Skip navigation
All Places > Education > Blog > 2018 > May

A variety of economic, geographic, and other factors influence a store chain to “stay regional” vs. going national or international.  Spatial patterns of where these regional chain stores are located often tell a story about where the headquarters is located, about population trends, demographics, climate, business tax rates, human behavior including commuting and buying habits, and much more. The patterns sometimes show that competing stores of the same type, often overlap, while at other times they are adjacent to each other.  This exercise uses a web based Geographic Information System (GIS) as an analytical tool to analyze the locations of regional convenience stores, and to site a new store in a community. 


To enable students and others to dig into these issues, to encourage them to think critically and spatially, and to engage them in using GIS tools, I have created a new lesson (REVISED NOVEMBER 2019) that uses Community Analyst to analyze national, regional, and local convenience stores (ATTACHED).  The lesson uses Community Analyst, a very rich online set of tools, data, and capabilities from Esri.   Also attached are a set of slides introducing Community Analyst (and Business Analyst Web, which is essentially the same toolkit as Community Analyst), and explaining how and why it can be used in education and beyond.  With Community Analyst you can create choropleth maps on hundreds of variables for many countries around the world on demographics, health, crime, and other variables, as well as on consumer preferences and lifestyles, you can create infographics and drive/walk time and distance buffers, you can map business locations, and so much more.  These tools are part of the ArcGIS platform, so your maps can be shared with ArcGIS Online, and conversely, you can bring maps from ArcGIS Online into Community Analyst/Business Analyst Web.


The lesson steps participants through analyzing the distribution of two regional convenience store chains - Allsup's and Casey's, asks them to make a variety of choropleth maps to understand population and markets, and finishes with a site selection for a new store in a community (I selected Columbia Missouri).  Concepts include understanding distributions, scales, business decisions, and site selection.  Tasks include filtering data, mapping point locations, computing drive time polygons, creating infographics, and more.  Several screen shots from the lesson appear below.  


The lesson could be taught in courses including geography, business, sociology, mathematics, and GIS.  It requires two  to three class periods or can be run online.  It can be taught in a community or technical college, a university, and even in an advanced high school course.  The lesson could be run in Business Analyst Web as well as in Community Analyst.  Since both of these tools are run online, no software is required.  An ArcGIS Online account is all that is needed to acesss the tools, make the maps, and conduct the investigations.  The lessons could be easily extended to other brands of convenience stores or other types of businesses.  I look forward to hearing your reactions. 



A truck stop travel center and convenience store.

A section of the Community Analyst lesson on convenience stores.

A section of the Community Analyst lesson on convenience stores.

A section of the Community Analyst lesson on convenience stores.

For those of us teaching with ArcGIS Pro (or currently migrating), attached is a training guide from our friends in Esri Training Services, containing a collection of web courses, lessons, tutorials, training seminars, MOOCs, etc.


Of course these resources are available at Esri Training, Learn ArcGIS, Documentation, etc. but the PDF contains logical groupings which may appeal to some. 

I created a new lesson in the ArcGIS Learn Library focused on siting a wind farm using the analytical tools in ArcGIS Online:   

The lesson will help you or your students build skills in these areas:

  • Conducting a site suitability study
  • Conducting drive time analysis
  • Creating a web app

What you will need to run the lesson:   

  • Publisher or Administrator role in an ArcGIS organization (see this link to get a free trial)
  • Estimated time: 1 hour.  

The lesson uses tools including filter, overlay (union), proximity, find locations, routing, as well as examining symbology, classification, and tabular information.  The lesson uses some wonderfully rich wind power data from the National Renewable Energy Lab (NREL), as well as electrical lines data, population data, and other layers.  You could run the lesson as part of your course in GIS, but also in a course on geography, energy, sustainable development, demography, or environmental studies.


Because the lesson uses ArcGIS Online, you could expand the lesson by adding additional layers to consider in your site suitability analysis, and by using additional analysis tools.   The lesson uses Colorado as its case study, but you could modify it for another state by accessing another state's wind data from NREL.  I thank the Platts company for the use of their generalized electrical data and my colleague Colin Childs on the Esri Learn Team for his help getting the lesson into the Learn format.

