GPS World

Category: Applications

  • Locating help: Mapping the homeless population

    By Troy Lambert

    Census data tries to describe for us what the homeless population looks like across the country. Typically the numbers contained in this data are considered to be low, as not all homeless individuals and families are “visible” so getting an accurate count can be challenging.

    An interesting interactive map has been created by Movoto that allows the user to look at the number of homeless per 100,000 people in each state. But Geographic Information Systems (GIS), community involvement, and app builders are helping gather and utilize data to truly make a difference.

    MOVOTO offers an interactive map.
    MOVOTO offers an interactive map.

    It’s not surprising to note that most of homeless shelter users have goals, both short and long term. Kelly A. Schwend , Maureen Cluskey , and Michael Cordell of Bradley University explored these in a study released early this year titled “Lifestyles and Goals of Male Homeless Shelter Users.” While most participants short term goals are focused on employment, almost all of them had medium to long term goals involving housing.

    The questions raised are several. How do we move the homeless from the streets into some kind of housing ladder, and who will assist them? GIS is helping to answer these questions in some of the larger population centers around the country. These programs are merely examples of what can be done elsewhere on a larger or smaller scale.

    San Francisco

    Over 10 years ago, then mayor of San Francisco Garvin Newsom promised that the worst of the homeless problem in one of the richest cities in the world would be gone. Ten years later, the city has moved nearly 20,000 homeless of the streets, but this hasn’t made a dent in the population. It seems that when one individual is helped, another takes their place.

    Photo Credit: CartoDB
    Photo Credit: CartoDB

    San Francisco Open Data contains information on the homeless population, counted by supervisory district. Taking this data, Bill Levay then overlays a San Francisco neighborhood shapefile. This not only shows where the homeless populations are concentrated, but by also adding in mapped locations of public and affordable housing locations, reveals if the resources are located near those in need. You can view the interactive map above here.)

    Photo Credit: CantoDB
    Photo Credit: CantoDB

    For instance, we can see on the map showing the intersection of this data that while a large portion of the homeless population is located near downtown and the South of Market area where there are only a few scattered public housing locations, there is much more public housing clustered together in Chinatown. While this issue has yet to be corrected, this information can be used to inform future decisions when locating resources.

    Los Angeles

    San Francisco is not the only populous city dealing with homelessness. Los Angeles is dealing with one of the largest homeless populations in the nation. A biennial survey taken in January, said to be the most rigorous and accurate so far according to City Labs, reveals 44,359 people sleeping on the streets, in their cars, and in shelters.

    A Los Angeles Survey shows an estimated a homeless population of 44,359, a 12% increase since 2013. (Credit: Los Angeles Times)
    A Los Angeles Survey shows an estimated a homeless population of 44,359, a 12% increase since 2013. (Credit: Los Angeles Times)

    A map created by the Los Angeles Times shows where this population ends up at night. Efforts are spotty at best, although the County’s Housing for Health program wants to have 10,000 permanent housing units created by 2018. Although Mayor Eric Garcetti says ending homelessness is a primary goal, and calls for funding for affordable housing, the problem continues to grow.

    It is hoped that mapping the concentration of the population to help resource teams know what locations to target, the revision of laws prohibiting sleeping in public, and discouraging police raids on homeless encampments will help.

    Baltimore

    Baltimore’s homeless population is smaller than that of Los Angeles, but still significant. The city is using both mapping and a survey taken every two years to locate the homeless and target resources.

    They’ve added another weapon to their arsenal, the Homeless Management Information System, (HMIS) spearheaded by the group The Journey Home and the Mayor’s Office for Health. Using this data, and a new web survey form, the city has obtained a more accurate picture of the homeless population, its location, and the resources still needed.

    The survey, called the Point in Time (PIT), this year counted 2,796 homeless, 88% of whom were housed in shelters. The survey also looked at Housing Information Count (HIC). The study showed some progress and some setbacks, and revealed growth in the category of unaccompanied youth.

    Photo Credit: Esri.com
    Photo Credit: Esri.com

    The map above shows the population, and the location of resources all within a one and a half mile radius. The program not only uses mapping, but employs other technology to attempt to create long term, sustainable, and creative solutions to the city’s homeless issues.

    New York City

    Perhaps the most innovative mapping program in the country involves several apps being used in New York City. Launched in early August the new app called NYC Map the Homeless lets users take a picture of the homeless which is tied to their location, and use hashtags like #man or #sleeping to categorize individuals. They can even choose #violent to let authorities know about individuals perceived to be dangerous.

    Photo Credit: NYC Map the Homeless

    The idea, according to the developer, is to “gather as much data as possible to make sense of the homeless issues we’re seeing.”

    He’s far from the first to try to use technology to address the increasing homeless issues in New York City, Homeless Helper, Feed it Forward, and WeShelter. WeShelter, provides direct assistance to the homeless, and wants create a behavior change from doing nothing to doing something, even if the user is not sure what to do.

    The app lets users donate money to the homeless at the tap of a button, and also send location information to WeShelter, which helps them send outreach teams to areas with the most need.

    Unlike Map the Homeless, WeShelter does not allow users to take pictures in the interest of privacy. it also keeps the location data it gathers closer to the vest, only making it available to homeless outreach groups.

