Understanding Data: It is Critical!

Think of spatial data as the fuel for your GIS engine.  It is fundamental to any spatial analysis.  On listservs, LinkedIn, GeoNet, in this blog, other blogs, and in our book, discussions about data are commonplace.  The volume of spatial data available has increased dramatically in recent years as have the formats in which that data is stored, and the means by which that data is delivered to the user—via web-mapping services, servers, portals, media, user-defined boxes, predefined tiles, and more.

In this avalanche of spatial data, it is more important than ever to encourage your users to fully understand the data they are using. Sometimes, stakeholders view anything on the computer as accurate and complete, including maps.  Maps are incredibly useful, but inherently full of errors and distortions, from the map projection they are drawn from, to missing data, to generalized lines.  Nowadays, anyone can make a digital map.  Help your the users of your data understand that data quality affects subsequent analysis.  For example, in a lesson I frequently teach on plate tectonics, I ask students to study 2001’s largest earthquake, below (south of) the tip of the arrow:

Using a measure tool, students determine that the earthquake is 4 kilometers off of the coast of Peru.  But then I ask them to consider the fact that the generalized coastline was digitized at 1:30,000,000 scale.  How confident are we based on this shoreline that the earthquake was offshore?  Consider the classic geography problem of calculating the length of the British (or any) coastline—the more detailed the scale, the longer the coastline becomes, because at larger and larger scales, the coastline begins to include every cape and bay.  Peru’s coastline may actually twist and turn here, so the earthquake could have occurred on the beach.  The “so what” and spatial thinking discussion continues with the impacts of coastal earthquakes versus underwater quakes, and possible tsunamis.

Encourage your data users – whether they are students, customers, managers, the general public, or others – to be critical of spatial data—knowing its source, who produced it, when and why it was produced, the scale at which it was produced, and its content.   Show them how to create and access metadata.   They will then be able to critically evaluate spatial information and decide whether they will use it in their present and future decision making.  And it is my hope that when they produce their own data, that they will tag and document it thoroughly.

2015 and Beyond: Who will control the data?

Earlier this year Michael F. Goodchild, Emeritus Professor of Geography at the University of California at Santa Barbara, shared some thoughts about current and future GIS-related developments in an article for ArcWatch. It was interesting to note the importance attached to the issues of privacy and the volume of personal information that is now routinely captured through our browsing habits and online activities.

Prof. Goodchild sees the privacy issue as essentially one of control; what control do we as individuals have over the data that are captured about us and how that data are used. For some the solution may be to create their own personal data stores and retreat from public forums on the Internet. For others, an increasing appreciation of the value of personal information to governments and corporations, may offer a way to reclaim some control over their data. The data could be sold or traded for access to services, a trend we also commented on in a previous post.

Turning next to big data, the associated issues were characterised as the three Vs:

• Volume—Capture, management and analysis of unprecedented volumes of data
• Variety—Multiple data sources to locate, access, search and retrieve data from
• Velocity—Real-time or near real-time monitoring and data collection

Together the three Vs bring a new set of challenges for data analysts and new tools and techniques will be required to process and analyse the data. These tools will be required to not only better illustrate the patterns of current behaviour but to predict more accurately future events, such as extreme weather and the outbreak and the spread of infectious diseases, and socio-economic trends. In a recent post on GIS Lounge Zachary Romano described one such initiative from Orbital Insights,  a ‘geospatial big data’ company based in California. The company is developing deep learning processes that will recognise patterns of human behaviour in satellite imagery and cited the examples of the number of cars in a car park as an indicator of retail sales or the presence of shadows as an indicator of construction activity. As the author noted, ‘Applications of this analytical tool are theoretically endless‘.

Will these new tools use satellite imagery to track changes at the level of individual properties? Assuming potentially yes, the issue of control over personal data comes to the fore again, only this time most of us won’t know what satellites are watching us, which organisations or governments control those satellites and who is doing what with our data.

