Rural settlement multi-dimensional evaluation analysis method and device

Abstract

The invention provides a rural settlement multi-dimensional evaluation analysis method and device, and the method carries out the comprehensive research analysis through combining the three dimensions of scale, form and distribution, and obtains the comprehensive variables of the three dimensions of scale, form and distribution through the linear fitting method and the landscape index fitting. And the three-dimensional comprehensive variable is used as a dependent variable of a space-time geographically weighted regression (GTWR) model, so that the accuracy and comprehensiveness of data analysis are improved. On the basis, main driving factors of rural settlement space-time evolution are explored by adopting a space-time geographically weighted regression model, and regression coefficients of the driving factors on a space distribution comprehensive variable, a scale comprehensive variable and a form comprehensive variable are quantitatively, qualitatively and intuitively expressed; the problem that in the prior art, due to the lack of multi-dimensional spatial attribute evaluation or research on spatial-temporal heterogeneity of related driving factors, comprehensiveness and universality are poor is solved, and recent and long-term planning management of the rural settlement land can be achieved more scientifically.

Description

Technical Field

[0001] The present invention belongs to the technical field of settlement land planning, and in particular relates to a rural settlement multi-dimensional evaluation and analysis method and device. Background Art

[0002] With the rapid development of social economy and the accelerated advancement of urbanization, rural settlements are facing unprecedented challenges and opportunities. In order to cope with the problems facing rural settlements, it is necessary to strengthen the planning and management of rural settlements, and pay attention to the protection of the ecological environment and historical and cultural heritage to achieve the sustainable development of rural settlements.

[0003] Taking the related technology CN117333530A as an example, specific research has been carried out on the spatial pattern evolution, spatial pattern optimization and driving factor optimization of specific types of rural settlements. However, the related research belongs to the technical solution of planning and analysis based entirely on image characteristics. There is a lack of comprehensiveness and universality due to the lack of multidimensional spatial attribute evaluation or research on the spatial and temporal heterogeneity of related driving factors, making it difficult to achieve more scientific short-term and long-term planning and management of rural settlement land. Summary of the invention

[0004] The present invention provides a rural settlement multi-dimensional evaluation and analysis method and device, which are used to solve the problems existing in the prior art.

[0005] In the first aspect, in order to achieve the purpose of the invention, the present invention provides a rural settlement multi-dimensional evaluation and analysis method, comprising:

[0006] S10: Use the map interaction module to reclassify and crop the land use data of the target study area.

[0007] We obtained rural settlement patches from multiple different periods;

[0008] S20: Convert rural settlement patches into points to obtain multiple rural settlement vector point data;

[0009] S30: Importing the rural settlement vector point data into the settlement analysis module, and calculating the landscape pattern index of the rural settlement in terms of spatial distribution, scale and morphological dimensions;

[0010] S40: Taking the landscape pattern index in spatial distribution, scale and morphological dimensions as independent variables, principal component analysis was used to calculate the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable;

[0011] S50: Taking the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable as the dependent variables of the spatiotemporal geographic weighted regression model, taking the various driving factors of the target study area as the independent variables of the spatiotemporal geographic weighted regression model, the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient are calculated respectively, and the graphical analysis results are obtained based on the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient.

[0012] Through the above technical scheme, the method of the present invention combines the three dimensions of scale, morphology and distribution for comprehensive research and analysis, obtains comprehensive variables of scale, morphology and distribution through linear fitting method combined with landscape index fitting, and uses the comprehensive variables of the three dimensions as the dependent variable of the spatiotemporal geographic weighted regression model, thereby improving the accuracy and comprehensiveness of data analysis. On this basis, the method of the present invention also adopts the spatiotemporal geographic weighted regression model to explore the main driving factors of the spatiotemporal evolution of rural settlements, and quantitatively and qualitatively expresses the degree of influence (regression coefficient) and related maps of driving factors on spatial distribution comprehensive variables, scale comprehensive variables and morphological comprehensive variables, which solves the problem of lack of comprehensiveness and universality in the prior art due to the lack of multidimensional spatial attribute evaluation or research on the spatiotemporal heterogeneity of related driving factors, and can more scientifically realize the short-term and long-term planning and management of rural settlement land.

