Latest Contributions by RichieCarmichael

Landsat Lens is a touch and mouse friendly application for browsing past and present Landsat satellite imagery hosted by Esri. Click here for the live app. Click here for the source code. Using a mouse, a lens can be moved around the map with a standard left mouse click and drag operation. Scrolling the mouse wheel will enlarge or decrease the size of a lens depending on the direction. With a touch device like an iPad, a lens can moved with an intuitive press and drag. To resize, pinch or expand two or more fingers within a lens. Likewise, rotating a lens is achieved by twisting two or more fingers. Unlike with a mouse, touch screens allow the user to manipulate two or more lenses concurrently. By default, the app starts with a lens dated 2017 located close to the Palm Jebel Ali in Dubai. To pick a preset location choose from one of the entries from the Bookmarks dropdown menu. Alternative you can pan or zoom to any area of interest. For one of the preset locations, or your own area of interest, you may want to view changes over time. To do so, use the Windows dropdown menu to add a window showing 2002, 2005, 2010, 2015 or 2017 imagery. By swiping lenses over the basemap and one another you can easily see changes in vegetation, coastlines, rivers and human activity. Use the last option in the dropdown menu to removal all lenses from the map. Known Issues: Support for W3C's touch events is extremely varied across browsers and operating systems. However the author notes that the most consistent behavior has been with Chrome browsers. The Esri hosted Landsat image services ms and ps currently contain imagery from the year 2000 til present. However the imagery is not uniformly distributed over time. Imagery prior to 2014 is fairly sparse. This will likely change over time. ... View more

This developer sample demonstrates how to interact with a multi-level building in three dimensions using Esri's ArcGIS API for JavaScript version 4.3. With a simple click/tap and drag operation a building can be intuitively and smoothly repositioned on the Earth's surface. Note that each floor is a separate graphic but are treated as a single entity when manipulated. Click here for the live application. Click here for the source code. It is important to note that the sample uses a few undocumented API calls to achieve this behavior. As such we caution against using these calls in a production environment as they are unsupported and will very likely change in the near future. The code may seem overly or unnecessarily complicated, below is a summary of what the code does: Building Construction For portability reasons, building floors are constructed from a set of hardcoded values. It is important to note that each floor is an individual graphic attributed with a building identifier. This identifier is used to group all adjacent floors for highlighting and spatial translation. Dragging The code is using the SceneView's drag event to respond to the three phases of a pointer's drag operation, namely start, update and end. During the "start" phase, a hittest is performed to identify the building floor (if any) under the pointer. If a floor is found, adjacent floors are identified and highlighted. In the "update" phase, two undocumented methods SceneView._stage.pick and SceneView._computeMapPointFromIntersectionResult are used to tracking pointer displacement in real world coordinates. These methods return the map location directly beneath the pointer, ignoring features and graphics. Disabling/Enabling Pointer Interaction When a building is dragged to a new location, it is necessary to disable map interaction so that map does not pan. To achieve this we used another undocumented method, SceneView.inputManager, to add and then restore handlers. Moving At present, it is not possible to update the geometry of an existing graphic. In order to show a graphic moving it must be deleted and then re-added. This may seem somewhat cumbersome but the performance hit is negligible. Throttling It is likely that the rate of the drag event is fired will exceed the display frame rate. If the display is performing at 60 frames per second (or better) then excessive drag events are ignored. Once again I would like to stress that this is a developer sample specifically for version 4.3 of the Esri JavaScript API. It is very likely that some of the code used is this application will be either obsolete or redundant (or both) in future releases. Special thanks to Johannes Schmid‌ for his technical expertise! ... View more

Arctic Dem is a prototype app developed a few years ago in conjunction with President Barack Obama's executive order calling to "enhance coordination of national efforts in the Arctic" . With a small preliminary dataset from the Polar Geospatial Center we created this proof of concept. Our intention was to experiment with the dynamic rendering of ArcGIS Image Services. For example, the first two sliders define the sun's position used by the image service to dynamically generate a hillshade from the elevation dataset. The second group of sliders are used to highlight a subset of elevation pixels that satisfy the height, slope and aspect criteria. Likewise, this rendering is performed dynamically using out-of-the-box rendering functions. Easter Egg: Click the "hillshade" label to toggle between the standard hillshade function and a multi-directional hillshade custom function. Please click here to access a global multi-directional hillshade. For a detailed description of the data and a user guide please click the orange buttons in the lower left hand corner of the application. Click here for the live application. Click here for the source code. For the production application please visit the ArcticDEM Explorer and read the associated press release. Special thanks to David Johnson‌ for preparing and republishing this service. ... View more

