Solar Eclipse Finder

Solar Eclipse Finder is a JavaScript-based web application that displays past and future solar eclipses that pass through a user defined location.  The source data is published as an AGOL hosted service with the paths of 905 solar eclipses from 1601 to 2200.  The eclipse paths were prepared by Michael Zeiler from data courtesy of Xavier Jubier.

The live application is available here.

The source code is available here.

Originally developed 2½ years ago as a Silverlight-based web application (see blog posting here), we wanted to confirm that the same performance and advanced symbology is achievable today with HTML5/JavaScript in modern browsers.

jQuery & Bootstrap

jQuery is a JavaScript framework for DOM manipulating.  It is important to note that jQuery is not a prerequisite for mapping apps using Esri‘s ArcGIS API for JavaScipt. It is however a prerequisite of many third party JavaScript libraries like Bootstrap, a popular user interface framework.  This application uses Bootstap’s popover tooltips in the fly-out attribute window and its modal dialog during start-up.

D3

The tapered symbol used by eclipse shadow paths is achieved using a linear gradient fill.  Linear gradient fills are not supported by ArcGIS API for JavaScript.  However linear gradient fills are supported by SVG, the underlying technology used by Esri’s JavaScript API for renderering vectors.  We used Mike Bostock‘s D3.js JavaScript library to insert and apply linear gradient fills directly to the map’s embedded SVG node.

Conclusion

Updating this application was a two step process.  First the eclipse dataset was republished as an AGOL hosted feature service and, second, the app was rewritten in HTML/JS.  Both tasks were relatively effortless and only took a couple of days in total.

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GeoJigsaw

GeoJigsaw is a community driven geographic jigsaw puzzle.  If you are feeling creative you can create a puzzle and share it with the puzzle community.  If you are feeling competitive, try beating the high score on someone else’s puzzle.

Click here to view the live application.
(works best in Microsoft Internet Explorer and Mozilla Firefox)

Click here to download the source code.

GeoJigsaw is a JavaScript-based web application inspired by a Silverlight application developed about two years ago called Puzzle Map.  Unlike a recently published geogame that uses Facebook, in this application we wanted to explore anonymous collaboration.  That is, anyone can anonymously create, share and play puzzles.

Voronois

In the app developed two years ago, the puzzle design was static.  In this application we wanted to offer puzzles of varying difficulty and size so we needed to implement a technique of dynamic puzzle creation.  After a little research we discovered this example of a voronoi tessellation using D3.  D3′s voronoi implementation and associated SVG-based visualization library are the basis of this game.

Unlike the D3 sample, our app did not use completely randomized points.  If a user created or selected an “impossible” puzzle then a 10 by 10 grid of points is created and nudged slightly before being turned into 100 piece voronoi diagram using D3.  This was only part of the puzzle (excuse the pun), each piece needed the addition of one or more tabs.  Tab addition is essential to give the game its recognizable jigsaw look. Through a process of iteration, tabs are appended to any side of sufficient length and reversed if an opposing tab exists.

SVG Filters

The finishing touch to give the puzzle a realistic look is the application of an inner bevel using a SVG filter.  SVG filters are hardware accelerated in Internet Explorer and Mozilla Firefox but not in Google Chrome.  Unfortunately the Chrome’s software rendering of SVG filters makes complex puzzles almost unplayable.  This may change in future releases of Chrome.

Security

Puzzles designs, ratings and scores are stored in ArcGIS Online (AGOL) hosted services.  We intended the application and associated services to be accessed by non-AGOL users.  This meant that AGOL user credentials could not be used to restrict access to prevent unanticipated malicious activity.  As such, we used the security model discussed in the previous post, that is, app registration and an intermediate web proxy.

Libraries Used

Conclusion

This project demonstrations that modern browsers are more than capable of impressive visualizations without the need of plugins such as Silverlight or Flex.  We also wanted to experiment with anonymous game play, time will tell if the lack of user identification is an incentive or disincentive to play.  Good luck!

