Archive

Archive for August, 2017

Evaluating GIS costs and benefits

August 28, 2017 1 comment

One of the themes in our book and this blog is to carefully evaluate the costs and benefits of geospatial data.  This should be considered if you are a consumer of data, and are debating whether to purchase data that may be “cleaned up”, thereby saving you time, or to download a free “pre-processed” version of that data, which saves you up-front money but may require quite a few hours or your time or your staff’s time.  However, a data producing organization should also evaluate costs and benefits when they decide how to serve it, and if and how to charge for it.

Chapter 4 of our book delves into these questions: “What is the true cost and value of spatial data?  How can the cost and value of spatial data be measured?  How do the policies determining cost and access ultimately affect the availability, quality, and use of spatial data?”

Other resources might be helpful:  One of my favorite pieces is this essay from Geospatial World on the Economic Value of Geospatial Data–The Great Enabler as is this economic studies for GIS operations document from NSGIC.  A series of 10 case studies are summarized in an e-book from Esri entitled Return on Investment, and here is the results of research of 82 cost-benefit assessments across multiple countries.  One of my favorite “benefits from GIS implementation” pieces is this recent brief but pointed document from Ozaukee County.  A dated but still solid chapter on this topic from Obermeyer is here, with a case study in Ghana here.  The economic impact infographic that has probably received the most attention is from Oxera’s well-done “Economic impact of Geo Services” study.

oxera

The top of the “Economic Impact of Geo Services” infographic from Oxera’s study.

What are your thoughts?  Should organizations still be charging for data in the 21st Century?  Should all geospatial data be open for anyone to use?  How should organizations pay for the creation and curation of geospatial data as the audience and uses for that data continue to expand?  Once geospatial data services are online, how can they best be updated and curated?

Advertisements

Best Available Data: “BAD” Data?

August 14, 2017 3 comments

You may have heard the phrase that the “Best Available Data” is sometimes “BAD” Data. Why?  As the acronym implies, BAD data is often used “just because it is right at your fingertips,” and is often of lower quality than the data that could be obtained with more time, planning, and effort.  We have made the case in our book and in this blog for 5 years now that data quality actually matters, not just as a theoretical concept, but in day to day decision-making.  Data quality is particularly important in the field of GIS, where so many decisions are made based on analyzing mapped information.

All of this daily-used information hinges on the quality of the original data. Compounding the issue is that the temptation to settle for the easily obtained grows as the web GIS paradigm, with its ease of use and plethora of data sets, makes it easier and easier to quickly add data layers and be off on your way.  To be sure, there are times when the easily obtained is also of acceptable or even high quality.  Judging whether it is acceptable depends on the data user and that user’s needs and goals; “fitness for use.”

One intriguing and important resource in determining the quality of your data can be found in The Bad Data Handbook, published by O’Reilly Media, by Q. Ethan McCallum and 18 contributing authors.  They wrote about their experiences, their methods and their successes and challenges in dealing with datasets that are “bad” in some key ways.   The resulting 19 chapters and 250-ish pages may make you want to put this on your “would love to but don’t have time” pile, but I urge you to consider reading it.  The book is written in an engaging manner; many parts are even funny, evident in phrases such as, “When Databases attack” and “Is It Just Me or Does This Data Smell Funny?”

Despite the lively and often humorous approach, there is much practical wisdom here.  For example, many of us in the GIS field can relate to being somewhat perfectionist, so the chapter on, “Don’t Let the Perfect be the Enemy of the Good” is quite pertinent.   In another example, the authors provide a helpful “Four Cs of Data Quality Analysis.”  These include:
1. Complete: Is everything here that’s supposed to be here?
2. Coherent: Does all of the data “add up?”
3. Correct: Are these, in fact, the right values?
4. aCcountable: Can we trace the data?

Unix administrator Sandra Henry-Stocker wrote a review of the book here,  An online version of the book is here, from it-ebooks.info, but in keeping with the themes of this blog, you might wish to make sure that it is fair to the author that you read it from this site rather than purchasing the book.  I think that purchasing the book would be well worth the investment.  Don’t let the 2012 publication date, the fact that it is not GIS-focused per se, and the frequent inclusion of code put you off; this really is essential reading–or at least skimming–for all who are in the field of geotechnology.

baddatabook.PNG

Bad Data book by Q. Ethan McCallum and others.