Measurements of the directivity of acoustic sound sources must be interpolated in almost all cases, either for spatial upsampling to higher resolution representations of the data, for spatial resampling to another sampling grid, or for use in simulations of sound propagation. The performance of different interpolation techniques applied to sparsely sampled directivity measurements depends on the sampling grid used but also on the radiation pattern of the sources themselves. Therefore, we evaluated three established approaches for interpolation from a low-resolution sampling grid using high-resolution measurements of a representative sample of musical instruments as a reference. The smallest global error on average occurs for thin plate pseudo-spline interpolation. For interpolation based on spherical harmonics (SH) decomposition, the SH order and the spatial sampling scheme applied have a strong and difficult to predict influence on the quality of the interpolation. The piece-wise linear, spherical triangular interpolation provides almost as good results as the first-order spline approach, albeit with on average 20 times higher computational effort. Therefore, for spatial interpolation of sparsely sampled directivity measurements of musical instruments, the thin plate pseudo-spline method applied to absolute-valued data is recommended and, if necessary, a subsequent modeling of the phase.

Geoscience Australia has been deriving raster sediment datasets for the continental Australian Exclusive Economic Zone (AEEZ) using existing marine samples collected by Geoscience Australia and …Show full descriptionGeoscience Australia has been deriving raster sediment datasets for the continental Australian Exclusive Economic Zone (AEEZ) using existing marine samples collected by Geoscience Australia and external organisations. Since seabed sediment data are collected at sparsely and unevenly distributed locations, spatial interpolation methods become essential tools for generating spatially continuous information. Previous studies have examined a number of factors that affect the performance of spatial interpolation methods. These factors include sample density, data variation, sampling design, spatial distribution of samples, data quality, correlation of primary and secondary variables, and interaction among some of these factors. Apart from these factors, a spatial reference system used to define sample locations is potentially another factor and is worth investigating. In this study, we aim to examine the degree to which spatial reference systems can affect the predictive accuracy of spatial interpolation methods in predicting marine environmental variables in the continental AEEZ. Firstly, we reviewed spatial reference systems including geographic coordinate systems and projected coordinate systems/map projections, with particular attention paid to map projection classification, distortion and selection schemes; secondly, we selected eight systems that are suitable for the spatial prediction of marine environmental data in the continental AEEZ. These systems include two geographic coordinate systems (WGS84 and GDA94) and six map projections (Lambert Equal-area Azimuthal, Equidistant Azimuthal, Stereographic Conformal Azimuthal, Albers Equal-Area Conic, Equidistant Conic and Lambert Conformal Conic); thirdly, we applied two most commonly used spatial interpolation methods, i.e. inverse distance squared (IDS) and ordinary kriging (OK) to a marine dataset projected using the eight systems. The accuracy of the methods was assessed using leave-one-out cross validation in terms of their predictive errors and, visualization of prediction maps. The difference in the predictive errors between WGS84 and the map projections were compared using paired Mann-Whitney test for both IDW and OK. The data manipulation and modelling work were implemented in ArcGIS and R. The result from this study confirms that the little shift caused by the tectonic movement between WGS84 and GDA94 does not affect the accuracy of the spatial interpolation methods examined (IDS and OK). With respect to whether the unit difference in geographical coordinates or distortions introduced by map projections has more effect on the performance of the spatial interpolation methods, the result shows that the accuracies of the spatial interpolation methods in predicting seabed sediment data in the SW region of AEEZ are similar and the differences are considered negligible, both in terms of predictive errors and prediction map visualisations. Among the six map projections, the slightly better prediction performance from Lambert Equal-Area Azimuthal and Equidistant Azimuthal projections for both IDS and OK indicates that Equal-Area and Equidistant projections with Azimuthal surfaces are more suitable than other projections for spatial predictions of seabed sediment data in the SW region of AEEZ. The outcomes of this study have significant implications for spatial predictions in environmental science. Future spatial prediction work using a data density greater than that in this study may use data based on WGS84 directly and may not have to project the data using certain spatial reference systems. The findings are applicable to spatial predictions of both marine and terrestrial environmental variables. You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