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Map Quiz

Map Quiz is fun JavaScript-based geo-game developed by the Applications Prototype Lab. The game tests your geographic knowledge with six randomly selected satellite images. Prove that you are a geo-genius at home, school or in your workplace!

Click here to access the live application.

Click here to download the source code.

Introduction

This project is a port of an application initially developed as a Windows Store application.  The purpose of the application is to present a fun geography-focused game (or “geo-game”) based on Esri technology.  The application is primarily based on Esri’s ArcGIS API for JavaScript and is powered by ArcGIS Online (AGOL) hosted services for scoring and questions.  Originally the Windows Store based application used AGOL-based authentically, in the JavaScript edition we decided to use Facebook authentication in an effort to appeal to a large audience.

Why Facebook?

One of the motivations of gaming is the thrill of competition, whether personally or with others.  In order to identify users so that they could monitor their scores, or those of others, there needed to be some sort of authentication.  The obviously choice is AGOL.  AGOL is the perfect choice when collaboration is needed within the GIS community but a large percentage of the target audience of this application may not have AGOL credentials. Arguably, Facebook may not be completely ubiquitous but certainly common.  As such, we decided to use Facebook and the Facebook API to authenticate users.  To ease privacy concerns this application requests and displays only a small subset of profile information, specifically, a person’s profile picture, last name and initial of first name.

Controlling Access to Hosted Services

The game’s questions, answers and scores are stored in two ArcGIS Online hosted feature services.  Hosted services are easy to create and allow for powerful spatial queries.  However access to hosted service is either unrestricted, or confined to an organization or to users that belong to specific groups.  Because this game is intended for non-AGOL users, we needed a way of restricting access to the hosted services to just the Map Quiz web application.

This was achieved by registering the app on AGOL.  The resulting app id and secret were then used in a web proxy that granted exclusive access to the hosted services to only the Map Quiz web application.  The proxy and instructions how to implement it are here.

Spinning

The spinning map on the landing page or the gradual zooming of each question is achieved using CSS3 animation and the animo JavaScript library (see above).  On modern browsers the animation effects are smooth and consistent.  With respect to the spinning map, map edges needed to be expanded outwards to avoid white patches appearing in the corners.  To avoid this we applied negative margins using a Pythagorean computation.  One disadvantage of the spinning map is that image seams are occasionally observed.

Silverlight vs. JavaScript

As a reformed Silverlight developer I have been pleasantly surprised with the performance and capabilities of JavaScript-based web applications.  To date, I have yet to encounter any Silverlight capability that could not be achieved with HTML5/CSS3.  The biggest issue has been the paradigm shift from Silverlight’s large well-documented framework to the necessity of working with a half dozen lightly documented open source libraries.

Libraries Used

As mentioned above, this project is based on a few open source libraries, these libraries are listed and described below.

  • animo.js by Daniel Raftery
    This is a small but powerful library that provides programmatic creation and management of CSS animations.
  • ArcGIS API for JavaScript by Esri
    Esri’s JavaScript library for mapping and analysis.
  • Bootstrap by Twitter Inc
    A useful and comprehensive UI framework.  This application leveraged a subset of bootstrap pertaining to buttons and input elements.
  • Facebook SDK for JavaScript by Facebook
    Allows easy authentication and access to user profile information.
  • jQuery by jQuery Foundataion Inc
    A JavaScript framework for DOM manipulation and a foundation for many other frameworks.

Conclusion

This  project was a fun and enjoyable exercise and we hope the reader is equally entertained by this application.  We showed that geo-games can be easily created with Esri’s JavaScript framework and cloud services together with common libraries like jQuery, bootstrap and Facebook.

