Urban contraction intelligent diagnosis and visualization system based on noctilucent remote sensing time sequence data

Abstract

The invention relates to a city contraction intelligent diagnosis and visualization system based on noctilucent remote sensing time sequence data, and the system comprises a data input module which is used for inputting the collected noctilucent remote sensing data of a target city to the system; the calculation and analysis module is composed of a basic calculation unit and a statistical test unit, and the basic calculation unit is used for calculating the change rate of the noctilucent remote sensing data input into the system; the statistical test unit can detect the overall monotonous trend of the noctilucent remote sensing data and verify the significance of the median of the change rate; the type judgment module is used for judging whether the target city belongs to a shrinkage city or not and judging the type of the shrinkage city; and the visual output module is used for automatically generating various contracted city analysis charts and outputting a contracted city structured analysis report. According to the method, the problems of insufficient long-term trend analysis, low analysis reliability, non-visual result presentation, single judgment standard and the like in the existing urban shrinkage analysis technology are solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of urban data analysis, and specifically relates to an intelligent diagnosis and visualization system for urban shrinkage based on single night light remote sensing time series data, through multiple statistical significance tests and scientific classification decision systems. BACKGROUND

[0002] Currently, urban shrinkage has become an important problem in the process of global urbanization, which is manifested as population loss, economic recession and spatial shrinkage. Traditional research mainly relies on population census data (such as statistical yearbooks) for urban shrinkage analysis, but such data has obvious limitations: first, the spatio-temporal resolution is low, usually with an annual update cycle and only covering administrative unit level, making it difficult to capture fine dynamic changes within the city; second, data acquisition is lagging, making it impossible to reflect the latest trends in urban development in a timely manner. In recent years, night light remote sensing data has shown a linear positive correlation with population and socio-economic data. Night light remote sensing data is increasingly being introduced into the field of urban shrinkage research due to its high spatio-temporal resolution (such as DMSP / OLS and NPP / VIIRS data, which can provide annual or even monthly coverage) and its long time series and full coverage characteristics.

[0003] However, the existing technology based on night light remote sensing time series data for urban shrinkage research still has the following shortcomings: (1) the patent with publication number CN111506879B uses multi-source data fusion, but only performs static point analysis, which cannot identify long-term shrinkage dynamics; (2) the patent CN112818747A relies on simple threshold determination, lacks statistical significance verification, and is difficult to distinguish between random fluctuations and real situations; (3) the patent CN119273013A focuses on population spatial distribution visualization and does not solve the problem of shrinkage feature recognition in the time dimension. In addition, the existing patent analysis process and visualization are disconnected, usually only outputting simple charts or statistical tables, which cannot visually display the spatio-temporal characteristics of shrinkage (such as continuous decline intervals or local turning points). Moreover, it ignores the scientific classification of shrinkage city types (such as the distinction between continuous shrinkage and frequent shrinkage), making it difficult for policy makers to take differentiated measures for different decline patterns. Therefore, there is an urgent need for an intelligent diagnosis and visualization method for urban shrinkage that supports integrated statistical testing and automatically outputs visual results. SUMMARY

[0004] In view of the above, the present application provides an intelligent diagnosis and visualization system for urban shrinkage based on night light remote sensing time series data, aiming to solve the following problems in existing urban shrinkage analysis technology:

[0005] 1. Lack of long-term trend analysis: only static point analysis is established, lacking long-term trend analysis;

[0006] 2. The analysis method is not reliable: lack of systematic statistical significance test framework, unable to distinguish between random fluctuations and real shrinkage problems;

[0007] 3. The results are not intuitive: the visualization function is weak, and it is difficult to clearly show the spatial and temporal characteristics of shrinkage;

[0008] 4. The determination standard is single: no scientific classification system of shrinkage types is established.

[0009] In order to achieve the above purposes, the present application adopts the following technical solutions:

[0010] The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data comprises a data input module for inputting the collected night light remote sensing data of the target city into the system;

[0011] The calculation and analysis module is composed of a basic calculation unit and a statistical test unit, the basic calculation unit is used to calculate the change rate of the night light remote sensing data input into the system; the statistical test unit can detect the overall monotonic trend of the night light remote sensing data and verify the significance of the median of the change rate;

[0012] The type determination module is used to determine whether the target city belongs to a shrinking city, and to determine the type of the shrinking city;

[0013] The visualization output module is used to automatically generate various analysis charts of shrinking cities and output structured analysis reports of shrinking cities.

[0014] Further, the night light remote sensing data requires at least Excel format containing "Year" and "Value" column fields, corresponding to the night light remote sensing values of different years of the target city.

[0015] Further, the basic calculation unit adopts the first-order difference method to calculate the annual relative change rate of the night light remote sensing data input into the system, and the change rate threshold for determining whether it is a shrinking city is-1% by default.

[0016] Further, the statistical test unit adopts Mann-Kendall trend test method to detect the overall monotonic trend of the night light remote sensing data;

[0017] Wilcoxon signed rank test method is used to verify the significance of the median of the change rate; t-test method is used to test whether the mean of the change rate is significantly lower than the change rate threshold;

[0018] The proportion test method is used to evaluate the significance of the proportion of the declining years, and the local MK test method is used to identify the local significant decline interval.

