Book Two of the Iliad notoriously contains a list of nearly 190 place names and includes the 29 contingents and that make up the Greek expedition to Troy.  Before launching into an over 250-line catalogue of the leaders of the Greek forces and the number of their ships, Homer appeals to the Muses to aid him in this tour-de-force of memory.  Without their help, he says:

I could not recount their numbers nor name them,
Not if I had ten tongues and ten mouths,
And an unbreakable voice and a brazen chest within,
If the Olympian Muses, daughters of aegis-bearing
Zeus, would remind me how many came under Ilium.

The Catalogue of Ships that follows this invocation can be mapped as an itinerary, or more precisely, three itineraries that traverse most of Greece.  The theoretical basis for the project I am undertaking with the Scholars’ Lab at the University of Virginia Library is already complete. In my recent book, Homer’s Trojan Theater (Cambridge University Press, 2011), I argue that Homer was able to recite the Catalogue by creating a mental journey that used the mnemonic techniques involving loci or places, well known from ancient rhetorical writers.  By envisioning a series of places, Homer could mentally walk – or sail – through Greece and produce a detailed catalogue. Our project will reproduce that journey by showing that the itinerary described follows the natural contours of Greek geography and the patterns of early Greek urban organization.

Mapping the Catalogue of Ships involves several steps.  “Least-cost path” GIS analysis by the Scholars Lab is revealing the terrain that must naturally be followed when taking a walking tour of the Greek mainland.  We are creating an interactive map that follows that path.  The Barrington Atlas of the Ancient World (2002) as well as the recent Historischer Atlas der antiken Welt (2007), The Homer Encyclopedia (2011) and the Pleiades Project, a collaborative database for ancient sites, have pinpointed locations for which we have evidence.  We will attempt to link the sites mentioned in Homer with archaeological material and useful bibliographies.  Finally, we hope to do in situ investigations by actually traversing the plotted itinerary at ground level to survey the terrain, and create extensive panoramic photography. Our main goal is to demonstrate that the arrangement of the Catalogue, far from a random list of place names, corresponds to the natural geography of Greece.  In cases where the position of a site is unknown or disputed, we hope that our analysis will provide plausible geographical and literary evidence to help identify its location.

Collaborators in this project include Ben Jasnow and Courtney Evans, two of my graduate students who worked with me on the Trojan Theater project and who are assisting with GIS analysis, under the guidance of Chris Gist and Kelly Johnston of the Scholars’ Lab. Wayne Graham and other members of the Scholars’ Lab R&D division are creating a presentational framework for our maps and text, and Jeremy Boggs is our lead designer.

Jenny Strauss Clay
William R. Kenan Jr. Professor of Classics

This map is a great example of proportional symbology and is of an interesting subject, especially when juxtaposed with modern oil trading.  Of course, the cartographic style is great too.  I hadn’t much thought of the cartographer or why the map was created until recently.  When I took a closer look, I found the cartographer’s name, Richard Edes Harrison (1901-1994), and that this was one in a series of maps he did for Fortune Magazine mainly in the World War II era.  He was a wonderful cartographer and his style intrigues me.  There is a large amount of information available about Harrison and his maps.

A website showing some of Harrison’s work: http://www.fulltable.com/vts/f/fortune/reh/mn.htm. (Almost all maps in this post are linked from this site).

An article in Imago Mundi about Harrison’s work: http://www.jstor.org/stable/1151400 (JSTOR access required).

A book showing much of Harrison’s work for Fortune: Look at the World, The Fortune Atlas for World Strategy.

Harrison employed many interesting techniques.

Cartograms

More information on cartograms

Interesting Extent Overviews

Series of Small Multiples

A Tufte favorite.  More information on small multiples.

Comprehensive Data Visualizations

Interesting Projections

More information on projections

The last two maps have very interesting projection lessons embedded in them.

Embedded Projection Descriptions

How nice is that?  Having a clear, concise description of projection and scale within the map is a great benefit to the reader.

In this map, Harrison used comparative scales to show changes in distance across map due to projection distortion.

This one is the best.  A clear description with a visual that anyone can understand.

Here is simplified version of the “world centrifuged” in a map called On Assignment.

A few weeks ago, I came across the following map.

What a beautiful projection!  It is know as the butterfly or Cahill projection (Named after the inventor.  Cahill even got a patent for a rubber ball that does the same thing.)  And as the embedded projection description describes, this is a great map for comparing circumnavigation routes because it does not distort distance, area or direction.  Not only that, it gives the “orange peel” figure to further give readers information on how this projection works.

I only learned of the Cahill projection after reading this xkcd cartoon.

Call for Help

If anyone know of other maps with this sort if imaginative visual explanations of projection embedded in maps, please contact me.

And cake.

In 2010 we threw open the Scholars’ Lab doors for folks to present geospatial lightning talks.  We were impressed by the breadth of GIS work ongoing across our community.

And lots of people came to hear these mappy stories.

And for cake.

Face it, you love maps.  We invite you to join our band of Virginia Mapheads as we celebrate World GIS day 2011 with a lightning-fast show of the world’s coolest geowork in the Scholars’ Lab on Wednesday, November 16 at 1:30pm.

