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FOSS4G 2017 Round Up

A month ago at this time I was in Boston for FOSS4G 2017 (Free and Open Source for Geospatial), which is the international conference for free and open source GIS enthusiasts, developers, educators, and practitioners. I updated my introductory GIS / QGIS workshop manual to 2.18 Las Palmas (which is slated to be the next long term service release once 3.0 comes out) and Anastasia, Janine, and I took the workshop on the road. We had a good turnout and an excellent class, and then were able to enjoy the three days of sessions. Here are some of the high-lights from sessions I attended.

  • The NYC Department of City Planning has hired their own, internal open source developer and is assembling a team called NYC Planing Labs. Their first project was to revamp the city facilities database and build the NYC Facilities Explorer, a web mapping interface that sits on top of the database and makes it easy for folks to browse and visualize.
  • There was an interesting talk from an independent research unit that’s affiliated with the University of Chicago. The speaker outlined their process for switching their team from ESRI to open source. The talk gave me appreciation for the amount of work that’s involved for transitioning a team of service providers from one set of tools to another. This group did things the right way, doing necessary background research and identifying short, medium, and long term plans for making the switch. Their biggest revelation was that they ended up shifting funds from purchasing licenses to staff, which has allowed them to expand their activities.
  • PostGIS 2.4 will add a number of functions that were only available for the geometry type, like ST_Centroid, to the geography type. PostgreSQL 10 is going to allow users with big databases to take greater advantage of parallel computing.
  • Some archaeologists are looking to adapt the schema used by Open Street Map to create an OpenHistoryMap. There aren’t many global standards for cataloging archaeological data; it’s primarily site and project specific. Unique challenges include the importance of scale (need to see that pot shard in a room, in a house, in the overall site, in the greater region…) and varying degrees of reliability. Once an artifact is found there isn’t absolute certainty regarding it’s age, provenance, or use. This project (Open History Map) is different from Open Historical Map; the latter relies on data that’s already in OSM and describes the past as a function of the present.
  • I tend to use GRASS GIS in limited circumstances, but am always pleasantly surprised when I follow up to see what’s new. The big selling points are stability, backwards compatibility, and the ability to do a lot via the command line. For version 7.2 there are several improvements: an improved GUI, a data catalog, a more sophisticated Python editor, easier vector legend tools, an advanced search feature for finding modules, and temporal algebra. I’m not a heavy raster user and never work in 3D, but these have always been and continue to be major strengths of GRASS. They also have a growing repository of 3rd party plugins and modules.
  • One of the plenary speakers gave us a demo of R markdown for creating websites, documents, and even for writing books. It gives you the ability to easily import data and write R code to produce a chart, graph, or map right in the same document as your narrative text. So instead of doing a basic analysis, creating a chart, and writing up your project in three different places you can do it all in one place and compile it to HTML or PDF. With the source, readers also have the benefit of seeing what you did and they can test the results.
  • R has really come a long way for geospatial analysis and visualization. I can’t remember it even being mentioned at the last FOSS4G I attended in 2011, but in 2017 it was a major component of the conference. In trying to figure how it fits in to the landscape, I assumed that it was a matter of background and preference. People who have a stats background and want to do geospatial work are going to gravitate towards it, while people with more of a programming or data processing background may be more disposed to using Python or Javascript. The plenary speaker framed R as an exploratory language that’s great for iterative work – let me quickly graph this data to see what it looks like, then I’ll write another piece to view it a different way. Other scripting languages tend to tackle more tasks in one large batch in a linear fashion.
  • The contingent of academic GIS and geospatial librarians and developers has been growing at this conference over the years. There was an opportunity for the Open Geoportal and Geoblacklight communities to get together and exchange notes. Both groups have a commitment to open metadata standards and resource sharing.
  • OSGeo folks have been active in promoting free and open source GIS education, and have created a directory of GIS labs around the world.
  • Just when you thought you were confused about which tool to use, here’s another one – Vega, a declarative JSON grammar for creating graphs and charts.
  • QGIS 3.0 is on the horizon; it will encompass a shift from Qt 4 to 5 and Python 2 to 3. There are a number of great new features: a task manager, a data source manager to replace the dozen individual buttons in 2.x, better support for metadata viewing and editing, more 3D tools, multiple map canvases, the ability to store different user profiles (to save your plugins and layouts on shared machines), better digitizing tools, and a whole lot more. A lot of plugins will disappear as they make their way into the processing toolbox, the stand-alone QGIS Browser will be dropped as its functions are integrated into QGIS Desktop, and map projects created in 3.x will not be backward compatible to 2.x. The time line says that 3.0 will be launched in late Nov 2017, and at that point 2.18 will become the LTS release. It will take another year, til Nov 2018, when 3.2 becomes the LTS. If you’re like me and favor stability over new features, you can stick with 2.x for the another year.
  • A good talk on community health mapping introduced a stack that you can use for data gathering (Fulcrum), analysis (QGIS) and publishing on the web (Carto). I’m well versed in the last two, but didn’t know about Fulcrum. Essentially it’s an app that you can use on phones and tablets to gather data out in the field, including GPS coordinates. On his blog he’s created a series of lab exercises that cover the entire stack, so the communities can learn the process and take ownership of it and the tools.

Planning for FOSS4G 2018 is well underway. The conference uses a three year rotation where it goes from Europe to North America to another continent. Next year it’s Africa’s turn as the conference heads to Dar es Salaam in Tanzania.

