130 results about "Spatial heterogeneity" patented technology

The invention provides a method for remote sensing and estimating woodland soil organic carbon. As image elements of a remote sensing image of a woodland vegetation-covered area are manifested to be the spectral signature of vegetation, and a remote sensing image vegetation index NDVI is the vegetation index which is applied most widely at present, is most applicable, and can be widely applied to the estimation study of the primary productivity of the vegetation. For this, according to the method, by means of a CASA ecological process model, when the NDVI remote sensing data utilizing the high-resolution remote sensing image for reflecting regional difference are applied to a traditional soil organic carbon estimating model, model parameters with the spatial heterogeneity are simplified into constants, the defect of lowering the estimation precision is overcome, a relation model of the soil organic carbon storage amount and soil foundation breathing is set up according to the soil foundation breathing coefficient having the close relation with the soil organic carbon storage amount, and the remote sensing and estimating study of the woodland soil organic carbon can be achieved.

The invention relates to a method and a system for quantitatively estimating carbon reserves of a native coastal wetland ecosystem. The method comprises the following steps: completing native coastalwetland mapping by adopting multi-source and multi-temporal remote sensing data; obtaining native coastal wetland soil and vegetation samples by adopting a traditional sample plot and quadrat investigation method, and realizing soil carbon reserve space inversion by adopting a geographically weighted regression method; adopting a remote sensing technology to realize biomass inversion of the nativecoastal wetland, and combining a biomass carbon coefficient to realize vegetation carbon reserve estimation of the native coastal wetland; and finally, estimating the carbon reserves of the native coastal wetland by combining the soil carbon reserves and the vegetation carbon reserves. The system and the method break through the limitation that the previous research on the native coastal wetlandis only limited to the estimation of experimental points, greatly improve the estimation precision of the carbon reserves, realize the intuitive and reliable analysis of the spatial heterogeneity andreserves of the carbon reserves, and can provide a scientific basis for the carbon cycle research of an ecological system.

The invention provides methods for calibrating spectral data acquisition systems. These calibration methods produce spectral data sufficiently accurate for use in tissue classification algorithms. The invention improves the accuracy of spectral-based tissue classification schemes, in part, by properly accounting for spatial variations, instrument-to-instrument variations, and patient-to-patient variations in the acquisition of spectral data from tissue samples. Effects that are accounted for include, for example, stray light effects, electronic background effects, variation in light energy delivered to a tissue sample, spatial heterogeneities of the illumination source, chromatic aberrations of the scanning optics, variation in wavelength response of the collection optics, and efficiency of the collection optics.

The invention discloses a pure remote sensing data driven drainage basin water circulation simulation method, comprising the following steps: building a drainage basin water circulation simulation framework including a vegetation interception and evapotranspiration process, a runoff production process and a runoff concentration process and compatible with spatial information; completing the simulation of drainage basin runoff production and concentration processes entirely based on remote sensing data, and getting the background values of total runoff yield and runoff volume of any grid point in a drainage basin; and adding a model parameter self-optimization module, constructing an objective function, carrying out random sampling to generate a simulation parameter sample combination to be calibrated, introducing a model for iterative operation, and determining eligible drainage basin water circulation simulation parameters and runoff production and concentration simulation results. The dependence on the observation data of a traditional meteorological site is reduced effectively, the spatio-temporal continuity and spatial heterogeneity of water circulation are better reflected, and the physical mechanism and multi-source data compatibility of the method are enhanced.

Exercise-induced T-wave alternans (TWA) in coronary artery disease patients reflects significant levels of spatial heterogeneity of repolarization, which may underlie the predictive utility of TWA in estimating risk of sudden cardiac death. A method for assessing spatial heterogeneity of repolarization of a heart of a patient includes the following steps: simultaneously sensing an ECG signal from each of a plurality of spatially separated leads attached to the patient; for a plurality of N beats in each of the ECG signals, identifying a JT interval of each beat; and for corresponding ones of the JT intervals of the ECG signals, calculating a second central moment indicative of spatial heterogeneity of repolarization.

