Earthquake emergency disposal-oriented urban plot real-time population distribution simulation method

Abstract

The invention discloses an earthquake emergency disposal-oriented urban plot real-time population distribution simulation method. The method comprises the following steps of: carrying out detailed classification on urban plots according to land utilization types; simulating the coverage range of each base station, superposing the coverage range with the urban plot to generate a sub-plot, and calculating the area of the sub-plot; based on an initial condition that average population density weights of various urban plots are equal, calculating a simulated population number of each sub-plot; carrying out normalization processing, and calculating new average population density weights of various urban plots in the research range; replacing the old average population density weight with the new average population density weight of each type of urban plot, and repeatedly carrying out iterative calculation until a calculation result is converged; calculating the real-time population quantity of each urban land parcel by using the new average population density weight of each urban land parcel during convergence; and generating a real-time plot population distribution diagram of earthquake emergency disposal based on the real-time population number, and applying the real-time plot population distribution diagram to earthquake emergency response.

Description

Technical Field

[0001] This invention relates to the field of spatial information technology, and in particular to a method for simulating the real-time population distribution of urban plots for earthquake emergency response. Background Art

[0002] Earthquake disasters are sudden and unpredictable. Urban earthquake emergency response requires timely access to real-time population data and spatial distribution in urban areas in order to make a reasonable and rapid response.

[0003] Current commonly used real-time population distribution simulation methods use urban grids as the calculation unit, and use data such as mobile phone signaling, GPS trajectory, smart card swiping, and mobile phone check-in as basic data. They combine data such as built environment, POI points of interest, and land use to perform spatial interpolation or geographic weighted calculation.

[0004] The results are presented as population heat maps in vector or raster form, which have low calculation accuracy and cannot provide real-time population data for specific urban areas. This limits their application in earthquake emergency response, as they cannot obtain the number of affected people in a timely and accurate manner, increasing the difficulty of rescue and the incidence of accidents. Summary of the Invention

[0005] This invention provides a method for simulating real-time population distribution in urban areas for earthquake emergency response. This method utilizes the periodic patterns of urban population movement to classify urban areas in detail, iteratively calculates the average population density weight for different urban area types using mobile phone signal population data, and then calculates the real-time population of each urban area, generating a real-time population distribution map for earthquake emergency response. This can reduce the difficulty of rescue efforts and lower the incidence and mortality rates of accidents. See the description below for details:

[0006] A method for simulating real-time population distribution in urban areas for earthquake emergency response, the method comprising:

[0007] Urban land parcels are classified in detail according to land use type;

[0008] Simulate the coverage area of ​​each base station, overlay it with urban plots to generate sub-plots, obtain the real-time population within the coverage area of ​​the base station of the sub-plot, as well as the plot ratio, average population density weight of the sub-plot, and calculate the area of ​​the sub-plot.

[0009] Based on the initial condition that the average population density weights of various urban plots are equal, the simulated population of each sub-plot is calculated using the area, plot ratio, average population density weights of each sub-plot, and the real-time population count within the coverage area of ​​each base station.

[0010] Normalization was performed to calculate the new average population density weights for various types of urban land parcels within the research scope;

[0011] Replace the old average population density weights with new average population density weights for various types of urban land parcels, and repeat the iterative calculation until the calculation results converge.

[0012] The real-time population of each urban plot is calculated using the new average population density weights of various urban plots and the total building area of ​​each urban plot at the time of convergence.

[0013] Real-time population distribution maps of urban plots are generated based on the real-time population count of each plot, and these maps are then used in earthquake emergency response.

[0014] The detailed classification of urban land parcels based on land use type is as follows:

[0015] Based on the periodic patterns of urban population travel, urban plots of different land use types are classified in detail. The average population density weight of each type of urban plot is used instead of the real-time population density weight for calculation. The plots are numbered sequentially and a mapping table between land use types and plot types is established.

[0016] Create new fields for "Plot Type" and "Average Population Density Weight" in the urban land parcel data. Based on the land use type and mapping table of urban land parcels, assign values ​​to the plot type of each urban land parcel within the study area, and specify the average population density weight of the nth type of urban land parcel as parameter ρ. n .

