Big Data-Based Land Use Change Monitoring Method

Abstract

The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method for monitoring land use changes based on big data. The method includes collecting each piece of data and land utilization rate data for each region, obtaining the influence degree of each piece of data on the land utilization rate, dividing each region into various region combinations, obtaining the weight adjustment factor for each region combination according to the influence degree of each piece of data on the land utilization rate and the correlation of each piece of data in each region combination, obtaining the adjusted weight value for each region combination according to the weight adjustment factor for each region combination and the initial weight of each region combination, obtaining the Moran index for each region at the latest sampling moment according to the land resource utilization rate of each region at the latest sampling moment and the adjusted weight value of each region combination, and further obtaining the abnormal region. The present invention improves the accuracy of identifying the abnormal region.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method for monitoring land use changes based on big data. Background Art

[0002] With the continuous growth of the global population and the acceleration of the urbanization process, the development and utilization of land resources are facing unprecedented pressures. Land use changes not only affect the ecological environment but also have a profound impact on social and economic development, sustainable management, and resource allocation. Traditional land use monitoring methods usually rely on manual surveys or low-frequency remote sensing data collection, making it difficult to reflect changes in real time and comprehensively, and being less efficient in a dynamic and complex natural environment. Therefore, methods for monitoring land use changes based on big data have received increasing attention, and related research and applications have shown important value in fields such as agriculture, environmental protection, and urban planning. These methods provide real, reliable, and timely data support for managers, promoting the achievement of sustainable development goals and driving the scientific and intelligent process of land use management.

[0003] The patent document currently authorized and announced as CN108564520B proposes a method for copyright authentication of GIS vector data based on the Moran index. By taking advantage of the stable spatial autocorrelation characteristics of geographical elements in GIS vector data and the feature that zero watermark uses important features of the original work to construct the original watermark signal, a zero watermark is constructed through the Moran index for copyright authentication of GIS vector data.

[0004] The Moran index is an index that describes the autocorrelation between land use rate and land spatial location, and can judge whether there are abnormal or unreasonable phenomena in land use in each region according to the correlation. However, when detecting the abnormal situation of land use in each region through the Moran index, since the weight in the Moran index only considers the distance between two regions, that is, the geographical locations of the two regions, ignoring other influencing factors between the two regions, such as the total area of the region, precipitation, etc., this will lead to inaccurate Moran indices for each region obtained, and thus the monitoring of the abnormal situation of land use in each region through the Moran index is not complete and accurate enough. Summary of the Invention

[0005] In order to solve the problem that when the weight in the Moran index only considers the distance between two regions, ignoring other influencing factors between the two regions, resulting in inaccurate Moran indices for each region obtained and affecting the abnormal detection, the present invention proposes a method for monitoring land use changes based on big data, and the method includes the following steps:

[0006] Collect each item of data in each area and the land utilization rate data; obtain the influence degree of each item of data on the land utilization rate; divide each area into area combinations;

[0007] Obtain the weight adjustment factor for each area combination:

[0008] , represents the weight adjustment factor of the i-th area combination; represents the mean value of the correlation of all items of data in the i-th area combination; represents the influence degree of the j-th item of data on the land utilization rate; represents the absolute value of the difference between the j-th item of data values of two areas in the i-th area combination at the k-th sampling moment; represents the mean value of the absolute value of the difference between the j-th item of data values of two areas in the i-th area combination at all sampling moments; J represents the number of data items; K represents the number of sampling moments;

[0009] Take the product of the weight adjustment factor of each area combination and the initial weight of each area combination as the adjusted weight value of each area combination; according to the adjusted weight value of each area combination, obtain the Moran index of each area at the latest sampling moment; based on the Moran index, obtain the abnormal area.

[0010] The innovation of the present invention lies in that by analyzing various influencing factors between different areas, the present invention obtains the influence degree of each item of data on the land utilization rate, which is convenient for subsequently obtaining the weight adjustment factor of each area combination according to the influence degree of each item of data on the land utilization rate to adjust the initial weight of each area combination, and calculating the Moran index of each area according to the adjusted initial weight, thereby making the calculation of the Moran index more accurate and also more accurate when detecting the unreasonable utilization of each area.

[0011] Preferably, the obtaining of the influence degree of each item of data on the land utilization rate includes:

[0012] ;

[0013] In the formula, represents the influence degree of the j-th item of data on the land utilization rate; represents the number of sampling moments; represents the number of areas; represents the j-th item of data value of the n-th area at the k-th sampling moment; represents the mean value of the j-th item of data of all areas at the k-th sampling moment; represents the land utilization rate data value of the n-th area at the k-th sampling moment; represents the mean value of the land utilization rate data of all regions at the k-th sampling moment; exp() represents the exponential function with the natural constant as the base.

