this study, so the space to the grid Source data can be utilized without transformation Since this information has the same national branch number-based grid geometry as the grid system of this study, source data can be utilized without spatial transformation into grid. However, the national statistical map service does not provide national data at once, but by city, county, and district. In the process of downloading and merging this information into national data, a grid of administrative boundaries is created as a multi-polygon. This is because, when two adjacent administrative districts are converted into grids and created, the grid overlapping administrative boundaries is included in both administrative districts. Therefore, in this study, the attribute values of overlapping grids were added up to collect national data and treated as one grid. (Fig. 9)

For the grid of vulnerable districts, the grid of the infant population and the grid of the elderly population were collected from the National Statistical Guidance Service. In order to construct information on vulnerable districts, the attribute values of the infant population grid and the elderly population grid must be summed, and since the two grids are composed of the same grid, it is possible to create a grid for vulnerable districts with a simple summation operation. In addition, since it has the same lattice geometry properties based on the ladle point number as in the lattice system of this study, the source data can be utilized without spatial transformation into the lattice.

To construct road, bridge, river, railway, reservoir, harbor, and fishing port data, polygon information such as road boundary, bridge, river boundary, railway, lake/reservoir, harbor, and fishing port was collected. To use as a grid-based typhoon risk information dataset, each piece of information was converted into a grid by the area weighting method. The information is annular to an area, so that the degree of exposure to damage caused by a typhoon can be distinguished. It can be seen that the larger the spatial area, the greater the exposure to typhoon damage. (Fig. 10)

Since statistical information and stream yield information for semi-underground households, disabled people, and recipients of basic livelihoods are provided in text format, it is necessary to process spatial information by spatially joining with administrative boundary information of cities, towns and villages. Information on semi-underground households is provided by the National Statistical Office of ‘households (general households)-si, county’ provided by the National Statistical Office, information on persons with disabilities is ‘statistics of registered disabled persons by city, county, disability level, and gender’, and information on basic livelihood recipients is ‘Status of beneficiaries of the second-class and single-parent families. -The data was constructed using the statistics of 'city, county, and district'. Administrative boundary information used the latest administrative district data of Korea in the form of polygons provided by geoservices. The year in which each statistical information data was constructed and the year in which the administrative boundary data (si, gun-gu) were constructed were spatially joined to each other and the data of the same construction year were spatially joined. Transformed into a grid by law. (Fig. 11)

Since steep slopes are provided as address-based text information, processing by geocoding is required. Geocoding refers to converting unique name information such as an address into point information composed of latitude and longitude coordinates. Since the steep slope is derived as point information through geocoding, it was converted into a grid by the number of points overlapping the grid to be used as a grid-based typhoon risk information dataset. (Fig. 12)

In general, the response process to a disaster situation consists of prevention, preparation, response, and recovery processes. A systematic disaster response system is required in order to effectively respond to emergency situations that change urgently from moment to moment, and various types of information are being used by each organization to build disaster response capabilities in each organization. In general, the basic task of each disaster response process is monitoring of the disaster situation, and identification of emergency situations and hazardous areas is very important. However, since there is a lot of data that needs to be referenced in the monitoring process for each disaster situation, performance degradation may occur in the process of displaying these information to the system at once. Also, even when various information is monitored, the analysis results vary greatly depending on the person who interprets it. The decision maker, who is currently in charge of disaster response, is a rotating public official, so it changes every time, and each person in charge has different strategies and knowledge to use in response according to his or her previous experience. It is difficult and each person in charge has difficulty in deriving the desired analysis result from the selected information. In order to solve such a problem, in the present invention, a data set is selected to display as a grid by merging information that causes harm, information exposed to typhoons, and information that can intensify typhoon damage into one.

To this end, the risk assessment methodology of UNISDR was applied to classify typhoon risk information into risk-exposure-vulnerability, and candidate groups for detailed indicators were selected based on related existing research cases and disaster annual reports. In this process, indicators were categorized by comparing the relationship between similar indicators and related indicators. In order to derive objective and valid indicators, only indicators that meet the purpose of typhoon risk of this study were reclassified. Finally, a total of 20 indicators were derived through expert review, and weights for each indicator were derived by the AHP method. Each index was targeted only for information that could be obtained, and the collected data was converted into a 1km grid according to the data conversion technique in Chapter 2.

Although the technical idea of the present invention has been specifically described in the preferred embodiment, it should be noted that the above-described embodiment is for the description and not the limitation. It is obvious to those skilled in the art that various changes and modifications are possible within the scope of the technical spirit of the present invention, and therefore, it is natural that such variations and modifications fall within the scope of the appended claims.