[0017] The real-time population count within the coverage area of ​​the base station is as follows:

[0018] The number of users at each base station within the study area is added together to obtain the total number of real-time base station users at several time points within the study area. The average number of users at several time points within the study area is then calculated.

[0019] Obtain the number of permanent residents within the study area from the recent census, and use the number of permanent residents within the study area and the average number of users at several time points within the study area to calculate the mobile phone signal population calibration coefficient;

[0020] Add a field for "real-time population within the base station coverage area" to the real-time base station user data at the target time. Calculate the real-time population within the coverage area of ​​each base station using the number of users and the mobile phone signal population calibration coefficient to obtain new real-time base station user data at the target time.

[0021] The simulated coverage area of ​​each base station is overlaid with urban land parcels to generate sub-parcels. This yields the real-time population within the base station coverage area of ​​each sub-parcel, as well as the sub-parcel's plot ratio, average population density weight, and the sub-parcel's area is calculated.

[0022] In the ArcMap document, use the "Add XY Data" tool to input the new target time real-time base station user data to obtain base station distribution data;

[0023] Using ArcGIS's "Create Thiessen Polygon" tool, input the base station distribution data, select "All" for the output field, and obtain the base station coverage data;

[0024] Using ArcGIS's "Intersection" tool, input urban plot data and base station coverage data to obtain urban sub-plot data. This data inherits the plot ID, plot ratio, plot type, and average population density weight field values ​​of the corresponding urban plots from the urban plot data, as well as the base station ID and real-time population count field values ​​of the base station coverage area from the base station coverage data.

[0025] Use ArcGIS's "Add Geometric Attribute" tool to add an area attribute to the city subplot data, and rename the generated POLY_AREA field to "Subplot Area".

[0026] The plot ratio of the urban land parcels is calculated by acquiring urban land parcel and building boundary data within the research scope and calculating the total building area and plot ratio of each urban land parcel.

[0027] The initial condition of equal average population density weights for various types of urban land parcels is used to calculate the simulated population of each sub-parcel based on its area, plot ratio, average population density weight, and real-time population count within the coverage area of ​​each base station.

[0028] Export the attribute table of urban sub-plot data into an urban sub-plot table, create a new "Theoretical Population" field, assume that the average population density weight is equal and equal to 1 in the initial iteration, and calculate the theoretical population of each sub-plot using the plot ratio and sub-plot area.

[0029] The theoretical population of each sub-plot is categorized and summarized according to the base station ID to obtain the theoretical population of each base station. The theoretical population of each base station is then linked to the city sub-plot table using the base station ID as the connection field, and used as the "theoretical population of each base station" field value for the corresponding sub-plot.

[0030] Create a new "Simulated Population" field to calculate the simulated population of each sub-plot using the theoretical population of the sub-plot, the theoretical population of the base station, and the real-time population within the base station's coverage area.

[0031] The normalization process described above, which calculates the new average population density weights for various urban land parcels within the research scope, is as follows:

[0032] In the city sub-plot table, the simulated population is categorized and summarized according to plot type. The summation method is to obtain the total population of each type of city plot.

[0033] Normalization was performed, and the new average population density weights for various types of urban plots were calculated using the total population and total building area of ​​each type of urban plot.

[0034] The total building area of ​​the various types of urban land parcels is obtained by classifying and summarizing the total building area of ​​urban land parcels according to land parcel type, thus covering the total building area of ​​various types of urban land parcels within the research scope.

[0035] Furthermore, the convergence condition is:

[0036]

[0037] in, The average population density weight of the nth type of urban land parcel obtained in the kth iteration; The average population density weight of the nth type of urban land parcels obtained in the (k-1)th iteration is denoted as .

[0038] The beneficial effects of the technical solution provided by this invention are:

[0039] 1. This invention classifies urban plots in detail according to land use type and establishes a correlation between population density and plot ratio of similar urban plots, resulting in relatively high accuracy of simulation results;

[0040] 2. This invention uses urban plots as units for population simulation calculation, which can provide real-time population data for specific urban plots, thereby providing detailed data support for earthquake emergency response, timely and accurately obtaining the number of people affected by the disaster, and meeting the needs of emergency response. Attached Figure Description

[0041] Figure 1 A flowchart of a method for simulating real-time population distribution in urban areas for earthquake emergency response;

[0042] Figure 2 This is a detailed flowchart of a method for simulating real-time population distribution in urban areas for earthquake emergency response. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific examples. It should be understood that the specific embodiments described herein are merely intended to explain the present invention and not to limit it. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of the invention.

