An idle and inefficient space identification method, device, equipment and product

Abstract

The application discloses an idle and inefficient space identification method, device, equipment and product, and relates to the technical field of land resource planning and urban renewal. The method considers population distribution changes in different time periods, realizes quantitative, spatial and accurate identification of idle and inefficient land use by performing spatial intersection analysis on distribution data of different land use types and population distribution data in corresponding time periods, can greatly reduce the investigation and statistics cost of idle and inefficient space, improve the accuracy and efficiency of identification, improve the scientificity of land resource planning, existing building management and urban renewal decision, and is helpful to promote high-quality urban development.

Description

Technical Field

[0001] This application relates to the field of land resource planning and urban renewal technology, and in particular to a method, apparatus, equipment and product for identifying idle and inefficient spaces. Background Technology

[0002] Currently, the identification of idle and inefficient land use is a crucial part of land resource planning and urban renewal. Existing methods for identifying idle and inefficient land use still rely on traditional approaches, primarily data collection, site surveys, and questionnaires. These methods rely heavily on manpower, experience analysis, and inductive synthesis, lacking quantitative, spatial, and precise technologies. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, equipment and product for identifying idle and inefficient spaces, so as to achieve quantitative, spatial and accurate identification of idle and inefficient land.

[0004] To achieve the above objectives, this application provides the following solution.

[0005] Firstly, this application provides a method for identifying idle and inefficient spaces, including: Obtain population location service data and land use status distribution data for the study area; Population location service data is divided into time periods to obtain population distribution data for different time periods; different time periods include residential time, working time, and consumption time. The land use status distribution data is classified according to land use type to obtain the distribution data of different land use types; the different land use types include residential land, industrial land and commercial service land. Spatial intersection analysis is performed on population distribution data and residential land distribution data during residential periods to generate data on the distribution of idle and inefficient residential land. Spatial intersection analysis is performed on population distribution data and industrial land distribution data during working hours to generate data on the distribution of idle and inefficient industrial land. Spatial intersection analysis is performed on population distribution data and commercial service land distribution data during consumption periods to generate data on the distribution of idle and inefficient commercial service land.

[0006] Optionally, population location service data and land use distribution data for the study area may be obtained, followed by: The population location service data is preprocessed to generate 24-hour population distribution data; Based on 24-hour population distribution data, create a 24-hour population distribution "fishnet" file using an 80m×80m grid. The land use status distribution data is preprocessed to generate a land use status distribution map; the 24-hour population distribution file and the land use status distribution map have the same projection coordinate system.

[0007] Optionally, the residential period is from 0:00 to 8:00, the working period is from 8:00 to 18:00, and the consumption period is from 18:00 to 24:00.

[0008] Optionally, spatial intersection analysis can be performed on population distribution data and residential land distribution data during different residential periods to generate data on the distribution of idle and inefficient residential land, specifically including: Based on population distribution data during residential periods and distribution data of residential land, plots of residential land that meet the first preset conditions are identified as idle and inefficient residential land; the distribution data of idle and inefficient residential land refers to the distribution data of different types of idle and inefficient residential land. The first preset condition is that the number of people living in a residential land parcel during the residential period is less than 20% of the theoretical population of the residential land parcel.

[0009] Optionally, spatial intersection analysis can be performed on population distribution data during working hours and industrial land distribution data to generate data on the distribution of idle and inefficient industrial land, specifically including: Based on population distribution data during working hours and industrial land distribution data, plots of land that meet the second preset condition are identified as idle and inefficient industrial land. The second preset condition is that the number of people on an industrial land parcel during working hours is less than 20% of the theoretical number of people on the industrial land parcel.

[0010] Optionally, spatial intersection analysis can be performed on population distribution data and commercial service land distribution data during consumption periods to generate data on the distribution of idle and inefficient commercial service land, specifically including: Based on population distribution data during consumption periods and distribution data of commercial and service land, land parcels that meet the third preset condition are identified as idle and inefficient commercial and service land. The third preset condition is that the population of a commercial land parcel during the consumption period is less than 20% of the theoretical population of the commercial land parcel.