[0013] Preferably, after step S20, the method of the present invention further comprises:

[0014] S300: Perform global spatial autocorrelation analysis, nearest neighbor analysis and density analysis on multiple rural settlement vector point data to analyze the spatial distribution characteristics of the target study area.

[0015] Preferably, the landscape pattern index in the spatial distribution dimension includes at least the Moran index, the clustered discrete characteristics of rural settlement evolution, and the kernel density of rural settlement vector points in each rural settlement patch. Step S300 includes:

[0016] S301: Using spatial autocorrelation analysis method, calculate the Moran index of multiple rural settlement vector point data in space;

[0017] S302: using the average nearest neighbor analysis method, the average distance between multiple groups of spatially adjacent two rural settlement vector point data is calculated as the agglomeration discrete characteristics of rural settlement evolution;

[0018] S303: Using the kernel density analysis method, calculate the kernel density of the rural settlement vector points in each rural settlement patch.

[0019] Preferably, the calculation formula of the average nearest neighbor analysis method is specifically:

[0020]

[0021] Among them, ANN is the agglomeration discrete characteristics of rural settlement evolution, d i is the actual distance between the i-th group of rural settlement vector point data, λ is the Conway constant, and n is the serial number of the rural settlement patch; if ANN<1, the rural settlement distribution pattern is a clustered pattern; if ANN>1, the rural settlement distribution pattern is a random pattern.

[0022] Preferably, the landscape pattern index in the scale dimension includes the number of rural settlement patches, the total area of ​​rural settlement patches and the area-weighted average patch area. Step S40 includes:

[0023] S401: Taking the number of rural settlement patches, the total area of ​​rural settlement patches and the area-weighted average patch area as independent variables, the principal component analysis method was used to calculate the scale comprehensive variable.

[0024] Specifically, the scale comprehensive variable expression after processing in step S401 is:

[0025] F1=k1Z NP +k2Z CA +k3Z AREA_AM (2)

[0026] Among them, F1 represents the comprehensive variable of scale, Z NP represents the number of rural settlement patches, Z CA represents the total area of ​​rural settlement patches, Z AREA_AM represents the area-weighted average patch area; k1, k2, and k3 are weight coefficients.

[0027] Preferably, the landscape pattern index in the morphological dimension includes a landscape shape index, an area-weighted average patch fractal dimension, and an average patch shape index. Step S40 includes:

[0028] S402: Using the landscape shape index, the area-weighted average patch fractal dimension and the average patch shape index as independent variables, principal component analysis was used to calculate the morphological comprehensive variable.

[0029] Specifically, the morphological comprehensive variable expression after processing in step S402 is:

[0030] F2=k4Z LSI +k5Z FRAC_AM +k6Z SHAPE_MN (3)

[0031] Among them, F2 represents the morphological comprehensive variable, Z LSI represents the landscape shape index, Z FRAC_AM represents the area-weighted average patch fractal dimension, Z SHAPE_MN represents the average plaque shape index; k4, k5, k6 are weight coefficients.

[0032] Preferably, the landscape pattern index in the spatial distribution dimension includes the aggregation degree of rural settlement patches, the density of rural settlement patches and the area-weighted average patch closest distance. Step S40 includes:

[0033] S403: Taking the aggregation degree of rural settlement patches, the density of rural settlement patches and the area-weighted average patch distance as independent variables, the principal component analysis method is used to calculate the comprehensive variable of spatial distribution.

[0034] Specifically, the spatial distribution comprehensive variable expression after processing in step S403 is:

[0035] F3=k7Z AI +k8Z PD +k9Z ENN_AM (4)

[0036] Among them, F3 represents the comprehensive variable of spatial distribution, Z AI represents the aggregation degree of rural settlement patches, Z PD represents the density of rural settlement patches, Z ENN_AM It represents the area-weighted average patch closest distance; k7, k8, and k9 are weight coefficients.