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Map Lens

Map Lens is a sample JavaScript-based web application that demonstrates lensing or perhaps a better description is “draggable map insets”.  Lensing offers many benefits over traditional swiping which is commonly used to horizontally transition between two web maps, for example, before and after a hurricane imagery.  In comparison, lensing offers the user an unlimited number of rectangular map overlays, for example, one lens per Esri basemap as shown above.

Click here for the live application.

Click here for the source code.

Lensing is achieved with the ArcGIS API for JavaScript and leverages jQuery and jQueryUI, specifically the draggable and resizeable methods.  The biggest challenge developing this sample was the handling (or suppressing) of the various mouse and navigation events.

Map lensing is not without its disadvantages.  Maps embedded in each lens cannot contain dynamic content, maps ideally should only contain tiled map or image service.  This is a performance consideration due to the number of navigation events invoked during lens interaction.

 

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Designing and Optimizing Image Services for High-Performance Analysis

Image services are not only for serving imagery; they can also perform dynamic pixel-level analysis on multiple overlapping raster datasets using chained raster functions.  Image services deliver blazing fast performance with pre-processed source imagery, especially when it is served from a tile cache.  Dynamic image services, on the other hand, may not respond as quickly because of the additional processing demands they place on the server.  High performance is important for dynamic image services because the data is re-processed automatically each time the user pans or zooms the map.  Dynamic image services typically produce sub-second responses for simple types of analysis.  But more complex analysis may take much longer depending on the complexity of the processing chain and the number of input datasets involved.  How can we get better performance in those situations?  To answer that question I ran a series of tests to see how the following factors affect the performance of dynamic image services:

  • Map scale and source data resolution
  • Resampling method
  • Source data format and compression
  • Project on-the-fly
  • Request size

In this article I present the results of those tests along with some suggestions for maximizing performance.  My testing machine is a desktop computer running Windows 7 SP1 and ArcGIS 10.2.1 with 18GB of RAM and a quad-core Intel Xeon W3550 processor running at 3 GHZ.  The test data was stored on an otherwise empty 2 TB SATA hard drive that I defragmented and consolidated prior to testing.   The tests were configured to determine the average response times of services under various conditions.  By “response time” I mean the time it takes a service to retrieve the source data, process it, and transmit an output image.  Transmission time was minimized by running the testing application directly on the server machine.

This information is written with the intermediate to advanced GIS user in mind.  I assume the reader has a general understanding of image services, raster data and analysis, raster functions, geoprocessing, mosaic datasets, map projections, and map service caching.

Map Scale and Source Data Resolution

The pixels that are processed for analysis by dynamic image services are generally not identical to the pixels stored in the source datasets.  Instead, the source data pixels are first resampled on-the-fly to a new size based on the current scale of the map.  This formula shows the relationship between map scale and resampling size when the map units of the data are meters:

Resampled pixel size = map scale * 0.0254/96

The resampled pixel size is analogous to the “Analysis Cell Size” parameter in the Geoprocessing Framework and is sometimes referred to as the “pixel size of the request”.  As you zoom out to smaller map scales, the resampled pixel size increases until eventually the service resamples from the pixels in the pyramids.  Resampling from pyramids helps to keep the performance of the service relatively consistent over a range of map scales.

Chart 1. Performance of an image service that performs a binary overlay analysis over a range of map scales.

Performance still varies depending on map scale and typically looks similar to chart 1.    I generated these results using an application configured to simulate a single user panning the map 100 times in succession at specific map scales.  The chart shows the average time the service took to process and transmit the output images for different map scales.  This particular service was configured with a raster function template to perform a binary overlay analysis on eleven overlapping rasters in a mosaic dataset.  The pixel sizes of the source datasets ranged from 91.67 to 100 meters.  The raster function template was configured to return a binary result, where each output pixel is classified as either “suitable” or “unsuitable” based on the analysis parameters.

