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Characterization of road surface by means of laser scanner technologies
Published in Maurizio Crispino, Pavement and Asset Management, 2019
M.R. De Blasiis, A. Di Benedetto, M. Fiani, M. Garozzo
There are a number of interpolation algorithms inserted in commercial software packages and the choice of the algorithm is a function of the type and density of the input data. We have chosen the Inverse Distance Weighting (IDW) interpolation method. This deterministic method is ideal for a regular configuration of points (Yang et al. 2004). Criteria used for the selection of the grid size are: The relation between the point density and grid step, for regular point samples is (Hengl 2006): ρ=0.5⋅ANwhere A is the is the studied area, in m2 and N is the number of points within the area A.The relation between the sample interval and the IRI computation: According to the (ASTM-E1926-98 2003), the accuracy of the computed value of IRI depends on the sample interval of profile, reducing the interval typically improves the accuracy. Moreover, they suggest a longitudinal sampling step less than or equal to 0.3 m. In addition to these norms and guidelines, (Olsen & Chin 2012) demonstrated that the IRI value is stable if it has been computed using a longitudinal sampling step in the range of about 12.5–27.5 cm.
Measuring stiffness of soils in situ
Published in Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto, Computer Methods and Recent Advances in Geomechanics, 2014
Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto
This study compares some interpolation methods to evaluate depths of bedrock from a limited number of boring data. Methods include IDW (Inverse Distance Weighting) method, Spline interpolation method (third order) and Ordinary Kriging.
Conventional top-view LiDAR topographic data
Published in Vorawit Meesuk, Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling, 2017
For determining these unknown values, a simple linear interpolation can be used for a homogeneous area, due to their this linear interpolation is fast, but their results may have fewer precision (Axelsson, 1999). It must be wise to use the more complex interpolation algorithms for more complex geometry. Inverse distance weighting (IDW), Kriging, and Spline algorithms are some of common interpolation algorithms. When topographic point cloud data are sparse, such algorithms could become more sensitive to replicate spatial cells of local surfaces. They may have some difficulties for the local grid refinements (Joe & Nigel, 1993; Filippova & Hanel, 1998; Durbin & Iaccarino, 2002). When topographic point cloud data are dense, applying Spline appears to show curve and smooth surface output cells. While using sophisticate-weighted average in Spline algorithm could be appropriately used for explaining variations in surfaces, their generated spatial cells can still be exceeded value range of samples and these cells may not pass through such samples. Applying IDW could be adequately used to represent proper details of local surface variations. The IDW algorithm is simple and relatively easy to explain. This algorithm determines an output cell value using a linear weighted combination set of samples. Such assigned weights can be applied as function of distances from an output cell location relative to input points. However, greater distances can result in less influences to output cell values. Other interpolation algorithms can be found elsewhere (Price & Vojinovic, 2011). In this section, the extracted top-view LiDAR point cloud were used to create three different types of digital elevation models (DEMs): (i) digital surface model (DSM), (ii) digital terrain model (DTM), and (iii) digital building model with DTM (DBM+), employing the IDW algorithms in the rasterization process. While the DSM represent all top surface elevations, e.g. top of canopies, the rooftop of buildings, the DTM, in contrast, represent only the bare Earth's surfaces of the ground, aka the terrain elevations. A distinguish between DSM and DBM+ is that the DSM typically corresponds all returned pulses of the raw top-view LiDAR data without filtering processes (Rottensteiner & Briese, 2002). The DBM+ corresponds to both the terrain and building key components (Fig. 3-15).
Spatial interpolation based on previously-observed behavior: a framework for interpolating spaceborne GNSS-R data from CYGNSS
Published in Journal of Spatial Science, 2023
Although not frequently employed in satellite data analyses, spatial interpolation is commonly used with other types of geospatial data that are often collected at point locations like soil moisture (e.g. (Yao et al. 2013)), precipitation (e.g. (Tait et al. 2006)), temperature (e.g. (Stahl et al. 2006)), aerosols (e.g. (Pfister et al. 2005)), and inundation extent (e.g. (Bales and Wagner 2009)). There are myriad spatial interpolation techniques. Some techniques, like linear, cubic, or spline interpolations assign smoothly-varying functions to interpolate between known data points. Inverse distance weighting (IDW) is another commonly-used spatial interpolation technique, in which interpolated points are calculated using a weighted average of neighboring observations, with the closest neighbors receiving the highest weights. Other interpolation methods, like the linear regression technique, use empirical relationships between the variable of interest and ancillary data to predict the variable at unsampled locations. For example, observed precipitation from rain gauges might be regressed against topographic variables like elevation, and then precipitation at unsampled locations will be interpolated using this regression (Yao et al. 2013).
GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques
Published in International Journal of Injury Control and Safety Promotion, 2021
Amir Mohammadian Amiri, Navid Nadimi, Vahid Khalifeh, Moe Shams
Hotspot mapping techniques can be categorized into three major groups: spatial analysis methods, interpolation methods and mapping cluster methods. Spatial analysis is the process of analysing attributes, locations and connections in spatial data, which can provide various valuable insights. This method includes several subcategories, such as kernel density estimation (KDE), point density estimation (PDE) and line density estimation (LDE). The second approach, interpolation, is the approximate judgment of surface values at the unknown points using the surface values of surrounding better-known points. Inverse distance weighting (IDW), Kriging, spline, and natural neighbour are the most well-known interpolation techniques. The third method of hotspot mapping, mapping cluster, is defined as the degree to which a set of spatial feature and the data values associated with it are applied. Average nearest neighbour, Getis-Ord (Gi*) and Moran’s I are among the most famous mapping clusters (Chainey et al., 2008).
Fluoride enrichment in groundwater and associated human health risk in a tropical hard rock terrain in South India
Published in Human and Ecological Risk Assessment: An International Journal, 2021
The spatial variation maps were created using the spatial analyst extension in ArcGIS 10.1. In this study, the inverse distance weighting (IDW) method is used, which assumes that the points near the known points assume similar comparable values than those falling away from the known points. The benefit of the IDW method is that it is intuitive and effective (Azpurua and Ramos 2010). This interpolation works best with evenly distributed points. The weights are assigned depending on the distance from a known point to an unknown point, and the points of equal distance will have similar weights. The weights can be calculated as shown below where, λi is the weight of point, Di is the distance between point i and the unknown point, α is the power ten of weight.