[0037] In a second aspect, in order to achieve the above-mentioned purpose of the invention, the present invention further provides a rural settlement multi-dimensional evaluation and analysis device, which uses the above-mentioned rural settlement multi-dimensional evaluation and analysis method to perform a rural settlement multi-dimensional evaluation on the target study area. The rural settlement multi-dimensional evaluation and analysis device includes:

[0038] The map interaction module is used to reclassify and crop the land use data of the target study area to obtain rural settlement patches in different periods;

[0039] The point conversion module is used to convert rural settlement patches into points and obtain multiple rural settlement vector point data;

[0040] The settlement analysis module is used to import rural settlement vector point data into the built-in model to calculate the landscape pattern index of rural settlements in terms of spatial distribution, scale and morphological dimensions;

[0041] The comprehensive variable calculation module is used to calculate the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable by using the landscape pattern index in the spatial distribution, scale and morphological dimensions as independent variables and adopting the principal component analysis method;

[0042] The graph analysis module is used to use the spatial distribution comprehensive variables, scale comprehensive variables and morphological comprehensive variables as the dependent variables of the spatiotemporal geographic weighted regression model, and the various driving factors of the target study area as the independent variables of the spatiotemporal geographic weighted regression model, to calculate the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient respectively, and to obtain the graph analysis results based on the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient.

[0043] The present invention has at least the following advantages:

[0044] 1) Compared with the existing technology, the method of the present invention has stronger universality and can more scientifically realize the short-term and long-term planning and management of rural settlement land.

[0045] 2) Compared with the existing technology, this method can conduct a further in-depth analysis of the comprehensive spatial distribution characteristics of the target study area, and the analysis results are more intuitive.

[0046] 3) Compared with the prior art, the algorithm operation rate of the method of the present invention is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 This is a flow chart of the multi-dimensional evaluation and analysis method for rural settlements in Example 1 of the present invention.

[0049] Figure 2 It is a distribution map of rural settlement patches at multiple different periods in step S10 in the first embodiment of the present invention.

[0050] Figure 3 This is a rural settlement kernel density analysis diagram of step S303 in the first embodiment of the present invention.

[0051] Figure 4 This is a graph of the change rate of the kernel density of rural settlements in Example 1 of the present invention.

[0052] Figure 5 This is a scale regression coefficient distribution diagram of step S50 in the first embodiment of the present invention.

[0053] Figure 6 This is a distribution diagram of the morphological regression coefficients in step S50 in the first embodiment of the present invention.

[0054] Figure 7This is a distribution diagram of spatial distribution regression coefficients in step S50 in the first embodiment of the present invention.

[0055] Figure 8 It is a schematic diagram of the structure of the multi-dimensional evaluation and analysis device for rural settlements in the second embodiment of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they cannot be understood as limitations on the present invention.

[0058] This embodiment discloses a rural settlement multi-dimensional evaluation and analysis method and device, which are used to solve the problems existing in the prior art.

[0059] Embodiment 1

[0060] like Figure 1 As shown, this embodiment discloses a multi-dimensional evaluation and analysis method for rural settlements, which includes:

[0061] S10: The map interaction module is used to reclassify and crop the land use data of the target study area to obtain rural settlement patches in different periods.

[0062] In this embodiment, a province is taken as the target research area, and the map interaction module adopts the existing ArcMap10.8 software. The multiple different periods mainly refer to the four periods of 1990, 2000, 2010 and 2020.

[0063] Specifically, Figure 2 As shown in the figure, the land use data of the target study area of ​​a province are reclassified and clipped to obtain the rural settlement patches in 1990, 2000, 2010 and 2020. Based on the visualization map, it can be seen that the rural settlement patches of the province can be used to conduct a preliminary analysis of the settlement evolution.

[0064] S20: Convert rural settlement patches into points to obtain multiple rural settlement vector point data.

[0065] Different from the existing atlas processing methods, the method of the present invention converts the visualized patches into rural settlement vector point data to facilitate quantitative analysis and related calculations of the data.

[0066] After step S20, the method of the present invention further includes:

[0067] S300: Perform global spatial autocorrelation analysis, nearest neighbor analysis and density analysis on multiple rural settlement vector point data to analyze the spatial distribution characteristics of the target study area.

[0068] Furthermore, the landscape pattern index in the spatial distribution dimension includes at least the Moran index, the clustered discrete characteristics of rural settlement evolution, and the kernel density of rural settlement vector points in each rural settlement patch. Step S300 includes:

[0069] S301: Use spatial autocorrelation analysis to calculate the Moran index of multiple rural settlement vector point data in space.