Final result after analysis

Final result after analysis is performed showing some of the layers used in the lesson. 


Wind turbine

Wind turbine.  Photo credit:  Joseph Kerski.

Huge thanks to Eric Shook (University of Minnesota), Patricia Carbajales (Clemson University) and Blake Lytle (Clemson University) for their fantastic presentations and follow up discussions on CyberGIS/HPC. 


The recording and slides are located here.

GIS Professional Tripp Corbin's book, the "ArcGIS Pro 2.x Cookbook" (2018, Packt Publishing) is new but I believe will quickly become a valued and oft-used resource. Mr Corbin's goal in writing this extensive (694 pages) resource is to help GIS professionals "create, manage, and share geographic maps, data, and analytical models using ArcGIS Pro." The audience for this book includes all who are learning GIS, or learning Pro, as well as those migrating from ArcMap to Pro.


Tripp's "cookbook" theme is evident throughout the book's format, where in each section and problem to be solved, he shows how to get ready, how to do it, how it works, and ... "there's more" (additional resources). That the book is from Packt is excellent, because Packt ( offers eBook versions of every one of its books, and also offers newsletters and tech articles. That Tripp is a full time trainer and instructor is evident--he understands the challenges in learning a rapidly-changing and complex technology inherent in GIS with just enough tips to keep the reader engaged. He also encourages the reader to think about how to apply each tool and method to his or her own work. He offers the reader the ability to download the sample data for the book, and the data bundle is also on GitHub. He also includes PDFs of all images of screen shots and diagrams.


I like Tripp's approach because, similar to my own instruction, he starts with data. He's not hesitant to discuss the benefits but also the limitations of each data format such as shp, gdb, and CAD files. He spends quality time in the book helping the reader understand how to convert data to the format that best fits his or her needs. His sections on linking tables from outside sources to existing data, on editing (in particular, a focus on topologies to improve data accuracy and increasing editing efficiency), and on 2D and 3D analysis are very helpful. I was pleased to see much attention to what I consider to be a chief advantage of Pro--the ability to more easily share content from Pro to ArcGIS Online and hence the wider community. Another wonderful new function in ArcGIS Pro is also included in the book--writing and using Arcade scripts, applied to symbology, classification, and analysis.


As a GIS book author myself, I know the challenges faced in writing such a book--what should be included, and what should be left out? Tripp does a nice job here as well, including the fundamentals that most users will touch. The book's chapters include: 1: Capabilities and terminology. 2: Creating and storing data. 3: Linking data together. 4: Editing spatial and tabular data. 5: Validating and editing data with topologies. 6: Projections and coordinate systems. 7: Converting data from one format to another. 8: Proximity analysis. 9: Spatial statistics and hot spots. 10: 3D maps and 3D analyst. 11: Arcade, labeling and symbology expressions. 12: ArcGIS Online, 13: Publishing your own content to ArcGIS Online. 14: Creating web apps using ArcGIS Online.


These chapters cover a great deal of ground. In the editing chapter, for example (Chapter 4), configuring editing options, reshaping existing, splitting, merging, aligning, creating new point line polygon features, creating new polygon feature using autocomplete, and editing attributes using attribute pane and in the table view, are all examined. The examples in the book are interesting and relevant, and not without some humor (Trippville is a community that is often studied). In my view, the book contains just the right amount of graphics. Tripp provides answers to the questions he poses, and then gives the explanation for each answer. Despite the "recipes" provided in the cookbook, not all of them require the previous recipe to be used, which is excellent for all of us in GIS who have limited time and want to select sections in a non-sequential order.