The invention discloses a method for estimating vegetation covering-managing factors of soil erosion by remote sensing, which includes the following steps of 1) classifying remote-sensing images to obtain multiple types of soil and dividing the multiple types of soil into three groups according to spatial heterogeneity and the characteristics changed with time of the types of the soil; 2) acquiring the vegetation covering-managing factors of the first group and the second group of the types of the soil by means of remote-sensing classifying; 3) performing linear-spectrum mixed pixel unmixing on the remote sensing images to obtain a green-vegetation component chart, a soil component chart and a shadow / water component chart, computing to obtain the vegetation covering-managing factor of the third group of the types of the soil according to the formula C=F soil / (1+F green vegetation+F shadow); 4) integrating the vegetation covering-managing factors of the three groups of the types of the soil to obtain the vegetation covering-managing factors of soil erosion of all the soil, wherein the F soil is soil component, the F green vegetation is green vegetation component. The method for estimating vegetation covering-managing factors of soil erosion by remote sensing has the advantages of reasonable estimating results and high universality.

The invention provides a method for predicting epidemic situation by a spatial heterogeneity-based infectious disease propagation model. If subareas comprising an area 1 and an area 2 exist in a city, a common rule that crowd flow causes the propagation of infectious diseases and the statistical data (obtained in the form of questionnaire and the like) about the dynamic flow of the crowd in a day can be explained according to a formula, and a method with better operability is established on the basis. The invention aims at reasonably describing a fact, namely the flow that people go out early and come home late for work, shopping and the like does not cause the migration of population macroscopically, but can cause transregional diffusion of the infectious diseases. As the daily periodic flow of the population increases the number of infected persons in a net input subarea, diseases are diffused in space to change the development speed of the epidemic situation. Particularly, at the initial stage of the epidemic situation, the transregional diffusion has an extremely high speed. The thought has heuristic significance for searching regional economic problems such as transregional consumption, labor input and export, and the like.

The invention discloses a county-level road accident forecasting method based on geographic weighting Poisson regression. Firstly history road accident frequency data of each county in a research scope are collected through data survey; then a model relation between county-level accident frequencies and influencing factors and a geographic position matrix form of influence coefficients of each variable quantity are set, and a geographic weighting function in a parameter estimation process is carried out; and parameter estimation is carried out on a Poisson regression model according to geographic weighting. A problem that existing county-level road accident forecasting does not give consideration to spatial heterogeneity to cause not-high forecasting accuracy is solved, and scientific and reasonable guidance is provided for county-level road accident forecasting and safety prevention and control policy formulating.

The present invention discloses a method and system of land cover verification considering POI (Point of Interest) data spatial heterogeneity. The method comprises: obtaining a POI data set from a Sina Weibo open platform, performing overlapping o the urban area building data provided by an OSM (Open Street Map) and the POI data set, dividing POIs of an urban region and a non-urban region, and correcting a division result; classifying the determined urban region POI, and obtaining the POI classification result of the urban region; classifying different types of POIs of the non-urban region, and obtaining the POI classification result of the non-urban region; generating grid data according to the POI classification result, employing a majority voting algorithm to determine the land cover model of each grid, and generating a land cover classification result based on the POI; and employing a confusion matrix to obtain the verification precision of GlobeLand30 land cover data products based on the POI, and realizing the land cover precision verification considering POI spatial heterogeneity.

The invention provides a method for quantifying the spatial and temporal distribution of traffic accidents based on a built environment and micro-blog data, which belongs to the technical field of urban traffic planning and traffic safety system. A built environment is added as an explanatory variable of the spatial and temporal distribution of traffic accidents, and the interpretability of the built environment to the temporal and spatial distribution of traffic accidents is proved by examples. The method considers the built environment variable as the explanatory variable of the spatial andtemporal distribution of traffic accidents, and proves the interpretability of the built environment variable to the temporal and spatial distribution of traffic accidents. Communities from differentgeographical locations show spatial heterogeneity under the influence of different built urban environments. The invention further presents a method for quantifying the spatial and temporal distribution of traffic accidents.