[0014] It is convenient to obtain the weight adjustment factor of each regional combination according to the influence degree of each item of data on the land utilization rate subsequently.

[0015] Preferably, the dividing of each region into each regional combination includes:

[0016] Denote any two regions as a regional combination, and a number of regional combinations are obtained.

[0017] Preferably, the obtaining of the correlation of all items of data of the regional combination includes:

[0018] Successively denote each item of data of each region at all sampling moments as each item of data sequence of each region; take the Pearson correlation coefficient between the data sequences of each item of the two regions in the i-th regional combination as the Pearson correlation coefficient of the j-th item of data of the i-th regional combination, and take the value obtained by dividing the sum of the Pearson correlation coefficient of the j-th item of data of the i-th regional combination plus one by two as the correlation of the j-th item of data of the i-th regional combination.

[0019] The greater the correlation of all items of data of the regional combination, the greater the weight adjustment factor of the regional combination.

[0020] Preferably, the obtaining of the initial weight of each regional combination includes:

[0021] Obtain the centroid of each region, and take the distance between the centroids of the two regions in any regional combination as the initial weight of this regional combination.

[0022] Preferably, the obtaining of the Moran index of each region at the latest sampling moment according to the adjusted weights of each regional combination includes:

[0023] Obtain the Moran index of each region at the latest sampling moment according to the land resource utilization rate of each region at the latest sampling moment and the adjusted weights of each regional combination.

[0024] The accuracy of the Moran index is improved.

[0025] Preferably, the obtaining of the abnormal region includes:

[0026] Preset the abnormal degree threshold T. If the abnormal degree of any region at the latest sampling moment is greater than the abnormal degree threshold T, this region is an abnormal region. At this time, a warning is issued and land resource replanning is carried out for this region.

[0027] The obtained abnormal region is more accurate.

[0028] Preferably, collecting each item of data of each area and the land utilization rate data includes:

[0029] The preset sampling time is once a week, and the collection duration is one year. The precipitation, humidity, and altitude of each area are collected by mobile meteorological equipment, and the average slope, total area, etc. of each area are collected through on-site measurement, so as to obtain each item of data of each area at each sampling time;

[0030] The land utilization rate data of each area is collected through on-site measurement, so as to obtain the land utilization rate data of each area at each sampling time.

[0031] The present invention has the following beneficial effects: The purpose of the present invention is to analyze various influencing factors between different areas, obtain the influence degree of each item of data on the land utilization rate, facilitate subsequent adjustment of the initial weights of each area combination according to the influence degree of each item of data on the land utilization rate, and calculate the Moran index of each area according to the adjusted initial weights, thereby making the calculation of the Moran index more accurate and also more accurate when detecting the unreasonable utilization of each area. Description of the Drawings

[0032] By referring to the following detailed description with reference to the drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become easily understood. In the drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0033] Figure 1 is a step flowchart of a method for monitoring land use change based on big data according to an embodiment of the present invention. Detailed Embodiments

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Next, the detailed embodiments of the present invention will be described in detail with reference to the drawings.

[0036] Please refer to Figure 1 , which shows a step flowchart of a method for monitoring land use change based on big data provided by an embodiment of the present invention. The method includes the following steps:

[0037] S001. Collect each item of data of each area and the land utilization rate data.

[0038] In the embodiment of the present invention, the area to be monitored for land use is divided according to subjective experience to obtain several areas;

[0039] The preset sampling time is once a week, and the acquisition duration is one year. The precipitation, humidity, and altitude of each area are collected by a mobile meteorological device, and the average slope, total area, etc. of each area are collected by on-site measurement to obtain each item of data for each area at each sampling time;

[0040] The land use rate data of each area is collected by on-site measurement to obtain the land use rate data of each area at each sampling time.

[0041] It should be noted that each item of data for each area is time-aligned with the land use rate data for each area.

[0042] S002. Obtain the influence degree of each item of data on the land use rate.

[0043] It should be noted that the Moran index is an index describing the spatial autocorrelation between land use rate and land location, and can judge whether there are abnormal or unreasonable phenomena in land use of each area according to the correlation. However, when detecting the abnormal situation of land use in each area through the Moran index, since the weights in the Moran index only consider the distance between two areas, that is, the geographical locations of the two areas, and ignore other influencing factors between the two areas, such as the total area of the area, precipitation, etc., this will lead to inaccurate Moran index for each area obtained, and thus the monitoring of the abnormal situation of land use in each area through the Moran index is not complete and accurate enough.