[0044] Current commonly used real-time population data simulation methods use urban grids as the calculation unit, and the results are presented as population heat maps in vector or raster form. The calculation accuracy is not high, and it cannot provide real-time population data for specific urban areas, which limits its application in urban earthquake emergency response.

[0045] Based on the above background, this invention utilizes the periodic patterns of urban population travel to classify urban plots in detail, uses mobile phone signal population data to iteratively calculate the average population density weight of different urban plot types, and then calculates the real-time population of each urban plot to generate a real-time population distribution map of plots for earthquake emergency response.

[0046] To facilitate understanding of this application, the following terms are explained in the embodiments of this invention:

[0047] Mobile signaling population data, which is real-time population data generated using mobile signaling data, can typically be obtained in two forms: real-time base station user data and real-time population heat maps.

[0048] Real-time base station user data, including the number of connected devices for each base station in real time. The data includes fields such as time, base station ID, base station coordinates, and number of users.

[0049] Real-time population heatmaps aggregate real-time base station user data onto a grid of a certain size (e.g., 200m×200m) to generate a population density map. The generated data includes both vector and raster data, which can be converted between each other. Vector real-time population heatmaps include: a planar grid graphic and fields such as time, grid ID, and grid population.

[0050] Urban plots are independent development plots within a city that have the same function. They are the basic units of urban development. The urban plot data required in this embodiment of the invention includes plot surface vector graphics and fields such as plot ID and land use type.

[0051] Building boundaries, the building outline boundaries in the city plan, and the building boundary data required in this embodiment of the invention include building boundary surface vector graphics and building height fields.

[0052] Floor area ratio (FAR) is a technical indicator used to measure the intensity of spatial development. Algorithmically, it refers to the ratio of the total above-ground floor area of ​​all buildings on an urban plot to the area of ​​the construction land.

[0053] Land use types, the classification of urban land use, according to the currently used national standard "Classification of Urban Land Use and Standard for Planning and Construction Land" (GB 50137-2011), urban construction land is divided into 8 major categories, 35 medium categories and 43 minor categories.

[0054] Base station coverage area generally refers to the range of mobile phone signal base station services. In the context of the real-time base station user data used in this embodiment of the invention, base station coverage area specifically refers to the spatial neighborhood range closest to a certain base station.

[0055] Population density weight is the ratio of "population density / floor area ratio" for each urban plot. Based on the periodic patterns of urban population travel, this embodiment of the invention assumes that the population density weight of urban plots of the same type at the same time is equal, and the average population density weight is taken.

[0056] Example 1

[0057] To address the technical problems existing in the background art, embodiments of the present invention provide a method for simulating real-time population distribution in urban areas for earthquake emergency response, see [link to relevant documentation]. Figure 1 The method includes the following steps:

[0058] 101: Classify urban land parcels in detail according to land use type;

[0059] 102: Simulate the coverage area of ​​each base station, overlay it with urban plots to generate sub-plots, obtain the real-time population within the coverage area of ​​the base station where the sub-plot is located, as well as the plot ratio, average population density weight of the sub-plot, and calculate the area of ​​the sub-plot.

[0060] 103: Based on the initial condition that the average population density weight of various urban plots is equal, the simulated population of each sub-plot is calculated using the area, plot ratio, average population density weight of each sub-plot and the real-time population of each base station coverage area.

[0061] 104: Perform normalization processing and calculate the new average population density weights for various types of urban land parcels within the research scope;

[0062] 105: Replace the old average population density weights with the new average population density weights for various types of urban land parcels, and repeat the iterative calculation until the calculation results converge;

[0063] 106: Using the new average population density weights of various types of urban plots at the time of convergence and the total building area of ​​each urban plot, calculate the real-time population of each urban plot.