I highly recommend using this book in conjunction with Tripp's other book on this topic, "Learning ArcGIS Pro." The Learning book focuses on installing, assigning licenses, navigating the interface, creating and managing projecrts, creating 2D and 3D maps, authoring map layouts, importing existing projects, creating standardized workflows using tasks, and automating analysis and processes using modelbuilder and python. The Learning ArcGIS Pro book ideally should be used first, before the ArcGIS Pro 2.x Cookbook, but if you are pressed for time, these two books could be used in tandem. Keep both of them handy--they will be very useful to you.


Tripp Corbin's GIS books.

The cover of Tripp Corbin's ArcGIS Pro Cookbook, left, along with his earlier book, Learning ArcGIS Pro.


Tripp Corbin's GIS book.

An example of the detailed screenshots that Tripp Corbin's ArcGIS Pro Cookbook contains. 


Tripp Corbin's GIS book.

Tripp Corbin's GIS book.

Additional examples of the details that Tripp Corbin's ArcGIS Pro Cookbook contains. 

The best teachers share a few characteristics. First and foremost, the students as individuals are more important than the subject, so you have to know and understand each kid deeply to help them. You need to know your subject matter intimately to engage different kids in different ways. You need to organize activities that challenge kids at a reasonable level, and kids don't handle all challenges equally. There are often ways to meet irksome rules while still meeting more important missions. And you must remain adaptable. In today's education parlance, the first three elements are generally called "differentiated instruction" or "whole child education." But the teachers who stand out grasp and implement the last two elements as well. And because of those, the teachers are sometimes called "mavericks." Retired but tireless teacher Randy Raymond, from Detroit, is a "maverick."


Randy got his BS and MS degrees in science in the early 70s, then did research on Isle Royale, the big island in Lake Superior. "We took the first boat out in May, and the last boat back in October." He did some teaching in northern Michigan, and ran a landscaping business. "But my first real teaching began in 1981, with 6th grade science in Detroit Country Day School, where I started addressing kids' needs, especially those needing something other than typical classes. I began an 'outdoor field study' program. All day every Friday, no matter the weather, every class period was outside field study for that hour. Kids liked coming to school." Prominent people liked the special projects underway, and found ways to support these with money or technology. And Randy made more connections with people in business, government, and nonprofits who could make things happen. "I saw GIS in 1987, ARC/Info on Unix, but didn't have the technology or time to cope with it, but knew it would be important."


With a reputation for success, Randy shifted to Cass Technical High School in 1991, teaching older kids. He earned more grants, and in early 1993, at the NSTA (National Science Teachers Assoc) Conference, Randy saw me in a booth, showing ArcView 1.0 for Windows. "I have money! I need to buy a school license!" It took Esri months to set up the mechanism, but Randy became the first teacher to buy this license, and his next 25 years became a blur.


With a special grant, "I got hardware and built a lab, and had students explore and tinker during the day, and taught adult ed classes in the evening." His students began doing projects. One group studied lead in the water in Detroit, mapping lead pipe water service; Randy had wondered if the problems some students exhibited with certain content, and thus on some critical tests, might be influenced by lead in the water. "Four good chemistry kids spent one year doing research, and the next year working out ways to relieve lead loading in the water that happens overnight." Available health data was not pinpoint geography, but showed over 6800 kids with blood lead poisoning. Randy and his students were set to present this at the opening of Esri's 1995 User Conference, but were diverted to the White House to receive the grand prize from the Seiko Environmental Youth Challenge.


GIS in K12 Education movie frames


Meanwhile, projects for Ford Motors and the City of Detroit earned even more attention, as seen in Esri's "GIS in K12 Education" video (1995) and Esri's book "Zeroing In" (1998). "Some kids and I worked on the city's $100m Empowerment Zone grant, downtown for four weeks every day after school, with our computer and printer there. On the day they had to submit it, I was putting the booklet together and they were holding a police car to get things to the airport and then to DC before the 5pm deadline. President Clinton said that, of all the proposals, ours was the most informative, especially in the first pages, with the maps."