The invention relates to flood disaster monitoring and early warning, in particular to an integrated flood disaster accurate monitoring and early warning method, which comprises the following steps of: acquiring heterogenous multi-scale monitoring data, and quickly extracting a flood inundation area from large-scale flood disaster data based on a multi-source satellite remote sensing platform through multi-scale super-pixel segmentation; and performing image splicing on the medium-scale flood disaster data based on the unmanned aerial vehicle platform based on the optimal suture line to obtain a flood disaster result of the unmanned aerial vehicle image, and obtaining multi-dimensional features from the small-scale flood disaster data based on the Internet of Things platform in combination with a multi-modal deep neural network and a structured sparse classification selection model. A Bayesian network risk assessment model is constructed, and a basis is provided for flood disaster early warning and decision making; according to the technical scheme provided by the invention, the defects that comprehensive integrated heterogeneous multi-scale monitoring data cannot be effectively and comprehensively utilized, and the problems of spatial heterogeneity, risk dynamic change and risk multi-level of flood disasters cannot be effectively solved can be effectively overcome.

The invention discloses a silicon nanowire chip and a mass spectrum detection method and detection method based on the silicon nanowire chip. The mass spectrum detection method comprises the followingsteps: step 1, manufacturing the silicon nanowire chip; carrying out surface washing pretreatment on the monocrystalline silicon wafer, carrying out metal-assisted etching, and carrying out alkali etching to obtain a silicon nanowire chip with a tip; carrying out surface chemical or nano material modification on the silicon nanowire chip; step 2, evaluating the mass spectrum performance of the silicon nanowire chip; step 3, carrying out top contact sampling and in-situ ionization mass spectrometry detection. According to the method, the nanostructure characteristic and the semiconductor characteristic of the silicon nanowire chip are fully utilized, contact type extraction transfer printing and matrix-free mass spectrometry detection are integrated, and the collection, pretreatment and detection processes of complex samples are greatly simplified; the silicon nanowire chip manufactured by the method has adsorption and extraction functions and a mass spectrum detection function at thesame time, and in-situ information of a sample with spatial heterogeneity can be reserved.

The invention discloses a high-space remote sensing estimation method for soil organic carbon density in a desertification region, by optimizing main model parameters in a soil organic carbon densityestimation process, and fusing the advantage of high spatial resolution of GF-1WFV data and the advantage of high temporal resolution of MODIS data, high-space (16m) and high-precision estimation of the organic carbon density of the soil in the desertification region are realized; and by utilizing remote sensing techniques, the problem of soil organic carbon density scale extrapolation can be wellsolved; the spatial heterogeneity of the organic carbon density distribution of the soil in the sand can be reflected more finely; possibility is provided for multi-scale soil respiration long-term quantitative estimation, scientific data reference is provided for land degradation monitoring and evaluation of desertification areas and implementation work of desertification control engineering, and the application capacity of domestic remote sensing data is improved.

The invention discloses a multi-scale spatial load predicting method balancing spatial heterogeneity, and relates to the field of electric planning and load predicting. According to the technical scheme, the method includes firstly dividing a study region irregularly according to spatial variation coefficients in the study region, performing cluster integration on irregular regions in a single scale according to similarity of the adjacent regions, and obtaining the irregular regions in multiple scales; adopting the C 5.0 decision tree algorithm, and obtaining conversion rules of the irregular regions in each multiple scale so as to drive cellular automata model simulating region land types to change; accordingly obtaining predicted load density of each lank type. The multi-scale spatial load predicting method balancing the spatial heterogeneity is provided on the basis of the spatial heterogeneity of geographical phenomena, distribution of power load in the space can be simulated in more reasonable and realistic manners, and accuracy of spatial load prediction is improved.

The embodiment of the invention provides a normalized difference vegetation index scale conversion model construction method and device. The method comprises the steps of obtaining an OLI remote sensing image of a target area; Wherein the OLI remote sensing image has a first spatial resolution; Preprocessing the OLI remote sensing image; Carrying out object-oriented classification on the OLI remote sensing image to obtain a fine classification result; Determining an acquisition window according to the target spatial resolution; carrying out pixel combination on the OLI remote sensing image according to the acquisition window; and calculating to obtain an NDVI value of the merged pixel, so as to obtain an NDVI upscale image with target spatial resolution, thereby verifying the NDVI image with the same target spatial resolution generated by the MODIS product according to the NDVI upscale image. According to the method, the images are finely classified, so that the NDVI scale effect caused by the surface spatial heterogeneity is more meticulous and deeper.