[0011] Alternatively, the theoretical number of people allowed on a plot of land can be calculated as follows: ; in, Let i be the theoretical number of people in the j-th plot of the i-th land use type, i=1,2,3. When i=1,2,3, the i-th land use type represents residential land, industrial land and commercial service land respectively. Let be the total population for the time period corresponding to the i-th land use type. Let i be the total area of ​​the i-th land use type within the study area. Let be the area of ​​the j-th plot of land of the i-th land use type. The symbol for rounding up.

[0012] Secondly, this application provides an idle and inefficient space identification device, which applies the above-mentioned idle and inefficient space identification method, and the idle and inefficient space identification device includes: The data acquisition module is used to acquire population location service data and land use status distribution data for the study area; The time period segmentation module is used to segment population location service data into time periods to obtain population distribution data for different time periods; different time periods include residential time periods, working time periods, and consumption time periods; The classification module is used to classify the current land use distribution data according to land use type, and obtain the distribution data of different land use types; the different land use types include residential land, industrial land and commercial service land; The first spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and residential land distribution data during residential periods to generate data on the distribution of idle and inefficient residential land. The second spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and industrial land distribution data during working hours to generate data on the distribution of idle and inefficient industrial land. The third spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and commercial service land distribution data during consumption periods to generate data on the distribution of idle and inefficient commercial service land.

[0013] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for identifying idle and inefficient space.

[0014] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for identifying idle and inefficient space.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects.

[0016] This application provides a method, apparatus, equipment, and product for identifying idle and inefficient spaces. This application takes into account changes in population distribution over different time periods. By performing spatial intersection analysis on the distribution data of different land use types and the population distribution data of the corresponding time periods, it achieves quantitative, spatial, and accurate identification of idle and inefficient land. This can significantly reduce the cost of investigating idle and inefficient spaces, improve the accuracy and efficiency of identification, enhance the scientific nature of land resource planning, existing building management, and urban renewal decisions, and help promote high-quality urban development. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for identifying idle and inefficient spaces according to an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of a method for identifying idle and inefficient spaces provided in an embodiment of this application.

[0020] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] In one exemplary embodiment, a method for identifying idle and inefficient spaces is provided, such as... Figure 1 As shown, it includes the following steps 101-106.

[0024] Step 101: Obtain population location service data and land use status distribution data for the study area.

[0025] Step 102: Divide the population location service data into time periods to obtain population distribution data for different time periods; different time periods include residential time, working time, and consumption time.

[0026] Step 103: Classify the current land use distribution data according to land use type to obtain distribution data for different land use types; the different land use types include residential land, industrial land and commercial service land.

[0027] Step 104: Perform spatial intersection analysis on the population distribution data and residential land distribution data during the residential period to generate data on the distribution of idle and inefficient residential land.

[0028] Step 105: Perform spatial intersection analysis on the population distribution data and industrial land distribution data during working hours to generate data on the distribution of idle and inefficient industrial land.

[0029] Step 106: Perform spatial intersection analysis on the population distribution data and commercial service land distribution data during consumption periods to generate idle and inefficient commercial service land distribution data.

[0030] Implementing steps 101-106 above can achieve quantitative, spatial, and precise identification of idle and inefficient land, which can significantly reduce the cost of investigating idle and inefficient spaces, improve the accuracy and efficiency of identification, enhance the scientific nature of land resource planning, existing building management, and urban renewal decision-making, and help promote high-quality urban development.

[0031] like Figure 2 As shown, data preprocessing is also performed during the acquisition of population location service data and land use distribution data. Specifically, this application preprocesses the population location service data to generate 24-hour population distribution data. Land use distribution data is also preprocessed to generate residential, industrial, and commercial land distribution data. Through the acquisition and preprocessing of population location service data and land use distribution data, the standardization of the required data is ensured, which helps to eliminate data inconsistencies and improves data reliability.

[0032] In another exemplary embodiment, step 102 above is followed by: The population location service data is preprocessed to generate 24-hour population distribution data.