Because of his GIS skills, Randy was moved in 1998 from Cass Tech to Detroit Public Schools Executive Services. "I did data and analysis, not politics." That made him extremely valuable, and students of any age with GIS skills very attractive. Randy taught GIS at colleges and Saturday academies at local high schools. "As a school administrator, I came with the background of a teacher who was accustomed to doing things that met needs, solved problems, and were possible even if not typical." Entrepreneurial associations grew, providing more kids experience with GIS, through collaborations between a mix of governmental, educational, non-profit, and private partners.


"In 2008, the city asked to collaborate on a lead study. We got 300,000 records from 1992-2008, with real addresses; 169,000 were really good, and 80,000 of those were currently in schools, across 13,000 blocks of the region. We published an article in 2013, showing 54% with lead damage when they were young. The results were so obvious that people asked if we rigged it, but we had a number of kids with tests from two or three different years, and we were clearly failing them. They were not being engaged in the special ways needed given the things that had happened to their bodies." (See Education Week's related article.) For publishing a study that exposed damage, Randy got in trouble, and retired in June of 2013.


For a quarter century, Randy has talked passionately, with anyone who would listen, about "purposeful applications of technology in school … It's what you do with the kids, that's more important than any subject you're teaching. Doing something good with them is always my goal… [GIS] is like a whirlwind, and some see the endless opportunities and dive in, while others just avoid it because they don't get it and just can't see the value … It's not magic. The longer they are involved with GIS in real world work, the more they get engaged in what they need to know and how interconnected things are, and they're iterating and editing constantly, making decisions to make something better. You don't just give an answer and have someone tell you that you got it right or wrong, you get the chance to investigate … Kids working with GIS get smarter even if you don't see it on a test … We want them to know that learning is a lifelong process and sometimes we stumble, and things change so we have to adapt … School is meant to be a 'terminal' thing, but learning is not; the more school is an end in itself, the less learning becomes the goal; we need to get people invested in learning rather than in school …"


And now? "The joy of retirement is that I'm only out of [a given project] if I want to be." His current mission is showing school and district administrators how to use GIS to enhance school safety. There are always new people waiting to be exposed and, fortunately, mavericks doing whatever they can to help people of all ages and roles grasp the power of GIS.

Randy with car and GISGUY license

Luc Anselin, a Fellow of the University Consortium for Geographic Information Science, recently remarked that "GIScience [is] morphing into spatial data science” (Anselin 1027).


Is it really?


Fresh from Harvard’s "Illuminating Space and Time with Data Science" conference, and thinking ahead to the upcoming CaGIS AutoCarto/UCGIS Symposium on "Frontiers of Geospatial Data Science", I aim to collect my thoughts about Luc's claim in this short article.


Depending on the origin stories you choose, both GIScience and Data Science began to take shape in the 1960s and 70s. Stanford professor David Donoho traces the origins of Data Science to the work of the maverick statistician John Tukey, then Donoho’s undergraduate thesis adviser at Princeton (and one of my own scholar-heroes; hence my choice of stories).


Donoho’s definition of data science as “a superset of the fields of statistics and machine learning which adds some technology for ‘scaling up’ to ‘big data’” belies his skepticism about the hype that surrounds the “contemplated field.” Indeed, Gartner reports that data science and machine learning began reaching the peak of their “hype cycle” in the past year. 


Concerns about predicted shortfalls of qualified practitioners have led organizations like the National Academies of Science, the National Science Foundation, and the National Institutes of Health to charge distinguished panels to develop strategic plans for Data Science, including data science education. These reports barely mention geospatial data and methods, and certainly don’t recognize GIScience as an integral part of Data Science.


Anselin (2017) might attribute this to “space skepticism,” the tendency of mainstream scientists not to consider spatial thinking “fundamental to the scientific process itself.” In our own higher education outreach, my colleagues in Esri’s Education team and I have noted a widespread belief among scientists beyond GIScience that spatial data are “just another data type." 


Contrary to that academic culture trait, there is some evidence of GIScience's convergence with Data Science. For instance, The University of Oregon’s Department of Geography established an undergraduate degree program in Spatial Data Science and Technology in 2016. The University of Southern California’s Spatial Sciences Institute offers a new cross-disciplinary MS Degree in Spatial Data Science. And Anselin himself founded a Center for Spatial Data Science within the Division of Social Sciences at the University of Chicago.