The invention discloses a reservoir incoming water quantity early warning and forecasting method and system based on medium and small basin flood forecasting, and the method comprises the steps: carrying out the interpolation of rainfall data in space, organically combining the grid rainfall data with the site rainfall data, and carrying out the model rainfall calculation; then, converting the potential evapotranspiration of the soil into the total evapotranspiration of the watershed; constructing a runoff production method library, and performing runoff production calculation by adopting different runoff production methods according to different climate types; combining the time area relation unit line of the drainage basin with the confluence speed, and obtaining the confluence flow process through a convolution formula by means of the surface net rainfall; fitting river section characteristic parameters, performing river flood calculation; and processing non-standard rainfall process data into a standard time period for parameter calibration. According to the method, the spatial heterogeneity of underlying surface attributes such as terrain and vegetation soil in the drainage basin is fully considered, the rainstorm flood process of the medium and small drainage basins is well simulated, and reliable technical support is provided for early warning of the water inflow of the reservoir.

The invention relates to a surface temperature downscaling method, system and device based on an XGBoost learning algorithm. The method includes carrying out region division on the research region according to the impermeable surface coverage; combining different influence degrees of low-resolution urban impervious surface coverage, vegetation coverage and road density in different regions on thesurface temperature, combining the influence of the spatial heterogeneity of the urban underlying surface on the surface temperature, and combining low-resolution thermal infrared image data for establishing a low-resolution nonlinear regression model; according to the high-resolution earth surface parameters and the nonlinear regression model, calculating the earth surface parameters. Compared with the prior art, the method has the advantages that high-resolution prediction of the surface temperature of the urban environment with complex spatial heterogeneity is achieved, the surface temperature of roads, buildings, vegetation and water can be distinguished more meticulously, and the accuracy of urban surface temperature prediction is improved.

The invention relates to flood disaster risk assessment, in particular to a flood disaster risk assessment method based on heterogenous multi-scale monitoring data, which comprises the following steps: acquiring heterogenous multi-scale monitoring data, establishing an index system after analysis, screening and determining a basic risk factor set, determining each network node and causal relationship. constructing a Bayesian network risk assessment model based on each network node and the causal relationship, performing fuzzy evaluation on the network nodes, performing defuzzification, calculating the posterior probability of the target event, and performing flood disaster risk assessment based on the posterior probability of the target event. According to the technical scheme, the defects that in the prior art, spatial heterogeneity of flood risks cannot be effectively handled, and the problems of risk dynamic changes and risk multi-level in the flood disaster breeding, occurrence and development process cannot be effectively solved can be effectively overcome.

The invention relates to an unsupervised image segmentation quality evaluation method based on dM, and the method comprises the steps: carrying out the bilateral filtering and two-dimensional Gabor filtering of an original image, so as to extract a spectrum feature vector and a spatial feature vector; carrying out the transformation of the obtained spatial features, extracting three principal components, and carrying out the superposition with the spectrum features too form a spectrum-spatial feature set; taking the spectrum-spatial feature set as a base map, and calculating the layered spatial heterogeneity of the segmentation region at each dimension; obtaining the intensity value of the layered spatial heterogeneity, and calculating the spatial autocorrelation of the segmentation results of all dimensions; calculating a mean value for representing the intensity value of the layered spatial heterogeneity of each dimension in the feature set and the mean value of Moran indexes, and constructing the space of two values; calculating the Mahalanobis distance between each point in the space and a point (1, 0), and finally carrying out the evaluation of the segmentation quality of theimage according to the Mahalanobis distance. The beneficial effects of the invention are that the method improves the quality evaluation precision and stability, and an evaluation result accords withthe visual features of the human being in a better way.

The invention provides a simulation method for topotactic transformation of a wireless sensing network, wherein generalized Euclidean distance between two sensor nodes is taken as cluster statistic, cluster analysis is performed on wireless sensing network topotactic transformation and impact factors influencing the wireless sensing network topotactic transformation so as to obtain a clustering result, each cluster in the clustering result corresponds to one subzone of one cellular space, zonal switching rules are used for replacing uniform switching rules, and a standard grid partitioning method is adopted for obtaining an asynchronous cellular evolutionary rate. Therefore, space-time difference, spatial heterogeneity of evolution laws and spatial differentiation of evolutionary rate are considered during a wireless sensing network topotactic transformation process.