[0064] 107: Generate a real-time population distribution map of each urban plot based on the real-time population of each plot, and use the real-time population distribution map of each plot in earthquake emergency response.

[0065] In summary, the embodiments of the present invention, through steps 101-107 above, use urban plots as units for population simulation calculation, which can provide real-time population data for specific urban plots, thereby providing detailed data support for earthquake emergency response, timely and accurately obtaining the number of people affected by the disaster, and meeting the needs of emergency response.

[0066] Example 2

[0067] The following describes Example 1 in further detail with specific examples. The method includes the following steps:

[0068] Figure 2 This invention provides a method for simulating real-time population distribution in urban areas for earthquake emergency response, such as... Figure 2 As shown, the real-time population distribution simulation method for urban plots for earthquake emergency response provided in this embodiment of the invention includes the following steps: S1 to S9.

[0069] S1: Obtain real-time base station user data covering the research area and perform calibration to obtain the real-time population of each base station's coverage area;

[0070] S2: Obtain urban plot and building boundary data within the research scope and calculate the total building area and plot ratio of each urban plot;

[0071] S3: Classify urban plots in detail according to land use type and calculate the total building area of ​​each type of urban plot within the research scope;

[0072] S4: Simulate the coverage area of ​​each base station, overlay it with urban plots to generate sub-plots, and calculate the area of ​​each sub-plot;

[0073] S5: Assuming that the average population density weights of various types of urban plots are equal, calculate the simulated population of each sub-plot;

[0074] S6: Perform normalization processing and calculate the new average population density weights for various types of urban land parcels within the research scope;

[0075] S7: Replace the old average population density weights with the new average population density weights for various types of urban land parcels, and repeat steps S5 and S6 for iterative calculation until the calculation results converge.

[0076] S8: Calculate the real-time population of each urban plot using the new average population density weights of various types of urban plots at the time of convergence.

[0077] S9: Generate a real-time population distribution map of urban plots based on the real-time population of the plots, and use the real-time population distribution map of the plots in earthquake emergency response.

[0078] In summary, the embodiments of the present invention, through the above steps S1-S9 using urban plots as units for population simulation calculation, can provide real-time population data for specific urban plots, thereby providing detailed data support for earthquake emergency response, timely and accurately obtaining the number of people affected by the disaster, and meeting the needs of emergency response.

[0079] Example 3

[0080] The following section, using specific examples and calculation formulas, details the implementation of steps S1 to S9 in Example 2.

[0081] In one possible implementation, step S1 includes:

[0082] S11: The research scope includes one or more municipal districts covering the target area;

[0083] S12: Obtain real-time base station user data for each base station covering the research area, including real-time base station user data at the target time, and real-time base station user data at 0:00, 1:00, 2:00, 3:00, and 4:00 on the previous working day before the target time.

[0084] The real-time base station user data is in tabular form, including fields such as time, base station ID, base station coordinates, and number of users.

[0085] S13: Add up the number of users at each base station within the study area at 0:00, 1:00, 2:00, 3:00, and 4:00 respectively to obtain the total number of real-time base station users at the five time points within the study area, and calculate the average number of users at the five time points within the study area.

[0086] S14: Obtain the number of permanent residents within the study area from the recent population census, and use the number of permanent residents within the study area and the average number of users at five time points within the study area to calculate the mobile phone signal population calibration coefficient. For the specific calculation method, please refer to formula (1).

[0087]

[0088] Where α is the mobile phone signal population calibration coefficient; P c P represents the number of permanent residents. s The average number of users at five time points within the study scope (from step S13).

[0089] S15: Add the field "Real-time population of the base station coverage area" to the real-time base station user data at the target time. Calculate the real-time population of each base station coverage area using the number of users and the mobile phone signal population calibration coefficient. For the specific calculation method, refer to formula (2) to obtain the new real-time base station user data at the target time.

[0090]

[0091] Among them, P s The real-time population size within the base station coverage area; α is the mobile phone signal population calibration coefficient (from step S14). This represents the number of users at the base station (from source data).

[0092] In one possible implementation, step S2 includes:

[0093] S21: Obtain urban plot and building boundary data within the study area;

[0094] The urban land parcel data includes land parcel area vector graphics and fields such as land parcel ID and land use type; the building boundary data includes building boundary area vector graphics and building height field.