[0033] Based on 24-hour population distribution data, create a 24-hour population distribution "fishnet" file using an 80m×80m grid.

[0034] The land use status distribution data is preprocessed to generate a land use status distribution map; the 24-hour population distribution file and the land use status distribution map have the same projection coordinate system.

[0035] Based on the preprocessed data, idle and inefficient land use can be identified, and idle and inefficient residential, industrial, and commercial service land plots can be accurately generated in 80m×80m grid units. It is applicable to the identification of spatial elements such as idle and inefficient public service land, park green space, and existing buildings.

[0036] In another exemplary embodiment, the process of preprocessing population location service data includes the following steps S11-S13.

[0037] Step S11: Obtain population location service data, including grid points, coordinate values, 24-hour population information, etc.

[0038] Step S12: Preprocess the population location service data to generate 24-hour population distribution data.

[0039] Step S13: Create a 24-hour population distribution fishing net file according to an 80m×80m grid.

[0040] In another exemplary embodiment, the above-mentioned residential period is 0:00-8:00, the working period is 8:00-18:00, and the consumption period is 18:00-24:00. The above-mentioned step 102 can be implemented by the following steps S21-S23.

[0041] Step S21: Preprocess the 24-hour population distribution fishing net file, extract the population distribution data from 0:00 to 8:00, and generate a population distribution map for the residential period; Step S22: Extract population distribution data from 8:00 to 18:00 and generate a population distribution map for working hours; Step S23: Extract population distribution data from 18:00 to 24:00 and generate a population distribution map for the consumption period.

[0042] In another exemplary embodiment, step 103 above can be implemented using the following steps S31-S33.

[0043] Step S31: Obtain land use status distribution data, including layer information such as residential, industrial, and commercial land; Step S32: Preprocess the land use status distribution data to generate a land use status distribution map with the same projection coordinate system as the 24 population distribution fishing net; Step S33: Preprocess the land use status distribution map to extract and generate distribution data for residential land, industrial land, and commercial land respectively.

[0044] In another exemplary embodiment, the above-mentioned data on the distribution of idle and inefficient residential land is the distribution data of different idle and inefficient residential land.

[0045] As a specific implementation method, in step 104 above, the plots of residential land that meet the first preset condition are identified as idle and inefficient residential land based on the population distribution data during the residential period and the distribution data of residential land. The first preset condition is that the number of people in the plots of residential land during the residential period is less than 20% of the theoretical population of the plots of residential land.

[0046] In another exemplary embodiment, step 104 above can be implemented using the following steps S41-S42.

[0047] Step S41: Spatially intersect the population distribution during the residential period with the residential land distribution layer to generate a residential land distribution map that includes population information during the residential period; Step S42: Extract the population distribution data of the residential period. Plots with a population value greater than or equal to 0 and less than 20% of the theoretical population are considered as idle and inefficient residential land, and generate a distribution map of idle and inefficient residential land.

[0048] In another exemplary embodiment, the above-mentioned data on the distribution of idle and inefficient industrial land is the distribution data of different idle and inefficient industrial land.

[0049] As a specific implementation method, in step 105 above, the land parcels that meet the second preset condition are identified as idle and inefficient industrial land parcels based on the population distribution data during working hours and the distribution data of industrial land. The second preset condition is that the number of people in the land parcels of the industrial land type during working hours is less than 20% of the theoretical number of people in the land parcels of the industrial land type.

[0050] In another exemplary embodiment, step 105 above can be implemented using the following steps S51-S52.

[0051] Step S51: Spatially intersect the population distribution data during working hours with the industrial land distribution layer (i.e., the distribution data of industrial land) to generate an industrial land distribution map that includes population information during working hours; Step S52: Extract the plots of land where the population during working hours is greater than or equal to 0 and less than 20% of the theoretical population as idle and inefficient industrial land, and generate a distribution map of idle and inefficient industrial land.

[0052] In another exemplary embodiment, the above-mentioned data on the distribution of idle and inefficient industrial land is the distribution data of different idle and inefficient industrial land.