Sadly, I know many of you don’t have the pleasure of working all day every day with maps and geodata, so treat yourself to a once-a-year map fix.  You know you deserve it!  And bring a friend.

Again, we have a compelling lightning talk lineup for 2011.

Your reward?  Two slices of our soon to be legendary 2011 GIS Day geocake to be revealed on GIS day!

2011 GIS Day Update

Over 70 folks enjoyed our 2011 GIS Day celebration in the Scholars’ Lab.

Speakers ranged from wily GIS veterans to those who’d recently started using geospatial tools.   Check the speaker list above to see the wide range of topics.

The Charlottesville CBS station sent their GIS Day team to cover the event interviewing Eric Johnson, Scholars’ Lab Head of Outreach and Consulting.

And we feasted on the already legendary Virginia-shaped geocake decorated with flags marking unusual place names.  All this was followed by delicious hot mulled cider.

Thanks to everyone who played a part in making GIS Day 2011 a mappy success!

• June, 2009 – Google Launches Fusion Tables, an online tool for mapping places
• July, 2010 – Wikileaks Publishes the Afghan War Diary, a massive dataset full of place names
• April, 2011 – Scholars’ Lab Launches “Spatial Humanities Step By Step”,  a source for peer-reviewed geo-tutorials
• October, 2011 – Devin Becker, University of Idaho Digital Initiatives Librarian, publishes Step By Step tutorials on mapping Wikileaks using Google Fusion Tables

So many new mapping tools.  So many online data sources.  So much interest in the Spatial Turn across disciplines.   Where to begin?

Participants in the NEH funded Institute For Enabling Geospatial Scholarship made it clear they wanted a reliable source of helpsheets and tutorials for working with data sources and mapping tools.  Spatial Humanities Step By Step is that growing resource.

We created Step By Step to help aggregate high-quality (yet simple-to-follow) tutorials and provide an opportunity for folks to receive professional acknowledgment for the work that goes into creating them.

In addition to Devin Becker’s newly published contribution on Mapping Wikileaks with Google Fusion Tables, we have in the pipeline tutorials on extracting and quantifying information from historic maps, using Google Earth as a gazetteer, and calculating least cost paths across a landscape.   Already posted are helpsheets for georeferencing historic maps, converting addresses to mapped locations through geocoding, mapping Global Positioning System datasets, and using Google Maps to create a HyperCities project.

We believe folks who invest the time to write easy-to-follow tutorials should receive credit for their work.  Is that you?  Then submit your own work to our supportive review board:

• Patrick Florance-Tufts University
• Chris Gist-University of Virginia
• Tracey Hughes-University of California, San Diego
• Kelly Johnston-University of Virginia, Editor
• Scott Nesbit-University of Richmond
• Bethany Nowviskie-University of Virginia
• Diana Stuart Sinton-University of Redlands
• Ginny White-University of Oregon

Our simple process: two reviewers take each submission for a spin to confirm the process is easy to follow, jargon free,  and just works.  Then with consensus, we publish.  If you’d like to volunteer to be called upon as a peer reviewer alongside our current review board please contact me.

I serve as the Step by Step editor and along with my colleagues on the review board we look forward to your contributions.  Check the site for more information: http://spatial.scholarslab.org/step-by-step

Ian McHarg

Ian McHarg was born around the industrial town of Glasgow in 1920.  After World War II, he came to the U.S. and started a career in city planning and landscape architecture.  He founded the Department of Landscape Architecture at the University of Pennsylvania and is considered innovative for his notion that design should work with the landscape instead of fighting or changing it.  He has also been credited with coming up, in the 1960s, with the idea of map overlay which is a fundamental GIS technique.

So, what do Richmond and McHarg have in common?  Before I can tell that story, I have to tell this one.  When I was a young struggling grad student, I happened to be at work in the Urban Planning Department at VCU one summer day when a PhD student from the University of California, Santa Barbara — John Cloud — strolled in. He told a small gathering of a few grad students and professors the story of how during the Great Depression the economic conditions were similar to those we face now. There was a foreclosure crisis and banks were not offering mortgages.  In an attempt to get the industry back on track, the Federal Housing Administration looked for ways to estimate neighborhood risk for mortgages.  They looked for indicators to predict how neighborhoods would fare at future dates. In partnership with the Richmond Planning Commission (RPC), the FHA used Richmond as one of its major study sites.  Mr. Cloud showed us a report he pulled from the National Archives (NARA) called Statistical Data Relative to Housing and other Planning Matters.

December 1935 Version

This report, produced as early as 1935, uses a series of tissue paper overlays to show various themes.  A color, loose-leaf card stock map of rent by block is used as the underlay.  Mr. Cloud has found this report to be the earliest American example of the use of such overlays during his research.  He also found parallel lines of work going on in Germany during the same time period.   Please find John Cloud’s detailed article on this story here.  There may be older examples out there waiting to be discovered!

RPC report showing tissue paper overlay and card stock underlay

Rental map under housing study area map

Homer Hoyt

Another player in this drama, economist Homer Hoyt, was an influential researcher at FHA.  He wrote a book in 1939 about housing research techniques called The Structure and Growth of Residential Neighborhoods in American Cities.