Welcome to the new Applications Prototype Lab GeoNet group!  Our blog, discussions and other updates are now hosted on the GeoNet Community for better integration with industry, product and developer forums.  For your convenience in the future many of our more popular historic blog postings will be ported to, and redirected to, our new GeoNet home. A little bit about us.  We are small group of twelve geospatial professionals engaged in pre-sales activities, corporate assignments and applied research and development.  We are based at Esri's headquarters in Redlands, California. Left to right, front to back: Lenny K., John Grayson, Bob Gerlt, David Johnson, Carol Sousa, Richie Carmichael, Al Pascual, Mark Smith (Manager), Witold Fraczek, Mark Deaton, Thomas Emge and Hugh Keegan. Please be sure to click "follow" in the top right corner of the overview page to be alerted to new announcements, apps, snippets or postings. If you're new to GeoNet, we also encourage you to check out the GeoNet Help group for tips and FAQs on how to get started and get the most out of your community experience. Thanks for joining us and we look forward to seeing your contributions! ... View more

The parallel fences generated in the X and Y directions by two separate runs of the Parallel Fences tool. Note:  The 3D Fences Toolbox was updated Feb 28, 2017.   This article introduces and discusses a geoprocessing toolbox that can perform geostatistics on vertical slices of three dimensional point clouds.Click here to download the toolbox. For years the users of ArcGIS and its Geostatistical Analyst extension have been able to perform sophisticated geostatistical interpolation of the data samples in two dimensions. With that, the creation of continuous maps of any phenomenon that was measured only at selected locations was feasible. With the growing popularity of analysis in 3D and the availability of 3D data, the necessity of an option to perform geostatistics on 3D data is in demand. Studies of atmospheric cross-sections, geologic profiles, and bathymetric transects became an integral part of GIS. In each one of these three classical elements (air, earth and water), we can measure natural phenomena, such as the gradient of air temperature, the content of an ore at various depth of the geologic strata, or the salinity of the ocean along adjacent vertical transect lines. In GIS we analyze not only the natural phenomena, but also those that are man-made. Human impacts on air, earth, and water are also measured. Among those human contributions to the environment that could be analyzed in 3D are plumes of air pollution and oil spills. The later may migrate down in the ground and be moved by the ground water drift, or migrate from the bottom of an ocean up to its surface while sometimes being dragged by ocean currents. Having an insight into such occurrences can greatly increase our understanding of the phenomena. This was the motivation for creating the 3DFences Toolbox. A slice is a vertical subset of the 3D data analogous to a slice of bread from a loaf. While typically narrow in one dimension it still is a 3D object. A fence is a 2D representation of a slice of 3D data. All points which belong to a slice are projected, or pressed onto a 2D plane. The term of a fence diagram, or a fence, is used in geology to illustrate a cross section of geologic strata generated from an interpolation of the data coming from a linear array of vertical drillings. The equivalent of a fence in the atmospheric sciences is usually referred to as a curtain. Top view of a slice of points shown in purple, and the resulting fence presented as a colored line in the center of the input points. Side view of the input slice points located on one side of the resulting fence. The points are the measurements of oil in sea water after an oil spill. To be explicit, the tools in the 3D Fences toolbox do not perform 3D interpolation. The approach offered by the 3DFences Toolbox does not implement geostatistical analysis directly on the vertical slices of the 3D data. Instead, the tools transform a slice of the 3D data, with its X, Y, Z, and the measure of a phenomenon component by rotating it by 90° to a horizontal 2D plane. The geostatistical interpolation method of Empirical Bayesian Kriging (EBK) is performed on these points producing either the geostatistical surface of Prediction, which is a continuous map of the concentration or intensity of something, or a map of the Prediction Standard Error, which can be explained as a map of a degree of confidence in the Prediction map at each location of the map. The resulting output is converted to a point dataset where the points represent the interpolated value at the center of the raster cells. The points are then placed back into the original coordinate space as a regular matrix of points resembling a fence. The fence is positioned in the center of the selected points of the initial slice, when displayed in ArcScene or ArcGIS Pro. The raster is converted to a point dataset because ArcGIS does not currently support display of raster data as a vertical plane and point symbology options provide added flexibility in displaying results. Any of the ArcGIS standard or geostatistical point interpolation tools could have been implemented in the tools. For the prototype, we have chosen the Empirical Bayesian Kriging (EBK) geostatistical method known for its best fitting default parameters and accurate predictions. The 3DFences toolbox consists of three separate tools to support different methods of generating fences. The Parallel Fences tool can generate sets of parallel fences in the directions that are related to either longitudes, latitudes or depths.  In other words, the output sets of parallel fences stretch from N to S, W to E, or through the Z dimension.  The number of these fences in each set is determined by the user. All of the tools support selection sets to create fences from a subset of sample point features. A fence created along digitized "S" shape. The Interactive Fences tool can generate fences based on lines digitized on the map. The user sets the buffer distance from the digitized line. All points that are located within the buffer will be used for the geostatistical analysis. The user may digitize multiple lines and even self-intersecting lines with many vertices. The third tool, called Feature based Fences, creates fences based on existing features in a polyline feature. In this case the fence shape is determined by the existing feature(s) and extends through the Z dimension of the selected sample points. As an example, it might be applied to investigate for oil leaks above an oil pipeline placed on the sea floor. All of the tools contain options enabling the user to determine the minimum number of sample points and fence size required to generate a reasonable geostatistical surface. The tools are also time aware. If the sample data contains a date-time field and the option is enabled, a fence will be generated for each time interval if the samples for that interval and location meet the minimum requirements set by the user. The resulting fences representing consecutive time windows are positioned at the same locations. Thus, to enable better visual analysis, these should be displayed as time animations. An example of an atmospheric curtain created from backscatter data acquired by NASA from its Calipso satellite orbiting at 32 km above the Earth's surface. Contributed by Bob G. and Witold F. ... View more