Claims (16)

In a system consisting of an operating computer with a built-in program (GIS-based integrated situation management system) for predicting storm and flood damage based on information related to natural disasters including wind and flood damage, and a monitor for simulation connected to the operating computer, A method of constructing and utilizing a map library dataset, characterized in that it is performed by the program as follows.
1) the process of inputting the natural disaster-related information into the program of the operating computer in a state where the disaster occurred location information and the disaster type information were combined;
2) In order to enable the disaster risk assessment by the program to be performed using only information by type of disaster among the natural disaster-related information input in step 1),
Information for each type of disaster is used to determine the size and extent of damage caused by the above program. It is to determine the exposure information of the damage target and the degree and scope to aggravate the damage.
The largely classified information for each type of disaster is detailed by the above program and classified into indicators that are the final dataset.
a process in which the indicators are sub-classified to correspond to the name of the mid-classified representativeness;
3) For the natural disaster-related information classified in step 2), the weight for each classification is determined by the program, and the final weight obtained by multiplying all the weights for each classification is obtained, and the final weight is obtained. a process in which weights are summed for each of the largely classified natural disaster-related information (risk information, exposure information, and vulnerability information) and evaluated as a disaster risk for each natural disaster-related information;
4) a process of geocoding by the program using only disaster occurrence location information among the natural disaster-related information input in step 1) and converting it into coordinates (x, y) of latitude and longitude;
5) The final weight of each major classified natural disaster-related information (risk information, exposure information, vulnerability information) obtained in step 3) by the program using the disaster occurrence location information geocoded in step 4) are superimposed on each point in the digital map space and simulated by the monitor,
In peacetime, the final weights obtained from only the information for each type of disaster classified as large, medium, and small for exposure information and vulnerability information in steps 2) and 3) are displayed overlappingly by point in the digital map space,
In the event of a disaster, the final weights obtained from only the information for each type of disaster classified as large, medium, and small for risk information and exposure information in steps 2) and 3) are overlapped and displayed on the digital map space by point,
In the case of disaster recovery, the final weights obtained from only the information for each type of disaster classified as large, medium, and small for exposure information and vulnerability information in steps 2) and 3) are displayed overlappingly by point in the digital map space.
In claim 1,
In the process 2) above, risk information relates to dangerous phenomena, substances, human activities or conditions that cause disasters, and exposure information relates to people, property, systems, and other factors that can be damaged by being located in a hazardous area. and how to build and utilize the map library dataset, characterized in that the vulnerability information relates to the characteristics and circumstances of organizations, systems, and properties that can easily receive dangerous damage.
delete In claim 2,
The risk information is a natural phenomenon that is a direct cause of damage and is used as information that can predict the size of the typhoon risk and the range of the area where damage occurs, and the exposure information is information that is the target of damage due to the typhoon information. It is used as information that can predict the extent of exposure to damage and the extent of the exposed area according to typhoon hazard factors, and the vulnerability information is a factor that can intensify typhoon risk damage and the degree to which damage can be aggravated according to typhoon hazard factors. and a method of constructing and utilizing a map library dataset characterized in that it is used as information that can predict the range of the Sihwa region.
In claim 4,
Rainfall and wind speed are the main causes of typhoon damage, such as flooding and collapse, so they are selected as risk-related indicators. Roads, bridges, railroads, rivers, and reservoirs are selected as indicators related to exposure because they are major infrastructure. The disabled, basic livelihood recipients, and the vulnerable population are vulnerable to typhoon response and are a factor that can intensify typhoon damage. How to build and utilize a map library dataset characterized by it.
delete In claim 2,
In the process 3), the Z-Score indicating the distance from the average was used for the index values to standardize between the indicators having each other size and unit, and the map library data, characterized in that the weight is determined as follows How to build and use the set.

z _i = Z-Score value, X _i = index value, X _mean = index mean value, X _std = standard deviation of index values
In claim 7,
In the case of Z-Score, values smaller than the average are displayed as negative numbers. To compensate for this, T-Score is applied as follows to build and utilize map library datasets.

In claim 1,
A method of constructing and utilizing a map library dataset, characterized in that the sum was derived by assigning weights to the detailed indicators constituting the risk in the process 3), and the formula is as follows.

H ₁ to H _n = indicators constituting the risk, w _n = weights, n = number of indicators
In claim 1,
A method of constructing and utilizing a map library dataset, characterized in that the sum was derived by assigning weights to the detailed indicators constituting the exposure in the process 3), and the formula is as follows.

E ₁ ~E _n = index constituting exposure, w _n = weight, n = index coefficient
In claim 1,
A method of constructing and utilizing a map library dataset, characterized in that the sum was derived by giving weights to the detailed indicators constituting the vulnerability in the process 3), and the formula is as follows.

V ₁ to V _n = indicator constituting vulnerability, w _n = weight, n = indicator coefficient
In claim 1,
In the process 3), the disaster risk was derived by adding risk, exposure, and vulnerability factors, and the calculation formula is as follows.

In claim 1,
In the process 3) above, weights for each indicator are selected based on the AHP method to generate grid-type typhoon risk area convergence information later. After creating a decision layer by dividing into large, medium, and small classification, weights are derived by constructing a comparison matrix, and the degree of consistency of answers is evaluated through CR (Consistency Ratio). A map library dataset construction and utilization method, characterized in that the final weight is derived through results less than 0.2.
In claim 1,
A grid transformation process of the collected data is added between steps 4) and 5) to construct a grid-based typhoon risk information dataset. A map library dataset, characterized in that it is selected from a method of using directly, ② a method of converting the collected source data into a grid, and ③ a method of processing the collected source data into new information and converting it into a grid. How to build and use it.
delete delete

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