    Regardless of the location or the methodology, it is clear that mapping the locations of the homeless population and the resources available to them is a step in the right direction. GIS plays a large role in aiding social action.

    Want to be a part of the solution? The Journey Home has some answers, but you can also get involved in your own community using the skills you have to aid in the eradication of homelessness. As WeShelter states, it’s all about a change in behavior from doing nothing to doing something.

  • Santa again tracked by NORAD on Christmas Eve

    Santa again tracked by NORAD on Christmas Eve

    NORAD employees volunteer for Santa tracking and response duty.
    NORAD employees volunteer for Santa tracking and response duty.

    NORAD is celebrating the 60th Anniversary of tracking Santa’s yuletide journey through an interactive website, smartphone apps and social media.

    NORAD, the North American Aerospace Defense Command, is based in Colorado Springs.

    The NORAD TracksSanta website, launched Dec. 1, features Santa’s North Pole Village, which includes a holiday countdown, games, activities and more. The website is available in eight languages: English, French, Spanish, German, Italian, Japanese, Portuguese and Chinese.

    NORAD

    Official NORAD Tracks Santa apps are also available in the Windows, Apple and Google Play stores, so parents and children can countdown the days until Santa’s launch on their smartphones and tablets. Tracking opportunities are also offered on Facebook, Twitter, YouTube and Google+. Santa followers just need to type “@noradsanta” into each search engine to get started.

    Also new this year, the website features the NORAD Headquarters in North Pole Village, and highlights of the program over the past 60 years.

    Santa visits NORAD.
    Santa visits NORAD.

    Starting at 12:01 a.m. MST (2:01 a.m. EST) on Dec. 24, website visitors can watch Santa make preparations for his flight. NORAD’s “Santa Cams” will stream videos on the website as Santa makes his way over various locations. Then, at 4 a.m. MST (6 a.m. EST), trackers worldwide can speak with a live phone operator to inquire as to Santa’s whereabouts by dialing the toll-free number 1-877-Hi-NORAD (1-877-446-6723) or by sending an email to [email protected].

    Any time on Dec. 24, Windows Phone users can ask Cortana for Santa’s location, and OnStar subscribers can press the OnStar button in their vehicles to locate Santa.

    NORAD Tracks Santa is a global experience, delighting generations of families everywhere. This is due, in large part, to the efforts and services of numerous program contributors.

    It all started in 1955 when a local media advertisement directed children to call Santa — only the number was misprinted. Instead of reaching Santa, the phone rang through to the crew commander on duty at the Continental Air Defense Command Operations Center, the predecessor to NORAD. Thus began the tradition, which NORAD has carried on since the agency was created in 1958.

  • New mapping technology helps Santa deliver toys more efficiently

    New mapping technology helps Santa deliver toys more efficiently

    This holiday season, Santa Claus is using innovative technology to become even more efficient. He will be using RippleNami, a cloud-based visualization platform, to efficiently and safely deliver presents.

    Our favorite jolly man is one busy fellow. Not only does he have to keep track of the naughty and nice kids all year long and make the appropriate number of toys and lumps of coal, he then has to visit the homes of children all over the world in a single night. There are many things Santa has to keep in mind as he’s planning his yearly trek across the globe — such as weather conditions, flight patterns of other aircraft, and which homes have chimneys. How does Santa possibly keep track of everything?

    RippleNami, a cloud-based visualization platform, allows users such as Santa to access data from countless sources and customize information layers into an easy-to-use map. Below is a snapshot of the data Santa can integrate into the RippleNami platform to efficiently and safely deliver presents this year.

    • Weather Conditions — Poor weather conditions can significantly impact Santa’s route. Fog in particular has traditionally been a difficulty, even with the help of Rudolph’s nose. RippleNami allows Santa to track fog in real time, so he knows when Rudolph’s nose is necessary, and when the reindeer can take a break.
    • Aviation Incidents — Santa’s sleigh isn’t the only thing flying on Christmas Eve. Tracking flight patterns and visualizing where aviation incidents have recently occurred help Santa avoid collisions and plan the fastest route.
    • Naughty and Nice Lists — Santa is diligent in keeping track of which kids are naughty and which are nice. He makes a list, and checks it twice! But even Santa could use help planning how many toys versus lumps of coal he needs to pack in his sleigh before the big night. And what if a child who has been nice all year long suddenly throws a fit Christmas Eve? With RippleNami, Santa will be alerted in real time and can pick up some extra coal accordingly.

    Here is a visual of what Santa sees when he’s using the platform.

    RippleNami

  • Leica adds to geodetic monitoring software

    Leica Geosystems has introduced two new additions to its Leica GeoMo deformation monitoring solution: Leica GeoMoS AnyData and GeoMoS API.

    Users of the system can now create comprehensible visualizations and customizable reports, which enables powerful sensor data fusion for applications, such as air or water quality monitoring and construction or building management.

    Leica GeosystemsWith GeoMoS AnyData and GeoMoS API, multiple open interface standards are accessible to provide more information to projects than just classic geodetic monitoring applications, according to a news release from Leica.