[0019] Further, the type determination module determines that the target city can be divided into shrinking cities and non-shrinking cities, wherein the shrinking cities can be further divided into continuous shrinking cities (or I-type shrinking cities) and frequent shrinking cities (or II-type shrinking cities) according to the determination standard. The determination standard of the continuous shrinking city is that the change rate of the night light remote sensing data significantly decreases for at least three consecutive years, and needs to pass the statistical test of any one test mode of the statistical test unit. The determination standard of the frequent shrinking city is that the proportion of years of decrease in the change rate of the night light remote sensing data is more than 1 / 3 of the total number of years of the statistical system input night light remote sensing data, and needs to pass the statistical test of any one test mode of the statistical test unit. If any of the above determination standards is not met, it can be determined as a non-shrinking city.

[0020] Further, the visualization output module can automatically generate a shrinking city analysis chart including a time series trend chart, an annual change rate column chart, a statistical test result comparison chart and a local MK test interval chart; and the output shrinking city structured analysis report includes key statistical indicators, and clearly marks the shrinking type and determination basis.

[0021] The present application also includes other components that can be used normally, which are conventional means in the art, and in addition, the devices or components not defined in the present application all adopt the existing technology in the art.

[0022] The beneficial effects of the present application are as follows:

[0023] 1. Long-time series dynamic analysis technology: The present application overturns the existing city shrinking analysis method, and innovatively develops a long-time series analysis algorithm of more than 10 years suitable for night light remote sensing data, which effectively solves the technical bottleneck of insufficient spatio-temporal resolution of traditional annual census data by processing long-time series night light remote sensing data.

[0024] 2. Four-fold statistical verification system: The present application innovatively constructs a combined verification framework of Mann-Kendall trend test, Wilcoxon sign rank test, t test and local MK test, which significantly improves the reliability of city shrinking determination, integrates multiple statistical test methods, and provides comprehensive significance verification.

[0025] 3. Spatio-temporal integrated visualization: By integrating local MK test and time series labeling technology, the present application innovatively realizes the synchronous visualization analysis of spatial dimension (local abnormal region) and time dimension (long-term trend change), automatically generates multiple professional analysis charts, and intuitively displays the spatio-temporal characteristics of shrinking.

[0026] 4. Scientific classification and decision-making system: A groundbreaking urban shrinkage classification system based on dual significance criteria has been established. Through a composite judgment model of Type I shrinkage (continuous shrinkage) cities and Type II shrinkage (frequent shrinkage) cities, it provides a quantitative basis for differentiated policy formulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a system structure diagram of the urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data in the present invention;

[0028] Figure 2 This is a flowchart of the workflow of the urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data in the present invention;

[0029] Figure 3 This is a diagram of an analysis chart interface of a shrinking city in Zhoukou City in the urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data in an embodiment;

[0030] Figure 4 This is a diagram of the Zhoukou City shrinking city analysis report interface of the urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data in the embodiment. DETAILED DESCRIPTION

[0031] The technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0032] It should be noted that the terms "upper", "lower", "front", "back", "inside", "outside" and so on indicating directions or positional relationships are based on the drawings and are only for the convenience of description.

[0033] Example

[0034] like Figure 1 As shown, the urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data includes a data input module, which supports the input of night light remote sensing data in Excel format containing at least "Year" and "Value" column fields into the system, corresponding to the night light remote sensing values ​​of the target city (taking Zhoukou City as an example) in different years.

[0035] The calculation and analysis module consists of a basic calculation unit and a statistical test unit. The basic calculation unit uses the first-order difference method to calculate the year-on-year relative change rate of the night light remote sensing data input into the system. The calculation formula is

[0036]

[0037] Where: CR t Indicates the rate of change of night light remote sensing data; X trepresents the night light remote sensing value of the target city in a certain year; X t+1 represents the night light remote sensing value of the target city in the next year of a certain year;

[0038] The default decline threshold of the change rate of the shrinking city is -1%.

[0039] The statistical test unit adopts a Mann-Kendall trend test method to detect the overall monotonic trend of the night light remote sensing data.

[0040] A Wilcoxon signed rank test method is used to verify the significance of the median of the change rate.

[0041] A t-test method is used to test whether the mean of the change rate is significantly lower than the change rate decline threshold.

[0042] A proportion test method is used to evaluate the significance of the proportion of the decline years.

[0043] A local MK test method is used to identify the local significant decline interval.

[0044] The type determination module determines that the type of the target city can be divided into shrinking cities and non-shrinking cities, and the shrinking cities can be divided into type I shrinking (continuous shrinking) cities and type II shrinking (frequent shrinking) cities according to the determination standard.

[0045] The determination standard of the continuous shrinking type city is that the change rate of the night light remote sensing data is significantly decreased for at least 3 consecutive years, and needs to pass the statistical test of any one of the statistical test units.