In that book, Hoyt demonstrated the value of techniques developed during the FHA’s neighborhood forecast research including the sector model and – you guessed it — map overlays.  He used mylar sheets in the book to do a series of overlays for Richmond, which is clearly a distilled version of the RPC report maps.

At right, all four of Hoyt's overlays together: Race, Age, Condition, and Rent

So, it wasn’t really Mr. McHarg who pioneered the use of overlay at all (sorry to all you landscape architects).  At least, the RPC, Mr. Hoyt, and German researchers did it some twenty-five years earlier.

On a related topic, in my recent correspondence with Mr. Cloud he informed me that the Library of Virginia (LVA) had another copy of the RPC report that was missing at least one overlay.  We agreed that I would scan the LVA’s copy and send him a copy.  In return, Mr. Cloud promised to get the missing pages from the NARA copy.  However upon further investigation, I have discovered that there are at least two versions of the document.  The NARA version is dated December, 1935, and the LVA version is dated January, 1938.  There are a few discrepancies between the versions and it is hard to tell whether they were produced with different overlays or whether some layers have been lost over the years.  Specifically, there are unique overlays in each document.  The following table is an inventory of the overlays for each document.

Mr. Cloud had concluded that purposeful reasons (i.e. a tear out) were to blame for the missing African American overlay from the LVA version of the RPC report. However, based on my comparison of the two different texts, I am not so sure.  I believe Mr. Cloud did not have all the information.  Did the 1939 version of the report originally have different overlays than the 1935 report?  Or were overlays removed/lost from each copy in the subsequent decades?   Of course, it wouldn’t be a surprise to find out that someone purposely removed specific overlays from the PRC report given the amount of revisionist history around Richmond.

Hoyt listed four factors (race, age, condition, and rent) in determining neighborhood risk using map overlays.  According to Hoyt’s analysis, we have race, specifically the number of African Americans, as a major indicator for forecasting neighborhood risk.  Is that enough to cement Richmond’s place in racial infamy?  Not quite.  However, much of his research went into a survey which concluded in a series of maps by the Home Owners’ Loan Corporation (HOLC).  The HOLC maps were used as proof of redlining,  as term coined by John McKnight in the late 1960s.

"Redline Map" of Richmond by the Home Owners' Loan Corporation

Undoubtedly, race — specifically the percentage of African Americans — played a large role in determining the hazard values for the HOLC maps.  The question is whether or not the HOLC maps caused institutional discrimination against African Americans after their production.  Amy Hillier, a researcher at the University of Pennsylvania, says no, at least not in Philadelphia.  According to her analysis, banks were giving mortgages in the redlined districts after the Philadelphia HOLC map was produced.  She surmises that the HOLC maps were not widely circulated outside the federal government and therefore were probably not known or used by lenders.  In fact, a majority of HOLC’s mortgages were in the hazardous end of their assessment scale.  However, Hillier does conclude that the FHA policies, which were formed partially from Hoyt’s research, were influential in the arena.  The legacy of these maps and policies must not be understated.

The University of Richmond has a excellent site that shows the HOLC map for Richmond and explains in detail the criteria and survey data used to determine hazard rankings of which presence of African Americans trumped all other factors.  The handwritten survey reports are shocking.

Next Steps

We have now finished the process of digitizing all the RPC and Hoyt overlays.  We next will do some geostatistical analysis to compare them to the Richmond HOLC map to see how well the data fit together.  I would also like to map the new mortgages and refinancing given from the 1940s to the 1960s, à la Amy Hillier, to see the rates in the different hazard zones of the HOLC map.  Jeremy Boggs, a historian in our group here at the Scholars’ Lab, has an interest in looking at the City of Richmond’s policies during this period to gauge how they were affected by Hoyt’s and HOLC’s research and maps.

GIS map showing overlay from RPC report and HOLC hazard areas

Article edited to make corrections to errors pointed out by Mr. Cloud in the comments section.

The USGS has up-to-the-minute maps for earthquakes all over the world.  For the latest Virginia events click here.  You can find their main earthquake page here.

8/23/2011 Virginia earthquakes - USGS

The USGS also has a crowd-sourced program – called Do You Feel It? – where internet users can gauge the quake at their location and report it back to help build the map below.  More on that program here.

Crowd-sourced intensity map - USGS

The good people at Development Seed created some cool maps just after the largest Virginia quake using publicly-available data and some tools from the guys at MapBox.  Please click here to see how they quickly mashed up the earthquake data to make some great maps.

http://tiles.mapbox.com/mapbox/#!/map/map_1314132938521

YouTube: http://www.youtube.com/watch?v=IKE7MLNdtcg

Did you know that Charlottesville once had streetcars?  Since moving to town, I’ve heard tales of the once-thriving transportation system that connected Fry’s Springs, UVa and downtown.  It wasn’t until an inquiry came in from a student looking for GIS data for the system that I investigated it.

Mules pulling streetcar on Main St. - Special Collections, University of Virginia Library

I first found the following 1890 map which shows the holdings and plans for the Charlottesville Land Company.  The map highlights the existing streetcar system and plans to extend the system into their new neighborhoods.  We also found a large number of streetcar-related images from the Holsinger Photo Collection.

