A prediction method for population demand of emergency shelters based on comprehensive disasters

Abstract

The present invention discloses a method for predicting population demand for emergency shelters based on comprehensive disasters, the method comprising: obtaining population density heat map data, determining permanent population density distribution data P of the area to be predicted, i And the area C corresponding to the data distribution i , get the total population M of the area to be predicted; determine the building height H in area j of the area to be predicted j 、Building area S j And the per capita area index K j ; Construct a population density redistribution coefficient formula model to obtain the resident population density distribution data P of the area to be predicted j ; Get the disaster risk area range D x , matching the disaster-affected area D of each land in the predicted area j ; Output the total number of refugees in emergency shelters in the area to be predicted M d This method proposes a more suitable and scientific prediction method for the differences in refugee population demand caused by different building and land types in the forecast area, providing a scientific basis for the site selection of urban emergency shelters.

Description

Technical Field

[0001] The invention belongs to the technical field of urban planning, and in particular relates to a method for predicting population demand for emergency shelters based on comprehensive disasters. Background Art

[0002] At present, the research on emergency shelters is mainly focused on responding to earthquake disasters. Whether the built emergency shelters can meet the shelter needs of comprehensive disasters still needs further research. The planning and site selection of new shelter facilities usually takes distance as the main influencing factor. The calculation using ArcGIS spatial accessibility analysis needs to be combined with some manual intervention methods to improve the reliability and objectivity of the results. The existing prediction methods for the population demand of emergency shelters usually directly use the population density accuracy only to the street and town level. The population number of plots and buildings at a smaller level cannot be accurately calculated. Summarizing the existing technical methods, there are several shortcomings:

[0003] 1. Emergency shelters are not comprehensive enough in terms of the types of disasters they prevent.

[0004] Different natural disasters require different emergency shelter spaces: tropical cyclones (typhoons), severe convective weather, heavy rains, floods, lightning, droughts, abnormally high temperatures, low temperatures and other types of disasters require emergency shelters with indoor spaces; fault activities, earthquakes, collapses, landslides, mudslides, ground collapses, ground subsidence, ground fissures, soft soil foundation subsidence and soil erosion and other types of disasters require emergency shelters with open outdoor areas. Therefore, the demand analysis for emergency shelters needs to be determined based on a comprehensive study of the comprehensive disaster type distribution characteristics and population agglomeration conditions.

[0005] 2. No deep integration with ArcGIS

[0006] Demand forecasting (creating optimal path planning based on planning maps) and facility point evaluation and site selection in cities should be research issues that are deeply integrated with geographic information science. They involve issues such as urban road connectivity, regional accessibility, and service area scope. They are comprehensive issues involving multiple disciplines and topics. If geographic information science methods cannot be effectively integrated into technical solutions, better results cannot be obtained to a certain extent.

[0007] 3. Population forecast data is not accurate enough

[0008] The planning and site selection of new facilities is often a subjective decision. The reason is that there is no scientific, reasonable and convenient method for facility site selection. Usually, only a simple model algorithm plus manual intervention can be used to select the site.

[0009] In summary, the prediction of population demand for emergency shelters based on comprehensive disasters is a more scientific prediction method that matches areas affected by various natural disasters on the basis of current population distribution and planned population forecast, and proposes differentiated shelter population demand for areas with different building and land compositions. Summary of the invention

[0010] The purpose of the present invention is to provide a method for predicting the population demand for emergency shelters based on comprehensive disasters, to propose a more suitable and scientific prediction method for the differences in the population demand for shelters caused by different building and land types in the predicted area, and to provide a more scientific basis for the site selection of urban emergency shelters.