[0095] S22: Use ArcGIS's "Add Geometry Attributes" tool to add area attributes to the urban plot and building boundary data. Rename the generated POLY_AREA field to "Plot Area" and "Building Base Area" respectively to obtain the first urban plot data and the first building boundary data.

[0096] S23: Using ArcGIS's "Intersect" tool, input the first city plot data and the first building boundary data to obtain the second building boundary data, which includes the plot ID, building floor area, and building height fields;

[0097] S24: Export the attribute table of the second building boundary data into the second building boundary table, create a new "building area" field, and use the building base area and building height fields to calculate the building area of ​​each building boundary graphic. For the specific calculation method, please refer to formula (3).

[0098]

[0099] Among them, S a S represents the building area; bH is the building's base area (from step S22); H is the building's height (from source data); h is the building's average floor height. The specific value of h has no impact on the calculation results of this embodiment of the invention. To simplify the calculation, it can be uniformly taken as 1. In specific implementation, this embodiment of the invention does not impose any restrictions on this.

[0100] S25: In the aforementioned second building boundary table, the building area is classified and summarized according to the plot ID. The summation method is summation to obtain the total building area of ​​each city plot. The summary result is saved as a plot total building area table, which includes plot ID and total building area fields.

[0101] S26: Using ArcGIS's "Join Field" tool, with the land parcel ID as the join field, connect the total building area field in the total building area table to the land parcel data of the first city to obtain the land parcel data of the second city;

[0102] S27: Create a new "Floor Area Ratio" field in the attribute table of the second city's land parcel data. Use ArcGIS's "Field Calculator" tool to calculate the floor area ratio of each city's land parcel. For the specific calculation method, refer to formula (4) to obtain the third city's land parcel data.

[0103]

[0104] Where F represents the plot ratio of each urban land parcel; S A The total building area of ​​each city plot (from step S25); S B The area of ​​each city plot is given (from step S22).

[0105] In one possible implementation, step S3 includes:

[0106] S31: Referring to the national standard "Classification of Urban Land Use and Standards for Planning and Construction Land Use" (GB50137-2011), and based on the cyclical patterns of urban population travel, urban land parcels are divided into residential land (R), administrative office land (A1), cultural facilities land (A2), higher education institution land (A31), secondary vocational school land (A32), primary and secondary school land (A33), special education land (A35), scientific research land (A34), sports stadium land (A41), sports training land (A42), hospital land (A51), health and epidemic prevention land (A52), special medical land (A53), other medical and health land (A59), social welfare facilities land (A6), cultural relics and ancient books land (A7), foreign affairs land (A8), religious facilities land (A9), retail commercial land (B11), wholesale market land (B12), catering land (B13), hotel land (B14), and business facilities land. Land is classified into 40 categories, including land for entertainment and sports facilities (B2), land for public utility business outlets (B4), land for other service facilities (B9), industrial land (M), land for logistics and warehousing (W), urban road land (S1), urban rail transit land (S2), transportation hub land (S3), transportation station land (S4), land for other transportation facilities (S9), land for supply facilities (U1), land for environmental facilities (U2), land for safety facilities (U3), land for other public utility facilities (U9), park green space (G1), protective green space (G2), and square land (G3). This ensures that the real-time population density weights of similar urban land parcels are similar. Therefore, the average population density weight of each type of urban land parcel can be used instead of the real-time population density weight for calculation. The parcels are numbered sequentially from 1 to 40. A mapping table between all land use types and land parcel types in the "Classification of Urban Land Use and Standard for Planning and Construction Land" (GB50137-2011) is established.

[0107] Of these, 40 categories represent general cases, which can be adjusted according to specific circumstances. This embodiment of the invention does not impose any restrictions on these categories.

[0108] S32: Create new fields "Plot Type" and "Average Population Density Weight" in the third city's land parcel data. Based on the land use type of the city's land parcels and the aforementioned mapping table, assign values ​​to the land parcel types of each city within the study scope, and specify the average population density weight of the nth type of city land parcel as parameter ρ. n We obtained the land parcel data for the fourth city.