Charlottesville Land Co., 1890 - Special Collections, University of Virginia Library

The Charlottesville Land Co. map is intriguing.  Not only does it show the eventual street grid for Belmont and Rose Hill, it has unrealized plans for the southwest quadrant of the city.  The extensive streetcar plan designed to service these new developments piqued my interest. After a little research, I found a book called “Forward is the Motto of Today”: Street Railways in Charlottesville, Virginia, 1866-1936 by Jefferson Randolph Kean.  The UVa Library has two copies, one in Special Collections and one in general circulation.  The book gives the entire history of the rail system from its modest beginnings with mule-drawn cars down Main St. to an extensive electric system.  One of the best qualities of the book is that it has maps drawn by the book’s publisher, Harold E. Cox.

Charlottesville streetcar system 1891 from "Forward is the Motto of Today": Street Railways in Charlottesville, Virginia, 1866 - 1936 - H.E. Cox, reproduced with permission

The 1891 map shows the main trunk line which connects the UVa Corner to the C & O station on the east end of downtown via Main Street. Notice the Fry’s Spring R.R. line from the train station area to the Hotel Albemarle.  This line was eventually abandoned and replaced with one following the Jefferson Park Avenue route.  The Belmont R.R. line servicing “The Grove” (now Belmont Park) was also later abandoned.

Charlottesville streetcar system 1895 from "Forward is the Motto of Today": Street Railways in Charlottesville, Virginia, 1866 - 1936 - H.E. Cox, reproduced with permission

The 1895 map shows planned expansion to Woolen Mills, Fifeville, and 10th and Page neighborhoods.  The section downtown (South Street, 1st Street, and Market Street) that was built but abandoned is particularly interesting.  The entire section was never more than two blocks from the main line which probably explains why it was removed.

Charlottesville streetcar system 1920 from "Forward is the Motto of Today": Street Railways in Charlottesville, Virginia, 1866 - 1936 - H.E. Cox, reproduced with permission

The 1920 map shows an expansion through UVa to service Lambeth Field and down Jefferson Park Ave. to Fry’s Spring.  This map also depicts the “pass-bys” (represented with the double lines in five locations), car barn (on Ridge St.), and turn-arounds at the end of all the lines.

GISing the Data

Reviewing Kean’s work and other maps led me to some interesting questions.  What exact streets did the trolleys use?  Are any of the facilities remaining?  If the Charlottesville Land Co.’s streetcar plan was implemented and still existed, how many Charlottesville citizens would be served?  How do you answer these questions?  Here is where GIS can help.

Steps in spatial analysis:

1. Georeference the maps.  Georeferencing is the process of taking scanned maps and geolocating them - generally through the use of control points – for use in GIS or other tools.  Check here for more information.
2  Digitize the relevant features using the ArcMap editor.  More information here.
3. Perform spatial analysis.  In this case, creating a service area around the lines using 1/4 mile buffer.  That distance is considered a serviceable walking distance.

Georeferenced Charlottesville Land Co. map

The above image shows the georeferenced Charlottesville Land Co. map with 40% transparency overlayed on an Open Street Map (OSM) layer.

1920 map with OSM base map

This copy of the 1920 map was the first one I acquired using a handheld camera (opposed to scanning as with the other version).  Notice my fingers in the lower left holding the book open.  While nowhere near optimal, this shows that even poor quality photographs of maps can be georeferenced with decent results. Once all the maps were georeferenced, I was able to digitize all the pertinent streetcar system features.  I chose to show all lines from the maps including conceptual, planned, and abandoned.  I also included all the pass-bys, turn-arounds and support buildings including the electric generation plant on the Rivanna River near Woolen Mills (not on the map above but found using aerial images).

Digitized Charlottesville streetcar system overlayed on modern aerial imagery

The above map shows all the streetcar routes, realized or otherwise.  It includes all the system features except the power plant adjacent to the Rivanna River.  A PDF showing the full extent of the system (with the ability to toggle layers on and off) is available here.

So what if the Charlottesville streetcar system was fully realized and still existed today?  How many Charlottesville citizens would it serve?   Using spatial analysis techniques, we can answer that question.  To do this, I downloaded the 2010 census population counts and joined them to a block-level boundary layer.  I then created the walking buffer of 1/4 mile around the streetcar routes.  I then aggregated the census blocks within the buffer to get the answer.

Proposed Charlottesville streetcar system with 1/4 mile service area buffer overlayed on 2010 census blocks

The above map shows the full streetcar system in black with the service buffer around it in red.  The background is the 2010 census blocks.  Notice the two donut holes and the white areas.  The white areas in the map are Albemarle County.  At this point, we are only interested in city residents.  However, if you wanted to get the full extent of the service population, the UVa Grounds would have to be included (the eastern section of the service area is mainly over the river and unpopulated areas).  The next step is to use a technique called a spatial join to aggregate the total population for each block to the streetcar service area.  More on that here.

Showing attributes of spatial join aggregation

You can see by the last column in the above table that the streetcar service area covers around 33,267 city residents.  I say around because any block that intersected the service area buffer boundary was counted in full even though a percentage of people living within that block may not be within the buffer.  Using our block level tabular data, we can gather some basic statistics including total population for the city.

