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Hangar: A new on-demand UAV Data Service

October 28, 2018 Leave a comment

We have written about the rapid evolution of imagery platforms and portals many times in these blog essays over the years.  One major recent advancement is the UAV service from Hangar, an Esri business partner.  This service allows data users to order UAV imagery and receive it according to their project specifications:
https://www.spar3d.com/news/uav-uas/hangar-esri-reality-data-arcgis/

In my way of thinking, it is sort of like an” Uber for Drones”.  Let’s say you don’t have a pilot’s license, or time, or equipment, or expertise to fly your own UAV imagery.  Hangar is a new UAV service covering all areas that may be of interest to a client requiring imagery.  For this service, Esri partners with Hangar, a company that holds hundreds of waivers to fly almost anywhere and the expertise and equipment to serve clients from just about any discipline and with any need.   For more information, read the article “Hangar Joins Esri Startup Program to Add ‘Task & Receive’ Aerial Insights ArcGIS:”   https://www.prweb.com/releases/2018/05/prweb15514744.htm. 

And two of the best examples of some of the Hangar imagery is this story map of the devastation from the Carr fire in California and this story map showing some of their imagery for Kilauea, shown below.   Be sure to zoom and pan the 360-degree UAV imagery shown in these story maps.  Warning!  They are highly addictive and fascinating.  And for those of us in education, they make for an attention-getting teaching tools which I have already used numerous times from primary school to university and beyond to teach about wildfires and volcanic hazards.

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-Joseph Kerski

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One size doesn’t fit all – global and local mapping solutions

October 22, 2018 1 comment

Couple of interesting articles posted recently highlighting some of work undertaken by local groups providing detailed, custom mapping to meet specific local requirements and ongoing frustrations with the lack of adoption of some of these open data sources.

For most of us, the lack of information on street gradient, footpath kerb height and ramp details in popular online map services is usually not an issue but for those with a physical disability, this can be a problem. The University of Washington’s Taskar Center for Accessible Technology has come up with one solution, AccessMap, developed on an OpenStreetMap/OpenSideWalks platform, to provide safe and accessible trip planning information (Salman, 2018). AccessMap, currently limited to the city of Seattle in NW USA, integrates information from the USGS National Elevation Dataset (NED) with sidewalks data from Seattle DoT and the HackcessibleMap – Sidewalks project, to provide a more detailed description of local conditions.

 

Shaikh (2018) describes the growing resistance in India to large corporations owning all the mapping data and not providing the level of detail necessary to help solve local community issues such as mapping fire catchment areas and managing litter collections. Many OpenStreetMap proponents in India, driven by different motivations but facing similar issues with respect to the lack of local detail, have stressed the importance of creating maps using local landmarks, knowledge and where possible providing the information in local languages. However, although detailed open data mapping resources have been used successfully in humanitarian aid projects and environmental monitoring schemes in India, there is sense that the detailed information now available in open data sources is not used to its full extent. One of the big challenges facing open data communities is to persuade government organisations and large companies to consider alternate open data sources for their mapping requirements.

 

 

Sensing air quality while photographing streets

October 15, 2018 1 comment

As described in an article in Business Wireair quality will be monitored on Google’s Street View vehicles starting with 50 cars in California.   Resulting from an agreement between Google and Aclima, carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5) will be sensed initially.  “This snapshot data will be aggregated and designated with a representativeness indicator and will be made available as a public dataset on Google BigQuery. The complete dataset will be available upon request to advance air quality science and research.”

Because Google Street View vehicles are already collecting in many countries (though not all, for quite a variety of reasons, as we mention in our book), monitoring air quality seems like an efficient partnership to gather this information.  Doing so by vehicle rather than via a standard fixed-position air quality monitoring station adds the benefit of monitoring in many areas, and over many time periods throughout the day in those areas.   One possible challenge in assessing the resulting data is that the points will be gathered in different places, with little repeated detection in the same place at the same time.  In a very real sense, the Google Street View vehicles become part of the Internet of Things.  I wonder if by having the air quality sensors on the vehicles whether Google will be sending the vehicles out more often than their standard street view updates require; i.e. whether the new goals will actually influence the schedule of the data gathering itself.   In a very real sense, if that happens, it is another example of the disruptive transformational nature of modern web GIS.

