Release of OS Open Map

In February this year, to coincide with International Open Data Day, Ordnance Survey (GB) announced the launch of a new suite of digital map products. Included in the product suite is OS Open Map – Local,  one of the most detailed vector map products (nominal viewing scale of 1:10,000) to be released under the OS OpenData initiative.

Contains OS data © Crown copyright [and database right] (2015)

OS Open Map – Local and the related products, including OS Open Rivers, OS Open Roads and OS Open Names, have just been made available on the OS web site and are now available to download in GML 3.2 and shapefile formats. The data are made available under the terms of the Open Government Licence (OGL).

GIS GIGO (Garbage In Garbage Out): 30 checks for data errors

Nathan Heazlewood, programme manager at the Auckland City Council in New Zealand, has written a wonderful essay about “garbage in, garbage out” in relation to geospatial data.  In it, he not only ties this oft-heard phrase to the importance of GIS data quality, but he also details the checks that GIS analysts should go through when they are assessing a data set.  This list is also valuable for data producers as they document their own work. And of course, these days, everyone in GIS is a potential data producer.

The list of items (at present, there are 30) is grouped under checks for positional accuracy, topological logic, geometric considerations, projections and coordinate systems, attribute and data structure checks, and attribute and data structure checks.  Extremely helpful are Nathan’s diagrams showing tables lacking null values for non-null attribute data, values outside permitted ranges, and orphan records in related tables.

Nathan includes many considerations that are not often discussed but can lead to enormous problems, such as the different standards of date formats being used around the world, from year-month-day to day-month-year to month-day-year (which Nathan dubs the “super dumb American date format”).  Another consideration is one I can identify with that gave me GIS problems during the first GIS workshop I taught in Turkey–the numbers in my data were formatted such as 100,000 for one hundred thousand, but the software in the university lab, given its location, was naturally configured for one hundred thousand to be coded as 100.000.

How might you be able to use this data error checklist in your own work?  What checks would you add or expand?

A section of Nathan Heazlewood’s very useful 30 checks for data errors.

OpenAerialMap

Just over a year ago we wrote about an OpenStreetMap project to support humanitarian aid with open UAV imagery following the destruction caused by Typhoon Haiyan in the Philippines in Nov 2013. Although there were some issues in coordinating the data collection, the benefits of having access to a managed resource of openly accessible aerial imagery were obvious.

One year on and the Humanitarian OpenStreetMap team (HOT) have established the OpenAerialMap (OAM) project, to host and share aerial imagery from a variety of sources including ‘traditional and nano satellites, manned and unmanned aircraft, mapping drones, balloons and kites‘. The project will not only provide access to the imagery but it will also make the management software available to download, to help support local access to the imagery.

Although the project is still in the early stages, one of the first objectives is to establish an imagery catalog to facilitate searches and display. One of the major problems in the Philippines was identifying which individual or agency collected the imagery the humanitarian relief teams needed access to. The next priority will be to create the map engine, or the OAM server, to make the imagery available as a web service.

 

 

Fee vs. Free Geospatial Data: Like a Snow Shovel?

One of my students recently shared something that I considered to be a thought-provoking analogy in the “fee vs. free” geospatial data debate that we included in our book and discuss on this blog.  The debate, in sum, revolves around the issue, “Should government data providers charge a fee for their geospatial data, or should they provide the data for free?”

The student commented, “I tend toward the “at cost” position of the debate for local governments and free side of the debate for federal data. For me, the “tax dollars are used to create the data so it has already been paid for argument” does not hold water. Taxpayers have no expectation (or shouldn’t have) of walking into the local parks department to borrow a shovel that in theory their tax dollars paid for.  The same logic could be applied to spatial assets.”  The student went on to say that the above argument should be applied to local and regional government data, because “federal level data […] tends to be more directly reflective of the population and the federal government more directly benefits from the economic opportunities created by free data.”

While I have tended to advocate on the side that geospatial data should be freely available, I believe that the student’s snow shovel analogy for local governments has merit.  Following this argument, a small fee for data requested that is over and above what that government agency provides on its website seems reasonable.  But I still am firmly on the side of that government providing at least some geospatial data for free on its website, citing the numerous benefits as documented in case studies in this blog and in our book.  These benefits range from positive public relations, saving lives and property in emergency situations, and saving time in processing requests from data users. Consider what one person can do with the snow shovel versus what one person could do with a geospatial data such as a flood dataset.  The shovel might help dredge a small section to help a few neighbors get out of their houses, but the flood dataset could help identify hundreds of houses at risk and provide a permanent, effectively managed solution.  There is an order of magnitude difference in the benefit to be gained from making geospatial data easily and freely available.

What are your thoughts on this important issue?  We invite you to share your thoughts below.

City of Boulder GIS Resources.

Crowdsourcing Phenology: Marsham’s Indications of Spring

Although there is perhaps a tendency to think that crowdsourcing data collection initiatives are a recent innovation, the practice of citizen science dates back to some of the earliest known recordings of natural and human-made phenomena. In a recent report by the BBC on the signs of spring ‘shifting’ in trees, the pioneering crowdsourcing work of English naturalist Robert Marsham, best known for his Indications of Spring, was acknowledged. Marsham’s interest was in what became known as phenology, the study of the periodic cycles of natural phenomena. His indications of those cycles, 27 altogether, included recordings of the first leafing of a number of trees such as elm, rowan, and oak, the first hearing of birds such as the cuckoo, swallow and nightingale, and the first croaks of certain amphibians. Marsham’s family continued with his observations after his death in 1797, providing almost 200 years of seasonal observations.

Today the same phenological surveys are supported through the Woodland Trust’s Nature’s Calendar survey, a resource for volunteers to record the signs of the changing seasons where they live. A number of live tracking maps are available, which allow visitors to the site to select a species, a year, a particular event such as a first flowering, and plot the results. I chose snowdrops, one of the signature flowers of spring in many parts of Europe. As of the 25 February there had been 487 recorded sightings of snowdrops this year.

Nature’s Calendar – Live Map

Although the spatial data are not available to download, summaries of the seasonal results are available as PDFs.