The first case study focuses on spatial interpolation for ground station-based environmental data. Spatial interpolation is a critical process in environmental science, where data from ground stations are used to estimate values in unmeasured locations. This case study showcases how our Common Workflow Language (CWL) based Workflow Management System (WfMS) can handle complex spatial data processes, ensuring accurate and efficient interpolation. In this case study, two spatial interpolation models were implemented. IDW (Inverse Distance Weighting) is a deterministic method for spatial interpolation, which estimates the values at unknown points using the values from known points weighted by the inverse of their distances. Kriging is a geostatistical interpolation technique that not only considers the distance between known and unknown points but also models the spatial autocorrelation among the measured points. It provides more accurate estimations by using a weighted average of known points, where the weights are determined based on the spatial structure of the data.

Abstract The Colombian Biogeographic Choco (CBC) and the La Plata Basin (LPB) are regions with high biodiversity. However, these areas are characterized by scarce climatological information, complex orography, and rain-gauge network unevenly distributed. Interpolated data from the ground station might overcome these aspects. For this reason, is necessary to identify the best technique for the spatial interpolation of rainfall. Hence, the spatial interpolation techniques were applied to annual and seasonal rainfall in the CBC and LPB. Geostatistical results and deterministic approaches were compared by cross-validation. Cokriging with spherical (gaussian) model is the best interpolator in the CBC (LPB), as indicated by the lowest root mean square error (RMSE) and a standardized RMSE close to one. The CBC shows three rainfall cores: the northern, 9,000 mm/year; the central-southern, 10,000 mm/year; and the southern, 7,000 mm/year. The LPB shows a west-east rainfall gradient, with a minimum to the west (450 mm/year) and a maximum in the mid-west (2,000 mm/year). To the north of the LPB, rainfall reaches 1,500 mm/year, while in the south it reaches only 900 mm/year. The results in our study may be useful for scientists and decision-makers for use in environmental and hydrological models for the CBC and the LPB.

Spatial interpolation techniques play an important role in hydrology as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, not all of them work consistently for hydrologic applications. One of the techniques, Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for interpolating hydrology data. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating hydrologic data and compare it with other widely used methods such as the inverse distance weighting (IDW), natural neighbor, and ordinary kriging. Comparison is performed quantitatively for using root mean square error (RMSE) and R2, visually for creating reasonable surfaces and computationally for ease of operation and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture are used for different areas in the United States. RMSE and R2 results show that LF is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to IDW and Kriging. Computationally, LF is faster than other methods in terms of speed when the datasets are not large. Overall, LF offers a robust alternative to existing methods for interpolating various hydrology data. Further work is required to improve its computational efficiency.

This dataset includes 3D µCT images of nine different specimen of 10 mm \times 10 mm of a carbon fiber reinforced polyamide 6 plaque produced in the long fiber reinforced thermoplastic direct (LFT-D) process. The position of the specimen in the plaque can be learned from the referenced publication (Blarr et al., Implementation and comparison of algebraic and machine learning based tensor interpolation methods applied to fiber orientation tensor fields obtained from CT images, Computational Materials Science, 2022). After small pre-processing steps, the fiber orientation tensor of each of the image stacks is determined with the help of the structure tensor based implementation of Pinter et al. The code can be found here: https://sourceforge.net/p/composight/code/HEAD/tree/trunk/SiOTo/StructureTensorOrientation/FibreOrientation/StructureTensorOrientationFilter.cxx#l186. Hence, nine .dat-files containing the fiber orientation tensor of second order are also included in this dataset. Most importantly, this dataset contains three different Python codes. The author implemented a different interpolation method in each of those codes; two algebraic and one machine learning based one. The component averaging method is the simplest; the decomposition method is mathematically more difficult. It works with the decomposition of the tensor into shape and orientation and subsequent separate invariant and quaternion weighting, before reassembling the then interpolated tensor. The deep learning based method is the only Jupyter notebook in this dataset, where an ANN is implemented for the same interpolation task. Please consider the reference paper mentioned before for details. For the visualization of the tensor glyphs, a Matlab function by Barmpoutis is used, which can be found here: https://de.mathworks.com/matlabcentral/fileexchange/27462-diffusion-tensor-field-dti-visualization.