Beyond the academy, there is evidence of convergence in the occupations as well. A search on “data scientist” at O*NET Online the U.S. Department of Labor’s database of occupationsproduces "Geospatial Information Scientists and Technologists" and "Remote Sensing Scientists and Technologists" among its top ten search results. 


First 20 occupations associated with the search term "data scientist" at the U.S. Department of Labor's O*Net OnLine web site.


(One might wonder, why does the Department of Labor database not include an occupation called "data scientist"? One explanation is that GIScience's hype cycle peaked much earlierarguably in 2003, when the U.S. Department of Labor highlighted “Geospatial Technology” as a high-growth technology industry. Advocacy for formal occupations crescendoed soon thereafter.)


The recent events hosted by Harvard's Center for Geographic Analysis, and soon by CaGIS and UCGIS, may reflect a widening interest in the intersection of GIScience and Data Science - among GIScientists, at least. Harvard's event attracted 26 presenters and panelists representing academic institutions, government agencies, and industry, and a record-high registration of over 250 participants in total. Organizers Matt Wilson (Professor Geography, University of Kentucky, and Visiting Scholar, Harvard), Wendy Guan (Executive Director, Harvard CGA), and I aimed to bring together mainstream data scientists and GIScientists, to review the status of both fields, and to explore commonalities.  


Here's a partial list of highlights of two keynote addresses and four panel sessions presented on Friday, April 27:


Keynoter Francesca Dominici (Professor Biostatistics, co-chair Harvard’s Data Science Initiative) described a research study that applied a neural network to predict a continuous, 1 km grid of daily air pollution levels across the continental U.S.. Fused with claims records for over 67 million Medicare patients, the research suggests that there is no “safe” level of fine particulate matter pollution (produced primarily by fossil-fueled power plants) for senior citizens. 


In a panel themed “Sensors, Smart Objects and Infrastructure for Data Science," Carlo Ratti (Director, SENSEable Cities Laboratory, MIT) focused his short presentation on a project called TrashTrack, which addresses the research question, “why do we know so much about the supply chain and so little about the ‘removal chain'?” The project mobilized volunteers in Seattle who attached small, cheap, location-aware sensors (designed in Ratti's Lab) to 3,000 trash objects. The visualized trajectories of tracked trash revealed far-flung, nationwide removal chains, and raised new questions about environmental justice. 


In the same session, Brendan Meade (Professor Earth & Planetary Sciences and Affiliate in Computer Science, Harvard), discussed how machine learning is changing the condition of possibility of earthquake prediction, and reported progress in using neural networks to predicting where aftershocks will occur.


A second panel titled "Crowdsourcing, Geocomputation, and Spatiotemporal Analysis" included Amen Mashariki (Urban Analytics program lead, Esri). Amen reflected on his former role as chief data scientist for the City of New York, and pointed out the prevalence of predictive policing in U.S. cities. Emphasizing the need for transparency in prediction algorithms, he described an outreach strategy to promote public understanding of algorithms in his new home, the City of Baltimore.


Alex Singleton (Professor of Geographic Information Science and Director of the University of Liverpool’s Geographic Data Science Lab) explained why traditional sources of social science data are under threat, including national censuses and large-scale social surveys. Emergent new data sources are challenging traditional modes of inquiry. 


In a third panel on "Data Science for Cities, Health and Environment," Björn Menze (Professor Computer Science, TU München) presented work on algorithm design for medical image processing, including CT Scans. Noting that hundreds of thousands of such images are available for analysis at national health information repositories, he demonstrated how machine learning enables new mappings of disease patterns. 


(Menze’s work came up earlier in the day, in a different context. Our host for the event, Jason Ur, (Professor Archeology and CGA Faculty Director) mentioned in his introductory remarks that Björn used similar algorithms to detect thousands of archaeological sites in remotely-sensing imagerydiscoveries that would have taken Jason years to uncover through traditional field methods.) 