[0011] To achieve the above purpose, the technical solution of the present invention is as follows:

[0012] S1: Obtain population density heat map data and determine the permanent population density distribution data P of area i in the predicted area i And the area C corresponding to the data distribution i , get the total population M of the area to be predicted;

[0013] S11: Convert the population density heat map data into a vector file through ArcGIS conversion tools to determine the permanent population density distribution data P of area i in the area to be predicted. i And the area C corresponding to the data distribution i , calculate the total population M of the area to be predicted:

[0014]

[0015] Where: M represents the total population of the area to be predicted; P i , C i They represent the population density distribution data of region i and the area corresponding to the population density distribution data respectively; m represents the total number of items of all i regions in the area to be predicted.

[0016] S2: Obtain the land use type and single building information of the construction land in the predicted area, and determine the building height H of area j in the predicted area j 、Building area S j And the per capita area index K j ;

[0017] S21: Enter the building height H of area j in the area to be predicted on the ArcGis platform j 、Building area S j , make a database of areas to be predicted; among them, land without buildings is classified by H j =0, S j =1Enter the database;

[0018] S22: Determine the per capita area index K according to national standards j .

[0019] S3: Based on the total population M and the building height H of the j area in the predicted area j 、Building area S j , Per capita area index K j , construct a population density redistribution coefficient formula model, allocate the total population according to the proportion, and obtain the permanent population density distribution data P of the j region in the predicted area j ;

[0020] S31: The formula for population density redistribution coefficient is:

[0021]

[0022] Where: P j is the resident population density distribution data of region j; j is the population density redistribution coefficient of region j; M is the total population of the region to be predicted; S j Represents the building area of ​​the jth region.

[0023] S32: Per capita area index K under national standards j The calculated population in region j is an ideal value and needs to match the actual population. The total population is apportioned according to the proportion to calculate the population density redistribution coefficient, where the formula is:

[0024]

[0025] Where: M j represents the number of people in the ideal state of region j under national standards; j is the population density redistribution coefficient of region j; H j , S j Respectively represent the building height and floor area of ​​area j; K j It represents the per capita area index of region j according to national standards; n represents the total number of items of all regions j in the area to be predicted.

[0026] S4: Based on different natural disaster risk data, perform spatial distribution analysis on different natural disaster risk indices to obtain the dangerous area D that meets the emergency shelter needs. x , matching the disaster-affected area D of region j in the area to be predicted j ; Construct a comprehensive disaster emergency shelter population prediction model, and use the permanent population density distribution data P in the j area to be predicted j and the area affected by the disaster D j Bring it into calculation;

[0027] The comprehensive disaster emergency evacuation population prediction model is:

[0028]

[0029] Where: M d P represents the number of people who need to take shelter in emergency shelters in the area to be predicted; j is the resident population density distribution data of region j; D j They represent the area affected by the disaster in region j; M represents the total population of the area to be predicted; H j , S j Respectively represent the building height and floor area of ​​area j; K j represents the per capita area index of region j according to national standards; n represents the total number of items in all regions j in the area to be predicted;

[0030] S5: Output the total population demand for emergency shelters in the predicted area M d .

[0031] Preferably, the step S1:

[0032] S11. Convert the population density heat map data into a vector file through ArcGIS conversion tools to determine the permanent population density distribution data P of area i in the area to be predicted. i And the area C corresponding to the data distribution i , calculate the total population M of the area to be predicted:

[0033]

[0034] Where: M represents the total population of the area to be predicted; P i , C i They represent the population density distribution data of region i and the area corresponding to the population density distribution data respectively; m represents the total number of items of all i regions in the area to be predicted.