[0046] The determination standard of the frequent shrinking type city is that the proportion of the years of the change rate of the night light remote sensing data is more than 1 / 3 of the total number of years of the system input night light remote sensing data, and needs to pass the statistical test of any one of the statistical test units.

[0047] If any of the above determination standards is not met, it can be determined as a non-shrinking city.

[0048] The visualization output module can automatically generate the shrinking city analysis chart, which includes a time series trend chart (annotating the significant decline interval), a year change rate column chart, a statistical test result comparison chart and a local MK test interval chart.

[0049] The output shrinking city structured analysis report includes key statistical indicators (p value, change rate, etc.), and clearly labels the shrinking type and the determination basis.

[0050] The software programs involved in each module of the city shrinking intelligent diagnosis and visualization system based on night light remote sensing time series data in the application adopt the prior art, which will not be described here.

[0051] As Figure 2 shown, the workflow of the present application is as follows:

[0052] 1) Data preparation phase:

[0053] Collect the night light remote sensing data of the target city;

[0054] Organize it into Excel format according to the specification.

[0055] 2) System running phase:

[0056] Import the data file;

[0057] Automatically perform the annual change rate calculation;

[0058] Run various statistical tests in parallel;

[0059] Comprehensive determination of contraction type.

[0060] 3) Result output phase:

[0061] Generate visual charts;

[0062] Output the analysis report;

[0063] Store the intermediate calculation results.

[0064] Taking Zhoukou City as an example:

[0065] 1) Data preparation:

[0066] File name: Zhoukou City.xlsx;

[0067] Data content: Annual night light DN values from 1995 to 2020.

[0068] 2) System running:

[0069] Import the data file;

[0070] Set parameters: decline threshold -1%, significance level 0.05, sliding window length 3 years;

[0071] Perform the analysis process.

[0072] 3) Result output:

[0073] Chart output: Zhoukou City_Analysis (png / pdf), as Figure 3 shown;

[0074] Report output: Zhoukou City_Report (png / pdf), as Figure 4 shown;

[0075] Comprehensive judgment: Zhoukou is a type of shrinking city (continuous decline from 2006 to 2009).

[0076] The technical solution of the present application is not limited to the above specific embodiments, and many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. Any technical modification made within the spirit and principles of the present application falls within the scope of the present application.

Claims

1. An intelligent urban shrinkage diagnosis and visualization system based on night light remote sensing time series data, characterized by: It includes a data input module for inputting the collected night light remote sensing data of the target city into the system; A calculation and analysis module, consisting of a basic calculation unit and a statistical test unit, wherein the basic calculation unit is used to calculate the rate of change of the night light remote sensing data input into the system; The statistical test unit is capable of detecting the overall monotonic trend of the night light remote sensing data and verifying the significance of the median of the rate of change; A type determination module is used to determine whether the target city is a shrinking city and the type of shrinking city; The visualization output module is used to automatically generate various shrinking city analysis charts and output shrinking city structured analysis reports.

2. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to claim 1 is characterized by: The night light remote sensing data is required to be in Excel format containing at least "Year" and "Value" column fields, corresponding to the night light remote sensing values ​​of the target city in different years.

3. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to claim 2 is characterized by: The basic calculation unit uses the first-order difference method to calculate the year-on-year relative change rate of the night light remote sensing data input into the system; the change rate decrease threshold for determining whether it is a shrinking city is -1%.

4. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to claim 3 is characterized by: The statistical test unit adopts the Mann-Kendall trend test method to detect the overall monotonic trend of the night light remote sensing data, and adopts the Wilcoxon signed rank test method to verify the significance of the median of the change rate.

5. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to claim 4 is characterized by: The statistical test unit can also use a t-test to test whether the mean of the change rate is significantly lower than the change rate decline threshold, use a proportion test to evaluate the significance of the proportion of declining years, and use a local MK test to identify local significant decline intervals.

6. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to claim 5 is characterized by: The type determination module determines that the target city can be divided into shrinking cities and non-shrinking cities, wherein shrinking cities can be further divided into continuously shrinking cities and frequently shrinking cities according to the determination criteria.

7. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to claim 6 is characterized by: The criteria for determining a continuously shrinking city are: a significant decrease in the rate of change of night light remote sensing data for at least three consecutive years, and the city needs to pass the statistical test of any one of the test methods of the statistical test unit.

8. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to claim 6 is characterized by: The criteria for determining a frequently shrinking city are: the proportion of years in which the rate of change of night light remote sensing data decreases exceeds 1 / 3 of the total number of years of night light remote sensing data input into the system, and the city needs to pass the statistical test of any one of the test methods of the statistical test unit.

9. The urban shrinkage intelligent diagnosis and visualization system based on night light remote sensing time series data according to any one of claims 1 to 8, characterized in that: The visualization output module can automatically generate shrinking city analysis charts including time series trend charts, annual change rate bar charts, statistical test result comparison charts and local MK test interval charts; the output shrinking city structured analysis report includes key statistical indicators and clearly marks the shrinkage type and judgment basis.

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