Using the basic statistics feature in ArcMap

The 2010 total population for Charlottesville is 43,475 which mean that approximately 76% of the city’s population would be served by the the streetcar system.

Some More Pictures

The Holsinger Collection in the UVa Library’s Special Collections has many great pictures of the streetcar system.  Here are some of my favorites along with a few other images.

Streetcar near Rotunda - Holsinger Collection - Special Collections, University of Virginia Library

Main St. looking east, electric street car about to pass Christian's Pizza (third full building on the right) - Holsinger Collection - Special Collections, University of Virginia Library

Looking north towards power plant, Rivanna River on the right

Working on the tracks, looking north up JPA Extended - houses in background exist today - Holsinger Collection - Special Collections, University of Virginia Library

Troops marching north up Rugby along tracks, Fayerweather Hall on the left, Mad Bowl on the right - Holsinger Collection - Special Collections, University of Virginia Library

Ridge St. with car barn in background, bridge over tracks and church exist today, car barn now the Greyhound station bus entrance - Holsinger Collection - Special Collections, University of Virginia Library

Installing tracks on University Ave. near the Corner - Holsinger Collection - Special Collections, University of Virginia Library

Of course once you know where to look, evidence of the streetcar system is around.

Intersection of University and Rugby looking southeast

If you look closely in the crosswalk there, you can see the old track as it turns off University Ave. onto Rugby Ave.

Closeup of exposed streetcar track in crosswalk across Rugby at University

In the Scholars’ Lab, we’re all about the spatial goodness.   Inspired by Kansas State University’s Seven Deadly Sins maps, we set about converting the qualities of life, liberty, and the pursuit of happiness into quantities we could visualize through maps.  Brainstorming commenced on how best to measure the unmeasurable.   Mappiness ensued.

We calculated a score for Life, for Liberty, and for the Pursuit of Happiness for each county in the continental U.S. and mapped how each county’s score deviated from the mean.  So our maps highlight extremes, both high and low.

Life mapped combined male and female Life Expectancy At Birth data for 1999.

Liberty mapped US Census datasets for 2000 to measure the institutionalized population as a percent of the total population.

Pursuit of Happiness mapped the ratio of arts, entertainment, and recreation establishments to the total population from the 2002 US Economic Census.

Transforming datasets from a spreadsheet to a map takes advantage of our human ability to consume mass quantities of information visually.  Rows of numbers stashed away in academic journals and US Census tables come alive when mapped to show comparisons with their neighbors both near and far.  Patterns and clusters appear.  New questions are asked. New answers come.  New maps emerge.

And here’s good news:  If you find our measures of Mr. Jefferson’s famous phrase lacking, software tools to combine and manipulate datasets that may share only common geographic markers can now make cartographers of us all.

Happy mapping!

http://spatial.scholarslab.org/

This site responds to needs identified in conversation with our 21 Institute faculty members and 56 participants (humanities scholars, software developers, and map & GIS librarians). It includes:

• an evolving, crowdsourced catalog of research resources, projects, and organizations;
• a set of framing essays on the spatial turn across the disciplines by Dr. Jo Guldi of the Harvard Society of Fellows;
• GIS-related feeds from Q&A sites and other forms of social media;
• and a peer-reviewed, occasional publication for step-by-step tutorials in spatial tools and methods.

Please help us keep this resource current by contributing to it! You can:

• use Zotero to freely upload research citations, projects, and links to groups;
• contribute your own tutorials and helpsheets in “Step By Step” format for peer review and formal publication;
• adopt the #geoinst hashtag on Twitter and Delicious;
• ask related questions and offer help on DH Answers or the GIS Stack Exchange;
• and post your commentary on the essays we’ve shared.

Learn more about our NEH Institute:

http://spatial.scholarslab.org/about/

and about how you can contribute to the “Spatial Humanities” site:

http://spatial.scholarslab.org/contribute/

Many thanks to the NEH, the staff of the Scholars’ Lab, our Institute advisory board and faculty, and the scores of Institute participants and fellows who helped to define the project!

The Omeka + Neatline project’s goal is to enable scholars, students, and library and museum professionals to create geospatial and temporal visualizations of archival collections using a Neatline toolset within CHNM’s popular, open source Omeka exhibition platform. Neatline, a “contribution to interpretive humanities scholarship in the visual vernacular,” is a project of the UVa Library Scholars’ Lab, originally bolstered by a Start-Up Grant from the Office of Digital Humanities at the National Endowment for the Humanities. Omeka is an award-winning web-publishing platform for the display of cultural heritage and scholarly collections and exhibits, funded by the Institute of Museum and Library Services, Alfred P. Sloan Foundation, Andrew W. Mellon Foundation, and Samuel H. Kress Foundation.

This two-year initiative will allow CHNM and the Scholars’ Lab to expand and regularize a partnership that developed informally between the two centers over the course of the past year. Collaboration has already resulted in improvements to the core functionality of Omeka by CHNM and has led the Scholars’ Lab to produce a number of prototype plugins making Omeka a more attractive and viable option for scholarly partnerships with larger libraries and cultural heritage institutions. These include: improved data import (including EAD, a common archival standard); Solr-powered searching and browsing; and Fedora-based repository services. Further development will improve existing plugins, add preservation workflows, and refine the Neatline toolset for integration and sophisticated editing and scholarly annotation of historical maps, GIS layers, and timelines. Enhancements to Omeka’s core APIs, improved documentation, regular “point” releases, and a new Exhibit Builder will strengthen Omeka’s already large and robust user and developer communities.