I suspect this is only the beginning.  Given increased demand for data at finer and finer scales, it only makes sense for government organizations, private companies, and nonprofit organizations to think about the existing platforms and mechanisms by which data is already collected, and broker relationships to attach their own data gathering to these existing platforms.  It is conceivable that the Street View vehicles could be outfitted with additional sensors, and, in a short time from now, the vehicles will be analogous to smartphones:  Because smartphones can do so much more than make calls and receive calls, calling has become only a minor part of their functionality.  Perhaps in only a year or two, people will have to be reminded that the Street View vehicles can actually take photographs of the neighborhoods they are passing through.

The Geospatial Data Act Passes

October 4, 2018 1 comment

Last year we wrote about a called the Geospatial Data Act, S1253,  The Act passed in a bipartisan manner in October 2018, as reported by the American Association of Geographers.  This legislation will save U.S. taxpayers millions of dollars because it allows government agencies for better coordination, avoiding duplication of efforts, and to procure geospatial expertise, technology, services, and data from across the full range of the dynamic and rapidly growing U.S. geographic and geospatial community.  Also key is that the Act establishes procedures and guidance for the Federal Geographic Data Committee (FGDC), which we have written about in this blog and in our book, and the National Geospatial Advisory Committee (NGAC).  After considering input from a variety of stakeholders, including the AAG, House and Senate committees finally settled on a streamlined bill stripped of the damaging provisions that would have limited federal procurement of geospatial data and services to a small segment of the geospatial community,” said the AAG.

As we described earlier, the Act should be a significant aid to visibility and advancement of geospatial technology.  Key segments of the Act include:

  • Section 2 defines the term ‘geospatial data’ for the US federal government.
  • Section 3 clarifies the role of a Federal Geographic Data Committee (FGDC).
  • Section 4 clarifies the role of a National Geospatial Advisory Committee (NGAC).
  • Section 5 describes the importance of a National Spatial Data Infrastructure (NSDI).
  • Section 8 describes the creation and operation of the ‘GeoPlatform’ as an electronic service that provides access to geospatial data and metadata for geospatial data.

Keep an eye on this blog and other resources to keep track of benefits resulting from the Geospatial Data Act.

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The passage of the Geospatial Data Act promises to be a positive step forward for the geospatial industry.  Photograph by Joseph Kerski.

Accessing USGS Topographic Maps on The Internet Archive – Archive.org

September 30, 2018 1 comment

For years, I have used the Internet Archive (https://archive.org/about/) for many things, from archiving multimedia that I created for my story maps to looking up information on historical web pages through their Wayback Machine, (as well as for listening to some old wonderful sound recordings) and through those efforts became aware of the wealth of information on the site.   And when I say wealth, I truly mean enormous – 279 billion web pages, 11 million books and texts, 4 million audio recordings (including 160,000 live concerts), 3 million videos (including 1 million Television News programs), 1 million images, and100,000 software programs. But did you know that The Internet Archive also houses some geospatial data?  The Internet Archive, a 501(c)(3) non-profit organization that has existed since 1996, states that its mission is to “provide Universal Access to All Knowledge,” so it makes sense that some geospatial data for the public good is there.

Let’s focus here on the USGS topographic map data on The Internet Archive, also known as Digital Raster Graphics (DRGs).  Start here for a list of these maps by state, and then underneath each state, a variety of search options are available.  It isn’t the most intuitive unless you know the specific map name that you are looking for, so a topographic map index may still come in handy; a scanned version of these is not easy to come by, but one such archive is here.  Formats include GeoTIFF, essential for use in a GIS.

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Interface on The Internet Archive for USGS Digital Raster Graphics. 