Take a look at the three points along the horizontal axis where the response time drops abruptly.  At those map scales the resampled pixel size is the same as the pixel sizes of the pyramids in the source data.  The processing time for resampling is the lowest at those scales because there is nearly a 1:1 match between source data pixels and resampled pixels.  Client applications which use this particular service will see dramatically faster response times if they are limited somehow to only those scales.  One way to do this is to use a tiled basemap layer.  Web mapping applications which use tiled basemaps are generally limited to only those map scales.  The most commonly used tiling scheme is the ArcGIS Online/Bing Maps/Google Maps tiling scheme (referred to hereafter as the “AGOL tiling scheme” for brevity).  The red-dotted vertical lines in the chart indicate the map scales for levels 7 – 12 of this tiling scheme.  Unfortunately those scales are not very close to the scales where this service performs it’s best.  There are two options for aligning source data pixels and tiling scheme scales:

  1. Build a custom basemap with a custom tiling scheme that matches the pixels sizes of the data.
  2. Sample or resample the data to a pixel size that matches the tiling scheme of the basemap.

Chart 2. Performance of the binary overlay analysis service with different source data pixel sizes

The horizontal axis in chart 2 represents the “pixel size of the request” rather than map scale as in chart 1.  The orange graph shows the response times of another service configured identically to the first one in blue, except it uses source datasets that were up-sampled to 38 meter pixels using the Resample geoprocessing tool.  Up-sampling to 38 meters aligned the service’s fastest response times with the AGOL tiling scheme scales, which resulted in a significant decrease in processing time at those scales from approximately 1.5 seconds to about 0.5 seconds.  Furthermore, notice that performance is improved at nearly all scales except for the very largest.  This is most likely due to having all the source data at the same resolution (38m) instead of three (91.67m, 92.5m, 100m), and/or because the source data pixels are also aligned between datasets (accomplished by defining a common origin point for each resampled raster using the “Snap Raster” environment setting).

Admittedly, using the Resample tool to prepare data for analysis is not ideal because it results in second-generation data that is less accurate than the original.  This may be perfectly acceptable for applications intended to provide an initial survey-level analysis;however, it’s best to generate new first-generation data at the desired pixel size whenever possible.  For example, if you have access to land-class polygons, you could use them to generate a new first-generation raster dataset at the desired pixel size using the Polygon to Raster tool, rather than resampling an existing land-class raster dataset.

To determine by how much performance improved with 38 meter pixels, I calculated the percentage change in average response times for each scale and averaged the values over multiple scales.

Up-sampling the source data to 38 meter pixels reduced response times by  63.8% at the poorest-performing -target map scales!  38 meters was not my only option in this example.  I could have chosen a size that corresponded to one of the other tiling scheme scales.  The following table lists all the map scales of the AGOL tiling scheme, and the corresponding pixel sizes in units of meters, feet and Decimal Degrees.   The three columns on the right provide suggested values for sampling raster data.  These suggestions are not set in stone.  It’s not necessary to sample your data to exactly these recommended sizes.  The key is to choose a size that is slightly smaller than one of the sizes of the target tiling scheme scales.

Map Scales and Pixel Sizes for the ArcGIS Online/Bing Maps/Google Maps tiling scheme

By the way, matching the pixel sizes of your data with a basemap tiling scheme is also useful for workflows that involve static imagery overlaid onto a tiled basemap.  For those cases, you can build mosaic dataset overviews for viewing at smaller scales instead of raster pyramids.  One of the great things about mosaic dataset overviews is that you can define the base pixel size of overviews as well as the scale factor to match your target tiling scheme.  This way you don’t have resample the source data to a new base pixel size in order to cater to any particular tiling scheme.

Resampling Method

The resampling method specified for an image service request also has an impact on performance.  The choice of which one to use should be based primarily on the type of data used in the analysis.  Chart 3 shows the performance of the binary overlay analysis service (with 38 meter data) with different resampling methods.