[0035] Preferably, the step S2: obtaining the land use type and single building information of the construction land in the area to be predicted, and determining the building height H of area j in the area to be predicted j 、Building area S j And the per capita area index K j The specific steps are:

[0036] S21. Enter the building height H of area j in the area to be predicted on the ArcGIS platform j 、Building area S j , make a database of areas to be predicted; among them, land without buildings is classified by H j =0, S j =1Enter the database;

[0037] Preferably, step S4: based on different natural disaster risk data, perform spatial distribution analysis on different natural disaster risk indices, and obtain the dangerous area D that meets the emergency shelter needs according to the spatial distribution of different natural disaster risk indices. x , matching the disaster-affected area D of each land in the predicted area j , specifically:

[0038] S41. Determine the comprehensive disaster types as rainstorm meteorological disasters, typhoon meteorological disasters, strong wind meteorological disasters, lightning meteorological disasters, high temperature meteorological disasters, low temperature meteorological disasters, drought meteorological disasters, flood geological disasters, and earthquake disasters; conduct spatial distribution analysis of disaster risk index and divide them into high-risk areas, relatively high-risk areas, low-risk areas, and relatively low-risk areas;

[0039] S42. Use ArcGIS conversion tools to convert the data element class of the comprehensive natural disaster risk index distribution map into a shapefile file, and obtain the dangerous area D that meets the emergency shelter needs based on the spatial distribution of different disaster risk indices. x , matching the disaster-affected area D of each land in the predicted area j .

[0040] Compared with the prior art, the method for predicting population demand for emergency shelters based on comprehensive disasters has the following advantages:

[0041] Beneficial effects:

[0042] (1) Fully consider the demand for emergency shelters in different natural disasters;

[0043] (2) The population density redistribution formula is used to improve the accuracy of refuge population demand;

[0044] (3) Fully integrate ArcGIS software to conduct more scientific and rigorous calculation research;

[0045] (4) Using the per capita area index under national standards, predict the population distribution in future construction land, and fully combine the disaster recurrence risk zoning to provide a basis for the planning and layout of emergency shelters; BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. The features and advantages of the present invention will be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 A flow chart of a method for predicting population demand for emergency shelters based on comprehensive disasters

[0048] Figure 2 Schematic diagram for converting population density heat map data into vector files

[0049] Figure 3 Mathematical Logic for Population Density Redistribution

[0050] Figure 4 The population distribution accuracy of a city’s population density heat map data after population density redistribution

[0051] Figure 5 To analyze the spatial distribution of disaster risk index

[0052] Figure 6 The spatial superposition of the distribution map data of the rainstorm meteorological disaster risk index and the population density distribution data after population density redistribution DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0055] The specific implementation methods of the technical solution of the present invention are further clearly and completely described below in conjunction with the embodiments and drawings. The specific implementation methods of the present invention are exemplary and are not intended to limit the present invention.

[0056] The following describes a method for predicting population demand for emergency shelters based on comprehensive disasters. The specific flow chart is as follows: Figure 1 As shown:

[0057] S1: Obtain the population density heat map data of a city and determine the regional permanent population density distribution data P i And the area C corresponding to the data distribution i , get the total population M of the region;

[0058] S11: Figure 2 As shown in the figure, the population density heat map data is converted into a vector file through the ArcGIS conversion tool to determine the regional permanent population density distribution data P i And the area C corresponding to the data distributioni , calculate the total population M of the region formula:

[0059]

[0060] Where: M represents the total population of a city; P i , C i They represent the population density distribution data of region i and the area corresponding to the population density distribution data respectively; m represents the total number of items of all i regions in the region.

[0061] Since the case is based on the entire city, the population density P i It is divided into 5 levels, so the total population of the region is calculated as M=P1C1+P2C2+P3C3+P4C4+P5C5.

[0062] S2: Obtain the land use type and single building information of the construction land in a certain city area, and determine the regional building height H j 、Building area S j And the per capita area index K j ;

[0063] S21: Enter the building height H of a city in the ArcGIS platform j 、Building area S j , create a city area database; among them, the land without buildings is classified by H j =0, S j =1Enter the database;

[0064] S22: Determine the per capita area index K according to national standards j .

[0065] S3: Based on the total population M and the building height H of the j area in the predicted area j 、Building area S j , and the per capita area index K j , construct a population density redistribution coefficient formula model, allocate the total population according to the proportion, and obtain the permanent population density distribution data P of a city area j ;

[0066] S31: The formula for population density redistribution coefficient is:

[0067]

[0068] Where: P j is the resident population density distribution data of region j; j is the population density redistribution coefficient of region j; M is the total population of the region to be predicted; S j They respectively represent the building area of ​​the jth area.