    The open solution offers flexibility; it is capable of automatically acquiring, processing and distributing intelligent information locally or via the Internet in real time. Leica GeoMoS integrates, processes and distributes all project data within one software program.

    With these additions to Leica GeoMoS, necessary information is made easily accessible via web-based visualization. The program provides an efficient way to convert raw data streams into intelligent information.

  • Sensor fusion: Low-cost, high-end

    Sensor fusion: Low-cost, high-end

    Integration for infrastructure monitoring, navigation

    By Desislava Staykova and Nico Zill

    Rapid development in the technology of combined sensors within complex systems has taken place over the last decade. Such systems provide different accuracy levels, offering the possibility of use in application areas such as surveying, railway and automotive engineering, land administration, and for navigation purposes.

    Multi-sensor integration and fusion is a comprehensive process of reading and combining sensor signals to ensure a higher level of data reliability and accuracy. Input data from every sensor and further combination with specially developed algorithms ensures the complete identification of observed features, which would be impossible with data from each individual sensor operating separately.

    Because of its flexibility and the possibility for fast and continuous data measurement, multi-sensor integration and fusion has evolved rapidly in different areas. The object of this article is to overview the use of high-end and low-cost system complexes and software solutions for the purposes of the engineering geodesy, transportation and navigation.

    Deformation Monitoring

    Geodetic measurements for monitoring and displacements analysis of various engineering objects have always played an important role in maintaining structures like bridges, dam walls, building columns, wind power generators, and other construction.

    This requires properly designed network schemes enabling continuous and highly accurate measurements. For such angular and length measurements of millimeter-level accuracy that must be performed in intervals of minutes, hours or a day, standard total stations are being replaced by automated ones (ATS) comprising precise servomotors, automatic target recognition sensors, electronic inclinometers, self-calibration control systems and other sensors.

    The synchronized process of high-accuracy measurements (angular accuracy better than one second and distance accuracy better than one millimeter) and simultaneously adjustment software enables real-time or post-processing deformation monitoring and analysis. This type of hardware and software combination is often used during the life cycle of a project for construction and reconstruction of objects and for regular monitoring of the object’s stability.

    Terrestrial Laser Scanning. The need for precise modeling and geometrical characterization of large structures and open areas as dams, mines, landslides and others cannot be covered by traditional surveying methods which require the use a huge number of points for describing the object’s surface. The development of laser scanning technology in the last decade offers a new way for deformations measurements and becomes part of the infrastructure monitoring.

    The high scanning speed, dense measurement of huge numbers of points and high accuracy gives terrestrial laser scanning (TLS) an advantage other technologies used for large structural monitoring. Compared with the technologies using single point monitoring approaches where the displacements detection is limiteded to specific benchmarks, TLS provides high data redundancy. Combined with proper software products, this technique offers the possibility for high-accuracy surface modeling and displacement detection at the millimeter level. The scanned object consists of a large number of points, which allows implementation of mathematical algorithms for modeling and analyzing the object’s behavior.

    Another advantage of TLS as a remote sensing measurement tool is the minimized impact of the operator over the observed points and network.

    A new method for structural monitoring has emerged recently, comprising the advantages of the TLS, GNSS, geotechnical and meteo-sensors, enabling wide-area coverage and surface monitoring. One such tool is shown in Figure 1.

    Figure 1. Terrestrial laser scanning combined with GNSS and other sensors enables wide-area coverage and survace monitoring. (Images courtesy of Leica Geosystems)
    Figure 1. Terrestrial laser scanning combined with GNSS and other sensors enables wide-area coverage and survace monitoring. (Images courtesy of Leica Geosystems)

    Mobile Laser Scanning

    For different navigation purposes, for monitoring and investigation of wide areas, static measuring methods are being replaced by complex mobile measuring combinations of both high-end and low-cost sensors, to ensure fast, continuous and accurate data acquisition.

    Recently mobile laser scanning (MLS) has experienced rapid development and proved its usage particularly in the railway and automotive sectors, for deformation analysis, for monitoring and documentation of as-built street and railway networks and the corresponding infrastructure objects.

    MLS for Rail and Road. The advantages of MLS for fast, high-accuracy and complete scanning of the surroundings make it an important part of current railway and road conditions monitoring.

    Continuous data acquisition and processing minimizes operator errors, and significantly reduces the time for performance of the surveying work and a-posteriori data analysis.

    Localization and recognition of infrastructure objects forming part of railway and road environment has long been of primary importance in the transportation sector.

    For determination and documentation of as-built railway and street networks from acquired data, Technet-Rail (Berlin, Germany) developed two software solutions, SiRailScan and SiRoadScan, for point-cloud analysis. The integrated mathematical algorithms ensure high-accuracy extraction and adjustment of the as-built left rail, right rail and center line, as well as of the roads’ border lines.

    The adjusted geometry forms the basis for driving speed control tests, determination of the as-built environment for clearance detection and documentation, investigation of catenary wire deviations, ballast and road settlements, traffic signal positions, and any changes in the existing situation (see Figures 2 and 3).