[0070] It should be explained that the Moran's index is a correlation index of the overall space, and its value range is [-1,1]. When the Moran's index is greater than 0, it means that the spatial distribution of the area shows obvious spatial correlation and has a tendency to aggregate, and as its value increases, the degree of aggregation also increases. When the Moran's index is less than 0, it means that the spatial distribution of the area shows a negative correlation, that is, it has a tendency to disperse. When the Moran's index is equal to 0, it means that the spatial distribution of the area is irrelevant and its distribution is random. Through step S301, the aggregation and dispersion tendencies of the target study area can be further analyzed based on the Moran's index.

[0071] S302: Using the average nearest neighbor analysis method, the average distance between multiple groups of spatially adjacent rural settlement vector point data is calculated as the agglomeration discrete characteristics of rural settlement evolution.

[0072] Specifically, the calculation formula of the above average nearest neighbor analysis method is:

[0073]

[0074] Among them, ANN is the agglomeration discrete characteristics of rural settlement evolution, d i is the actual distance between the i-th group of rural settlement vector point data, λ is the Conway constant, and n is the serial number of the rural settlement patch; if ANN<1, the rural settlement distribution pattern is a clustered pattern; if ANN>1, the rural settlement distribution pattern is a random pattern.

[0075] S303: Using the kernel density analysis method, calculate the kernel density of the rural settlement vector points in each rural settlement patch.

[0076] After the above steps S301-S303 and related data analysis, the calculation results shown in Table 1 can be calculated.

[0077] Table 1

[0078]

[0079] As can be seen from Table 1, the global spatial autocorrelation analysis and average nearest neighbor analysis were used to study the rural settlement patches, revealing the evolution law of aggregation and discreteness of rural settlements. When the p value is less than 0.05, Moran's I<1, which reveals that the rural settlements of the province as a whole are spatially clustered, while the ANN of each year shows a downward trend, which indicates that the degree of spatial agglomeration of rural settlements in the province is getting higher and higher. During the period from 1990 to 2020, the densely populated areas of rural settlements were mainly distributed in the western and eastern regions, the northern region was basically a sparse area, and the central triangular area gradually evolved from a relatively dense area in 1990 to a sparse area. Combined with Figure 3-Figure 4 From the perspective of time, the maximum value of the kernel density gradually decreases, indicating that the number of settlements per unit area in the densely populated rural settlement area is gradually decreasing. The rural settlements in the target study area showed a trend of agglomeration between 1990 and 2020. As time goes by, the overall medium and high density areas gradually shrink towards the western and eastern regions, and the overall agglomeration trend gradually increases. From the perspective of space, the density distribution of rural settlements in the target study area has shown significant changes in the past 30 years: high in the east and west, and low in the northern and central triangle areas.

[0080] After the above step S20, the method of the present invention further comprises:

[0081] S30: Import the rural settlement vector point data into the settlement analysis module to calculate the landscape pattern index of the rural settlement in terms of spatial distribution, scale and morphological dimensions.

[0082] It should be noted that the above step S300 and step S30 can be executed in parallel.

[0083] In this embodiment, the settlement analysis module uses the existing Fragstats 4.2 software, which has built-in scale and morphological analysis algorithms. After the above-mentioned rural settlement vector point data (TIFF data file) is imported into the Fragstats4.2 software, a number of landscape pattern indicators that have not yet been classified will be output in real time. Different from the prior art, the present invention mainly selects 9 landscape pattern indicators as principal components to participate in subsequent calculations. Among them, the landscape pattern index on the scale dimension includes the number of rural settlement patches (NP), the total area of ​​rural settlement patches (CA) and the area-weighted average patch area (AREA_AM); the landscape pattern index on the morphological dimension includes the landscape shape index (LSI), the area-weighted average patch fractal dimension (FRAC_AM) and the average patch shape index (SHAPE_MN); the landscape pattern index on the spatial distribution dimension includes the aggregation degree (AI) of rural settlement patches, the density (PD) of rural settlement patches and the area-weighted average patch nearest distance (ENN_AM).

[0084] S40: Taking the landscape pattern index in spatial distribution, scale and morphological dimensions as independent variables, principal component analysis was used to calculate the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable.

[0085] Specifically, the above step S40 includes:

[0086] S401: Taking the number of rural settlement patches, the total area of ​​rural settlement patches and the area-weighted average patch area as independent variables, the principal component analysis method was used to calculate the scale comprehensive variable.

[0087] S402: Using the landscape shape index, the area-weighted average patch fractal dimension and the average patch shape index as independent variables, principal component analysis was used to calculate the morphological comprehensive variable.

[0088] S403: Taking the aggregation degree of rural settlement patches, the density of rural settlement patches and the area-weighted average patch distance as independent variables, the principal component analysis method is used to calculate the comprehensive variable of spatial distribution.

[0089] Furthermore, the scale comprehensive variable expression after processing in step S401 is:

[0090] F1=k1Z NP +k2Z CA +k3Z AREA_AM (2)

[0091] Among them, F1 represents the comprehensive variable of scale, Z NP represents the number of rural settlement patches, Z CA represents the total area of ​​rural settlement patches, Z AREA_AMrepresents the area-weighted average patch area; k1, k2, and k3 are weight coefficients.

[0092] Furthermore, the morphological comprehensive variable expression after processing in step S402 is:

[0093] F2=k4Z LSI +k5Z FRAC_AM +k6Z SHAPE_MN (3)

[0094] Among them, F2 represents the morphological comprehensive variable, Z LSI represents the landscape shape index, Z FRAC_AM represents the area-weighted average patch fractal dimension, Z SHAPE_MN represents the average plaque shape index; k4, k5, k6 are weight coefficients.

[0095] Furthermore, the spatial distribution comprehensive variable expression after processing in step S403 is:

[0096] F3=k7Z AI +k8Z PD +k9Z ENN_AM (4)

[0097] Among them, F3 represents the comprehensive variable of spatial distribution, Z AI represents the aggregation degree of rural settlement patches, Z PD represents the density of rural settlement patches, Z ENN_AM It represents the area-weighted average patch closest distance; k7, k8, and k9 are weight coefficients.

[0098] It should be explained that for the province studied in this embodiment, k1 is 0.656, k2 is 0.604, k3 is 0.453, k4 is 0.704, k5 is 0.703, k6 is -0.102, k7 is 0.70637, k8 is -0.70565, and k9 is 0.05639. The above weighting coefficients can be calculated based on the actual comprehensive variable values. After steps S401-S403, it is also necessary to test the significance of the above formulas (2)-(4) to verify the rationality of the design of the above weighting coefficients.

[0099] In other embodiments, if the research object is other provinces, the weight coefficient will change.

[0100] More specifically, as shown in Table 2, the weighted coefficient of each landscape index in the comprehensive variable is the arithmetic square root of the corresponding landscape pattern index load matrix divided by the initial eigenvalue of the corresponding landscape pattern index. For example, the coefficients of each index in the scale index formula are calculated by dividing the principal component load matrix in the table by the arithmetic square root of 2.034; the calculation of the morphological index and distribution index coefficients is the same.

[0101] Table 2

[0102]

[0103] S50: Taking the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable as the dependent variables of the spatiotemporal geographic weighted regression model, taking the various driving factors of the target study area as the independent variables of the spatiotemporal geographic weighted regression model, the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient are calculated respectively, and the graphical analysis results are obtained based on the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient.

[0104] It should be explained that, unlike the prior art, as shown in Table 3, the driving factors selected by the present invention from natural environmental factors and socio-economic factors include elevation, slope, annual sunshine duration, annual average temperature, annual average precipitation, local GDP, road mileage, population density, night light data and farmland production potential data.

[0105] Table 3

[0106]

[0107] As shown in Table 4, after placing the above driving factors into the spatiotemporal geographic weighted regression model, with the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable as dependent variables, the spatiotemporal geographic weighted regression results and collinearity test statistics can be obtained to analyze the influence of each driving factor on the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable.

[0108] Table 4

[0109]

[0110] As shown in Table 5, in order to make the spatiotemporal geographically weighted regression model more reliable, the regression coefficients can be averaged over many years.

[0111] Table 5

[0112]

[0113] like Figure 5As shown in the figure, the scale regression coefficient is plotted, and the scale index regression results show that from 1990 to 2020, considering the average absolute value of the regression coefficient, the influence degree is from high to low, which is highway mileage, elevation, farmland production potential, annual sunshine duration, night lights and annual average temperature. The influence degree of highway mileage and elevation on the scale of rural settlements is greater than other factors. Among them, the impact of highway mileage on the scale of rural settlements is positive, and its positive influence gradually weakens with the passage of time. From a spatial perspective, the regression coefficient of highway mileage is higher in the southwest and lower in the northeast. As a basic research perspective of socioeconomic factors, roads can well improve the connectivity and accessibility of various rural settlements and provide very favorable conditions for the development of rural settlements. The terrain of the province slopes from the northeast to the southwest and south, and rural settlements often choose to develop in areas with lower elevation and flat terrain, so the same degree of roads has a greater influence on the southwest. With the development of society, the road system has gradually improved reasonably, so with the passage of time, the positive influence of highway mileage on the evolution of rural settlement scale has gradually decreased, but it still shows a positive promotion effect. The impact of elevation on the scale of rural settlements has both positive and negative effects, but the negative effects are dominant. From a spatial perspective, the regression coefficient of elevation has the greatest negative impact in the southwest, and the main affected area is western Guangdong. Its negative impact gradually weakens as it moves northeastward, and gradually produces a positive impact in the eastern region.

[0114] like Figure 6 As shown in the figure, the morphological regression coefficients are plotted, and the morphological index regression results show that from 1990 to 2020, the degree of influence is from high to low: elevation, farmland production potential, road mileage and population density. The influence of elevation and farmland production potential on rural settlement morphology is greater than other factors. Among them, the impact of elevation on the scale of rural settlements is more complex, mainly showing a positive impact in some counties and districts in the northern region and the eastern region, and a negative impact in the central triangle region and the western region.

[0115] The spatial distribution regression coefficients were plotted and the distribution index regression results showed that from 1990 to 2020, the impact levels were as follows: population density, elevation, farmland production potential, road network density, and average annual precipitation. Figure 7 As shown, population density and elevation have a greater impact on the distribution of rural settlements than other factors. Among them, the impact of population density on the distribution of rural settlements is mainly positive. The impact of elevation on the distribution of rural settlements is more complex, mainly showing a negative impact in the western region and some northern regions of the province, and a positive impact in the central triangle region and the eastern region. In other embodiments, the method of the present invention can also be applied to other provinces.

[0116] Through the above technical scheme, the method of the present invention combines the three dimensions of scale, morphology and distribution for comprehensive research and analysis, and obtains comprehensive variables of scale, morphology and distribution through linear fitting method combined with landscape index fitting, and uses them as dependent variables of spatiotemporal geographic weighted regression model, thereby improving the accuracy and comprehensiveness of data analysis. On this basis, the method of the present invention also adopts spatiotemporal geographic weighted regression model to explore the main driving factors of spatiotemporal evolution of rural settlements, and quantitatively and qualitatively expresses the degree of influence (regression coefficient) of driving factors on spatial distribution comprehensive variables, scale comprehensive variables and morphological comprehensive variables, solving the problem of lack of comprehensiveness and universality in the prior art due to lack of multidimensional spatial attribute evaluation or research on spatiotemporal heterogeneity of related driving factors, and can more scientifically realize the short-term and long-term planning and management of rural settlement land.

[0117] It should be noted that, as shown in Table 6, the regression coefficients obtained by the method of the present invention, according to the spatial autocorrelation results, show that each regression coefficient has a high spatial autocorrelation in each year, which meets the spatial heterogeneity requirements of spatial weighted regression. Then, the standardized data of the selected driving factors and the comprehensive dependent variables obtained by principal component analysis were tested for collinearity and significance. The results show that the variance inflation factor VIF of each selected factor is less than 6.5, and the P value is less than 0.05, which meets the requirements of collinearity and significance. From the perspective of the fit of the regression results, R2 is greater than 0.7, indicating that the selected driving factors can explain at least 70% of the change results, and the regression model has a good fitting effect.