[0069] S32: Per capita area index K under national standards j The calculated population in region j is an ideal value and needs to match the actual population. The total population is apportioned according to the proportion to calculate the population density redistribution coefficient, where the formula is:

[0070]

[0071] Where: M j represents the number of people in the ideal state of region j under national standards; j is the population density redistribution coefficient of region j; H j , S j Respectively represent the building height and floor area of ​​area j; K j It represents the per capita area index of region j according to national standards; n represents the total number of items of all regions j in the area to be predicted.

[0072] like Figure 3 As shown in the figure, the mathematical logic of population density redistribution is to further improve the accuracy of the population heat map through the national standard on construction land, and to redistribute the concentrated population originally on non-construction land (pond water surface, agricultural and forestry land) to construction land.

[0073] like Figure 4 As shown in the figure, after the population density heat map data of a certain city was redistributed, the accuracy of population distribution was further improved.

[0074] S4: Based on different natural disaster risk data, perform spatial distribution analysis on different natural disaster risk indices to obtain the dangerous area D that meets the emergency shelter needs. x , matching the area D affected by the disaster in the area to be predicted j ; Construct a comprehensive disaster emergency shelter population prediction model, and use the permanent population density distribution data P j and the area affected by the disaster D j Bring it into calculation;

[0075] S41: Select rainstorm meteorological disasters from the comprehensive disaster types to conduct spatial distribution analysis of disaster risk index, and divide them into high-risk areas, relatively high-risk areas, low-risk areas, and relatively low-risk areas, such as Figure 5 As shown;

[0076] S42: Using ArcGIS conversion tools, convert the rainstorm meteorological disaster risk index distribution map data feature class into a shapefile file, and obtain the dangerous area D that meets the emergency shelter needs according to the spatial distribution of different disaster risk indices. x , matching the disaster-affected area D of each land in the predicted areaj , as shown in Table 1.

[0077] Heavy rain disaster risk level Impact area (square kilometers) Low risk 228.72 Lower risk 520.21 Higher risk 422.54 High risk 137.92 total 1309.39

[0078] S5: Output the total population demand for emergency shelters in the predicted area M d .

[0079] S51: Output the total population demand for emergency shelters in the predicted area M d Specifically:

[0080]

[0081] Where: M d P represents the number of people who need to take shelter in emergency shelters in the area to be predicted; j is the resident population density distribution data of region j; D j They represent the area affected by the disaster in region j; M represents the total population of the area to be predicted; H j , S j Respectively represent the building height and floor area of ​​area j; K j It represents the per capita area index of region j according to national standards; n represents the total number of items of all regions j in the area to be predicted.

[0082] Calculate M d The formula of the quantitative relationship is as follows Figure 6 As shown in Table 2, the distribution map data of the rainstorm meteorological disaster risk index is spatially superimposed with the population density distribution data after population density redistribution to obtain the number of people in the affected area.

[0083]

[0084]