    Figure 2. Adjusted as-built rail geometry with SiRailScan used as basis for performance of clearance analysis and documentation in chainage based railway system.
    Figure 2. Adjusted as-built rail geometry with SiRailScan used as basis for performance of clearance analysis and documentation in chainage based railway system.
    Figure 3. Adjusted with SiRoadScan road border lines. Detection and recognition of the roads signals.
    Figure 3. Adjusted with SiRoadScan road border lines. Detection and recognition of the roads signals.

     

    In response to the growing interest in application of the MLS technique and a-posteriori data adjustment for monitoring purposes, Technet-Rail developed additional tools for deformation analysis of structures such as tunnel bodies, railway bridges and road surfaces. The integrated software solutions enable comparison between the designed and as-built situation, epoch-wise analysis, modeling of the structure, development into 2D followed by color-coded deformation map (see Figures 4 and 5).

    Figure 4. Tunnel deformation analysis performed with SiRailScan based on the as-built rail geometry. Automated calculation of differences between designed and as-built tunnel structure.
    Figure 4. Tunnel deformation analysis performed with SiRailScan based on the as-built rail geometry. Automated calculation of differences between designed and as-built tunnel structure.
    Figure 5. Tunnel deformation analysis with SiRailScan based on a pre-defined form and direction.
    Figure 5. Tunnel deformation analysis with SiRailScan based on a pre-defined form and direction.

    MLS for Navigation. Multi-sensor integration is the basis for operation of the moving measuring systems integrating hardware devices such as laser scanning devices, GNSS, inertial measurement units (IMU), distance measuring instruments (DMI) and specific software algorithms for data synchronization. A milestone in the development of such systems is the measurement and navigation in indoor places or in areas with low or no GNSS coverage.

    The need for safe and reliable navigation in transportation systems such as train control systems, intelligent vehicle systems, system tracking, in urban environments, underground areas, and other areas with no available GNSS signal stimulated much research in the area of multi-sensor integration and fusion. the main scope of some studies is the integration of different sensors delivering information for the attitude, velocity, acceleration such as the IMUs, inclinometers, wheel sensors, and correspondent filtering algorithms to achieve the best possible position accuracy without usage of GNSS signals.

    Conclusion

    For decades, infrastructure objects such as dam walls, bridges, tunnels, roads and railway tracks form a substantial part of civil engineering and engineering geodesy. The integrity of their structure requires deep knowledge of the behavior of these objects and the various methods for their optimal and high accurate monitoring. The rapid development evolution of multi sensor integration in combination with laser scanning technology makes it an essential method for accurate, continuous and dense measurement for the purposes of the engineering surveys.

    Desislava Staykova and Nico Zill are engineers with Technet-rail 2010 GmbH, Berlin, Germany.

  • flyGarmin app makes piloting easier

    The new flyGarmin app for Windows simplifies avionics database updates such as navigation, charts and more, while also accommodating the distribution of Jeppesen charts, Garmin said.

    The flyGarmin app is intended to give pilots a streamlined experience that makes database updates easier, requiring less time at their computers. Jeppesen charts are available for ChartView-enabled devices, plus subscribers can download Jeppesen charts alongside other databases purchased from Garmin.

  • 2G Robotics laser systems used to create deep ocean surveys

    2G Robotics laser systems used to create deep ocean surveys

    2G Robotics has delivered two deep-rated ULS-500 subsea laser systems to Oceaneering International Inc.’s business unit, Oceaneering Survey Services, a provider of deepwater seafloor mapping and subsea surveys.

    Including these two ULS-500 systems, Oceaneering now uses six of the ULS-500 systems with its autonomous underwater vehicles (AUVs) as part of its advanced survey and inspection services for assessing pipeline and flowline integrity for the oil and gas industry.

    The ULS-500 deep-rated system.
    The ULS-500 deep-rated system. (Photo: 2G Robotics)

    Oceaneering has been using the ULS-500 since 2013 to perform dynamic flowline and pipeline inspections with its AUVs, and most recently used the 2G Robotics ULS-500 system to inspect 2,500 kilometers of pipeline and flowline.

    The ULS-500 can be used to perform high-quality stationary scans, but the system delivers greater operational value when integrated with subsea vehicles to perform dynamic scanning, 2G Robotics said. The ULS-500 is designed for dynamic scanning with development focused on subsea vehicle integration, high sample rates, and timing synchronization for efficient and accurate data acquisition.

    The ULS-500 uses PPS (pulse per second) time synchronization because it provides better timing accuracy than a standard NTP (network time protocol) time synchronization approach, ensuring better data accuracy.

    The 3D point cloud models generated by the ULS-500 provide Oceaneering with the detail needed to accurately assess pipelines and flowlines, and measure displacements and deformations.

    ULS-500_2G Robotics scans
    (Center) The National Oceanic and Atmospheric Administration (NOAA) used the ULS-500 to explore the Monohansett shipwreck site at the Thunder Bay National Marine Sanctuary. The ULS 500 captured detailed 3D models of the Monohansett at a range of approximately 3m to 5m. (Credit: 2G Robotics)
  • Leica releases GNSS monitoring on board stand-alone receiver

    Leica releases GNSS monitoring on board stand-alone receiver

    A GNSS monitoring solution integrated into a stand-alone receiver detects fast movements of man-made and natural structures in real time.