Accurate mapping and disaggregation of key health and demographic risk factors have become increasingly important for disease surveillance, which can reveal geographical social inequalities for improved health interventions and for monitoring progress on relevant Sustainable Development Goals (SDGs). Household surveys like the Demographic and Health Surveys have been widely used as a proxy for mapping SDG-related household characteristics. However, there is no consensus on the workflow to be used, and different methods have been implemented with varying complexities. This study aims to compare multiple modelling frameworks to model indicators of human vulnerability to malaria (SDG Target 3.3) in Senegal. These indicators were categorised into socioeconomic (e.g., stunting prevalence, wealth index) and malaria prevention indicators (e.g., indoor residual spraying, insecticide-treated net ownership). We compared three categories of the commonly used methods: (1) spatial interpolation methods (i.e., inverse distance weighting, thin plate splines, kriging), (2) ensemble methods (i.e., random forest), and (3) Bayesian geostatistical models. Most indicators could be modelled with medium to high predictive accuracy, with R2 values ranging from 0.40 to 0.86. No method or method category emerged as the best, but performance varied widely. Overall, socioeconomic indicators were generally better predicted by covariate-based models (e.g., random forest and Bayesian models), while methods using spatial autocorrelation alone (e.g., thin plate splines) performed better for variables with heterogeneous spatial structure, such as ethnicity and malaria prevention indicators. Increasing the complexity of the models did not always improve predictive performance, e.g., thin plate splines sometimes outperformed random forest or Bayesian geostatistical models. Beyond performance, we compared the different methods using other criteria (e.g., the ability to constrain the prediction range or to quantify prediction uncertainty) and discussed their implications for selecting a modelling approach tailored to the needs of the end user.

Urmia Lake, the second largest hyper-saline lake in the world, has experienced a significant drop in water level during the last decade. This study was designed to examine the water quality of Urmia Lake and to characterize the spatial heterogeneity and temporal changes of the physico-chemical parameters between October 2009 and July 2010. Two spatial interpolation methods, Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), were used and compared with each other to derive the spatial distribution of ionic constituents as well as TDS and density along the lake. Results showed that the main dominant cations and anions in Urmia Lake were Na+, Mg++, K+, Ca++, Cl- , SO4--, and HCO3-, respectively. Although water quality of the lake is homogeneous with depth, it differs between the northern and southern parts. Water quality also varies seasonally, determined by river inflows and the lake bathymetry. Moreover, with the present salinity level, salt precipitation is likely in Urmia Lake and is becoming one of the principal factors determining the distribution of solutes within the lake. This study shows that the combined use of temporal and spatial water quality data improves our understanding of complex, large aquatic systems like Urmia Lake.

Additional file 2: Benefits of the 3Cs model and indicator selection rationale. Our rationale for choosing the indicators and questions included in this study, as well as for aligning them to the 3Cs model, is detailed further in Appendix B. The authors believe this rationale is important to include, but not essential for and potentially distracting from understanding the study Methods section, so included this rationale as additional data file 2. Source [37] is only referred to in Appendix B, not within the manuscript.

One of the most common problems related to meteorological information is the missing registers. This lack of data generates uncertainties in the analysis of climate, hydrology, and natural disasters. In Mexico, very often, this problem is present in all the meteorological stations of the country. In this study, we apply two well-established spatial interpolation methods that have report competitive performance in the specialized literature: the Inverse Distance Weighting (IDW) and Modified Inverse Distance Weighting (MIDW); and they are compared with a proposal of spatio-temporal regression using an artificial neural network of the kind of multilayer perceptron (MLP). The results show that using a combination of spatial and temporal data with a low number of predictors is competitive with the comparing methods using a high number of predictors. We compare the methods through statistical measures of the error for 31 meteorological stations of the Jalisco state in the period of 2002-2006.

