Maryland State GIS Conference (TuGIS)

The workshop evaluations are in

(if you want to cut to the chase, the workshop results are here).

I had a great time teaching our two workshops at the TuGIS conference.  In the morning, my students and I presented Spatial SQL: A Language for Geographers, and in the afternoon we taught Python for Geospatial.

We knew expectations would be high: both courses sold-out in 2 days, and we even expanded the class size to 38 people for each workshop!!  I knew that teaching 38 people would be a challenge, but it would also be a great lesson to see if we could corral so many cats into a single, technical workshop.  The workshop evaluations would be crucial to determine if we met our objectives.

The workshop evaluations were overwhelmingly positive.  For example:

  1. over 90% said they enjoyed the workshop.
  2. over 83% said it was much better than other GIS training they have been to.
  3. on a scale of 1-10, 95% of the attendees rated the course a 7 or above.
  4. 93% said they learned something new in the workshop.
  5. 89% said the workshops would help them in their careers.
  6. 91% said they would apply these skills to their job.

I decided to throw one curve-ball on the evaluation sheet and asked:

This was a half-day workshop. Most one-day GIS training classes cost around $600/day. If we developed other in-depth full-day workshops on topics like this for under $250, how likely would you be to participate in it?

it turned out that 89% of the respondents rated a 7 or higher, indicating that almost 90% of the people valued the training enough to pay $250 for a full day course (opposed to $600 for most GIS courses).  This means it is possible to offer really good, low cost training to GIS professionals.  Keep an eye out on this, as I am very likely to take these training classes on the road.

The comments the participants provided were great – it confirmed our belief that this was an excellent training course, and that the course needed to be expanded to 8 hours, rather than 4 hours – most everyone felt like their was simply too much information to absorb.

If you would like to see the results of the workshop evaluation, click the link below:

TuGIS Workshops – Google Forms

Finally, if you can’t make it to a live workshop, all of my video training courses are $30 or less, when you visit www.gisadvisor.com.  These courses can’t get into the level of depth that a live course gives, but you’ll see that after thousands of students taking the courses, close to 90% of them give the course 4 starts out of 5!

 

How do I do that in Arcpy

howdoiIn 2004, I created a little document with my students titled How do I do that in ArcGIS/Manifold.  To our surprise, the document really took off, and had tens of thousands of downloads from all over the world.  It was that document, and the response, that got me to realize how as a Professor, I could have a far reaching impact on people learning GIS.

As the years passed, I came out with a number of other documents in the series: How do I do that in Manifold SQL, How do I do that in QGIS, and How do I do that in PostGIS.  These documents have also been used by thousands of people (although, nowhere near the reach of the original document).

So  today I am posting my latest book in the series: How do I do that in Arcpy: Illustrating Classic GIS Tasks.  I love this book: it is short and to the point, and actually provides users with code to illustrate all the commands that the USGS’s 1988 document A Process for Evaluating Geographic Information Systems said should be in any GIS.  Between Manifold, PostGIS, QGIS, and Arcpy, I can’t keep all these languages straight (the effects of aging, I suppose).  So, I keep each of these books right next to my computer for a quick reference on how to do virtually any GIS task.


ithaca
I welcome you to download the .pdf and make use of it yourself with the accompanying geodatabase..  You are free of course to send it to whomever you like, but I would appreciate it if you simply provided people with a link to my site so they can see more of what I’m doing here.

 

If you want to learn more about how to program geospatial tasks with Python, or how to use Free and Open Source GIS like QGIS and PostGIS, check out all my courses here.  And, in celebration of the book, you can get my course Python for Geospatial for $20 here.

Undergraduate Geospatial Python Projects

earthballThis week my GIS Programming students presented their programming projects to ESRI. First, I cannot say enough to thank ESRI for taking time out of their schedule to meet with our students – the staff was helpful, encouraging, and provided great feedback to the students – what an honor it was to get their feedback.  I am so thankful to be a part of a GIS community that is so supportive of one another.