[0109] S33: Export the attribute table of the fourth city land parcel data into the first city land parcel table, classify and summarize the total building area of ​​the city land parcels according to the land parcel type, and sum the summation method to obtain the total building area of ​​each type of city land parcel.

[0110] In one possible implementation, step S4 includes:

[0111] S41: Create a new ArcMap document, select a suitable projection coordinate system, use the "Add XY Data" tool, select the new target time real-time base station user data in the table, select the data columns corresponding to the base station coordinates in the X and Y fields respectively, generate point-like base station data, export it as a Shapefile format, and obtain the base station distribution data;

[0112] The attribute table of base station distribution data includes fields such as base station ID and real-time population count within the base station coverage area.

[0113] S42: Use ArcGIS's "Create Thiessen Polygons" function, input base station distribution data, and select "All" for Output Fields to obtain base station coverage data;

[0114] In this data, each polygon in the base station coverage data corresponds to the coverage area of ​​each base station.

[0115] S43: Using ArcGIS's "Intersect" tool, input the fourth city's land parcel data and base station coverage data to obtain the first city's sub-land parcel data;

[0116] Among them, the graphic unit of the first city's sub-plot data is the sub-plot, which inherits fields such as plot ID, plot ratio, plot type, and average population density weight parameter from the fourth city's plot data, as well as fields such as base station ID and real-time population number within the base station coverage area from the base station coverage data.

[0117] S44: Use ArcGIS's "Add Geometry Attributes" tool to add an area attribute to the first city sub-plot data, rename the generated POLY_AREA field to "Sub-plot Area" to obtain the second city sub-plot data.

[0118] In one possible implementation, step S5 includes:

[0119] S51: Export the attribute table of the second city sub-plot data into a city sub-plot table, create a new "theoretical population" field, assume that the average population density weight is equal and equal to 1 in the initial iteration, and calculate the theoretical population of each sub-plot using the plot ratio and sub-plot area. For the specific calculation method, please refer to formula (5).

[0120]

[0121] in, F represents the theoretical population of the sub-plot; b The plot ratio of the sub-plot (from steps S27 and S43); S c The area of ​​the sub-plot (from step S44); ρ b The average population density weight for sub-plots, with values ​​ranging from {ρ1, ρ2, ..., ρ3}. 40 The initial iteration assumes that the average population density weights are equal, and takes ρ1 = ρ2 = ... = ρ 40 =1.

[0122] S52: Classify and summarize the theoretical population of sub-plots according to base station ID. The summation method is to obtain the theoretical population of each base station. Then, using the base station ID as the connection field, connect the theoretical population of the base station to the city sub-plot table as the value of the "theoretical population of the base station" field for the corresponding sub-plot.

[0123] S53: Create a new "Simulated Population" field. Calculate the simulated population of each sub-plot using the theoretical population of the sub-plot, the theoretical population of the base station, and the real-time population of the base station coverage area. For specific calculation methods, see formula (6).

[0124]

[0125] in, The simulated population size for the sub-plot; The theoretical population of the sub-plot (from step S51); P represents the theoretical population size of the base station (from step S52); s The real-time population size within the coverage area of ​​the base station (from step S15).

[0126] In one possible implementation, step S6 includes:

[0127] S61: In the city sub-plot table, the simulated population is classified and summarized according to the plot type. The summation method is to obtain the total population of each type of city plot.

[0128] S62: Perform normalization processing, and use the total population of various types of urban plots and the total building area on various types of urban plots to calculate the new average population density weight of various types of urban plots within the research scope. For the specific calculation method, please refer to formula (7).

[0129]

[0130] in, The new average population density weight for the nth type of urban land parcel; S represents the total population of the nth type of urban land parcel (from step S61); nThe total building area on the nth type of urban land parcel (from step S33).

[0131] In one possible implementation, step S7 includes:

[0132] S71: Replace the old average population density weights with the new average population density weights for various types of urban land parcels, and repeat steps S5 and S6 for iterative calculation to obtain the new average population density weights for various types of urban land parcels.

[0133] S72: After each iteration, determine whether the calculation result has converged. If it has converged, stop the iteration. The convergence condition is given in formula (8).

