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Archive for May, 2019

Nebraska: New Digital Hub and Geospatial Data Portal

May 26, 2019 2 comments

I recently gave the new Nebraska digital hub and geospatial data portal a test run.  As some of the GIS-based lessons I teach are based in Nebraska, some of which are in the Exercises section of this blog (including the fire tower site selection exercise), I have been using GIS portals in the state for at least 20 years.  As I have stated in reviews of other data portals, my preference is to have searching tools as well as the ability to browse categories of data, and this site provides both.

As detailed in a recent article in Enterprise Digitalization, the unique characteristic of the Nebraska portal is that it includes data from every single state agency, served in one location.  Crime, childcare, gas stations, and other information is all supposed to be here.  My searches for fuel or gas came up with nothing, as was my search for robberies, crime, and soils.   My search on land cover netted me unrelated items.   Admittedly, these were my first experiences with the site, and no doubt the site as well as my skills in using it will improve over time.  I did find some interesting data sets that I had not been able to find in the Nebraska data portals I had been using up to now.  I was also pleased to see that many of the data sets are offered as downloads and also as  streaming data services.  Plus, metadata is easily found and understandable.

I know it is a challenge to pull together a state portal, and I salute the efforts of those behind this one.  Admittedly, all I am interested in is geospatial data, and not the other data on the site.  For now, I will use this portal, but because I could not find all of the data sets I am interested in, I will continue to use the Nebraska DNR site, the state’s open data portal, and the sites I have found useful in the past at the University of Nebraska.   I only hope that if those other sites are removed at some point, that the developers do not do so before verifying that every single GIS dataset is moved to the new portal (please!).

Nebraska data portal.

–Joseph Kerski

Verifying location data with blockchain cryptography

May 20, 2019 1 comment

Following on from Joseph’s recent post on some of the issues associated with the plethora of image resources we now have access to, another interesting aspect of verifying those data sources relates to the basic premise of proof. How can a data provider, whether that’s an individual or global company, prove the data they collect and publish are an authoritative and accurate representation of the locations they seek to record? The problems associated with Geolocation and GPS Spoofing are not new, with many protocols and procedures now in place to help prevent this type of deception. Conversely, GPS simulators are generally available, making it relatively easy for location hackers to interfere with GPS signals.

So how do data providers prove entities, in both the physical and human-made environments, really do exist at a particular location? One company, XYO, has been working on an alternative to satellite networks as a source of verified location information – the XYO Network. By augmenting our increasingly interconnected network of digital devices with location tracking technologies that incorporate blockchain cryptography, these co-opted devices (acting as sentinels or bridges) can be configured to recognise, validate and confirm the location of each other. As each device acts as a witness to the location of other devices; the more witnesses there are confirming a device’s location, the less chance there is that location is incorrect. The end result is a decentralised location data network that is arguably at less risk of being compromised.

Bound witnesses (sentinel and bridge devices) in San Francisco – https://matrix.xyo.network/map

Using device networks in this manner is an interesting new development in evolution of geospatial data and an emerging technology to watch.

 

Imagery: It is what it is: Well, not always.

May 12, 2019 2 comments

Being critical of all data, including geospatial data, is a chief theme of this blog and our book:  Use it wisely.  Know where it came from.  Know what has been done to it. Imagery has always been an important component of geospatial data, and today, it takes many forms, ranging from images taken on the ground with a phone or camera to those taken by webcams, UAVs, aircraft, satellites, and more.  Doesn’t this imagery “tell it like it is” and is it therefore exempt from us casting a critical eye on it?   I contend that no, images must be viewed critically so that you can determine if they are fit for your use in whatever project you are working on.

As one example in a larger story, consider the example of the removal of individuals from 20th Century photographs in the USSR. But lest we think such image doctoring was a thing of the past, there are dozens of articles that arise in current web searches highlighting modern day image manipulation, from fashion magazines to science journals, some inadvertent, some on purpose, some for purposes to benefit society and some unfortunately to mislead.  In the geospatial world, consider the image offsets on Google Maps in China that we wrote about not long ago.

Let’s discuss another aspect of image manipulation.  Consider the satellite images below.  They cover many different dates and are derived from many different sources.  But pay attention to what’s on–or off-the highways in the images.   These images all cover the same location—Interstate 30 – 35E interchange on the southwest side of downtown Dallas Texas USA, known locally as the Mixmaster.  Constructed between 1958 and 1962, you can spot, in some images, recent much-needed improvements due to the substantial increase in traffic volume in the past 60 years.

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Study area in ArcGIS Online (first image) and Mapquest (second image).

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Study area in Yahoo maps (first image) and Bing maps (second image).

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Study area in Google Maps (it appears the same way in Google Earth). 

Notice anything missing on the Google Maps image?  No traffic. This would be unheard of, day or night, in this busy part of one of the largest cities in the USA.  Obviously, a vehicle-removal algorithm has been applied to the image. It is actually quite impressive, because the algorithm seems to only be applied to moving vehicles–cars, buses, trains–and not to stationary vehicles.  No doubt it is applied to make the process of viewing and interpreting the now-clearer imagery easier.  Interestingly, the moving-vehicle removal seems to only, at the present time, be present in running Google Maps on the web on a tablet or laptop:  Running the Google Maps app on a smartphone shows moving vehicles (below).

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Study area in Google Maps app on smartphone. 

Key takeaway points and teachable moments here include:  (1) Images that can be obtained in web mapping services, in geodatabases, and portals should not be considered the same view that would be seen if someone were to be peering over the side of a UAV, airplane, spacecraft, or standing next to a webcam when the image was captured.  They can and are manipulated for a variety of purposes.   They need to be viewed critically.  At the same time, we can marvel and appreciate the many choices we have nowadays in imagery for use in geospatial analysis.   (2)  The machine learning and artificial intelligence techniques applied to imagery are further evidence for the enterprise nature of geotechnologies:  GIS is increasingly connected to mainstream IT trends and innovations.

–Joseph Kerski