    The new product from Leica Geosystems, dubbed VADASE, runs onboard Leica reference stations and monitoring receivers. The Leica Velocity and Displacement Autonomous Solution Engine (VADASE) provides an in-depth look into fast movements using unique processing algorithms. In real time, accurate high-rate velocity and displacement information of various activities and structures are provided to engineers and researchers for a complete, precise and reliable monitoring solution, Leica said.

    Leica GeosystemsLeica VADASE delivers actionable information independent of any GNSS real-time kinematic (RTK) correction service in real time. Displacement events are recorded on board a single stand-alone GNSS receiver, and the user can be notified by email. With this instant information, professionals receive a deeper understanding of how structural movements occur and can take necessary actions to mitigate damages and potentially save lives, the company said.

    Leica VADASE does not require additional hardware or infrastructure for differential processing (such as one or more reference stations or global correction services for precise point positioning); it provides autonomous processing capability with no extra equipment or services needed.

    Users can also apply the latest versions of Leica SpiderQC, Leica GeoMoS or any other customized software for advanced data visualization, analysis, threshold verification and notification.

  • JAVAD TRIUMPH-LS Short Baseline Accuracy and Precision with 1 Hz and 5 Hz Corrections

    By Matt Johnson

    In a previous article, “JAVAD GNSS 5 Hz “Beast Mode” RTK Base Station Corrections Reduce the Time to Acquire a Fix by 72 Percent,” the benefits of RTK base station correction rates greater than 1 Hz were discussed. This article will investigate and compare the accuracy and precision of JAVAD TRIUMPH-LS RTK positions with a JAVAD TRIUMPH-2 base station with both 1 Hz and 5 Hz corrections in an open-sky environment with a short baseline.

    Procedure

    A TRIUMPH-LS RTK rover and TRIUMPH-2 base station were set up on tripods in a farm field. The base and the rover were adjusted to be at the same height as checked with a 4-foot level. The horizontal distance between the base and rover was measured with a tape measure to be 2.93’.

    A TRIUMPH-2 and TRIUMPH-LS set up on tripods in a farm field as the sun sets in the background.
    A TRIUMPH-2 and TRIUMPH-LS set up on tripods in a farm field as the sun sets in the background.

    Using the TRIUMPH-LS’s field software, J-Field, the TRIUMPH-LS was configured to automatically accept collected points and continuously collect points until manually stopped.

    “How to Stop?” configuration screen in J-Field set to collect 10 epochs, Auto Accept points and Auto Re-Start.
    “How to Stop?” configuration screen in J-Field set to collect 10 epochs, Auto Accept points and Auto Re-Start.

    Six sessions of points were collected: points with 10, 30 and 60 logged epochs with both 1 Hz and 5 Hz corrections rates. The RTK engines were configured to automatically reset after each point was collected.

    Results

    JAVAD TRIUMPH-LS

    Analysis

    • All points had good horizontal precision and 95.4 percent of all points (2 standard deviations) fell within 0.027’ of the average position in the worst group of “1 Hz 10 Epochs.”
    • The point groups had good horizontal accuracy. The physically measured distance between the base and rover matched the averaged RTK groups’ position within 0.014’.
    • All points had good vertical precision and 95.4% of all points (2 standard deviations) fell within 0.052’ of the average position in the worst groups of “1 Hz 60 Epochs” and “5 Hz 10 Epochs.”
    • The point groups had good vertical accuracy. The base and rover were set up at the same height. The error between the averaged heights of the groups was at most 0.041’.
    • The vertical precision tended to improve as the number of epochs collected increased. The horizontal precision had marginal improve as the number of epochs collected increased.
    • The base station broadcast rate does not appear to have any substantial effect on the precision but allowed points to be collected with less time. With 1 Hz corrections, a point with 60 epochs requires 60 seconds to collect, but with 5 Hz corrections, it only requires 12 seconds to collect.

    Conclusion

    In an open sky environment with a short baseline, the RTK position precision is only marginally improved as more epochs are collected. Higher RTK broadcast rates made possible with JAVAD RTK systems allow points to be collected faster.

  • Boxes and Boxes of Professional-Grade Tools

    Geospatial data is everywhere. Many times I’ve shown the following photo I shot at the Esri User Conference several years ago. At the Field Technology Conference in November, I talked about this. Actually, I believe I’ve talked about the topic at nearly every Field Technology Conference since the inaugural event in 2010. Geospatial data long ago left the user domain of thousands and is rapidly headed toward billions.

    GeospatialConsciousness

    One of the many developments driving that growth was the appearance of Google Earth in 2004, sprung from Google’s acquisition of Keyhole. Suddenly there was easy-to-use software to visualize geospatial data. At about the same time, Navteq (now HERE) and TeleAtlas (now TomTom) — two of the premiere geospatial data companies at the time — were gaining tremendous momentum in the exploding GPS car navigation market because they were, and still are, the two companies that provide the vast majority of the map data to the Garmins and TomToms (and others) of the world.