Chart 3. Response times of the binary overlay analysis service with different resampling methods

Bilinear resampling is the default method.  Here is how the response times for the other methods compared to bilinear averaged over the five map scales tested:

Raster Format

The storage format of the data can have a huge impact on performance.  For example, the response time of the binary overlay analysis service averaged over all map scales was 36% lower when the data was stored in the GeoTIFF format versus file geodatabase managed raster.  The Data Sources and Formats section of the  Image Management guide book recommends leaving the data in its original format unless it is in one of the slower-performing formats such as ASCII.  GeoTIFF with internal tiles is the recommended choice for reformatting because it provides fast access to the pixels for rectangular areas that cover only a subset of the entire file.

Pixel Type and Compression

The pixel type determines the precision of the values stored in the data and can have a huge impact on performance.  In general, integer types are faster than floating-point types, and lower-precision types are faster than higher-precision types.  Compression of imagery can potentially increase or reduce performance depending on the situation.   For more information about the affect of compression on file size refer to the Image Management guide book section on Data Sources and Formats.  To assess the impact of pixel type and compression on the performance of data stored on a local hard drive, I tested a group of image services configured to perform an extremely intensive overlay analysis on 15 raster datasets.  The services were configured identically except for the pixel and compression types of the analysis data.  The tests were run at the map scale corresponding to the pixel size of the data.

Charts 5 & 6. Avg. response time and storage size vs. compression type for an image service that performs a complex overlay analysis

The following table shows the percentage change in response times with the reformatted datasets versus the original double-precision floating-point dataset.

On-the-fly Projection

On-the-fly projection is a very important feature of the ArcGIS platform.  It has saved GIS users like me countless hours of work by eliminating the need to ensure that every dataset in a map is stored in same coordinate system.  However, in some cases this flexibility and convenience may be costly when ultra-fast performance is required.   The following chart shows one of those cases.

Chart 8. Performance of a dynamic image service configured to perform a weighted overlay analysis.

Chart 8 shows the performance of a service which performs a weighted overlay analysis  on six datasets in an Albers projection.  The upper graph shows the performance when the output of the service is set to Web Mercator (Auxiliary Sphere).  The lower graph shows the performance when the output of the service is the same coordinate system as the data.  Performance without reprojection to Web Mercator improved by an average of 45% over all map scales.  This is a fairly extreme example.  The performance cost of reprojection is related to the mathematical complexity of the input and output projections.  Equal-area projections such as Albers are mathematically complex compared to cylindrical projections such as Mercator.  I have not run tests to prove this, but I expect that the performance cost of reprojection between two cylindrical projections such as UTM and Web Mercator would be less costly than seen in this example, and that a simple projection from geographic coordinates to Web Mercator would be even less costly.

To avoid on-the-fly projection you must ensure that all of your data is in the same coordinate system, including the basemap.  Most of the basemap services currently available from Esri on ArcGIS Online are in Web Mercator (auxiliary sphere).  So if you are going to use one of those basemaps, you would have to convert your data to the same coordinate system.  This can be an acceptable solution for some situations, but keep in mind that it results in second-generation data with less positional accuracy than the original source data.  Alternatively, you can create your own basemap in the same coordinate system as your data, and either publish it to an ArcGIS Server site or upload it to ArcGIS Online as a hosted map service.  If you take this approach, I recommend caching the basemap using a custom tiling scheme with scale levels that match the pixel sizes of your data.

Request Size

Request size is directly related to the size of the map window in the application and is specified in the REST API as the number of rows and columns of pixels in the output image.  To measure its impact on performance, I ran a series of tests at different request sizes on the weighted overlay analysis service that I used for the reprojection-on-the-fly tests.  I measured the average response times for request sizes ranging from 400×400 to 2200×2200, increasing at 100 pixel increments (e.g. 500×500, 600×600, etc…).  All of the tests were run at the map scale of 1:113386, which corresponds to the 30 meter pixel size of the source raster datasets.

Chart 9. Average response in MP/s for different request sizes for the weighted overlay service.