[0044] It should be further noted that since the influence degree of each item of data on the land use rate is known to be different, the present invention first needs to obtain the influence degree of this item of data on the land use rate according to the similarity between any item of data and the land use rate data of all areas at all sampling times. The greater the similarity, the greater the influence degree of this item of data on the land use rate.

[0045] In the embodiment of the present invention, obtain the influence degree of the j-th item of data on the land use rate:

[0046] ;

[0047] In the formula, represents the influence degree of the j-th item of data on the land use rate; represents the number of sampling times; represents the number of areas; represents the value of the j-th item of data of the n-th area at the k-th sampling time; represents the mean value of the j-th item of data for all regions at the k-th sampling moment; represents the land use rate data value of the n-th region at the k-th sampling moment; represents the mean value of the land use rate data for all regions at the k-th sampling moment; exp() represents the exponential function with the natural constant as the base; represents the similarity between the j-th item of data and the land use rate data of the n-th region at the k-th sampling moment. The closer the ratio is to 1, the higher the similarity between the j-th item of data and the land use rate data of the n-th region at the k-th sampling moment, and the smaller the influence degree of the j-th item of data on the land use rate. Therefore, comprehensively considering the similarity between the j-th item of data and the land use rate data of all regions at all sampling moments, the influence degree of the j-th item of data on the land use rate is obtained.

[0048] S003. Divide each region into various region combinations, and obtain the weight adjustment factor for each region combination according to the influence degree of each item of data on the land use rate and the correlation of each item of data in each region combination.

[0049] It should be noted that since the weight in the Moran index only considers the distance between two regions to obtain the initial weight between the two regions and does not consider other influencing factors between the two regions, it is first necessary to divide each region into several region combinations to facilitate obtaining the weight adjustment factor for each region combination to adjust the initial weight of each region combination.

[0050] The acquisition logic of the weight adjustment factor for each region combination is as follows: The greater the correlation of each item of data in any region combination, the greater the weight adjustment factor of the region combination. And the greater the fluctuation degree of any item of data between the two regions in the region combination at all sampling moments, the smaller the correlation between the two regions in the region combination, and at this time the weight adjustment factor of the region combination is smaller. However, if the influence degree of this item of data on the land use rate is greater, it means that more attention should be paid to the fluctuation degree of any item of data between the two regions in the region combination at all sampling moments.

[0051] In the embodiment of the present invention, any two regions are recorded as a region combination to obtain several region combinations;

[0052] Sequentially record each item of data of each region at all sampling moments as the data sequence of each item of data of each region; The Pearson correlation coefficient between the data sequences of each item of data of the two regions in the i-th region combination is used as the Pearson correlation coefficient of the j-th item of data of the i-th region combination, and the value obtained by dividing the sum of the Pearson correlation coefficient of the j-th item of data of the i-th region combination plus one by two is used as the correlation of the j-th item of data of the i-th region combination;

[0053] Obtain the weight adjustment factor of the \(i\)-th area combination:

[0054] ;

[0055] In the formula, represents the weight adjustment factor of the \(i\)-th area combination; represents the mean value of the correlation of all item data of the \(i\)-th area combination; represents the influence degree of the \(j\)-th item data on the land use rate; represents the absolute value of the difference between the \(j\)-th item data values of two areas in the \(i\)-th area combination at the \(k\)-th sampling moment; represents the mean value of the absolute value of the difference between the \(j\)-th item data values of two areas in the \(i\)-th area combination at all sampling moments; \(J\) represents the number of data items; \(K\) represents the number of sampling moments; represents the correlation degree of the \(j\)-th item data of the \(i\)-th area combination at the current sampling moment. The larger its value, the more correlated the \(j\)-th item data of the two areas in the \(i\)-th area combination, and the larger the weight adjustment factor of the \(i\)-th area combination at the current sampling moment; represents the fluctuation degree of the \(j\)-th item data of the two areas in the \(i\)-th area combination at all sampling moments. The larger its value, the lower the correlation of the \(j\)-th item data of the two areas in the \(i\)-th area combination, and the smaller the weight adjustment factor of the \(i\)-th area combination. At this time, if the influence degree of the \(j\)-th item data on the land use rate is greater, it means that more attention should be paid to the fluctuation degree of the \(j\)-th item data of the two areas in the \(i\)-th area combination at all sampling moments. Therefore is used to correct the value; finally, considering the fluctuation degree of all item data of the two areas in the \(i\)-th area combination at all sampling moments, the weight adjustment factor of the \(i\)-th area combination is obtained.

[0056] S004. According to the weight adjustment factor of each area combination and the initial weight of each area combination, obtain the adjusted weight value of each area combination. According to the land resource utilization rate of each area at the latest sampling moment and the adjusted weight value of each area combination, obtain the Moran index of each area at the latest sampling moment, and then obtain the abnormal area.