 

The Ecological Land Units Map and Data Sets of the World

In my view, one of the most important new global datasets released in the past year is the Ecological Land Units of the world.  This dataset came from a collaboration between Esri and Dr. Roger Sayre and others from the USGS. It was officially launched in late 2014 the ACES 2014: A Community on Ecosystem Services meeting in Washington, DC. For the data set, the collaborators undertook a massive biophysical stratification of the planet at the finest yet-attempted spatial resolution of 250 meters to produce this first-ever map of distinct physical environments and their associated land cover. Project leaders also offered a concept for delineating ecologically meaningful regions that is essentially both classification-neutral and data-driven. Their intent was to provide scientific support for planning and management (including as an important variable for GIS geodesign models and apps), and to enable understanding of impacts to ecosystems from climate change and other disturbances; hence for valuation of ecosystem services.  This site is a manifestation of Software as a Service (SaaS) that we discuss in our book.

The data from the map is offered in both the public domain and as ArcGIS Online content with considerable value-added analytical and visualization functionality. Project leaders encourage the community to test and use the data, to develop apps based on the data, and to consider delineating their own ecological regions using the ecophysiographic stratification approach with local, finer resolution datasets.

There are multiple ways to learn more and to access the map and the underlying data:

1. As a presentation:  ACES 2014 Town Hall presentation by Roger Sayre (USGS) and Dawn Wright (Esri) via this link.
2. As a story map:  Introduction to the ecological land units project via esriurl.com/elu
3. As an App to explore the data via esriurl.com/EcoTapestry and search for a place of interest.
4. As a technical report, to learn more about how the ecological land units were derived via www.aag.org/global_ecosystems
5. As data in ArcGIS Online: Get started using this content in ArcGIS (with an ArcGIS Online for Organizations subscription) via esriurl.com/landscape
6. As raw data as global rasters from USGS via ftp: http://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/
7. As a white paper to give further guidance to our use case testers thoughout the academic community, and to offer the associated conceptual and technical support pro bono.
8. For general reading, via an article in Wired magazine: New Map Shows the World’s Ecosystem in Unprecedented Detail.

Project researchers are already making improvements to the data, initially by swapping the GLOBCOVER 2009 data that they used with the new 2010 epoch Global Land Cover map (v 1.4). There will be more improvements to come as the appropriate data become available.

Ecological Land Units Map and Data, here, as a story map.

VGI Data Sources: Assessing Completeness and Correctness

In a recent article published in the ISPRS International Journal of Geo-Information, Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany, the authors investigated some of the concerns regarding data quality and data usability often levelled at Volunteered Geographic Information (VGI) data sources.

The objective of the study, based in the Rhine-Neckar region of southern Germany, was to compare OSM data to the authoritative land use and land cover (LULC) data set ATKIS Base DLM version 6.0. published by the LGL mapping agency (Baden-Württemberg State Office for Geoinformation and State development).

The results for the OSM data completeness and correctness comparison were variable across the different classes of land use in the study area. However some general trends emerged including:

• Areas with a high percentage of forest cover were the areas with the highest level of completeness and correctness.
• Other classes (incl. farmland and urban areas) had low levels of completeness but higher levels of correctness; features present were mapped accurately but some features were missing.
• Other areas (incl. quarry and lakes) had high levels of completeness (most features mapped) but had a greater percentage of incorrectly mapped features.
• There was a marked difference between rural and urban areas; the study identified higher OSM coverage and thematic accuracy in densely populated areas (more people available/interested in collecting the data?).
• Some land use classes demonstrated both high levels of completeness and correctness, suggesting they had been mapped for a specific purpose.

Although not intended as a definitive statement of OSM data quality, the study suggested that if full coverage and accurate LULC data was a requirement for a project, then OSM data (at present) may not be the best option. However for certain land use classes, where the LULC information was available it was mostly correct so depending on project requirements OSM data may be a suitable alternative.

As we’ve said many times before on Spatial Reserves, it is not whether the data are good, but rather if they are good enough to meet your requirements.

Ref:
Dorn, H.,Törnros, T. and Zipf, A. (2015). Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany. ISPRS Int. J. Geo-Inf. 4, pp. 1657 – 1670