Chart 10. Average response time at different request sizes for the weighted overlay service.

Chart 9 shows that the throughput for this service levels off at a request size of approximately 1000×1000 pixels to about 1.5 – 1.6 MP/s.  Chart 10 shows that request size has a linear impact on performance.  This service is capable of providing sub-second response times for requests up to about 1,440,000 pixels, or a request size of 1200×1200.

Summary

Raster analysis can involve many stages of data processing and analysis.  Complex on-the-fly processing chains can place heavy processing loads on a server and contribute to sluggish performance.  Huge performance improvements can be achieved in some cases by pre-processing the data into a more efficient format for resampling and on-the-fly processing.

For applications which use tiled basemap layers, the greatest performance improvements are likely to be achieved by aligning the pixel sizes of the data with the scales of the basemap tiling scheme.  The section “Map Scales and Source Data Resolution” describes the theory behind this approach and provides a table with recommended pixel sizes for applications which use basemaps with the ArcGIS Online/Bing Maps/Google Maps tiling scheme.  Alternatively, developers can build basemaps with custom tiling schemes to align with the existing pixel sizes of the analysis data.

Another way to significantly reduce the processing load on a server in some cases is to avoid on-the-fly projection of the analysis data.  This is accomplished by ensuring that the basemap and the analysis data are in the same coordinate system.  The performance impact of on-the-fly projection varies depending on the input and output coordinate systems and is discussed in the section titled “On-the-fly Projection”.

The file format, pixel type, and compression type of the analysis data can also have a huge impact on performance.  GeoTIFF with internal tiles is recommended for situations where it’s necessary to re-format the data from a slower format.  Lower-precision pixel types give better performance than higher-precision types.  Pixel compression has the potential to either increase or decrease performance depending on the how the data is stored and accessed by the server.  These topics are discussed in the sections titled “Raster Format” and “Pixel Type and Compression”.

Client applications can also play a role in dynamic image service performance.  Service response times are the lowest when applications specify nearest neighbor resampling, followed by bilinear resampling.  And there is a direct relationship between service performance and the size of the map window in an application.  These topics are discussed in the sections titled “Resampling Method” and “Request Size”.

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Landsat Pivot Viewer

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The live application is available here.

The source code is available here.

The landsat program recently celebrated its 40th birthday. Since the launch of the first satellite in 1972 the program has amassed more than 3,000,000 images.

The USGS has published this archive as a single ArcGIS Image Service called LandsatLook. The prototype described in this posting uses this service in an HTML5 web mapping application.

While the map view is perfect to identify an area of interest, it is not so useful for sorting through hundreds or thousands of overlapping images. This prototypes uses a control developed by LobsterPot called the PivotViewer to present a sortable collection of imagery. Using the map and pivotviewer together, the presenter in the video above was able to quickly find and download a recent cloud-free image of London.

Contributed by Richie C.

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Thank You – Latest Innovations from APL session @EsriDevsummit

A huge thank you to all of you who attended our sessions yesterday at Esri Developer Summit in Palm Springs. There were many topics and technologies covered during the sessions and we hope that you found our work interesting, informative and worthwhile. For those who didn’t get a chance to attend our sessions, looks like one of the session got recorded so you might get a chance to catch up soon via online videos soon.

We were enthused and thrilled to have such huge attendance for both the sessions, you were a great group and your enthusiasm and comments helped make our time together both productive and fun.

Please join us at the Esri Labs area in the upcoming Esri User Conference this year at San Diego for more awesome stuff we are working on. Follow us on @esrilabs twitter account for more updates and details closer to the event.

Here are a few pictures and tweets about the sessions yesterday, Thanks and see you all soon!


Click here to view tweets on Storify

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Proximity Map for Windows Store

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This video demonstrates peer-to-peer collaboration in a mapping application developed using the ArcGIS Runtime SDK for Windows Store apps.  The app uses Windows 8 native proximity capability to establish an ad hoc Wi-Fi Direct connection that is fast, low latency, secure and doesn’t require any existing network.