The invention discloses a land use combination form determination method related to road traffic accident risks. The land use combination form determination method comprises steps of collecting history data and other parameters of each traffic analysis district in a research range, performing dividing on a land use combination form through a clustering method, establishing a geographic weighted regression model according to a partitioning result and extracting a risk land use form highly correlated to traffic accidents, constructing a determination analysis function, establishing determination methods for various land use combination forms, performing determination on a land use combination form of a newly-provided traffic analysis district on the basis of a determination analysis function, and determining risk land use characteristics. The land use combination form determination method related to road traffic accident risks overcome a past problem of lack of variety of land use characteristic representations and spatial heterogeneity, can accurately improve result accuracy and can provide a scientific and reasonable guidance to land planning and traffic safety prevention and control policy.

The invention belongs to the technical field of traffic safety, and particularly relates to a traffic accident prediction method based on hybrid geographically weighted regression, which comprises thefollowing steps: step 1, dividing a spatial research area of a traffic accident, and collecting influence factor data; 2, explaining variables through multiple colinearity verification, and deletingunreasonable explaining variables; step 3, constructing a space weight function as a Gaussian function and a double square function; 4, determining that the bandwidth selection type is a fixed bandwidth and an adaptive bandwidth, and determining that a bandwidth optimization criterion is a corrected red pool information criterion; step 5, constructing and determining an optimal geographically weighted Poisson regression model; step 6, respectively bringing in explanatory variables as global variables to construct a hybrid geographically weighted Poisson regression model to perform a comparisontest; and step 7, constructing and determining an optimal hybrid geographically weighted Poisson regression model. The invention provides a traffic accident prediction method based on hybrid geographically weighted regression, which is sufficient in spatial heterogeneity consideration and high in prediction model precision.

A method, including a computer-implemented method, is provided for in vivo detection of epidermal growth factor receptor (EGFR) mutation status within peritumoral edematous tissue of a patient. The method includes performing quantitative pattern analysis of magnetic resonance imaging (MRI) data corresponding to MRI of in vivo peritumoral edematous tissue to determine a level of spatial heterogeneity or similarity within the in vivo peritumoral edematous tissue. EGFR mutation status is assigned as one of negative or positive based on the level of spatial heterogeneity or similarity determined. A non-transitory computer-readable storage medium and a system are also provided.

The invention discloses a method for recovering the development and production of vegetation of a sandy pasture. In the method, sheep and cattle herding is replaced by poultry culturing on the sandy pasture to recover the vegetation on the sandy pasture. The method for recovering the development and production of the vegetation of the sandy pasture takes the climate features, the topography and landscape features and the vegetation features of four sandy pastures in China and the spatial heterogeneity and diversity thereof into consideration, the operation is easy, food and cost are saved, the growth period is long, diseases are fewer, the meat quality is good, and the economic benefit is high; the pasture yield can be increased, and the local ecology environment is protected; and meanwhile, the income of herdsmen is increased, so the method is good for increasing the income of general herdsmen.

The invention discloses a soil organic carbon density spatial heterogeneity-considered Kriging interpolation model. According to the Kriging interpolation model, influences, on common Kriging methods, of low-spatial heterogeneity and spatial abnormal values are reduced by combining land utilization information; and according to different trend term functions, the Kriging interpolation model comprises a virtual variable regression Kriging method, a mean centralized Kriging method and a median centralized Kriging method. By utilizing the model, high-precision SOCD prediction and mapping are realized, and finally, an optimum prediction method and a prediction result thereof are selected according to model evaluation indexes. The reliability, prediction rationality, prediction precision and decision coefficient R2 of the model are remarkably improved; and meanwhile, the model realizes high-precision SOCD spatial mapping, so that significance is provided for realizing precision agriculture and improving the agricultural production efficiency.