Now, this was a really special class of undergraduates – and some of them were part of that special group of students that presented their research at an undergraduate conference.  It was small, so we could do some really cool things.  In fact, in the middle of the semester, the students wrote a paper comparing the geocoding accuracies of Google Maps and the United States Census Bureau.

Things were going so well that I decided in lieu of a final exam, we expanded their final projects a little more, and arranged for the staff at ESRI Charlotte and ESRI Redlands to join us on a WebEx that included demonstrations and a code walk-through.  Below are each students’ presentation, and some of the Q&A from ESRI:

noahNoah Krach.  Noah is an amazing undergraduate.  Recently, we lost one of our graduate research associates, and Noah stepped in to provide technical support on a National Science Foundation project in Lake Victoria.  Without missing a beat, Noah was all over the project, and he used his time in my class to create an Arcpy tool to extract, translate, and load (ETL) gigabytes of Landsat imagery.  This tool does a lot, and I can’t even begin to describe all he did, you’ll simply have to watch and learn.

Check out his video, and you’ll see why we are so excited that Noah will be around for another semester.

cc

Caitlin Curry.  If you follow my blog, you’ve already met Caitlin.  She finished her summer internship I told you about, and during the middle of it, her boss wrote us to say what an excellent worker she was (he prefaced his email by saying he never does that, but was so impressed with Caitlin, he had to let us know).  We are impressed with Caitlin, too.  And, as I have now grown to expect, Caitlin did an amazing job with another ETL type tool using Arcpy, where she downloaded, unzipped, and processed earthquake data and critical infrastructure.

I did a lot of emergency response work with earthquakes in a previous life, and what Caitlin did here would have been so useful.  I think you will enjoy seeing how she integrated many different Python packages with Arcpy to provide an early warning application for emergency responders.  And just as a heads-up, Caitlin uses Python to download everything while the script is running – so you just give the script to a user and it works without any operator knowledge of the underlying data = really cool, and efficient.

mb

Matthew Bucklew.  After my first lecture this semester, Matt told me he built his own computer this summer – just for fun.  So, I knew he wasn’t your ordinary  geographer – he likes to try new things, and if something is done in a conventional way, Matt is going to try and be more innovative.  Matt created a great Arcpy application to locate renewable engery stations needed by automobiles.  His Python scripts use ArcGIS for analysis, but at the same time, seamlessly brings in the Google APIs to provide directions to the nearest locations.  For good measure, he also brings in other packages like heapq.

At the moment, Matt’s program works on a desktop, but his hope it to turn this application into a cloud based solution for use with mobile phones.  Keep an eye out for what Matt comes up with, and if you watch this, you’ll see it is an excellent tutorial on how to mash up bunches of Python packages with Arcpy.

jmJessica Molnar.  Like Caitlin, Jessica is another student you’ve seen before.  She’s got such a big heart, and is always looking for ways to apply GIS to humanitarian and ecological solutions.  In this project, Jessica created an Arcpy application to identify locations for community gardens in Baltimore City with special consideration for locations within food deserts, near churches and schools, and on suitable soils for growing food.  Jessica’s program also found those locations that were already owned by the City, but were vacant.  Let’s hope the City makes use of this to build a more beautiful Baltimore (BTW, Jessica wrote her program to work in any location in the State of Maryland, so any community can use this tool!).  I think Jessica may eventually roll this into a cloud based solution – hey Jessica, I think we found a project for graduate school!

 

jtJohn Tilghman.  John’s family owns an orthodontist practice, and John decided to use PostGRES/PostGIS along with a number of other different Python packages to perform market area analysis.  John integrated PostGRES, Google, and the Pygal libraries to create the first stages of a geodashboard to assess the effectiveness of marketing strategies, and other metrics.  In the video, you’ll also see how he created a distance decay algorithm in SQL to determine at what point customers drop off from visiting the practice.  With just a little bit of information (addresses and marketing strategies), John was able to extract a ton of business information – in fact, our guests from ESRI were surprised the John wasn’t already a business major!

This is an excellent presentation to watch for those of you who are interested in using Python with Open Source GIS – you’ll learn how to integrate FOSS4g and Python for a business analytics tool.