Ürümqi in China is the remotest location on Earth, geographically speaking of course. This discovery and the analysis behind it are discussed in a recently published story map entitled Poles of Inaccessibility. Vilhjalmur Stefansson, an Icelandic explorer, introduced the world to the concept of inaccessibility with his 1920's computation of the Arctic's pole of inaccessible. Story map authors Dr Witold Frączek and Mr Lenny Kneller recomputed this Arctic location and inaccessible locations in six continents. Frączek and Kneller computed and compared remote locations using geodesic and planar computations. Differences were small with the exception of the Eurasian continent which has a variation of approximately 11 kilometers. The authors discovered that South America was essentially bi-polar with respect to inaccessibility. While both locations are located in Brazil, and separated by 1,400 km, the difference in inaccessibility was less than a kilometer. It is conceivable that the order of remoteness may change with the interpolation and generalization of the coastline. Lastly, I would encourage you to take a moment to read and view Frączek and Kneller's Poles of Inaccessibility. ... View more

Hydro Hierarchy is an experimental web application for visualizing the US river network. Click here to view the live application. Source code is available on agol and github. There are approximately a quarter of a million rivers in the United States, but only 2,500 are displayed in this application.  This subset represents streams with a Strahler stream order classification of four or greater.  The stream data used by this application is derived from the USGS's National Hydrographic Dataset and has undergone significant spatial editing.  Streams geometries have been adjusted to ensure connectivity, generalized for small scale mapping and re-oriented in the direction of flow. River flow data was acquired from the USGS's WaterWatch website.  Each river segment is attributed with the average flow for each month in 2014 and the ten year monthly average.  Computed values, in cubic feet per second, represent the flow at the downstream end of each river.  Flow data is displayed as a column chart on the left hand side of the browser window whenever the user's mouse passes over a stream. The preview animated image at the beginning of this post may look sped up.  It is not.  Upstream and downstream rivers are highlighted in real time as the user moves his or her cursor over the hydrologic network.  This performance is achieved using connectivity information loaded when the application first starts from this file.  The file was creating in ArcMap from a network dataset that included the river feature class.  Using this script, connectivity information for each network node was extracted and arranged into a hierarchical data structure. The radial and column charts on the left hand side of the application are generated using the D3 graphics library.  The column chart displays 2014 flow data for any river segment that is directly below the user's mouse.  The horizontal red line represents the ten year mean monthly flow.  Note that for most river segments, only one or two months ever exceeded the ten year average.  This is indicative of 2014's drought, at least with respect to river flows over the past decade.Contributed by Richie C. and Witold F. ... View more