Omeka + Neatline is one of six contract awards made by the Library of Congress in a program that aims both to improve the Library’s own content management and content delivery infrastructure and to contribute to collaborative knowledge sharing among broader communities concerned with the sustainability and accessibility of digital content. In July of 2010, the Library of Congress targeted approximately $3,000,000 toward Broad Agency Announcements covering three areas of research interest related to these goals. Technical proposals were openly solicited from expert, multi-disciplinary communities in both academic and commercial settings in three areas: Ingest for Digital Content, Data Modeling of Legislative Information, and Open Source Software for Digital Content Delivery.

In addition to guiding software development work at the Scholars’ Lab and CHNM, project directors Tom Scheinfeldt and Bethany Nowviskie will use the Omeka + Neatline project as an opportunity to document and disseminate a model for open source, developer-level collaborations among library labs and digital humanities centers.

I originally went to Social Explorer (SE) to view and map the data. While SE allowed me to easily view data and make some quick thematic maps, it has some limitations. SE only allows you to map one attribute at a time and does not allow for custom attributes (like the rate change in bicycle riding between 2005 and 2009). Also SE only goes back to 2006 for the ACS.  My journey next took me to American FactFinder (AFF), the U.S. Census’s data portal.

In AFF, I was able to get all the transportation survey data and decided to map rate changes in mass transit, bicycling and walking.  I had to manipulate the downloaded Excel file to make it “GIS ready.”

There are some interesting things going on within this map.  First, why do a majority of the counties have no data?  The ACS only surveys communities with greater than 65K in population.  Therefore, a majority of U.S. counties are not included in the ACS.  Several counties have data for either 2005 or 2009 but rate change can only be calculated for counties with both.  Secondly, how can adjacent counties have such wild differences in rates of change for bicycle usage?  This question is much tougher to answer.  However, the most likely issue is inaccuracies in the ACS survey results stemming from small sample sizes and differences in the way questions are asked.  The LAB article does mention these issues in passing.

Keeping in mind the classification bins are not uniform across these maps, I think there may be some interesting parallels between counties that have lost mass transit and gained walkers/bicyclists, etc.

I made a few more maps showing the density of cyclists in 2005 and 2009 at a larger scale.

If you would like more information about the U.S. Census, the ACS, Social Explorer, GIS or bicycling, please contact me at cgist[at]virginia.edu.  Larger version of the maps are also available.

• Identifying map projections and coordinate systems
• Interpreting map scale
• Understanding techniques of cartographic relief
• Interpolating latitude & longitude
• Calculating geographic extent rectangles

Too easy?  Well the profession I’m describing is not Geographic Information Systems guru or Cartographer or Neogeographer.   In fact, my list describes just a few of the skills you’ll need to be a first-rate Map Cataloger.

Thanks to a thoughtful invitation from Jennifer Roper, I learned a bit about map cataloging alongside her UVA Library Cataloging and Metadata Services team at a workshop led by one of the very best, Paige Andrew of Penn State University.  Of course Mr. Andrew sports a world map necktie.

The session made clear to me many of the skills needed to be a strong map cataloger are the same skills needed to be proficient in Geographic Information Systems.  That skills list above would serve as a good starting outline for a GIS 101 syllabus and all those skills are key to the map cataloging workflow.

And Mr. Andrew’s traveling map collection was a treat to explore with examples of bleeding edge maps, segmented maps, pop-up maps, remote sensing maps, tourist maps, and maps with and without covers and neatlines.  Who knew map catalogers must be skilled in both map unfolding for preservation and map re-folding (harder!) for panel measurements.

Today was eye-opening for me in thinking about using a geographic search to discover paper maps in a library catalog.  With catalogers now capturing the latitude and longitude of each corner of the map as part of their workflow, finding every paper map that intersects any point on the earth becomes possible.  It’s sometimes called searching by geographic extent.  Just point to any spot on a map of the world and find all the maps in the collection that cover that spot.

Geographic search relies on the kind of extent rectangle metadata map catalogers now create every day.  We’ve implemented geographic search for digital datasets in our GIS Portal http://gis.lib.virginia.edu by creating just such extent rectangle metadata for every digital dataset.

Seems map catalogers are setting the stage for that same kind of discovery for paper map collections.  Paper maps are getting smarter every day.

During the summer of 2010, U.Va. History professor and Scholars’ Lab GIS collaborator Max Edelson took a berth aboard the NOAA Ship Thomas Jefferson as it charted the waters off the western Keys of Florida. For a week, he learned about modern coastal surveying and hydrography first hand and interviewed the TJ’s officers and scientists about their experiences using sonar-based sensing to measure the extent of the Deepwater Horizon oil spill. To better get a grasp on the first rigorous colonial surveys of Florida created in the 1760s and 1770s, he enlisted some of the crew to recreate early modern methods by tracing the contours and measuring the depths of a harbor in Key West. When asked what they were up with their lead lines and sextants by puzzled crew mates, they replied, “We’re off to do some old-school hydro.”