[0057] It should be noted that next, after correcting the initial weight of each area combination according to the weight adjustment factor of each area combination, the adjusted weight value of each area combination is obtained. Finally, according to the adjusted weight value of each area combination and the land resource utilization rate of each area at the current sampling moment, the Moran index of each area is obtained, and the abnormal situation of the land use of each area is detected through the Moran index.

[0058] In the embodiment of the present invention, the centroid of each region is obtained, and the distance between the centroids of two regions in any region combination is obtained as the initial weight of the region combination; the weight adjustment factor of the i-th region combination is multiplied by the initial weight value of the i-th region combination to obtain the adjusted weight value of the i-th region combination; according to the land resource utilization rate of each region at the latest sampling moment and the adjusted weight value of each region combination, the Moran index of each region at the latest sampling moment is obtained;

[0059] A box plot is constructed for the Moran index of each region at the latest sampling moment, and the degree of abnormality of each region at the latest sampling moment is obtained;

[0060] A preset abnormality degree threshold T is set. If the abnormality degree of any region at the latest sampling moment is greater than the abnormality degree threshold T, the region is an abnormal region. At this time, a warning is issued in time, and land resource replanning is carried out for the region. In the embodiment of the present invention, T = 0.7 is preset. In other embodiments, the implementer can preset the value of T according to the specific implementation situation.

[0061] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A land use change monitoring method based on big data, characterized in that: include: Collect every data and land utilization data of each area; Obtain the impact of each data on land utilization; Divide each area into various area combinations; ； In the formula, Represents the impact of the j-th data on land utilization rate; Represents the number of regions; Represents the jth data value of the nth region at the kth sampling moment; Represents the mean of the j-th data of all regions at the k-th sampling time; Represents the land utilization rate data value of the nth area at the kth sampling time; Represents the mean of the land utilization rate data of all regions at the kth sampling time; exp() represents an exponential function with a natural constant as the base; obtain the weight adjustment factor for each regional combination: , represents the weight adjustment factor of the i-th regional combination; Represents the mean of the correlation of all item data of the i-th region combination; Represents the impact of the j-th data on land utilization rate; Represents the absolute value of the difference between the jth data values ​​of the two regions in the i-th region combination at the k-th sampling time; represents the mean of the absolute values ​​of the differences between the j-th data values ​​of two regions in the i-th region combination at all sampling times; J represents the number of data items; K represents the number of sampling times; The product of the weight adjustment factor of each regional combination and the initial weight of each regional combination is used as the adjustment weight of each regional combination; according to the adjustment weight of each regional combination, the Moran index of each region at the latest sampling time is obtained; based on the Moran index, the abnormal area is obtained.

2. The land use change monitoring method based on big data according to claim 1 is characterized in that: The division of each area into various area combinations includes: Any two regions are recorded as a region combination, and several region combinations are obtained.

3. The land use change monitoring method based on big data according to claim 1 is characterized in that: The acquisition of the correlation of all item data of the regional combination includes: Each data item of each area at all sampling moments is recorded as each data sequence of each area in turn; the Pearson correlation coefficient between each data sequence of two areas in the ith area combination is taken as the Pearson correlation coefficient of the jth data item of the ith area combination; the value of the Pearson correlation coefficient of the jth data item of the ith area combination plus one and divided by two is taken as the correlation of the jth data item of the ith area combination.

4. The land use change monitoring method based on big data according to claim 1 is characterized in that: The acquisition of the initial weights of the regional combinations includes: The centroid of each region is obtained, and the distance between the centroids of two regions in any region combination is obtained as the initial weight of the region combination.

5. The land use change monitoring method based on big data according to claim 1 is characterized in that: The method of obtaining the Moran index of each region at the latest sampling time according to the adjusted weight of each region combination includes: The Moran index of each region at the latest sampling moment is obtained according to the land resource utilization rate of each region at the latest sampling moment and the adjustment weight of each regional combination.

6. The land use change monitoring method based on big data according to claim 1 is characterized in that: The obtaining of the abnormal area comprises: A threshold T of abnormality is preset. If the abnormality of any area at the latest sampling moment is greater than the threshold T, the area is an abnormal area. At this time, an early warning is issued and the land resources of the area are replanned.

7. The land use change monitoring method based on big data according to claim 1 is characterized in that: The data collected for each area and the land utilization rate data include: The preset sampling time is one week / time, and the collection time is one year. The precipitation, humidity, and altitude of each area are collected through mobile meteorological equipment. The average slope and total area of ​​each area are collected through field measurement to obtain each data of each area at each sampling time. The land utilization data of each area is collected through field measurement to obtain the land utilization data of each area at each sampling time.

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