[0134]

[0135] in, The average population density weight of the nth type of urban land parcel obtained in the kth iteration; The average population density weight of the nth type of urban land parcels obtained in the (k-1)th iteration is denoted as .

[0136] In one possible implementation, step S8 includes connecting the new average population density weights of various types of urban plots to the first urban plot table at convergence, calculating the real-time population of each urban plot, and obtaining the second urban plot table by referring to formula (9) for the specific calculation method.

[0137] P B =ρ B ×S A ; Formula (9)

[0138] Among them, P B ρ represents the real-time population of the urban plot; B The new average population density weight for this type of urban land parcel at convergence (from step S7); S A The total building area of ​​the urban plot (from step S22).

[0139] In one possible implementation, step S9 includes:

[0140] S91: Using ArcGIS's "Join Field" tool, with the land parcel ID as the join field, connect the real-time population field of the urban land parcels in the second urban land parcel table to the fourth urban land parcel data to obtain the fifth urban land parcel data;

[0141] S92: Load the fifth city plot data into an ArcMap document, select a suitable projection coordinate system, open the layer properties dialog box, select the real-time population number field of the city plot as the label field in the Labels tab, select "Categories-Unique values" in the Symbolology tab, select the real-time population number of the city plot in the Value Field, select any gradient color band in the Color Ramp, apply the changes and export the image to obtain the real-time population distribution map of the plot for earthquake emergency response, and use the real-time population distribution map of the plot in earthquake emergency response.

[0142] In summary, this invention utilizes the periodic patterns of urban population travel to classify urban plots in detail, uses mobile phone signal population data to iteratively calculate the average population density weight of different urban plot types, and then calculates the real-time population of each urban plot. The simulation results are highly accurate and can provide real-time population data for specific urban plots, generating real-time population distribution maps for earthquake emergency response. This provides detailed data support for earthquake emergency response, enabling timely and accurate acquisition of the number of affected people and meeting the needs of emergency response.

[0143] It should be noted that the embodiments of the present invention take mobile signal population data in the form of real-time base station user data as an example. If real-time population heat map data is used, steps S41 and S42 are not required. It is only necessary to convert it into a vector real-time population heat map, and use grid range, grid ID, and grid population to replace base station coverage range, base station ID, number of users, etc. for calculation. The embodiments of the present invention do not impose any restrictions on this.

[0144] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0145] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulating real-time population distribution in urban areas for earthquake emergency response, characterized in that, The method includes: Urban land parcels are classified in detail according to land use type; Simulate the coverage area of ​​each base station, overlay it with urban plots to generate sub-plots, obtain the real-time population within the coverage area of ​​the base station of the sub-plot, as well as the plot ratio, average population density weight of the sub-plot, and calculate the area of ​​the sub-plot. Based on the initial condition that the average population density weights of various urban plots are equal, the simulated population of each sub-plot is calculated using the area, plot ratio, average population density weights of each sub-plot, and the real-time population count within the coverage area of ​​each base station. Normalization was performed to calculate the new average population density weights for various types of urban land parcels within the research scope; Replace the old average population density weights with new average population density weights for various types of urban land parcels, and repeat the iterative calculation until the calculation results converge. The real-time population of each urban plot is calculated using the new average population density weights of various urban plots and the total building area of ​​each urban plot at the time of convergence. Real-time population distribution maps of urban plots are generated based on the real-time population count of each plot, and these maps are then used in earthquake emergency response.

2. The method for simulating real-time population distribution in urban areas for earthquake emergency response as described in claim 1, characterized in that, The detailed classification of urban land parcels based on land use type is as follows: Based on the periodic patterns of urban population travel, urban plots of different land use types are classified in detail. The average population density weight of each type of urban plot is used instead of the real-time population density weight for calculation. The plots are numbered sequentially and a mapping table between land use types and plot types is established. Create new fields for "Plot Type" and "Average Population Density Weight" in the urban plot data. Based on the land use type and mapping table of urban plots, assign values ​​to the plot type of each urban plot within the study area, and specify the average population density weight of the nth type of urban plot as parameter ρ. n .