The invention discloses a construction method of a spatial variable coefficient PM2.5 concentration estimation model based on a Re-ESF algorithm. The construction method comprises the following steps:step 1, multi-source AOD product fusion; step 2, data processing of related influence factors and PM2.5 concentration; step 3, obtaining independent variable values and PM2.5 concentration values ofall parts of the monitoring site; step 4, constructing an adjacent matrix; step 5, centralizing the spatial adjacency matrix and calculating a feature value and a feature vector; step 6, solving a regression coefficient of an SVC model based on the Re-ESF; step 7, judging whether variables in the model are remarkable or not; Step 8, model precision evaluation; step 9, 10-fold cross validation; step 10, judging the relation between the number k of neighborhoods and the number n of monitoring station points; and step 11, selecting an optimal model according to the precision evaluation. On the basis of effectively eliminating the influence of spatial heterogeneity and spatial autocorrelation on PM2.5 concentration modeling, a random effect and spatial variable coefficient method is introduced, so that the precision of the concentration estimation model is further improved, and the model resolving time is shortened.

The invention provides an artificial fish nest. The artificial fish nest comprises a floating structure and an adhesion structure connected with the floating structure, wherein the adhesion structurecomprises a columnar frame and an adhesion matrix positioned in the columnar frame, and the columnar frame is provided with at least one opening. The artificial fish nest provided by the invention canfully utilize the space of a water body to provide a spawning place for viscous egg-producing fishes, is favorable for hatching floating fish eggs, provides a shelter place and a baiting place for juvenile fishes, and has a long service life, the space of a water surface and an underwater water column can be fully utilized, the spatial heterogeneity of a habitat environment of fishes is improved,the biodiversity is improved effectively, and the ecology of the water body is restored.

The invention discloses a spatial heterogeneity analysis method for the sensitivity of the roadside parking demand to the parking fee. The distribution rule of sensitivity of the roadside parking demand to the parking price in different spatial positions is analyzed based on a geographical weighted regression model and historical data, a spatial weight matrix is adopted to describe the spatial heterogeneity of the sensitivity of the parking demand to price, the factors affecting the spatial heterogeneity are analyzed based on a generalized linear regression model, and a roadside parking demand elasticity prediction model is built. The method makes up for the defect that the parameters are globally fixed and lacks the consideration on spatial heterogeneity in previous parking demand and parking fee relationship modeling, and a relationship model between road section and resident information and the demand elasticity is built, and the demand elasticity difference between different spatial positions can be analyzed in a differentiated manner. Instances show that the spatial heterogeneity analysis method for the sensitivity of the roadside parking demand to the parking fee put forward by the invention lays a good theoretical foundation for regional dynamic parking fee charging, and is better than previous models.

The invention relates to a method and a device for analyzing spatial correlation of an agricultural product price, and aims to solve the problems that the spatial correlation and the spatial heterogeneity of the agricultural product price cannot be accurately reflected in the prior art. The method comprises the following steps: acquiring agricultural product price information; preprocessing the agricultural product price information and matching the agricultural product price information to a map; establishing a highway network data set; calculating an optimal path of every two agricultural product wholesale markets; determining the distance of the optimal path as a traffic distance space weight coefficient between the two agricultural product wholesale markets; calculating global spatial autocorrelation analytical statistic and local spatial autocorrelation analytical statistic of the agricultural product price information according to the traffic distance space weight coefficient between the two agricultural product wholesale markets; generating an agricultural product price spatial correlation statistical graph according to the global spatial autocorrelation analytical statistic, and generating an agricultural product price local autocorrelation distribution diagram according to the local spatial autocorrelation analytical statistic.

The embodiment of the invention discloses a method for simulating the spatial and temporal migration trend distribution of organic pollutants and relates to the field of organic pollutant migration distribution simulation. The method comprises the following steps that: S1, a database is established; S2, grid division is performed on a research area; S3, a four-level escape model is established, specifically, an escape calculation formula, a Z value calculation formula, a D value calculation formula and an advection calculation formula are integrated, and the formulas are programmed through a Matlab programming platform; and S4, four-stage multi-medium escape model simulation is performed by adopting the four-stage escape model and on the basis of advection rules. According to the method, spatial heterogeneity is solved by gridding the research area; a set of independent parameter files is configured for each grid and is corresponding to years in which simulation is performed, and therefore, the method more conforms to an actual situation; the advection rules are added; and the migration of the pollutants among grids is achieved through the advection rules, so that a simulation result is closer to an actual situation. Compared with an existing method, the method is closer to the actual situation.