While I still find the interface on the other main DRG archive, LibreMap, to be a bit easier to use, LibreMap is not maintained any longer, and is starting to return some errors during certain searches.  The Esri USGS Historical Map Explorer, and the USGS TopoView, reviewed here, is more modern approach to obtaining topographic maps, with the added benefit of historical editions.  USGS topographic maps are part of the set of basemaps available inside ArcGIS Online as data services, which is increasingly part of modern GIS workflows, rather than downloading the data and using it locally.  Still another archive is that from Historical Aerials, which I reviewed here. 

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A section of my all-time favorite USGS topographic map, for Mitchell Indiana, simply because of the intricacies of the depression contours and disappearing streams in the magnificent karst landscape depicted. 

A review of Google’s search engine for Open Data

September 17, 2018 4 comments

An article in Nature described Google’s new search engine for open data, and since geospatial data is a fundamental part of open data, and after all these years, still challenging at times to find, I was immediately interested in testing it.

The tool, called Google Dataset Search, is accessible on this link.  Like Google Scholar and Google Books both of which I make heavy use of, this is a “specialized search engine.”

The utility of this tool will depend on metadata tagging. Indeed, as the article points out, “those who own the data sets should ‘tag’ them, using a standardized vocabulary called Schema.org, an initiative founded by Google and three other search-engine giants (Microsoft, Yahoo and Yandex)”. The schema.org dataset markup is the standard used here, but others are supported, such as CSVs, imagery, and proprietary formats.  I had to laugh at the open-ness of the last line in the list of what could qualify as a dataset, “Anything that looks like a dataset to you.”

Many data search engines and portals have a vast amount of data but very little geospatial data.  But with this tool, in my test searches, I found many useful geospatial data sets, some of which I knew and some that were new to me.  I have had challenges finding stream gauging data services for Australia recently, and with this tool found some new leads to investigate.  Being Google based, the searches were rapidly returned, with what I considered enough information to decide whether or not to investigate more fully (see screenshot below).  The data format was featured prominently, as was the coverage, both of which I appreciated.  NOAA was an early adopter of the indexing, and so it makes sense that I could find many NOAA data sets using this search engine.

I wonder if data in Github, or in Esri’s Living Atlas, or on state, national, and international portals will be findable.  I also wonder how the sheer importance of Google will influence how organizations tag their data in the future, and the influence this will have on agencies that perhaps did not put as much time on metadata as they perhaps should have.  Time will tell, but if Google Scholar and Google Books are any indication, the Google Dataset Search could indeed prove to be extremely useful for many of us in GIS research and education.

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Result of stream gauge search in the new Google data search engine. 

Location, Privacy and Google

September 10, 2018 1 comment

Recent revelations about Google’s continued tracking of personal location information despite the Location History setting being disabled have been widely reported (Business Insider, The Guardian). Google responded robustly, acknowledging incidental location information was also collected under other application settings (specifically Web and App Activity) but insisting they provided clear guidelines on what the various settings entailed. Many observers and users of Google services remain unimpressed by what appears at be the rather insidious tracking of location information. Disabling Location History should mean NO location information is tracked regardless of which Google service was used.

Having just reread their Privacy Policy, Google does make it clear that location information is tracked through a variety of other sources, including public data, business and marketing. The Location History setting means location information is saved to a Google account timeline – if that’s what it means, then perhaps the setting should be Save Location History to Timeline.

All of this leads into a more general discussion on privacy; what do we assume privacy means, what do we expect to remain private and what information about us are we prepared to be in the public domain. I typed define: privacy into Google search and the response was … ‘a state in which one is not observed or disturbed by other people‘. Clearly this is not the baseline for Google’s policy and perhaps another example where some rewording of the policy headline would help clarify exactly what a user of Google’s services can expect. Maybe Information Collection and Reuse Policy would be more transparent so there is no misunderstanding, no expectation of privacy and users make informed decision at to what personal information they are prepared to handover in exchange for access to online services.

 

 

 

Categories: Public Domain Data