Average Distance to Public Libraries in the US

A few months ago I had a new article published in LISR, but given the absurd restrictions of academic journal publishing I’m not allowed to publicly post the article, and have to wait 12 months before sharing my post-print copy. It is available via your local library if they have a subscription to the Science Direct database (you can also email me to request a copy). .

Citation and Abstract

Regional variations in average distance to public libraries in the United States
F. Donnelly
Library & Information Science Research
Volume 37, Issue 4, October 2015, Pages 280–289
http://dx.doi.org/10.1016/j.lisr.2015.11.008

Abstract

“There are substantive regional variations in public library accessibility in the United States, which is a concern considering the civic and educational roles that libraries play in communities. Average population-weighted distances and the total population living within one mile segments of the nearest public library were calculated at a regional level for metropolitan and non-metropolitan areas, and at a state level. The findings demonstrate significant regional variations in accessibility that have been persistent over time and cannot be explained by simple population distribution measures alone. Distances to the nearest public library are higher in the South compared to other regions, with statistically significant clusters of states with lower accessibility than average. The national average population-weighted distance to the nearest public library is 2.1 miles. While this supports the use of a two-mile buffer employed in many LIS studies to measure library service areas, the degree of variation that exists between regions and states suggests that local measures should be applied to local areas.”

Purpose

I’m not going to repeat all the findings, but will provide some context.

As a follow-up to my earlier work, I was interested in trying an alternate approach for measuring public library spatial equity. I previously used the standard container approach – draw a buffer at some fixed distance around a library and count whether people are in or out, and as an approximation for individuals I used population centroids for census tracts. In my second approach, I used straight-line distance measurements from census block groups (smaller than tracts) to the nearest public library so I could compare average distances for regions and states; I also summed populations for these areas by calculating the percentage of people that lived within one-mile rings of the nearest library. I weighted the distances by population, to account for the fact that census areas vary in population size (tracts and block groups are designed to fall within an ideal population range – for block groups it’s between 600 and 3000 people).

Despite the difference in approach, the outcome was similar. Using the earlier approach (census tract centroids that fell within a library buffer that varied from 1 to 3 miles based on urban or rural setting), two-thirds of Americans fell within a “library service area”, which means that they lived within a reasonable distance to a library based on standard practices in LIS research. Using the latest approach (using block group centroids and measuring the distance to the nearest library) two-thirds of Americans lived within two miles of a public library – the average population weighted distance was 2.1 miles. Both studies illustrate that there is a great deal of variation by geographic region – people in the South consistently lived further away from public libraries compared to the national average, while people in the Northeast lived closer. Spatial Autocorrelation (LISA) revealed a cluster of states in the South with high distances and a cluster in the Northeast with low distances.

The idea in doing this research was not to model actual travel behavior to measure accessibility. People in rural areas may be accustomed to traveling greater distances, public transportation can be a factor, people may not visit the library that’s close to their home for several reasons, measuring distance along a network is more precise than Euclidean distance, etc. The point is that libraries are a public good that provide tangible benefits to communities. People that live in close proximity to a public library are more likely to reap the benefits that it provides relative to those living further away. Communities that have libraries will benefit more than communities that don’t. The distance measurements serve as a basic metric for evaluating spatial equity. So, if someone lives more than six miles away from a library that does not mean that they don’t have access; it does means they are less likely to utilize it or realize it’s benefits compared to someone who lives a mile or two away.

Data

I used the 2010 Census at the block group level, and obtained the location of public libraries from the 2010 IMLS. I improved the latter by geocoding libraries that did not have address-level coordinates, so that I had street matches for 95% of the 16,720 libraries in the dataset. The tables that I’m providing here were not published in the original article, but were tacked on as supplementary material in appendices. I wanted to share them so others could incorporate them into local studies. In most LIS research the prevailing approach for measuring library service areas is to use a buffer of 1 to 2 miles for all locations. Given the variation between states, if you wanted to use the state-average for library planning in your own state you can consider using these figures.

To provide some context, the image below shows public libraries (red stars) in relation to census block group centroids (white circles) for northern Delaware (primarily suburban) and surrounding areas (mix of suburban and rural). The line drawn between the Swedesboro and Woodstown libraries in New Jersey is 5-miles in length. I used QGIS and Spatialite for most of the work, along with Python for processing the data and Geoda for the spatial autocorrelation.

Map Example - Northern Delaware

The three tables I’m posting here are for states: one counts the 2010 Census population within one to six mile rings of the nearest public library, the second is the percentage of the total state population that falls within that ring, and the third is a summary table that calculates the mean and population-weighted distance to the nearest library by state. One set of tables is formatted text (for printing or just looking up numbers) while the other set are CSV files that you can use in a spreadsheet. I’ve included a metadata record with some methodological info, but you can read the full details in the article.

In the article itself I tabulated and examined data at a broader, regional level (Northeast, Midwest, South, and West), and also broke it down into metropolitan and non-metropolitan areas for the regions. Naturally people that live in non-metropolitan areas lived further away, but the same regional patterns existed: more people in the South in both metro and non-metro areas lived further away compared to their counterparts in other parts of the country. This weekend I stumbled across this article in the Washington Post about troubles in the Deep South, and was struck by how these maps mirrored the low library accessibility maps in my past two articles.