3. The method for simulating real-time population distribution in urban areas for earthquake emergency response as described in claim 1, characterized in that, The real-time population count within the coverage area of ​​the base station is: The number of users at each base station within the study area is added together to obtain the total number of real-time base station users at several time points within the study area. The average number of users at several time points within the study area is then calculated. Obtain the number of permanent residents within the study area from the recent census, and use the number of permanent residents within the study area and the average number of users at several time points within the study area to calculate the mobile phone signal population calibration coefficient; Add a field for "real-time population within the base station coverage area" to the real-time base station user data at the target time. Calculate the real-time population within the coverage area of ​​each base station using the number of users and the mobile phone signal population calibration coefficient to obtain new real-time base station user data at the target time.

4. The method for simulating real-time population distribution in urban areas for earthquake emergency response as described in claim 1, characterized in that, The coverage area of ​​each simulated base station is overlaid with urban land parcels to generate sub-parcels. This yields the real-time population within the base station coverage area of ​​each sub-parcel, as well as the sub-parcel's plot ratio, average population density weight, and the area of ​​the sub-parcel. In the ArcMap document, use the "Add XY Data" tool to input the new target time real-time base station user data to obtain base station distribution data; Using ArcGIS's "Create Thiessen Polygon" tool, input the base station distribution data, select "All" for the output field, and obtain the base station coverage data; Using ArcGIS's "Intersection" tool, input urban plot data and base station coverage data to obtain urban sub-plot data. Inherit the plot ID, plot ratio, plot type, and average population density weight field values ​​of the urban plot where the sub-plot is located, as well as the base station ID and real-time population count field values ​​of the base station coverage area of ​​the base station where the sub-plot is located. Use ArcGIS's "Add Geometric Attribute" tool to add an area attribute to the city subplot data, and rename the generated POLY_AREA field to "Subplot Area".

5. The method for simulating real-time population distribution in urban areas for earthquake emergency response according to claim 4, characterized in that, The plot ratio of the urban land parcel is calculated by acquiring urban land parcel and building boundary data within the research scope and calculating the total building area and plot ratio of each urban land parcel.

6. The method for simulating real-time population distribution in urban areas for earthquake emergency response according to claim 1, characterized in that, Based on the initial condition that the average population density weights of various urban land parcels are equal, the simulated population of each sub-parcel is calculated using the area, plot ratio, average population density weights, and real-time population counts within the coverage area of ​​each base station: Export the attribute table of urban sub-plot data into an urban sub-plot table, create a new "Theoretical Population" field, assume that the average population density weight is equal and equal to 1 in the initial iteration, and calculate the theoretical population of each sub-plot using the plot ratio and sub-plot area. The theoretical population of each sub-plot is categorized and summarized according to the base station ID to obtain the theoretical population of each base station. The theoretical population of each base station is then linked to the city sub-plot table using the base station ID as the connection field, and used as the "theoretical population of each base station" field value for the corresponding sub-plot. Create a new "Simulated Population" field to calculate the simulated population of each sub-plot using the theoretical population of the sub-plot, the theoretical population of the base station, and the real-time population within the base station's coverage area.

7. The method for simulating real-time population distribution in urban areas for earthquake emergency response according to claim 1, characterized in that, The normalization process is performed, and the new average population density weights for various types of urban land parcels within the research scope are calculated as follows: In the city sub-plot table, the simulated population is categorized and summarized according to plot type. The summation method is to obtain the total population of each type of city plot. Normalization was performed, and the new average population density weights for various types of urban plots were calculated using the total population and total building area of ​​each type of urban plot.

8. A method for simulating real-time population distribution in urban areas for earthquake emergency response, as described in claim 7, is characterized in that... The total building area on various types of urban land parcels is: the total building area of ​​urban land parcels is classified and summarized according to land parcel type to obtain the total building area on various types of urban land parcels covering the research scope.

9. A method for simulating real-time population distribution in urban areas for earthquake emergency response, as described in claim 1, is characterized in that... The convergence condition is: in, The average population density weight of the nth type of urban land parcel obtained in the kth iteration; The average population density weight of the nth type of urban land parcels obtained in the (k-1)th iteration is denoted as .

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