This talk describes the art and science of surveying and mapmaking in and around the Florida Keys across 250 years.

All Scholars’ Lab events are free and open to all. No registration is required.

We hope to see you in the Scholars’ Lab! And check out our full calendar of events for the Fall semester.

All two-dimensional maps (as opposed to globes) are projected. That is, the curved three-dimensional surface of the Earth is transformed onto a flat two-dimensional surface. This can be done in an infinite variety of ways, many of which have been mathematically characterized and named by cartographers, for whom they are necessary tools. We must note, however, that no such transform can obtain a perfect representation of a section of the Earth. The mapmaker must choose which qualities to preserve and in what measures. Is it more important to provide an accurate depiction of relative areas or of relative lengths? Is the area around Greenland to be kept in the focus of accuracy, or that around New Zealand?

Each map therefore carries with it from its creation certain choices like these, part of the arguments the map makes about the world by its very construction. We chose WMS on which to start building our tools because, amongst other reasons, it allows for the transmission of projection information as part of its operation. This fact allows us to produce imagery from historical maps (themselves in any number of projections) and maintain the original choices the mapmaker made. Google Maps and Open Street Map are not WMS sources. They can be described as tile caches, huge reservoirs of rendered imagery. As such, they offer their own choices about how the world is to be projected. (Google’s choice has become so closely associated with Google that it is known widely as “the Google projection”.)

Now we come to an important technical distinction; WMS services are able (depending on the capabilities of the specific software in use) to reproject their contents. That is, in response to a specific request for imagery, they can produce the imagery in a projection different from the one in which it was stored. GeoServer, the software we are using for Neatline, has a library of thousands of projections to which users can add more as desired. This allows us to take imagery from a WMS source and lay it under a historical map layer while maintaining the original projection for that of the map as a whole. Tile caches, by and large, do not allow for this. (Google Maps offers its one projection, and Open Street Map offers two.) This means that in order to lay historical map imagery over a layer from one of these sources, we would have to reproject the foreground (historical imagery) overriding the choices of the mapmaker and introducing additional choices of our own about what facets of the geographies at stake are to be preserved and which abandoned.

(Neogeographers will remark that georectifying a digital image introduces similar issues. This is true, but unavoidable for our purposes. We would like to avoid compounding the matter in a way that is subtle and hard to detect.)

We are working out means by which we can provide the undeniable utility of popular tilecaching services in a way that is respectful of the historical context and story of map artifacts. Until we do, we will continue to concentrate on the more flexible and sophisticated apparatus provided by WMS.

The Scholars’ Lab/NEH Institute for Enabling Geospatial Scholarship was held at the University of Virginia Library May 25-27, 2010 and concluded with a set of two-minute, three-slide lightning talks by Institute attendees on their own spatial humanities projects and works-in-progress.

We’re crowd-sourcing the keynote to the final round of the Scholars’ Lab/NEH 2009-2010 Institute for Enabling Geospatial Scholarship. With all of these fantastic attendees on hand — not to mention the Institute faculty — how could we let the opportunity slip by?

Frontiers in Spatial Humanities:
Lightning Presentations

We are pleased to host 40 rapid-fire, 2-minute demos of boundary-pushing projects in spatial humanities. The scholars presenting their work come from 27 different institutions, and were competitively selected to attend this prestigious program, funded by the National Endowment for the Humanities. Some of our Institute faculty will also offer brief glimpses of their work as part of a whirlwind tour of emerging work in humanities GIS.

While admission to the Institute itself is now closed, “Frontiers in Spatial Humanities” and the reception that follows are open to the public!

I’d like to thank the NEH for its generous funding of our training program, and the University of Virginia Library for supporting the Scholars’ Lab — as well as the “Frontiers” reception, to which you’re all invited!

Thursday, May 27th, 3:30-5:00pm
Harrison-Small Auditorium

For more information about the SLab and our NEH-funded Institute for Advanced Topics in the Digital Humanities, please visit:
http://lib.virginia.edu/scholarslab/geospatial/

First a little digression to explain latitude and longitude, and why computer GISs generally prefer decimal degrees when expressing lat/lon as a coordinate pair.

Latitude and Longitude is a spherical coordinate system for describing location upon a sphere (or upon an object that’s approximately a sphere, like the Earth).  Just as there are 360 degrees in a circle, there are 360 degrees of longitude (numbered 180 W (-180) to 0 to 180 E (+180) on either side of the Greenwich Prime Meridian) and 360 degrees of latitude (numbered 90 S (-90) to 0 to 90 N (+90) from the south Pole to the Equator (the Prime Parrallel, so to speak) to the North Pole… and back again on the other side of the globe, to complete the circle).  Each degree can be subdivided into 60 minutes, and each minute subdivided into 60 seconds.  Further subdivision of seconds is expressed as fractions or decimals.  Thus you could express the geographic location of the Scholars’ Lab at UVA as being at 38º 02’ 12.3540” N, 78 º 30’ 19.7928” W (or +38º 2’ 12.3540”, -78 º 30’ 19.7928”).