 

jyJosh Young.  Josh created an Arcpy script to assemble tons of location based data that might be useful for someone thinking about moving to a particular location.  Now, in Josh’s case, he chose location based data he deemed important for the neighborhood (download speeds, elementary school, crime statistics, distance to the downtown, etc.).  But ultimately, what Josh has shown us is how to create a template that integrates multiple Python packages and online data to provide very useful information.

It would be so easy to take Josh’s work and roll it into a site specific location-based analysis engine.  In fact, one of the people watching Josh’s presentation mentioned that he was moving, and saw how useful this could be for a community.  The best part of it is that Josh did it with all freely available online data for the State of Maryland, so any community can spin this up into a cloud-based solution.

 

image2

Robbie Stancil.  Robbie is our only non-geography major.  You’ve met him before when he worked with me on a National Science Foundation project to use Spatial Hadoop.  Like John, Robbie’s project used Postgres/PostGIS and the Google API to do something quite interesting: he created a mesh of points over community to determine how far the Google API will search in order to find a property address, and compared the concave hull of each series of points for an address to the actual property parcel.  This project got us thinking about some very creative uses – you’ll have to watch it until the end to see the interesting things we came up with.

 

Again, I have to give a huge shout out to the ESRI staff – they were wonderful guests, and really excellent mentors during the Q&A. As these students get ready to graduate in May, I know they will make excellent employees or graduate students – the future is really bright for them. If you are in academia, I hope that you are inspired to expect the very best of your students as I do, and you’ll be so pleased to see what they are capable of doing.

want to learn how to program geospatial solutions like these students? Check out the geospatial courses at gisadvisor.com.   

A Typical Class Project at Salisbury University: Evaluating Geocoding Accuracies

I’ve always been proud of our Salisbury University GIS students, and love to push them as far as their little minds can handle it.  You may recall that last Spring I had my Advanced GIS students perform independent GIS projects and present those projects as posters at an Undergraduate Research Conference.  Well, this Fall I am teaching GIS Programming, and have 7 awesome students (pictures and bios to follow).  We started the year off learning spatial SQL with Postgres and PostGIS.  We have now moved into Python, which includes Arcpy as well as other Python packages.

The semester was going so well, and the students were so responsive to anything I asked, I said what the heck, let’s try something crazy.  So, I showed the students how to use two Python geocoding packages (geocoder and censusgeocode) and then said:

why don’t we conduct a research project over the next week to test the match rates and positional accuracies of the Google API and the United Census Bureau API.  

So yeah, I gave them a week to put this together: design, code, analyze, and write.  And, like most of my students at this level, they didn’t disappoint me.  This meant they had to integrate a lot of what they have learned over the years (programming, GIS, statistics, etc.).

I just uploaded their work onto researchgate:

 Click for ResearchGate Article  

I was surprised by how little there is out there in terms of quantitative assessment of geocoding accuracies.  I hope you have a chance to click on the link and check out the working paper (we will submit it to a journal sometime soon).  Also, I included a short abstract below so that you can see the results of our work (note: our paper includes the original data and the source code for performing the geocoding):

Undergraduate Research in Action: Evaluating the positional differences between the Google Maps and the United States Census Bureau geocoding APIs

Abstract:  As part of a class assignment in GIS Programming at Salisbury University, students evaluated 106 geographically known addresses to determine the match rate and positional accuracy obtained using the Google and the United States Census Bureau geocoding application programming interface (API)s. The results showed that 96.2% of the addresses supplied by the Google API were successfully geocoded, while 84% of the addresses supplied by the Census Bureau API were successfully geocoded.  Further, the Google API matched 90% of the addresses with a ROOFTOP designation.  The average positional accuracy of the Google derived addresses were 80m overall, and 65m for those geocoded with the ROOFTOP designation while the Census Bureau positional accuracy was 271.09m.  

So yeah, this is what you can do with 7 GIS undergraduates at Salisbury University: they work hard, fast, and are a very creative bunch.

paper