[0118] Table 6

[0119]

[0120] Embodiment 2

[0121] like Figure 8 As shown, this embodiment provides a rural settlement multi-dimensional evaluation and analysis device, which uses the rural settlement multi-dimensional evaluation and analysis method in Example 1 to perform a rural settlement multi-dimensional evaluation on a target study area, and the device includes:

[0122] The map interaction module is used to reclassify and crop the land use data of the target study area to obtain rural settlement patches in different periods;

[0123] The point conversion module is used to convert rural settlement patches into points and obtain multiple rural settlement vector point data;

[0124] The settlement analysis module is used to import rural settlement vector point data into the built-in model to calculate the landscape pattern index of rural settlements in terms of spatial distribution, scale and morphological dimensions;

[0125] The comprehensive variable calculation module is used to calculate the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable by using the landscape pattern index in the spatial distribution, scale and morphological dimensions as independent variables and adopting the principal component analysis method;

[0126] The graph analysis module is used to use the spatial distribution comprehensive variables, scale comprehensive variables and morphological comprehensive variables as the dependent variables of the spatiotemporal geographic weighted regression model, and the various driving factors of the target study area as the independent variables of the spatiotemporal geographic weighted regression model, to calculate the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient respectively, and to obtain the graph analysis results based on the spatial distribution regression coefficient, scale regression coefficient and morphological regression coefficient.

[0127] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc. Various media that can store program codes.

[0128] For those skilled in the art, various other corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all of these changes and deformations should fall within the protection scope of the claims of the present invention.

Claims

1. A multi-dimensional evaluation and analysis method for rural settlements, characterized in that: include: S10: Use the map interaction module to reclassify and crop the land use data of the target study area to obtain rural settlement patches in different periods; S20: Convert the rural settlement patches into points to obtain a plurality of rural settlement vector point data; S30: importing the rural settlement vector point data into a settlement analysis module, and calculating the landscape pattern index of the rural settlement in terms of spatial distribution, scale and morphology; S40: Taking the landscape pattern index in spatial distribution, scale and morphological dimensions as independent variables, principal component analysis was used to calculate the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable; S50: Using the spatial distribution comprehensive variable, the scale comprehensive variable and the morphological comprehensive variable as the dependent variables of the spatiotemporal geographic weighted regression model, using the various driving factors of the target study area as the independent variables of the spatiotemporal geographic weighted regression model, respectively calculating the spatial distribution regression coefficient, the scale regression coefficient and the morphological regression coefficient, and obtaining a graphical analysis result based on the spatial distribution regression coefficient, the scale regression coefficient and the morphological regression coefficient.

2. The rural settlement multidimensional evaluation and analysis method according to claim 1 is characterized in that: After step S20, the method of the present invention further comprises: S300: Performing global spatial autocorrelation analysis, nearest neighbor analysis and density analysis on multiple rural settlement vector point data to analyze the spatial distribution characteristics of the target study area.

3. The rural settlement multidimensional evaluation and analysis method according to claim 2 is characterized in that: The landscape pattern index in the spatial distribution dimension at least includes the Moran index, the clustered discrete characteristics of rural settlement evolution, and the kernel density of rural settlement vector points in each rural settlement patch. The step S300 includes: S301: Using spatial autocorrelation analysis method, calculate the Moran index of multiple rural settlement vector point data in space; S302: using the average nearest neighbor analysis method, the average distance between multiple groups of spatially adjacent two rural settlement vector point data is calculated as the agglomeration discrete characteristics of rural settlement evolution; S303: Using the kernel density analysis method, calculate the kernel density of the rural settlement vector points in each rural settlement patch.

4. The rural settlement multidimensional evaluation and analysis method according to claim 1 is characterized in that: The landscape pattern index in the scale dimension includes the number of rural settlement patches, the total area of ​​rural settlement patches and the area-weighted average patch area. Step S40 includes: S401: Taking the number of rural settlement patches, the total area of ​​rural settlement patches and the area-weighted average patch area as independent variables, the principal component analysis method was used to calculate the scale comprehensive variable.

5. The rural settlement multidimensional evaluation and analysis method according to claim 4 is characterized in that: The scale comprehensive variable expression after the processing in step S401 is: F1=k1Z NP +k2Z CA +k3Z AREA_AM (2) Among them, F1 represents the comprehensive variable of scale, Z NP represents the number of rural settlement patches, Z CA represents the total area of ​​rural settlement patches, Z AREA_AM represents the area-weighted average patch area; k1, k2, and k3 are weight coefficients.