Accurate mapping and disaggregation of key health and demographic risk factors have become increasingly important for disease surveillance, which can reveal geographical social inequalities for improved health interventions and for monitoring progress on relevant Sustainable Development Goals (SDGs). Household surveys like the Demographic and Health Surveys have been widely used as a proxy for mapping SDG-related household characteristics. However, there is no consensus on the workflow to be used, and different methods have been implemented with varying complexities. This study aims to compare multiple modelling frameworks to model indicators of human vulnerability to malaria (SDG Target 3.3) in Senegal. These indicators were categorised into socioeconomic (e.g., stunting prevalence, wealth index) and malaria prevention indicators (e.g., indoor residual spraying, insecticide-treated net ownership). We compared three categories of the commonly used methods: (1) spatial interpolation methods (i.e., inverse distance weighting, thin plate splines, kriging), (2) ensemble methods (i.e., random forest), and (3) Bayesian geostatistical models. Most indicators could be modelled with medium to high predictive accuracy, with R2 values ranging from 0.40 to 0.86. No method or method category emerged as the best, but performance varied widely. Overall, socioeconomic indicators were generally better predicted by covariate-based models (e.g., random forest and Bayesian models), while methods using spatial autocorrelation alone (e.g., thin plate splines) performed better for variables with heterogeneous spatial structure, such as ethnicity and malaria prevention indicators. Increasing the complexity of the models did not always improve predictive performance, e.g., thin plate splines sometimes outperformed random forest or Bayesian geostatistical models. Beyond performance, we compared the different methods using other criteria (e.g., the ability to constrain the prediction range or to quantify prediction uncertainty) and discussed their implications for selecting a modelling approach tailored to the needs of the end user.

We have developed a statistical gap-filling method adapted to the specific coverage and properties of observed fugacity of surface ocean CO2 (fCO2). We have used this method to interpolate the Surface Ocean CO2 Atlas (SOCAT) v2 database on a 2.5°×2.5° global grid (south of 70°N) for 1985-2011 at monthly resolution. The method combines a spatial interpolation based on a 'radius of influence' to determine nearby similar fCO2 values with temporal harmonic and cubic spline curve-fitting, and also fits long term trends and seasonal cycles. Interannual variability is established using deviations of observations from the fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on the spatial and temporal range of the interpolation. Tests of the method using model data show that it performs as well as or better than previous regional interpolation methods, but in addition it provides a near-global and interannual coverage.

This dataset contains the code and data used in the case study mentioned in the paper "A kriging interpolation model for geographical flows".Title A kriging interpolation model for geographical flowsAbstractThe kriging model can accommodate various spatial supports and has been extensively applied in hydrology, meteorology, soil science, and other domains. With the expansion of applications, it is essential to extend the kriging model for new spatial support of high-dimensional data. Geographical flows can depict the movements of geographical objects and imply the underlying mobility patterns in geographical phenomena. However, due to the bias, sparsity, and uneven quality of flow data in the real world, research about flows remains hindered by the lack of complete flow data and effective flow interpolation methods. In this study, we design a kriging interpolation model for flows based on several flow-related concepts and the autocorrelation of flows. We also analyze the second-order stationarity and anisotropy in the flow spatial random field. To illustrate the effectiveness and applicability of our method, we conduct two case studies. The former case study compares several experiments of flow density interpolation using Beijing mobile signaling data and illustrates the conditions of applicable areas. The latter case study extends our model to other flow attributes, such as travel time uncertainty, using Beijing taxi origin-destination flow data. The results of these cases demonstrate the effectiveness and high accuracy of our model.

Inverse Distance Weighting (IDW) is a spatial interpolation technique used to estimate values at unsampled locations based on known values at nearby points. The method assumes that points closer to the location of interest have a greater influence on the predicted value than those farther away. IDW calculates the predicted value by taking a weighted average of the known values, where the weights are inversely proportional to the distances between the known points and the prediction location, raised to a power parameter. This power parameter controls the rate at which the influence of the known points decreases with distance, with higher values giving more weight to closer points. IDW is widely used in fields such as geostatistics, meteorology, and environmental science to interpolate spatial data like rainfall, temperature, and pollution levels.

The data and code for manuscript: An explainable spatial interpolation method considering spatial stratified heterogeneity.1.Environments: -MATLAB R2023a-'System Identification Toolbox'-'Mapping Toolbox'-'Statistics and Machine Learning Toolbox'-'Parallel Computing Toolbox'2.Data:-Raw data and the code for the simulated experiment are located in the 'simulated data' folder. -To generate the simulated data, execute 'sysDataGeneration.m'.-The soil organic matter data cannot be shared publicly due to data protection restrictions.3.Description of data files:-'simulateddata.csv', 'simulateddata-train.csv', and 'simulateddata-test.csv': These files contain all samples, training samples and testing samples, respectively.-Each column in the above tables represents the point ID, stratum ID, x-coordinate, y-coordinate, feature 1, feature 2, coefficient 1, coefficient 2, observation value, and the training sample label, in that order.4.Codes-The code for X-SSHM is stored in the 'X-SSHM' folder. -X_SSHM.m: Code for the X_SSHM model, which includs training of intra-stratum and inter-strata learner, fusing of intra-stratum and inter-stratum features, and interpolation of unknown points . Data for explanation can also be obtained from the workspace of MATLAB after interpolation.-generate_st_feature.m: Code for constructing spatial features.-between_samples_gener.m: Code for generating inter-strata samples for each stratum.-gwr.m: Code for Geographically Weighted Regression model (GWR).-bisquare.m: Code for bi-square kernel function for GWR.-guass.m: Code for Guassian kernel function for GWR.-condnum.m: Code for calculating condition number.-fminsearchbnd.m: Code for finding minimal value with bound constraints.-fminsearchcon.m: Extension of fminsearchbnd.m with general inequality constraints.-golden_section.m: Code for golden selection.-gwr_select_bandwidth.m: Code for selecting optimal bandwidth for GWR.-parseagrs.m: Code for parsing name-value pairs.-rsquare.m: Code for calculating RMSE and R square (R2).-startmatlabpool.m and closematlabpool.m: Code for start and close the parallel pool.-To train the model, interpolate and obtain the data for the explanation, execute 'run.m'.5.Visualization-The GSHAP data can be visualized use the code in 'visualization' folder.-GSHAP_dependence_graph.m: Code for GSHAP dependence graph.-GSHAP_summary_graph.m: Code for GSHAP summary graph.

Peer reviewd paper in the journal Computers and Geosciences. This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called “Universal Kriging” (UK) and “Kriging with External Drift” (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, a physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different software environments, both statistical and GIS.

Website:

http://www.sciencedirect.com/science/article/pii/S0098300407001008

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Result of 4 kinds of different spatial interpolation.

Title of program: JMPDIS Catalogue Id: ACYW_v1_0

Nature of problem Any spatial or temporal data set with sharp jump discontinuities may be interpolated by this subroutine JMPDIS. The data set can have a finite number of discontinuities (less than 10 in this case) or no discontinuity at all.

Versions of this program held in the CPC repository in Mendeley Data ACYW_v1_0; JMPDIS; 10.1016/0010-4655(79)90093-6

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of seabed mud content across the Australian Exclusive Economic Zone (AEEZ), which indicated that machine learning methods are generally among the most accurate methods. In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK) and inverse distance squared (IDS). We aim to identify the most accurate methods for spatial interpolation of seabed mud content in three regions (i.e., N, NE and SW) in AEEZ using samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods and their combinations with OK or IDS) is compared using a simulation experiment. The prediction accuracy changes with the methods, inclusion and exclusion of slope, search window size, model averaging and the study region. The combination of RF and OK (RFOK) and the combination of RF and IDS (RFIDS) are, on average, more accurate than the other methods based on the prediction accuracy and visual examination of prediction maps in all three regions when slope is included and when their searching widow size is 12 and 7, respectively. Averaging the predictions of these two most accurate methods could be an alternative for spatial interpolation. The methods identified in this study reduce the prediction error by up to 19% and their predictions depict the transitional zones between geomorphic features in comparison with the control. This study confirmed the effectiveness of combining machine learning methods with OK or IDS and produced an alternative source of methods for spatial interpolation. Procedures employed in this study for selecting the most accurate prediction methods provide guidance for future studies.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html