    Professional Mapping

    Today, Google Earth and Google Maps are still the defacto standard for “desktop mapping” by the general consumer. Google Earth Pro, the company’s offering to the high-end mapping market, formerly available on a subscription basis, will soon be free, as of January 2016. Previously the user received the following, and one supposes the same will continue to hold true:

    • Advanced measurements: Polygon area measurement. Determine affected radius.
    • High-resolution printing: Print images up to 4800 x 3200 pixels.
    • Pro data layers: Demographics, parcels, traffic count.
    • Import spreadsheet data: Import up to 2,500 addresses at a time.
    • Import Esri and MapInfo-formatted data: Import .shp and .tab files.
    • Make HD movies: Make Windows Media and QuickTime HD movies.

    To download Google Earth Pro, register for a license key and download for Windows or Mac.

    Creating 3D Visualizations

    Trimble offers another cool geospatial tool that was once part of the Google portfolio.  SketchUp is a powerful software for creating 3D visualizations (think 3D structures and objects).

    Sketchup
    Building that was modeled in SketchUp and overlaid in Google Earth

    Both free SketchUp and fee-based SketchUp Pro versions are available. If your work includes generating renderings for clients, the latter can be valuable. You can download a free trial version here.

    SketchUp pro is designed for architects, engineers, and design and construction professionals, as well as members of the global maker community.  Its capabilities include:

    • Professional Drafting: Using a 2D drawing and documentation tool, users can manage drawings and display data from their information models, applying object classifications and accessing that info with an annotation tool.
    • Modeling Tools: With a 3-point arc tool, users can draw arced edges four different ways. A rotated rectangle tool allows for drawing precise rectangles unbound by default axes.
    • 3D Warehouse: Models of popular brand-name building products are among a broad free content offering, more than 2.5 million models.

    Integrating with Other Geospatial Tools

    In coordination with Google, Esri has prepared a transition offer to ArcGIS for Google Earth Enterprise and Google Maps Engine customers and partners. ArcGIS provides 2D and 3D mapping and analysis in desktop, server and hosted environments. The system provides an infrastructure for making maps and geographic information available throughout an organization, across a community and openly on the Web.

    Among its features:

    • Geoprocessing: a 3D analyst incorporating a LAS dataset toolset and visibility toolset; and conversion, data management, multi-dimension and spatial analyst toolboxes.
    • Geodata: connections to read-only databases or geodatabases in Oracle.
    • Extensions: 3d analayst and spatial analyst extensions.

    Esri will provide no-cost software to replace Google Earth Enterprise or Google Maps Engine technology, and will include no-cost training in ArcGIS.

    Realizing the value and momentum of Google Earth to reach the consumer users of geospatial technology, Esri has also announced ArcGIS Earth, and its website says it is accepting beta testers.

    At Play in the Fields of Google Earth Pro

    For just a quick-and-dirty exercise, I imported some unsmoothed, 1-foot contour lines generated from a UAV flight and overlaid them in Google Earth Pro.

    Planimetric-W
    Planimetric view

    Then, in true Google Earth fashion, I zoomed in to have an oblique ground view (with Mt. Hood in the background, some 74 kilometers in the distance).

    UAV-GE-GroundView1-W
    Zoomed in oblique ground view

    Finally, following is the UAV imagery overlaid in Google Earth Pro.

    UAV-GE1-W
    Screenshot of the UAV imagery overlaid in Google Earth Pro

    Actually, the Google Earth Pro imagery looks pretty good, but you start to see the differences as you zoom in. It’s hard to beat UAV orthophoto resolution.

    UAV-GE-CloseUP1_Track
    Google Earth imagery
    UAV-CloseUP1_Track
    UAV imagery shot with a 12-megapixel camera at 200 feet AGL (above ground level.)

    Last month, I wrote that I’d post the presentations from the Field Technology Conference. Well, they aren’t quite ready, so we’ll have them for next month. There’s a great mix of presentations on GPS/GNSS, mobile devices, UAVs for mapping, laser rangefinders, various sensors and GIS software.

    Happy Holidays and cheers to a prosperous New Year!

    See you next month.

    Follow me on Twitter at @GPSGIS_Eric.

  • You be the judge: To eLoran or not?

    eloran-survey
    eLoran Antenna Park of 13, 200-meter masts at Anthorn, UK.

    Readers of GPS World, its e-newsletters, website — and all interested PNT parties — are invited to register their opinion in the current poll at gpsworld.com/janpoll.

    Should the U.S. government install a full eLoran network of broadcast stations to back up GPS in case of jamming, interference or other emergencies?

    • Yes.
    • No.
    • More study is needed before answering this question.
    • Don’t know.

    Voters may enter their name in a drawing to receive a $50 gift card. Vote by Jan. 11, 2016.

    Results will be published in the February issue of GPS World magazine.

  • PPP for hydrography

    PPP for hydrography

    A new high-accuracy technique using one dual-frequency GNSS receiver, precise point positioning (PPP) offers the possibility of cost-effectively obtaining coordinates. This study investigates the accuracy of kinematic PPP for hydrographic applications on rivers, and shows results comparable to double-difference solutions.

    By Ashraf Abdallah and Volker Schwieger

    PPP_opening_W
    Duisburg Harbor, Germany: site of the PPP survey.