Finally, in a fourth panel on "Geography, Civic Engagement, and the Future of Data Science," Robert Chen (Director CIESIN, Columbia University) described an effort to mobilize the "data revolution" to advance the United Nation's Sustainable Development Goals.  


Michael Goodchild (Professor Emeritus Geography, UC Santa Barbara) offered a second keynote address entitled "The Landscape of GIScience." Goodchild, who coined the term “Geographic Information Science” in 1992, wondered if the name "Data Science" isn't "retrograde," given that "information is data fit for purpose." Still, he agreed that rise of data science does provide opportunities for GIScience. "Carpe diem," Mike concluded.


Should GIScience converge with Data Science?


Allowing that some evidence supports Anselin's claim that GIScience is morphing into spatial data science, a second question remains: should it? Answers will vary depending on one's viewpoint and values. I'm an educator first and foremost, and my primary sense of duty is for my students' successbefore and after they graduate. From that perspective I think about spatial data science in context of the evolution of work in an age of automation. 


I hear a growing chorus of economists, tech leaders, and forward-looking historians anticipate fundamental disruption of traditional employment by increasingly capable machines. Management consultants Richard and Daniel Susskind, authors of The Future of the Professions (2016, p), foresee that “in the long run, increasingly capable machines will transform the work of professionals … leaving most … to be replaced by less expert people and high-performing systems.” Kelleher and Tierney (2018, 67), for example, suggest that "data science is best understood as a partnership between a data scientist and a computer."


Recognizing that the outsourcing of work to machines is nothing new, and that observers are notoriously bad at anticipating the new jobs that disruptive technologies eventually create, the Susskinds don’t predict future occupations that may replace the traditional professions. Instead, they suggest twelve future roles for which education should help people prepare. Those roles are:
Future roles in a post-professional economy (Susskind and Susskind 2015)


As the search results of the Department of Labor's O*Net database (above) suggest, "data scientist" is a role that workers in many occupations will be expected to play. I, for one, am becoming convinced that graduates of GIS-related degree and certificate programs should be prepared to play that role, to a greater extent than their predecessors already do. 


One implication is that tomorrow’s spatial data scientists – professionals with specialized competence with georeferenced data “wrangling,” analysis, visualization, and story telling – will need skills and abilities that span all three industry sectors of the Department of Labor's Geospatial Technology Competency Model: positioning and data acquisition, analysis and modeling, and software and app development. A corollary to that point is the need for future revisions of the GTCM to incorporate data science skills and technologies, including machine learning techniques and greater emphases on statistics and programming. 


While GIScience may be "morphing into spatial data science," the fact remains that few data scientists recognize that spatial data are special, as Goodchild first argued in 1992. However, that "space skepticism" may yet be overcome by "successful use cases ... demonstrating indisputable business advantages" and "unequivocal evidence that the incorporation of an explicit spatial perspective leads to better solutions..." (Anselin 2017). 


Carpe diem indeed!



Anselin, Luc (2017) Space Skepticism. University Consortium for Geographic Information Science blog, October 24. 
Berman, Francine, Rob Rutenbar, Brent Hailpern, Henrik Christensen, Susan Davidson, Deborah Estrin, Michael Franklin, Margaret Martonosi, Padma Raghavan, Victoria Stodden, and Alexander S. Szalay (2018) Realizing the Potential of Data Science. Communications of the ACM, 61:4, 67-72. 
Donoho, David (2017). 50 Years of Data Science. Journal of Computational and Graphical Statistics, 26(4), 745-766.
Kelleher, John D., and Brendan Tierney (2018). Data Science. MIT Press Essential Knowledge series.
National Academies of Sciences (2018). Data Science for Undergraduates: Opportunities and Options
National Institutes for Health (2018) Strategic Plan for Data Science [draft].
Susskind, Richard E., and Daniel Susskind (2015). The Future of the Professions: How Technology will Transform the Work of Human Experts. Oxford University Press.


Filter Blog

By date: By tag: