Method for measuring spatial function characteristics of rural areas based on multi-source geographic information data

Abstract

The present application provides a kind of village area spatial function characteristic measure method based on multi-source geographic information data, comprising: obtaining the geographic information space data of regional space, pre-processing geographic information space data;Participatory evaluation method is used to data analysis to the pre-processed geographic information space data;The data after analysis is counted, and multi-source geographic information data is obtained;Multi-source geographic information data is fused;The accurate identification of regional production-life-ecological space is carried out to the data after fusion;According to the identification result, the spatial function measure index system based on production-life-ecology-vitality is constructed;Based on the spatial function measure index system, the function of the region is measured, and the space resource is distributed according to the measurement result;The present application focuses on village scale, and carries out household survey, and this research scale and method selection has unique advantage.

Description

Technical Field

[0001] This invention belongs to the field of regional functional characteristic measurement, specifically relating to a method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data. Background Technology

[0002] Agricultural modernization lags significantly behind urbanization and industrialization, the development level of rural tertiary industries is low, and there are numerous gaps in rural spatial planning, a lack of necessary technical basis, and a slow pace of updates. This leads to problems such as disorderly housing construction, chaotic infrastructure layout, and irrational land use structure. Leveraging the guiding role of rural planning and optimizing rural spatial and industrial layout is crucial. Introducing the "three-life space" theory to integrate and optimize the current state and development space of villages can help address bottlenecks in rural development. Conducting research on the evolution and measurement of village spatial functions is a powerful guarantee and important foundation for promoting agricultural modernization, improving rural planning and management, and achieving the economical and intensive use of land.

[0003] Currently, most research on "three-life spaces" (production, living, and ecological spaces) focuses on land optimization, land consolidation, and land use classification, but a consensus on the division of these three types of spaces has not yet been reached. Domestic scholars are reclassifying the land use of these three-life spaces based on current land use classification or planned land use classification, and then implementing this classification spatially. Existing research on the identification of "three-life spaces" is divided into meso-macro and micro scales, and employs methods such as land use type merging and indicator system calculation. The land use type merging method directly merges land use types into different "three-life space" types; the indicator system calculation method constructs an indicator system based on natural, economic, and social factors affecting the "three-life spaces," and identifies these spaces through comprehensive evaluation.

[0004] The spatial structure of "production, living, and ecology" exhibits different patterns of change over time. Studying its dynamic evolution and driving mechanisms can lay a theoretical foundation for optimizing regional "production, living, and ecology" spaces. The characteristics and influencing factors of rural spatial evolution are also early research hotspots. Related studies indicate that globalization, counter-urbanization, industrialization, and rural leisure industries are the main factors driving rural spatial evolution. Rural spatial development can be divided into three stages: decentralization, centralization without rural towns, and rural town centralization. Domestic scholars have conducted in-depth research on the development, evolution, and structural adjustment of rural "production, living, and ecology" spaces, explored the driving factors of spatial reconstruction, and proposed a strategy for the functional reconstruction of rural "production, living, and ecology" spaces that combines "bottom-up" and "top-down" approaches, based on the reconstruction methods and implementation paths of production-living-ecological spaces. In addition, many scholars, based on survey data of farmers, have explored the intrinsic relationship and mechanism between farmers' behavior and changes in the "three-life" space through a combination of qualitative and quantitative methods. They have analyzed the driving mechanism that affects the changes in the "three-life" spatial pattern, and believe that the evolution of rural spatial structure originates from the production and life activities of rural residents themselves. It is an inevitable result of the interaction between production labor and social relations in spatial location. On this basis, they have proposed optimization and regulation strategies for the "three-life" space.

[0005] Current technologies for studying village spatial functions mostly employ large-scale approaches, such as town or even county-level studies. While this large-scale approach can grasp overall trends from a macro perspective, it struggles to delve into the micro-level within villages. For example, a town often comprises multiple villages, each with significant differences in geographical environment, economic development level, and socio-cultural aspects. Large-scale studies often average these differences, neglecting the unique characteristics of each village and failing to highlight some subtle but crucial features. This results in research findings lacking specificity and operability when guiding the planning and development of specific villages. Most existing technologies primarily focus on analyzing the current state of village spatial functions, paying less attention to the historical evolution of village spatial forms and functions. However, the development of village spatial functions is a dynamic process, influenced by a combination of factors at different times. Without understanding its historical evolution, it is difficult to deeply comprehend the roots and internal logic of current spatial function formation. Current technologies for measuring village spatial functions largely focus on the study of "three-life" spaces (production, living, and ecology). While the theory of "three-life" spaces provides an important framework for village spatial function research, it still has certain limitations. Firstly, a unified standard for the division of "three-life" spaces has not yet been established, and different studies differ in indicator selection and classification methods, leading to a lack of comparability in research results. Secondly, existing indicator systems primarily focus on the functional attributes of spaces, insufficiently considering factors such as human vitality and development potential within the village. Research on village spatial functions requires substantial, accurate, and detailed data support, including geographic information data and socioeconomic data. However, current technologies face numerous difficulties in data acquisition. Firstly, the data statistics system in rural areas is relatively incomplete, with some data missing or outdated. Secondly, data from different sources differ in format, standards, and accuracy, making integration difficult. Low data quality and integration difficulties affect the reliability and accuracy of existing research results, making it difficult to provide strong decision-making support for village planning and development. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention proposes a method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data. This method includes: acquiring regional spatial geographic information data; preprocessing the spatial geographic information data; performing data analysis on the preprocessed spatial geographic information data using a participatory assessment method; statistically analyzing the analyzed data to obtain multi-source geographic information data; fusing the multi-source geographic information data; accurately identifying the regional production-living-ecological spaces based on the fused data; constructing a spatial function measurement index system based on the identification results; measuring the regional spatial functional characteristics based on the spatial function measurement index system; and allocating spatial resources according to the measurement results.

[0007] The beneficial effects of this invention are:

[0008] This invention breaks through the limitations of traditional indicator systems by constructing a comprehensive and multi-layered evaluation index for village spatial functions, centered on farmers. It innovatively adopts a four-in-one model of "living-production-ecology-vitality," adding a vitality dimension compared to traditional indicator systems that only focus on living, production, and ecology. This newly added vitality function is measured from three levels: population vitality, economic vitality, and cultural vitality. Population vitality is visually presented through the population attraction index and population agglomeration index, reflecting the flow and concentration of the village population; economic vitality uses the Engel coefficient to reflect residents' living standards and economic development; and cultural vitality is reflected by the proportion of the population with a high school education or above, indicating the level of cultural and educational development in the village. This four-in-one indicator system can more accurately and comprehensively reflect the spatial functions of villages.

[0009] This invention focuses on the village scale and conducts household-by-household surveys, a choice of research scale and methodology that offers unique advantages. Compared to the town scale, the village scale is more micro-level and specific, capable of delving into the internal structure and details of the village, fully showcasing its uniqueness and diversity. Each village has its unique geographical environment, resource endowment, historical culture, and socio-economic characteristics, all of which collectively influence the formation and development of village spatial functions. Research at the village scale allows for a more precise understanding of these characteristics, providing a basis for developing personalized development plans for different villages. Household-by-household surveys are a crucial means of obtaining micro-level data within the village. By visiting each household, detailed information on their land use, production and management status, living needs, and aspirations can be obtained, yielding the most authentic and detailed data. Attached Figure Description

[0010] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] A method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data, such as Figure 1As shown, the method includes: acquiring regional spatial geographic information data and preprocessing the spatial geographic information data; using a participatory evaluation method to analyze the preprocessed spatial geographic information data; statistically analyzing the analyzed data to obtain multi-source geographic information data; fusing the multi-source geographic information data; accurately identifying the regional production-living-ecological spaces in the fused data; constructing a spatial function measurement index system based on production-living-ecology-vitality based on the identification results; measuring the regional spatial function characteristics based on the spatial function measurement index system; and allocating spatial resources based on the measurement results.

[0013] The complete process of this invention consists of three parts: data collection and acquisition, data fusion and spatial identification, and measurement methods, which will be described in turn.

[0014] In this embodiment, data collection and acquisition include:

[0015] Data content includes: high-resolution remote sensing imagery from recent years covering typical mountainous villages, UAV aerial survey imagery, large-scale measured topographic and cadastral data (1:2000), DEM data, rural real estate cadastral survey data, geographic national conditions monitoring data, third national land survey data, land use status data, farmland grading data, arable land quality grading data, high-standard basic farmland data, nature reserve red line data, ecological protection red line data, geological survey data, village-level administrative boundary data, rural homestead and collective construction land use rights confirmation and registration, rural housing real estate registration, and other multi-source vector data.

[0016] Data collection methods: Data can be obtained through relevant data platforms, geographic information departments, remote sensing satellite companies, and professional surveying and mapping institutions. For example, high-resolution remote sensing imagery and UAV aerial surveying data can be obtained through cooperation with relevant remote sensing satellite operating companies or professional UAV surveying and mapping companies; large-scale measured topographic and cadastral data can be obtained by commissioning professional surveying and mapping institutions to conduct on-site measurements; other types of data can be obtained from relevant government departments such as geographic information departments and land and resources departments.

[0017] Methods: Spatial analysis techniques were employed to perform orthorectification and fusion correction on the acquired image data to establish a base map for the geographic information spatial data survey. For example, orthorectification tools in GIS software were used to orthorectify remote sensing images to eliminate geometric distortions; image fusion techniques were used to fuse multispectral images with high-resolution panchromatic images to improve image resolution and information richness.

[0018] First, high-resolution panchromatic and multispectral images of the study area were acquired, along with a digital elevation model (DEM) and sensor rational polynomial coefficient (RPC) files covering the same area. Orthorectification based on the RPC model was then performed on the panchromatic and multispectral images. This involved reading the RPC parameters from the image metadata, utilizing the elevation information provided by the DEM, and employing inverse digital differential correction. Specifically, for each pixel in the output orthorectified image, the elevation Z was interpolated from the DEM based on its geographic coordinates (X, Y), and then substituted into the inverse RPC function to calculate the corresponding sub-pixel coordinates on the original image. Finally, grayscale resampling was performed using bilinear interpolation or cubic convolution to eliminate geometric distortions caused by terrain undulations and sensor pose, generating orthorectified panchromatic and orthorectified multispectral images with accurate geocoding. Based on this, image fusion using Gram-Schmidt transform involves: linearly weighting each band of the low-resolution multispectral image to simulate a low-resolution panchromatic band; using this simulated band as the first vector to orthogonally decompose the multispectral bands to obtain a set of orthogonal components; then, histogram matching is used to make the grayscale distribution of the high-resolution orthophoto panchromatic image consistent with the simulated panchromatic band, and the first orthogonal component is replaced with the matched panchromatic band; finally, inverse Gram-Schmidt transform is performed to reconstruct the high-resolution multispectral fused image. Subsequently, based on the fused high-resolution image, combined with field photographs and existing thematic data, visual interpretation of land features in the study area is carried out: for typical land types such as water bodies, vegetation, built-up land, and bare land, their spectral characteristics (brightness range of each band), texture structure (uniformity, roughness), geometric morphology (shape, size, shadow), and spatial distribution patterns are recorded in detail, supplemented by qualitative descriptive text, to establish a land feature interpretation marker library for the study area. Image enhancement techniques such as color compositing, linear / nonlinear transformation, histogram transformation, and principal component analysis (PCA) are employed to highlight the spectral and textural differences of the target land cover types, achieving targeted feature enhancement. Finally, based on prior knowledge provided by the interpretation marker library and referencing other auxiliary data, a method combining human-computer interaction and automatic computer classification is used to extract typical land cover patches: First, supervised classification (support vector machine or random forest) or object-oriented classification (multi-scale segmentation + rule set) is used to automatically perform initial classification of the enhanced image, obtaining preliminary patches; then, the classification results are overlaid on the fused image, and misclassified and omitted areas are visually checked. GIS editing tools are used to manually correct patch boundaries, merge fragments, and remove misclassified categories; finally, the confusion matrix and Kappa coefficient are calculated using field verification points to assess accuracy, ensuring that the classification results meet application requirements. The final output is a vector patch layer of typical land cover areas in the study area.

[0019] In this embodiment, a non-spatial data survey is conducted based on the participatory rural assessment (PRA) method:

[0020] Data content: Using a combination of questionnaires, individual interviews, and small focus groups, semi-structured interviews based on the participatory rural assessment method (PRA) were conducted to obtain socioeconomic data on farmers' population, households, employment, income, consumption, housing, and living conditions.

[0021] Data collection methods: The survey team went deep into the village to conduct door-to-door visits and questionnaire surveys; some representative households were selected for in-depth individual interviews; and some villagers were convened to hold small-scale seminars to discuss relevant issues.

[0022] Methods: During the questionnaire survey, interview techniques were used to guide farmers to actively participate and obtain authentic and detailed information.

[0023] In this embodiment, departmental statistics:

[0024] Data content includes: annual district and county statistical yearbooks, rural economic statistical annual reports, township national economic development plans, township land use master plans, village and town land consolidation plans, rural construction land reclamation, rural land rights confirmation and registration, village and town industrial plans, ecological protection plans, forestry plans, village and town plans, rural plans, urban-rural integration plans, new rural construction plans, agricultural development plans, geological disaster prevention and control plans, geological disaster assessments, arable land quality grades, agricultural land classification data, high-standard basic farmland construction plans, soil survey data, meteorological data, annual land use change data and statistical ledgers, economic census data, population census data, etc.

[0025] Data collection methods: Data can be obtained through government departments' official websites, data sharing platforms, and other channels.

[0026] Methods: Collected departmental statistical data were organized and analyzed, and data mining techniques were used to extract information related to village spatial functions. For example, data analysis software was used to conduct trend analysis on economic data in statistical yearbooks over the years to understand changes in village economic development; geographic information system (GIS) technology was used to overlay and analyze land use planning data with geographic information spatial data to understand the planning status of village land use.

[0027] In this embodiment, data fusion and spatial recognition:

[0028] Multi-source geographic information data fusion processing and analysis in village areas: Based on a GIS platform and combined with "3S" technology, orthophoto processing was performed on multiple periods of high-resolution remote sensing imagery and low-altitude aerial survey imagery from UAVs. This data was then fused and corrected using a 1:2000 large-scale existing topographic map. Through visual interpretation and field verification, a basic survey base map of spatial geographic information data for typical mountain villages was established. Methods such as coordinate correction, projection transformation, and base map registration were used to uniformly transform other spatial data, including rural real estate cadastral surveys, national geographic condition monitoring, land surveys, ecological red lines, basic farmland, and geological disaster data, onto the base survey map. For example, coordinate correction tools in GIS software were used to convert spatial data from different coordinate systems into a unified coordinate system; projection transformation methods were used to unify the projection of spatial data from different projection methods. Simultaneously, seamless integration technology for geospatial and non-spatial data was integrated, and non-spatial attribute data such as rural land use planning, land consolidation planning, rural construction land reclamation, village and town planning, agricultural development planning, and other related special plans were overlaid onto the base survey map using spatialization methods. For example, planning information on different land use types in land use planning can be mapped onto a base map according to their geographical location, thus achieving the integration of non-spatial and spatial data. This leads to the establishment of a basic geographic information database for village-level spaces.

[0029] In this embodiment, the village's production, living, and ecological spaces are accurately identified:

[0030] Based on a typical mountain village spatial geographic information database, combined with historical remote sensing imagery from different periods and data collected by various departments, the boundaries of land reclamation and agricultural land consolidation plots from recent years in land consolidation and land ticket transactions are overlaid on the base map.

[0031] By combining PRA participatory rural surveys with various methods such as farmer recollections, confirmation of land certificates and property ownership certificates, plot identification, and image verification, the changes in village geospatial information over different periods are accurately traced, and maps of the village's land use status at different times are drawn. For example, based on farmer recollections and land certificate information, the location and extent of farmers' homesteads at different times are determined; based on plot identification and image verification, the planting types and utilization methods of agricultural land at different times are determined.

[0032] By applying spatial-nonspatial data fusion processing techniques to collect nonspatial economic and social data of different farmers obtained from questionnaire surveys, basic geographic information and socio-economic development datasets for typical villages at different periods are constructed. For example, farmers' income, employment, and other socio-economic data are linked with their geographical location information to establish spatial-nonspatial datasets.

[0033] Based on the theory and scientific connotation of village-level production-living-ecological space, this paper analyzes the composition and interrelationship of the village-level three-space system. According to the national standard for land use classification, and in combination with the correspondence between land use classification and the division of the three-space system, following the principle of "bottom-up and top-down", the paper explores the classification system of production space, living space and ecological space respectively, and establishes a village-level three-space division and evaluation system based on land use classification.

[0034] Based on GIS spatial analysis, this study utilizes basic geographic information and socio-economic development datasets of typical villages in Chongqing from different periods in recent years to accurately identify the production, living, and ecological spaces of typical villages in Chongqing at different times. For example, spatial analysis tools in GIS software, such as overlay analysis and distance analysis, are used to analyze land use maps and socio-economic datasets from different periods to identify the scope and characteristics of production, living, and ecological spaces in different periods.

[0035] In this embodiment, the measurement method includes: combining economic and social development data such as population and GDP of typical mountain villages at different periods and farmer survey data, and in accordance with the development goals of intensive and efficient production, livable and moderate living, beautiful ecology and clear waters, and vibrant life, the method comprehensively applies econometrics, landscape ecology and input-output analysis methods to construct a "four-in-one" village spatial function measurement index system from multiple dimensions such as quantity scale, utilization structure, spatial form and quality and benefits.

[0036] Ecological space function indicators: taking into account factors such as disaster situation (number of geological disaster sites, number of historical floods, number of historical meteorological disasters), human activities (proportion of homesteads within the ecological protection red line, harmless treatment rate of domestic waste), and ecological service value (ecological service value of cultivated land, forest land, grassland, wetland, and water area).

[0037] Living space functional indicators: taking into account factors such as population (proportion of homesteads within the ecological protection red line, harmless treatment rate of domestic waste), living conditions (reasonableness of per capita living area, homestead vacancy rate, number of dilapidated houses), infrastructure and public service facilities (distance from urban area, distance from the nearest town, drinking water compliance rate, parking facility satisfaction rate, 15-minute coverage rate of educational facilities, 15-minute coverage rate of medical facilities, and 15-minute coverage rate of commercial facilities);

[0038] Production space functional indicators: taking into account factors such as income and employment (average annual income of villagers, proportion of villagers employed in the village), primary industry (output per unit area of ​​primary industry, ratio of labor force to cultivated land, rate of non-grain use of cultivated land, and rate of abandoned cultivated land), secondary industry (output per unit area of ​​secondary industry), and tertiary industry (total output of tertiary industry and number of tourist visits).

[0039] The vitality space function index considers factors such as the status of intangible cultural heritage (quantity, types, and number of inheritors), tangible cultural heritage (quantity), protection of distinctive resources (quantity and scale), level of cultural facilities (whether there is a village history museum, number of other cultural facilities, and number of historical and cultural protection units), and cultural activities (number of participants in cultural activities). This reflects the people-centered rural revitalization development concept. Combining research methods such as hierarchical analysis, the spatial function index of the village area is measured at different periods, the spatial function characteristics of the village area at different periods are assessed, the spatial evolution characteristics of typical villages in Chongqing at different periods are analyzed, and the interrelationship characteristics between village areas at different periods are revealed.

[0040] In this embodiment, the construction of a multifunctional evaluation index system for rural settlements includes: based on the above understanding of the relationship between the system structure and function of rural settlements, from the perspective of the "four-in-one" integration of production, living, ecology and vitality, the hierarchical analysis method is used to comprehensively construct a multifunctional evaluation index system for rural settlements, which integrates elements such as land, buildings, facilities, industry and population structure; as shown in Table 1.

[0041] Table 1 Evaluation Index System for "Four Functions"

[0042]

[0043] In this embodiment, the indicator data processing includes: based on the multifunctional evaluation indicator system for rural settlements constructed above, in order to accurately measure the functional status of rural settlements in various aspects, it is necessary to carry out data processing for each indicator data. The following will explain the data sources and data processing for each functional indicator from four spatial dimensions: ecology, living, production, and vitality.

[0044] In this embodiment, the calculation of the rural settlement function index includes: to eliminate the influence of the units of measurement of the evaluation indicators, the extreme value method is used to normalize the original data; to overcome the interference of subjective factors on the evaluation results, the entropy weight method is used to calculate the entropy value and the difference coefficient, and then the weight is calculated; on this basis, the weighted summation method is used to calculate each function index and to conduct a multi-functional evaluation of rural settlements.