    Precise Point Positioning (PPP) is a challenging surveying technique for high-accuracy 
results. It offers the advantage of using one dual-frequency GNSS instrument. Estimation of a PPP solution is based on the ionosphere-free linear combination for code data and carrier-phase data.

    Bernese Software. Bernese software V. 5.2 is a GNSS post-processing software, using GNSS measurement data for static and kinematic surveying. It processes the data in double-difference (differential GNSS) and zero-difference (PPP solution) techniques. The software was developed at the Astronomical Institute of the University of Bern.

    Bernese software contains a group of different tools or programs to complete the processing for double-difference or zero-difference mode. The estimation of the two techniques has the same processing schedule in most of the pre-processing stages. The change appears later within the parameter estimations section.

    As shown in Figure 1, the processing starts with downloading the related orbits from the CODE (Center for Orbit Determination in Europe) FTP server. The orbit tools include the updating of the Earth orientation parameters to be in Bernese format, converting the satellite data to a specific format and generating the standard orbit format for Bernese software. A preprocessing program contains the smoothing of the RINEX data from outliers and cycle slips.

    Figure 1. Bernese software processing schedule.
    Figure 1. Bernese software processing schedule.

    This smoothing step is following by converting the RINEX into Bernese binary format. The receiver clock is synchronized with respect to the GPS time and stored to observation files using clock synchronization tools. Using the code solution, a kinematic file is written to be inserted in the next parameter estimation procedure. For double-difference solution, a baseline is created, and this baseline is corrected from cycle slips for phase data. Parameter estimation is carried out by least-square estimation for the phase and code GNSS observations.

    Kinematic PPP Solution. Bernese software provides the possibility to obtain the PPP solutions in automatic script (Bernese Protocol Engine [BPE]). The satellite orbit and clock ephemeris data from CODE center were used with intervals of 5 seconds to obtain highly accurate results. Satellite and receiver phase center offsets are considered. Tropospheric correction is applied using the Global Mapping Function (GMF) model for the hydrostatic and wet delay estimation. Regarding ionospheric correction, the estimation of the PPP solution is based on the linear ionospheric-free combination, with high-order ionospheric parameters to improve the estimation.

    The ocean tidal loading correction is considered in the PPP estimation. Atmosphere tidal loading is also corrected.
    Figure 2 gives the analysis flowchart. Some outputs of the PPP solution could be visualized, such as the satellite phase and code residuals. The high residuals might come from the lower elevation angles of the satellites. Moreover, the residuals appear because of the effect of the remaining observation errors, such as atmospheric delay, multipath, or even the satellite orbit and clock residuals.

    Figure 2. Flowchart of analysis strategy.
    Figure 2. Flowchart of analysis strategy.

    Regarding kinematic PPP solution, the error values in the east, north and ellipsoidal height are calculated with respect to the double-difference solution from Bernese software. The root-mean-square (RMS) error, which refers to the double-difference solution, and the standard deviation (SD), which is related to the mean value of the PPP solution error, are calculated, and the frequency histogram is plotted.

    An antenna and a receiver were mounted on the surveying vessel to collect the GNSS data with an interval of 1 second during two days.
    An antenna and a receiver were mounted on the surveying vessel to collect the GNSS data with an interval of 1 second during two days.

    Experimental Work. Two kinematic trajectories were observed on the Rhine River in Duisburg, Germany, as a part of the project “HydrOs — Integrated Hydrographical Positioning System.” The project was launched in cooperation with Department M5 (Geodesy) of the German Federal Institute of Hydrology (BfG) and the Institute of Engineering Geodesy at the University of Stuttgart (IIGS) .

    An antenna and a receiver were mounted on the surveying vessel (inset photo, opener) to collect the GNSS data with an interval of 1 second during two days. The virtual SAPOS (SAtellitenPOSitionierungsdienst der deutschen Landesvermessung) reference station was considered as a reference station, provided from the SAPOS-NRW team. SAPOS is a continuously operating reference station (CORS) GNSS service collecting data throughout Germany.

    Results and Discussions

    The layout of the first trajectory, which was observed for more than three hours, is presented in Figure 3. The measurements started from the inner harbour in Duisburg. The left figure shows the overview layout, and the right figure illustrates a zoom-in of the trajectory below two bridges. The white line refers to the kinematic PPP trajectory; the cross-hatched white line shows interpolated points between two solved points from the PPP solution. Because of loss of GNSS signals from the bridges, the yellow line indicates the actual vessel trajectory below bridges.

    Figure 4L-W

    Figure 3. Layout of the first trajectory [DOY: 2014/126], zoom-in on bottom. (Photo: Google Earth)
    Figure 3. Layout of the first trajectory [DOY: 2014/126], zoom-in on bottom. (Photos: Google Earth)
    As mentioned before, the double-difference solution of the Bernese software is considered as the reference solution for the PPP solution. The PPP residuals for phase and code observations (not using double-difference solution) are presented in Figure 4. Here the residual values in phase and code have a gap because of the loss of GNSS signals, which starts from epoch 438 to 486 [GPS week second = 199845: 200115]. Additionally, there are some cycle slips from epoch 883 to 892 [GPS week second = 202105: 202150].