6. The rural settlement multidimensional evaluation and analysis method according to claim 1 is characterized in that: The landscape pattern index in the morphological dimension includes the landscape shape index, the area-weighted average patch fractal dimension and the average patch shape index. The step S40 includes: S402: Using the landscape shape index, the area-weighted average patch fractal dimension and the average patch shape index as independent variables, principal component analysis was used to calculate the morphological comprehensive variable.

7. The rural settlement multidimensional evaluation and analysis method according to claim 6 is characterized in that: The morphological comprehensive variable expression after the processing in step S402 is: <h2 style=";text-align:left;direction:ltr">F2=k4Z<h2 style=";text-align:left;direction:ltr"> LSI <h2 style=";text-align:left;direction:ltr"> +k5Z<h2 style=";text-align:left;direction:ltr"> FRAC_AM <h2 style=";text-align:left;direction:ltr"> +k6Z<h2 style=";text-align:left;direction:ltr"> SHAPE_MN <h2 style=";text-align:left;direction:ltr"> (3) Among them, F2 represents the morphological comprehensive variable, Z LSI represents the landscape shape index, Z FRAC_AM represents the area-weighted average patch fractal dimension, Z SHAPE_MN represents the average plaque shape index; k4, k5, k6 are weight coefficients.

8. The rural settlement multidimensional evaluation and analysis method according to claim 1 is characterized in that: The landscape pattern index in the spatial distribution dimension includes the aggregation degree of rural settlement patches, the density of rural settlement patches and the area-weighted average patch closest distance. The step S40 includes: S403: Taking the aggregation degree of rural settlement patches, the density of rural settlement patches and the area-weighted average patch distance as independent variables, the principal component analysis method is used to calculate the comprehensive variable of spatial distribution.

9. The rural settlement multidimensional evaluation and analysis method according to claim 8 is characterized in that: The spatial distribution comprehensive variable expression after processing in step S403 is: <h2 style=";text-align:left;direction:ltr">F3 = k7Z<h2 style=";text-align:left;direction:ltr"> AI <h2 style=";text-align:left;direction:ltr"> +k8Z<h2 style=";text-align:left;direction:ltr"> PD <h2 style=";text-align:left;direction:ltr"> +k9Z<h2 style=";text-align:left;direction:ltr"> ENN_AM <h2 style=";text-align:left;direction:ltr"> (4) Among them, F3 represents the comprehensive variable of spatial distribution, Z AI represents the aggregation degree of rural settlement patches, Z PD represents the density of rural settlement patches, Z ENN_AM It represents the area-weighted average patch closest distance; k7, k8, and k9 are weight coefficients.

10. A multi-dimensional evaluation and analysis device for rural settlements, characterized in that: The rural settlement multidimensional evaluation and analysis method according to any one of claims 1 to 9 is used to perform a multidimensional evaluation of rural settlements in a target study area, wherein the rural settlement multidimensional evaluation and analysis device comprises: The map interaction module is used to reclassify and crop the land use data of the target study area to obtain rural settlement patches in different periods; A point conversion module is used to convert the rural settlement patches into points to obtain a plurality of rural settlement vector point data; A settlement analysis module, used to import the rural settlement vector point data into a built-in model to calculate the landscape pattern index of the rural settlement in terms of spatial distribution, scale and morphological dimensions; The comprehensive variable calculation module is used to calculate the spatial distribution comprehensive variable, scale comprehensive variable and morphological comprehensive variable by using the landscape pattern index in the spatial distribution, scale and morphological dimensions as independent variables and using the principal component analysis method; The graph analysis module is used to use the spatial distribution comprehensive variable, the scale comprehensive variable and the morphological comprehensive variable as the dependent variables of the spatiotemporal geographic weighted regression model, and the various driving factors of the target study area as the independent variables of the spatiotemporal geographic weighted regression model, to calculate the spatial distribution regression coefficient, the scale regression coefficient and the morphological regression coefficient respectively, and to obtain the graph analysis results based on the spatial distribution regression coefficient, the scale regression coefficient and the morphological regression coefficient.

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