[0085] The above description is only a preferred embodiment of the present invention. For example, there may be multiple combinations of different configurations and characteristic parameter settings. The embodiments of the present invention only show example parameters and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting population demand for emergency shelters based on comprehensive disasters, characterized in that: The method comprises the following steps: S1: Obtain population density heat map data and determine the permanent population density distribution data P of area i in the predicted area i And the area C corresponding to the data distribution i , get the total population M of the area to be predicted; S2: Obtain the land use type and single building information of the construction land in the predicted area, and determine the building height H of area j in the predicted area j 、Building area S j And the per capita area index K j ; S3: Based on the total population M and the building height H of the j area in the area to be predicted j 、Building area S j , Per capita area index K j , construct a population density redistribution coefficient formula model, allocate the total population according to the proportion, and obtain the permanent population density distribution data P of the j region in the predicted area j ; The population density redistribution coefficient formula model constructed in step S3 is: S31. The formula for population density redistribution coefficient is: Where: P j is the resident population density distribution data of region j; j is the population density redistribution coefficient of region j; M is the total population of the region to be predicted; S j represents the building area of ​​the jth region; S32. Per capita area index K under national standards j The calculated population in region j is an ideal value and needs to match the actual population. The total population is apportioned according to the proportion to calculate the population density redistribution coefficient, where the formula is: Where: M j represents the number of people in the ideal state of region j under national standards; j is the population density redistribution coefficient of region j; H j , S j Respectively represent the building height and floor area of ​​area j; K j represents the per capita area index of region j according to national standards; n represents the total number of items in all regions j in the area to be predicted; S4: Based on different natural disaster risk data, perform spatial distribution analysis on different natural disaster risk indices to obtain the dangerous area D that meets the emergency shelter needs. x , matching the disaster-affected area D of region j in the area to be predicted j ; Construct a comprehensive disaster emergency shelter population prediction model, and use the permanent population density distribution data P in the j area to be predicted j and the area affected by the disaster D j Bring it into calculation; The comprehensive disaster emergency evacuation population prediction model is: Where: M d P represents the number of people who need to take shelter in emergency shelters in the area to be predicted; j is the resident population density distribution data of region j; D j They represent the area affected by the disaster in region j; M represents the total population of the area to be predicted; H j , S j Respectively represent the building height and floor area of ​​area j; K j represents the per capita area index of region j according to national standards; n represents the total number of items in all regions j in the area to be predicted; S5: Output the total population demand for emergency shelters in the predicted area M d .

2. The method for predicting population demand for emergency shelters based on comprehensive disasters according to claim 1, characterized in that: The step S1: S11. Convert the population density heat map data into a vector file through ArcGIS conversion tools to determine the permanent population density distribution data P of area i in the area to be predicted. i And the area C corresponding to the data distribution i , calculate the total population M of the area to be predicted: Where: M represents the total population of the area to be predicted; P i , C i They represent the population density distribution data of region i and the area corresponding to the population density distribution data respectively; m represents the total number of items of all i regions in the area to be predicted.

3. The method for predicting population demand for emergency shelters based on comprehensive disasters according to claim 1, characterized in that: Step S2: Obtain the land use type and single building information of the construction land in the area to be predicted, and determine the building height H of area j in the area to be predicted. j 、Building area S j And the per capita area index K j The specific steps are: S21. Enter the building height H of area j in the area to be predicted on the ArcGIS platform j 、Building area S j , make a database of areas to be predicted; among them, land without buildings is classified by H j =0, S j =1Enter the database; S22. Determine the per capita area index K according to national standards j .

4. A method for predicting population demand for comprehensive disaster emergency shelters according to any one of claims 1 to 3, characterized in that: Step S4: Based on different natural disaster risk data, spatial distribution analysis is performed on different natural disaster risk indices, and the dangerous area D that meets the emergency shelter needs is obtained according to the spatial distribution of different natural disaster risk indices. x , matching the disaster-affected area D of each land in the predicted area j , specifically: S41. Determine the comprehensive disaster types as rainstorm meteorological disasters, typhoon meteorological disasters, strong wind meteorological disasters, lightning meteorological disasters, high temperature meteorological disasters, low temperature meteorological disasters, drought meteorological disasters, flood geological disasters, and earthquake disasters; conduct spatial distribution analysis of disaster risk index and divide them into high-risk areas, relatively high-risk areas, low-risk areas, and relatively low-risk areas; S42. Use ArcGIS conversion tools to convert the data element class of the comprehensive natural disaster risk index distribution map into a shapefile file, and obtain the dangerous area D that meets the emergency shelter needs based on the spatial distribution of different disaster risk indices. x , matching the disaster-affected area D of each land in the predicted area j .

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