    Figure 4. Satellite residuals for the first trajectory [DOY: 2014/126].
    Figure 4. Satellite residuals for the first trajectory [DOY: 2014/126].
    To assess the accuracy of the PPP solution for this hydrographic trajectory, Figure 5 illustrates the analysis results for this trajectory between the double-difference and PPP solutions. The X-axis refers to the number of observations (one epoch/5 seconds), and the Y-axis indicates the error value in meters. Figure 5.1 shows the error plot (m) in east, north and height. As shown previously, the error values have a gap in the solution because of the loss of lock below the bridges. Moreover, there are some cycle slips later on, which decrease the estimated kinematic PPP accuracy.

    Figures 5.2 and 5.3 provide the error plot for the east and north and east and height directions. The blue points refer to the errors, and the red cross refers to the mean value. Table 1 summarizes the PPP results.

    Table 1. Statistical results of the first trajectory [DOY: 126/2014].
    Table 1. Statistical results of the first trajectory [DOY: 126/2014].
    Five percent of the PPP errors are eliminated to get outlier-free results. The SD (95%) of the kinematic PPP solution is obviously improved to reach 5.0 cm, 1.20 and 5.0 cm in east, north and height directions, respectively.

    To distinguish between the standard deviation and the standard deviation based on 95 percent of the data, Figure 5 shows additionally the histogram of SD in Figures 5.4, 5.5 and 5.6 for east, north and height respectively. Figures 5.7, 5.8 and 5.9 provide the error with 95 percent of the results. Absolutely, the error range is improved by eliminating 5 percent of the data including outliers.

    Figure 5. Analysis results for the first trajectory. Standard deviations shown in plots on the left, with outliers excluded, right.
    Figure 5. Analysis results for the first trajectory. Standard deviations shown in plots on the left, with outliers excluded, right.

    Second Data Set. The second trajectory on the Rhine River was observed [DOY: 127] for more than 5 hours (see Figure 6). Sixteen satellites were observed during the measurement time.

    Figure 6. Layout of the second trajectory [DOY: 127/2014]. (Photo: Google Earth)
    Figure 6. Layout of the second trajectory [DOY: 127/2014]. (Photo: Google Earth)
    In Figure 7, the phase and code residuals are plotted. Some outliers are reported in this graph, which refers to cycle slips during the observations.

    Figure 7. Satellite residuals for the second trajectory [DOY: 127/2014].
    Figure 7. Satellite residuals for the second trajectory [DOY: 127/2014].
    Figure 8 illustrates the PPP results for this kinematic trajectory. Figure 8.1 shows the PPP error values in the east, north and height directions with respect to the double-difference solution from Bernese software.

    Figure 8. Kinematic PPP solution for the second trajectory. Standard deviations shown in plots on the left, with outliers excluded, right.
    Figure 8. Kinematic PPP solution for the second trajectory. Standard deviations shown in plots on the left, with outliers excluded, right.

    The first 40 minutes of that trajectory were realized in a quasi-static observation technique (nonmoving vessel) from GPS week second 281660: 284060. The result obtained from this solution is more accurate due to the high number of satellites, and the trajectory did not include the bridges area. Figure 8.2 and 8.3 show errors in east and north, and east and height.

    As shown in Table 2, the maximum and minimum values for the error range, which are presented in detail in Figure 8.4, 8.5 and 8.6, are reported in the east, north and height directions. These figures show the frequency histogram for the PPP errors. The RMS error from the solution is 2.10 cm and 2.90 cm in east and north respectively, with an RMS error of 5.60 cm in height. The standard deviation is definitely improved after eliminating 5 percent of the PPP errors as outliers. The standard deviation for 95 percent of the results shows 1.5 cm in east and north and 3 cm in height. The error histograms for 95 percent of the data are provided in Figures 8.7, 8.8 and 8.9.

    Table 2. Statistical results of the second trajectory [DOY: 127/2014].
    Table 2. Statistical results of the second trajectory [DOY: 127/2014].
    The second trajectory clearly provides a higher accuracy than the first. Its data has a higher number of satellites and lower outliers than the first. Figure 8 shows the histogram of the second trajectory is similar to the Gaussian distribution curve.

    Acknowledgments

    The authors would like to thank Annette Scheider for receiving the GNSS measurements through the HydrOs project, our BfG partners Harry Wirth and Marc Breitenfeld, and Bernhard Galitzki form SAPOS-NRW for providing us with the reference stations.

    This article is based on a peer-reviewed paper presented at the FIG Working Week, May 2015, in Sofia, Bulgaria.

    Manufacturers

    A Leica 1203+ antenna and GX1230+ GNSS receiver collected the data shown here.

    Ashraf Abdallah is an assistant lecturer in engineering, Aswan University, Egypt, and a Ph. D. student at the Institute of Engineering Geodesy (IIGS), Stuttgart University, Germany. He received a master’s degree from Aswan University in applications of single-frequency GNSS. 


    Volker Schwieger is a full professor at the University of Stuttgart and director of the IIGS. He received a Ph.D. from the University of Hannover, focusing on GPS for monitoring applications.