The first part of the video shows extent synchronization or “shared navigation”.  As a map on one device is panned or zoomed, its extent is immediately broadcast to the other device.

The second part is somewhat more advanced.  We developed a map-friendly overlay, similar to a graphics layer, that could broadcast graphics as they are being digitized.  Graphics, or partial graphics, are serialized to JSON and transmitted to the other device.  Note: the ability to digitize to a graphics layers will be part of the next release of the ArcGIS Runtime (see Map.Editor.RequestShapeAsync()).

The last part of the video shows the ability to offset one screen from the other.  Any extents that are transmitted or received are automatically shifted to give the illusion that the screens are connected.  This would be useful if someone wanted to create a single map display from multiple devices.

We have shown that map collaboration with Windows 8 is viable even in the absence of a local network.  The proximity capability at Windows 8.0 is still rather limited, for example, only two devices can participate in a Wi-Fi Direct connection.  Later this year when Windows 8.1 is released, proximity will be expanded to support ad hoc networks with two or more nodes using either Wi-Fi or Bluetooth.

Cinematography by Mark D.

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QR Map for Windows Store

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This video demonstrations a proof-of-concept that uses a dynamic QR code to communicate map information from a presenter’s screen to mobile devices.  Whenever the presenter’s map changes, the map’s coordinate extent is compressed and encoded into a QR code using the ZXing.Net.

The same app used by presenter is also installed on the mobile device, in this instance, a Microsoft Surface.  Rather than a “presenter mode”, the app on the mobile device is placed in a “following” mode, this turns on the devices back facing camera and uses ZXing.net to scan for, and decode, coordinate extents.

We agree that by itself it may seem somewhat unremarkable.  We found it intriguing because the communication, while one way, was established without traditional connectivity such as Bluetooth or Wi-Fi.  This technique illustrated here may be useful in classroom settings, seminars and briefings.

References:

Video filmed by Mark D.

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Geometric Network Configuration Manager

This post discusses the release of a sample utility called Geometric Network Configuration Manager.  In some instances when it is necessary to temporarily remove a geometric network, this tool can recreate the geometric network from a backed definition file.

The add-in can be downloaded from here.

The source code is available here.

Configuration Manager has a very long lineage.  More than a decade ago the Prototype Lab published Geodatabase Designer for documenting geodatabases and exchanging schema.  Designer is now obsolete but fortunately much of its capabilities are now incorporated into ArcMap or other tools such as ArcGIS Diagrammer and XRay for ArcCatalog.

However the one feature from Designer that has yet to be replicated is the ability to save and restore geometric networks.  This useful if loading large amounts data or performing a schema change like switching a feature class from a simple edge to a complex edge.

Prerequisites

Libraries Used

The following section will walkthrough the steps required to backup geometric network, remove it and then restore it.  The use case for this workflow could be for bulk data loading or transferring a geometric network from a test server to a production server.

Walkthrough

Following the successful installation of the add-in.  Display the Geometric Network Tools toolbar and click the first button to launch the main dialog.  Drag and drop a geometric network into the configuration manager window.

The complete definition of the geometric network will be loaded into the dialog.  The four tabs below the ribbon make it is possible to review and, in some cases, modify classes, weights and connectivity rules.

The geometric network definition is current stored in memory and should really be saved to a file so that it can backed-up or restored at a later time.  Click the save or Save As button to export the definition to a file with a esriGeoNet extension.

Recreating a geometric network is just a matter of loading an esriGeoNet file and clicking Export.  The application will prompt the user for the name and location of the exported geometric network.  If the geometric network already exists, it will be overwritten.

Known Issues:

  • The dialog that appears when the Export button is click may be hidden by the Configuration Manager window.  Either minimize or move the window to the side to continue with the export operation.
  • Add and removing of network classes and weights is currently not supported.
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