[0053] As a specific implementation method, in step 106 above, the land parcels of the commercial service land type that meet the third preset condition are identified as idle and inefficient commercial service land based on the population distribution data during the consumption period and the distribution data of commercial service land. The third preset condition is that the population of the commercial service land type land parcel during the consumption period is less than 20% of the theoretical population of the commercial service land type land parcel.

[0054] In another exemplary embodiment, step 106 above can be implemented using the following steps S61-S62.

[0055] Step S61: Spatially intersect the population distribution data during the consumption period with the commercial service land distribution layer (i.e., the distribution data of commercial service land) to generate a commercial service land distribution map that includes population information during the consumption period; Step S62: Extract land parcels with a population of 0 or more but less than 20% of the theoretical population during the consumption period as idle and inefficient commercial service land, and generate a distribution map of idle and inefficient commercial service land.

[0056] In another exemplary embodiment, the theoretical number of people on the plot is calculated as follows: in, Let i be the theoretical number of people in the j-th plot of the i-th land use type, i=1,2,3. When i=1,2,3, the i-th land use type represents residential land, industrial land and commercial service land respectively. Let be the total population for the time period corresponding to the i-th land use type. Let i be the total area of ​​the i-th land use type within the study area. Let be the area of ​​the j-th plot of land of the i-th land use type. The symbol for rounding up.

[0057] In another exemplary embodiment, in step S12 above, a geographic coordinate system Shapefile (vector geospatial data) format file is generated using GIS (Geographic Information System) software based on the coordinate values ​​of the population location service data. The geographic coordinate system Shapefile is then converted into a population distribution Shapefile in a specified projection coordinate system using a GIS projection tool. In step S13 above, an 80m × 80m fishing net file is created based on the boundaries of the population distribution Shapefile (the boundaries refer to the geographic boundary limits of the area covered by the population distribution Shapefile, including the northernmost latitude, southernmost latitude, easternmost longitude, and westernmost longitude). Using a GIS spatial connection tool, the fishing net is connected to the population distribution Shapefile, and 24-hour population data is associated to generate a 24-hour population distribution fishing net Shapefile.

[0058] In steps S21-S23 above, based on the 24-hour population distribution fishing net Shp file, add fields for "Number of people during residential time", "Number of people during working time", and "Number of people during consumption time". According to 0-8:00, 8-18:00 and 18-24:00, respectively, summarize the population values ​​of residential time, working time and consumption time for each fishing net grid, and generate population distribution Shp files for residential time, working time and consumption time.

[0059] In step 103 above, obtain the current land use distribution data, including Shp layers for residential, industrial, and commercial land. If necessary, use GIS coordinate transformation and projection tools to convert the current land use Shp file to the same projection coordinate system as the population distribution Shp file, generating Shp files for residential, industrial, and commercial land distribution.

[0060] In step 104 above, a spatial intersection analysis is performed between the residential time period population distribution Shhp file and the residential land distribution Shhp file to generate a residential land distribution Shhp file containing the population figures for each residential time period. A "Residential Land Efficiency" field is added, using the average population count per grid period as the benchmark. When the total grid population value is 0, the field is assigned the value "Idle Residential Land"; when 0 < total grid population value ≤ average grid population (theoretical number) × 0.2, the field is assigned the value "Inefficient Residential Land". An idle and inefficient residential land distribution map is generated and visualized in a chart.

[0061] In step 105 above, a spatial intersection analysis is performed between the working-hour population distribution Shhp file and the industrial land distribution Shhp file to generate an industrial land distribution Shhp file containing the working-hour population. An "Industrial Land Efficiency" field is added, using the average number of grid residents during the working hours as a benchmark. When the total grid population is 0, the field is assigned the value "Idle Industrial Land"; when 0 < total grid population ≤ average grid population (theoretical number) × 0.2, the field is assigned the value "Inefficient Industrial Land". An idle and inefficient industrial land distribution map is generated and visualized in a chart.