[0045]

[0046] In the formula: is the standard value of the multi-functional evaluation index for rural settlements; n is the number of villages. Weights for evaluation indicators; For the functional indices of rural settlements, the Jenks method in ArcGIS was used to classify the intensity of living, production, and ecological functions into three levels: high, medium, and low. Specifically, these include:

[0047] (1) Indicator normalization processing:

[0048] The range method is used to map each index value to the [0,1] interval, eliminating the influence of dimensions. The formula is:

[0049]

[0050] in, The original value, , These are the minimum and maximum values ​​of the indicator.

[0051] (2) Weight determination:

[0052] Objective weights are calculated using the entropy weight method, and then combined with expert subjective judgment using the analytic hierarchy process (AHP) to form a comprehensive weight. The formula for the entropy weight method is:

[0053]

[0054] in, Let be the information entropy of the j-th indicator.

[0055] (3) Calculation of functional index:

[0056] The production-living-ecological-vitality function index is calculated using a weighted summation model, and the formula is as follows:

[0057]

[0058] in, Let i be the functional index of the i-th spatial unit. As the indicator weight, This is the normalized index value.

[0059] (4) Spatial differentiation analysis:

[0060] The functional indices were divided into three levels—high, medium, and low—using the ArcGIS natural breakpoint method (Jenks) to generate a spatial functional zoning map of the village area. Spatial autocorrelation analysis (such as Moran's I) was used to identify the clustering characteristics of the functional indices, providing a basis for spatial optimization, such as analyzing the spatial correlation between high-value areas and low-value areas.

[0061] (5) Dynamic evolution analysis:

[0062] By combining data from multiple periods, the rate of change of the functional index is calculated to analyze the dynamic evolution trend of village spatial functions.

[0063] This invention aims to overcome the shortcomings of existing technologies from three key dimensions: scale, time, and comprehensiveness. Regarding scale, existing technologies often focus on research at a larger scale, with relatively little exploration at the micro-scale. While this large-scale research approach can grasp macro trends, it easily overlooks many small but crucial characteristics within the village. This invention, however, focuses on a more micro-scale approach, conducting household-by-household surveys. Through in-depth visits to each household, data can be accurately obtained at the household level, thus providing a more comprehensive and detailed view of the village's subtle characteristics.

[0064] In terms of time dimension, existing technologies are mostly limited to analyzing current data and situations, with little mining and retrospection of historical data. This makes existing technologies significantly limited in understanding the development and evolution of villages and exploring their internal driving factors. This invention, by collecting historical remote sensing images, land use data, and farmer survey data from different periods, combined with participatory rural survey methods to obtain farmers' memories and cognition, can accurately trace changes in village geospatial information, deeply explore the historical logic and development patterns behind the evolution of village spatial functions, and provide a solid historical basis for predicting future development trends of villages and formulating scientific and reasonable development strategies.

[0065] In terms of comprehensiveness, most existing technologies fail to construct a comprehensive village spatial function measurement index system that integrates production, living, ecology, and vitality from multiple dimensions such as quantity, scale, utilization structure, spatial form, and quality and efficiency. Their index systems are often one-sided and fail to fully and comprehensively reflect the true state of village spatial functions. The index system constructed in this invention, however, possesses high comprehensiveness, starting from four dimensions: living, production, ecology, and vitality, with each dimension further subdivided into multiple specific indicators. The living dimension covers aspects such as housing conditions and livelihood security; the production dimension considers both agricultural and non-agricultural production; the ecology dimension focuses on the natural environment and cultural landscape; and the vitality dimension measures population, economic, and cultural vitality. This comprehensive and multi-layered index system can accurately and comprehensively assess village spatial functions, providing a scientific and accurate quantitative basis for optimizing the layout of village land space.

[0066] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data, characterized in that, include: Acquire geographic spatial data of the region and preprocess the geographic spatial data; Participatory evaluation methods were used to analyze the preprocessed geospatial data. The analyzed data are statistically analyzed to obtain multi-source geographic information data; the multi-source geographic information data is then fused. Accurately identify regional production, living, and ecological spaces from the integrated data; Based on the identification results, a spatial function measurement index system based on production, living, ecology, and vitality is constructed; the regional spatial function characteristics are measured based on the spatial function measurement index system, and spatial resources are allocated according to the measurement results.

2. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, Geographic information spatial data includes: multi-period high-resolution remote sensing imagery data of the region, UAV aerial survey imagery data, measured large-scale current topographic and cadastral data, DEM data, real estate cadastral survey data, geographic national conditions monitoring data, third national land survey data, land use status data, farmland grading data, arable land quality grading data, high-standard basic farmland data, nature reserve red line data, ecological protection red line data, geological survey data, village-level administrative boundary data, confirmation and registration of rural homestead and collective construction land use rights, and rural housing real estate registration.

3. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, Preprocessing of geographic information spatial data includes: using spatial analysis algorithms to perform orthophoto processing and fusion correction on the acquired image data.

4. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, The participatory evaluation method is used to analyze the preprocessed geographic information spatial data, including: obtaining living space function indicators and production space function indicators based on the geographic information spatial data; and revising the ecological space function indicators, living space function indicators, and production space function indicators.

5. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, The statistical analysis of the analyzed data includes: using GIS spatial analysis tools to perform quantitative statistics on the preprocessed geographic information data, generating spatial feature parameters directly related to the "four-in-one" indicator system; for the acquired non-spatial data, spatialization processing is used to couple it with the geographic information data, and the coupled data is labeled onto the vector data of each household's homestead through coordinate matching, and the attribute table is updated.

6. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, The fusion processing of multi-source geographic information data includes: using 3S technology to process orthophotos of multi-period high-resolution remote sensing images and low-altitude aerial survey images from UAVs; fusing and correcting the processed orthophotos with existing topographic maps, and establishing a base map for spatial data of typical mountain villages through visual interpretation and field verification; using coordinate correction, projection transformation, and base map registration methods to uniformly convert spatial data from real estate cadastral surveys, geographic national condition monitoring, land surveys, ecological red lines, basic farmland, and geological disasters onto the base map; and using seamless integration technology of integrated geospatial and non-spatial data to overlay non-spatial attribute data from rural land use planning, land consolidation planning, rural construction land reclamation, village and town planning, agricultural development planning, and other relevant special plans onto the base map using spatialization methods to obtain a spatial basic geographic information database.

7. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, The process of accurately identifying regional production-living-ecological-vitality spaces from the fused data includes: production space, living space, ecological space, and vitality space; using high-resolution remote sensing imagery as the primary data source, radiometric correction, geometric fine correction, and image fusion preprocessing are performed; administrative village boundary vector data, third national land survey data, and change survey data are acquired and used as auxiliary reference data; spectral features, texture features, and geometric features are extracted from the fused images, and a random forest classifier is used for supervised classification; the classification results are mapped to the three-dimensional spaces based on the auxiliary reference data; POI data and nighttime light data are acquired, fused, and the fused data is weighted and superimposed with POI density and nighttime light; the comprehensive index and global mean of the superimposed POI density and nighttime light are calculated, and graticules with a comprehensive index higher than the global mean + 0.5 standard deviations are identified as the core area of ​​the vitality space.

8. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, The spatial function measurement index system based on production, living, ecology, and vitality is constructed as follows: The index system includes 4 target layers and 16 indicator layers. The 4 target layers are respectively the four aspects of ecology, living, production, and vitality. The 16 indicator layers include disaster situation, impact of human activities, value of ecosystem services, population situation, living conditions, infrastructure and public service facilities, population structure, income and employment situation, primary industry, secondary industry, tertiary industry, intangible cultural heritage situation, tangible cultural heritage situation, protection of characteristic resources, level of cultural facilities, and cultural activities.

9. The method for measuring the spatial functional characteristics of rural areas based on multi-source geographic information data according to claim 1, characterized in that, The measurement of regional spatial functional characteristics based on the spatial function measurement index system includes: normalizing the values ​​of each index using the range method; calculating objective weights using the entropy weight method; and incorporating expert subjective judgments through the analytic hierarchy process (AHP) to form a comprehensive weight; calculating the production-living-ecological-vitality functional index using a weighted summation model; dividing the functional index into high, medium, and low levels using the ArcGIS natural breakpoint method to generate a village spatial functional zoning map; identifying the clustering characteristics of the functional index through spatial autocorrelation analysis; and calculating the rate of change of the functional index by combining multi-period data to analyze the dynamic evolution trend of village spatial functions.

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