Esri International User Conference July 23 27 San Diego Convention Center Surface Creation & Analysis with 3D Analyst Khalid Duri
Surface Basics Defining the surface Representation of any continuous measurement with one value for a given x-y location. z = ƒ(x,y) Elevation Pollution Epidemiology Ground stability Unlimited possibilities More than just topography!
Surface Data Types Overview of Raster & Vector Surfaces Raster Surface Created by interpolation Rectangular matrix of cells arranged in rows & columns Source measurement precision generalized to cell size GDB & file based formats Vector Surface Created by triangulation Irregularly spaced data points connected by edges Source measurement precision maintained TIN, terrain, & LAS dataset
Vector Surface Formats Supports irregular distribution of nodes, providing higher resolutions where measurements vary greatly and lower resolution in areas w/ less variability. TIN Single-resolution Terrain Multi-resolution Supports LAS attributes Suited for large scale, archival data storage Loaded into memory for display Suited for smaller collections of high density data LAS Dataset Dynamic resolution Designed for airborne lidar Quick to create, references data
Delaunay Triangulation Vector Surface Concepts Avoids long, thin triangles Maximizes smallest interior angle of each triangle No vertex lies within circumcircle of another triangle
Surface Feature Types Vector Surface Concepts Mass points: Measurements used for triangulation Erase polygon: Interior areas of no data Replace polygon: Assigns a constant z value Clip polygon: Defines the interpolation zone Also supports: Break lines Tag fill polygon Note: Tag fill polygons provide a means for applying classification attributes (e.g. land use codes).
Surface Feature Types: Break Lines Vector Surface Concepts Surface measurements that capture linear features (e.g. roads, ridges, shorelines, etc ) Densified to ensure Delaunay triangulation rules Impact is visible when exporting raster Without Break Lines With Break Lines Note: Densification of break lines in a TIN can be ignored by specifying constrained Delaunay triangulation. This reduces overall size, while disabling natural neighbor interpolation.
Surface Feature Type: Hard vs. Soft Vector Surface Concepts Qualifiers for line and polygon based surface feature types Hard features denote sharp break in slope Soft features denote gradual change in slope Soft Break Lines Hard Break Lines Note: Impact of soft vs. hard designation is only reflected in the raster exported from the triangulated surface using Natural Neighbor inteprolation.
Triangulated Irregular Network (TIN) Overview Single resolution Recommended 15-20 million node limit Typically used for high-precision modeling of smaller areas Advantages Interactive editing Rendered in ArcScene Supports constrained Delaunay triangulation at break lines Note: Maximum allowable size of a TIN varies relative to free, contiguous memory resources. It s recommended to cap the size at a few million nodes for usability.
TIN Creation & Editing Demo
Terrain Dataset
Terrain Dataset Overview Multi-resolution TIN Stored in a geodatabase feature dataset Commonly used in bathymetric & topographic mapping Typical data sources include lidar, sonar, & photogrammetry Advantages Control over scale display resolution Grouping options for non-mass point surface features types Supports anchor points & LAS attributes (class codes, returns, etc ) Note: Terrain dataset is not supported in ArcScene & can only be rendered in ArcMap.
Z-Tolerance vs. Window Size Pyramids Terrain Concepts Scale-dependent subsets of source measurements designed for optimizing display and analysis performance. Window Size Partitions data into small areas & selects one or two source measurements to contribute to the next pyramid level Suited for urban landscapes Quick to build Z-Tolerance Determines the minimum number of points to ensure vertical accuracy within the defined z- tolerance from source data points More accurately depicts data characteristics Note: Click here for more information on terrain pyramids.
Terrain Construction Demo
LAS Dataset
LAS Dataset Overview Dynamic resolution Designed for airborne lidar References data from LAS files Treats LAS measurements as mass points Advantages Provides rapid display of LAS Provides interactive LAS classification editing Supports anchor points Can be rendered in ArcScene
LAS Dataset Creation & Editing Demo
Raster Interpolation
Inverse Distance Weighted (IDW) Raster Interpolation Cell values are determined using linearly weighted set of sample measurements. Weight is a function of inverse distance. Strengths Suited for densely sampled measurements Fast processing Supports barrier features Note: Interpolated values fall within Z-range of sample measurements.
Kriging Raster Interpolation Predictive geostatistical method that assumes spatial autocorrelation Multi-step process involving exploratory statistical analysis & variogram modeling Weight is determined by distance & spatial arrangement Ordinary Kriging assumes no trend, unversal Kriging assumes overriding trend Strengths Offers multiple semivariogram models Provides variance prediction raster to indicate level of confidence in predicted value Diverse applications (e.g. health sciences, geochemistry, geology) Semivariance Empirical Semivariogram Distance Note: Choosing the most appropriate estimation method requires interactive investigation of the sample measurement s spatial behavior.
Natural Neighbor Raster Interpolation Applies weights to closest subset of source measurements to a query point Weight based on proportionate of overlap between Voronia polygons around source measurements & query point Strengths Surface passes through the sample measurements Smooth except at sample measurements Note: Interpolated values fall within Z-range of sample measurements. Does not infer trends nor capture sharp features that are not found in source measurements (e.g. ridges &valleys)
Spline Raster Interpolation Estimates values using mathematical function that minimizes curvature between source measurements Curve must fit a specified number of data points Regularized method creates smooth surface; tension method is more constrained to source measurement range Strengths Surface passes through the sample measurements Supports barriers Infers trend Note: Interpolated values will exceed Z-range of sample measurements.
Topo To Raster Raster Interpolation Specifically designed for creation of hydrologically correct elevation models Imposes contraints that ensure connected drainage structure Produces correct representation of ridges and streams from contours Strengths Supports point elevation measurements, contours, streams, sinks, boundary and lake polygons Parameters can be saved to a file and reused Note: A minor bias in the interpolation algorithm causes contours to have a stronger effect on the resulting surface at the location of the contour.
Trend Raster Interpolation Fits polynomial function to source measurements Supports up to 12 th order polynomials Logistic trend option generates prediction model for presence/absence of certain phenomena Strengths Ideal for fitting sample points when surface varies gradually from region to region (e.g. air pollution) Useful for examining effects of longrange/global trends Note: Resulting surfaces are highly susceptible to outliers.
Raster Interpolation Demo
Surface Analysis
Surface Analysis Overview Provides wide range of functionality including data management & conversion, to surface analysis. Interactive Tools Rapid results on full resolution data Available via following toolbars: - 3D Analyst - TIN Editing - LAS Dataset Geoprocessing Tools Can be leveraged for automation Large collection of functionality - Data management & QA\QC - Surface derivatives & analysis - Visibility
Surface Analysis Demo
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