[0062] In step 106 above, a spatial intersection analysis is performed between the population distribution shapefile during the consumption period and the commercial service land distribution shapefile to generate a commercial service land distribution shapefile containing the population during the consumption period. A "Commercial Service Land Efficiency" field is added, using the average population of each grid during the consumption period as a benchmark. When the total population value of each grid is 0, the field is assigned the value "Idle Commercial Service Land"; when 0 < total population value of each grid ≤ average population value of each grid (theoretical number of people) × 0.2, the field is assigned the value "Inefficient Commercial Service Land". An idle and inefficient commercial service land distribution map is generated and visualized in a chart.

[0063] According to the specific embodiments provided in this application, this application has the following technical effects.

[0064] This application ensures the standardization of required data through the acquisition and preprocessing of population location service data, helping to eliminate data inconsistencies and improve data reliability. It also establishes a standardized technical process for identifying idle and inefficient land use, capable of accurately generating 80m×80m grid units for idle and inefficient residential, industrial, and commercial service land parcels. This method is applicable to the identification of spatial elements such as idle and inefficient public service land, parks and green spaces, and existing buildings. These methods can significantly reduce the cost of identifying idle and inefficient spaces, improve the accuracy and efficiency of identification, enhance the scientific nature of land resource planning, existing building management, and urban renewal decisions, and contribute to promoting high-quality urban development.

[0065] This application does not rely on massive and complex data. The data requirements are simple, efficient, and without consistency issues. It only needs to obtain population location service data (including grid points, coordinate values, and 24-hour population) from a population mobile data service provider. By performing spatial intersection analysis with existing land use data, it can identify idle and inefficient land parcels in 80m×80m grid units. If the population mobile data service provider provides higher-precision population location service data, it can achieve higher-precision identification of idle and inefficient land parcels. If spatial element data such as existing buildings can be obtained, the above method can be used to calculate and generate spatial element distribution data such as idle and inefficient buildings. This application can be implemented using software such as ArcGIS and QGIS, or through programming such as Arcpy and Python. It is easy to operate, has a rigorous process, is simple and efficient, easy to replicate and promote, and has a wide range of application scenarios. It can greatly reduce the cost of investigating idle and inefficient spaces, improve the accuracy and efficiency of identification, enhance the scientific nature of land resource planning, existing building management, and urban renewal decision-making, and contribute to high-quality urban development.

[0066] Based on the same inventive concept, this application also provides an idle and inefficient space identification device for implementing the aforementioned idle and inefficient space identification method. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more idle and inefficient space identification device embodiments provided below can be found in the limitations of the idle and inefficient space identification method described above, and will not be repeated here.

[0067] In one exemplary embodiment, an idle and inefficient space identification device is provided, comprising: The data acquisition module is used to acquire population location service data and land use status distribution data for the study area; The time period segmentation module is used to segment population location service data into time periods to obtain population distribution data for different time periods; different time periods include residential time periods, working time periods, and consumption time periods; The classification module is used to classify the current land use distribution data according to land use type, and obtain the distribution data of different land use types; the different land use types include residential land, industrial land and commercial service land; The first spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and residential land distribution data during residential periods to generate data on the distribution of idle and inefficient residential land. The second spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and industrial land distribution data during working hours to generate data on the distribution of idle and inefficient industrial land. The third spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and commercial service land distribution data during consumption periods to generate data on the distribution of idle and inefficient commercial service land.

[0068] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a method for identifying idle and inefficient storage space.

[0069] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0070] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0071] In one exemplary embodiment, a computer program product is provided, comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps in the above-described method embodiments.

[0072] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0073] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0074] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0075] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying idle and inefficient spaces, characterized in that, include: Obtain population location service data and land use status distribution data for the study area; Population location service data is divided into time periods to obtain population distribution data for different time periods; different time periods include residential time, working time, and consumption time. The land use status distribution data is classified according to land use type to obtain the distribution data of different land use types; the different land use types include residential land, industrial land and commercial service land. Spatial intersection analysis is performed on population distribution data and residential land distribution data during residential periods to generate data on the distribution of idle and inefficient residential land. Spatial intersection analysis is performed on population distribution data and industrial land distribution data during working hours to generate data on the distribution of idle and inefficient industrial land. Spatial intersection analysis is performed on population distribution data and commercial service land distribution data during consumption periods to generate data on the distribution of idle and inefficient commercial service land.

2. The method for identifying idle and inefficient spaces according to claim 1, characterized in that, Acquire population location service data and land use status distribution data for the study area, followed by: The population location service data is preprocessed to generate 24-hour population distribution data; Based on 24-hour population distribution data, create a 24-hour population distribution "fishnet" file using an 80m×80m grid. The land use status distribution data is preprocessed to generate a land use status distribution map; the 24-hour population distribution file and the land use status distribution map have the same projection coordinate system.

3. The method for identifying idle and inefficient spaces according to claim 1, characterized in that, The hours for residence are from 0:00 to 8:00, the hours for work are from 8:00 to 18:00, and the hours for consumption are from 18:00 to 24:

00.

4. The method for identifying idle and inefficient spaces according to claim 1, characterized in that, Spatial intersection analysis is performed on population distribution data and residential land distribution data during different residential periods to generate data on the distribution of idle and inefficient residential land, specifically including: Based on population distribution data during the residential period and distribution data of residential land, the plots of residential land that meet the first preset condition are identified as idle and inefficient residential land. The first preset condition is that the number of people living in a residential land parcel during the residential period is less than 20% of the theoretical population of the residential land parcel.

5. The method for identifying idle and inefficient spaces according to claim 1, characterized in that, Spatial intersection analysis is performed on population distribution data during working hours and industrial land distribution data to generate data on the distribution of idle and inefficient industrial land, specifically including: Based on population distribution data during working hours and industrial land distribution data, plots of land that meet the second preset condition are identified as idle and inefficient industrial land. The second preset condition is that the number of people on an industrial land parcel during working hours is less than 20% of the theoretical number of people on the industrial land parcel.

6. The method for identifying idle and inefficient spaces according to claim 1, characterized in that, Spatial intersection analysis is performed on population distribution data and commercial service land distribution data during consumption periods to generate data on the distribution of idle and inefficient commercial service land, specifically including: Based on population distribution data during consumption periods and distribution data of commercial and service land, land parcels that meet the third preset condition are identified as idle and inefficient commercial and service land. The third preset condition is that the population of a commercial land parcel during the consumption period is less than 20% of the theoretical population of the commercial land parcel.

7. The method for identifying idle and inefficient spaces according to any one of claims 4-6, characterized in that, The theoretical number of people allowed on the land parcel is calculated as follows: ; in, Let i be the theoretical number of people in the j-th plot of the i-th land use type, i=1,2,3; Let be the total population for the time period corresponding to the i-th land use type. Let i be the total area of ​​the i-th land use type within the study area. Let be the area of ​​the j-th plot of land of the i-th land use type. The symbol for rounding up.

8. A device for identifying idle and inefficient spatial information, characterized in that, The idle and inefficient space identification device applies the idle and inefficient space identification method according to any one of claims 1-7, and the idle and inefficient space identification device comprises: The data acquisition module is used to acquire population location service data and land use status distribution data for the study area; The time period segmentation module is used to segment population location service data into time periods to obtain population distribution data for different time periods; different time periods include residential time periods, working time periods, and consumption time periods; The classification module is used to classify the current land use distribution data according to land use type, and obtain the distribution data of different land use types; the different land use types include residential land, industrial land and commercial service land; The first spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and residential land distribution data during residential periods to generate data on the distribution of idle and inefficient residential land. The second spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and industrial land distribution data during working hours to generate data on the distribution of idle and inefficient industrial land. The third spatial intersection analysis module is used to perform spatial intersection analysis on population distribution data and commercial service land distribution data during consumption periods to generate data on the distribution of idle and inefficient commercial service land.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the idle and inefficient space identification method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the idle and inefficient space identification method according to any one of claims 1-7.

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