Computer GIS programs all want the northing and easting coordinate pair saved as just two numbers (one number for latitude, one number for longitude) instead of three different fields to contain the degrees, minutes, and seconds for latitude and another three fields for longitude.  This makes it much easier for the computer to plot location.  Many GIS programs also prefer the coordinates to be ordered longitude, latitude since that mimics X, Y coordinates.  Since there are 60 minutes in a degree and 60 seconds in a minute (or 3600 seconds in a degree [60 x 60]), you could write the location of the Scholars’ Lab as -78 – (30/60) – (19.7928/3600), 38 + (02/60) + (12.3540/3600) which is -78.505498°, 38.036765° in decimal degrees.

Now back to the emergent tartan-plaid problem.  What would happen if instead of converting to decimal degrees, you simple wrote out the degrees-minutes-seconds numbers in the format DD.MMSSssss ?  The Scholars Lab location would become -78.30197928, 38.02123540.  But the computer GIS would still interpret this as decimal degrees, and would compress all points falling within a 1×1 degree box into just the first 6/10ths x 6/10ths of the box, with a gap without points filling the rest of the box.  Spread over a large region, this would result in a tartan plaid-like pattern emerging.

So what is supposed to look like this:

Would end up looking like this:

And both the correct and incorrect versions together:

Not that this is a common mistake people using GIS make by any stretch, but when someone has a question about why their points are all coming out misaligned with strange empty striping patterns, it can take a little while to deduce what’s going on if you’ve never seen the results of such a mistake before.

We’re developing a new web-based tool for visualizing these historic boundary changes and it’s nearly ready for prime time.  We’ll announce the beta release here soon.

So with the knowledge that US state boundaries have already been subject to drastic change over time, let’s have some fun with geographic information systems to visualize drastic mathematically-induced changes to those familiar US state boundaries.

For our experiment, let’s keep all our current state capital cities right where they are since they are laden with the necessary infrastructure of government.  But we’ll move the state boundary lines Voronoi-style so anywhere you travel in each of our new states you’ll be closer to the state capital than any other state capital.  In other words, when you’re standing anywhere inside our newly outlined Virginia, you will always be closer to the Virginia state capital, Richmond, than any other state capital.  That seems very efficient.  Let’s have a look.

Here’s that familiar grade-school wall map of the lower 48 US states and their capital cities.   Now let’s tweak the map with GIS software to reconfigure the states, Voronoi-style.

Wow, what a difference Voronoi makes.

Let’s measure just how much the states have changed in our new layout.   In absolute terms, Utah and New Mexico make the biggest land grabs while Texas and California lose the most real estate.  But as a percentage of their current area, Rhode Island is the big winner ballooning in size by over 240% while Massachusetts shrinks 60%.

To visualize the state-by-state changes, Todd Burks from neighboring Clemons Library overlayed the two maps.

Intrigued?  Read more about Voronoi and Thiessen polygon GIS techniques.

The 1907 Massie Map of Albemarle Co., VA

While going through our archives of scanned maps, we recently ran across a copy of Frank A. Massie’s 1907 “A new and historical map of Albemarle County, Virginia” [Special Collections, University of Virginia Library], commonly referred to as the Massie map, which contains a wealth of detailed historical information for the county in which the University of Virginia sits.

After obtaining a more recent scan of the map from Special Collections and Andrew Curly (of the Library’s Digital Production Services), we georectified the digital map and added it to our geospatial data portal. Accessing the map through our portal (here or here) allows you to not only view the map at high resolution in your browser, but also to view the Massie map in Google Earth, as well as being able to overlay the Massie map over other base maps and other geospatial data layers (for example, by making a WMS call to our portal’s server from your desktop GIS; or by pulling the Massie map into your webpage-embedded dynamic map using Open Layers).

Among the many interesting historical features on the maps, are the locations of:

• The residences and named estates of prominent county landowners, including the oldest house in the county, plus various churches and schools, numerous dams, mills, and quarries, and local geologic and mineral resources;
• Where Revolutionary war prisoners where held (the Barracks that held Hessian troops and from which Barracks Road was named, as well as where Baron and Baroness de Reidesel resided) plus old militia rendezvous points;
• Routes and encampments of various military campaigns (notably those of British Lieutenant Colonel Banastre Tarleton’s 1781 raid on Charlottesville and Gov. Thomas Jefferson’s subsequent flight, General T. J. “Stonewall” Jackson’s Confederate Army’s march in 1862, General George A. Custer’s unsuccessful Union Army raid in 1864, and General Philip Sheridan’s Union Army’s march in 1865 along with locations of many of the mills and bridges Sheridan had destroyed);
• Existing and proposed rail lines of many now-nonexistent railroads;
• Locations where various murders and river drownings occurred.

Additionally, the map contains tables showing city and county data from the 1900 census and a 1906 household survey (listing, among other things: total property values and taxes; numbers of livestock (1000’s of cattle, horses, hogs, and sheep, but only 24 goats!), wagons, watches, clocks, sewing machines, and pianos; number of manufacturers with their capital, number employees, and total wages; farm acreage; and enrollment figures for the white and colored public schools.

There’s also an inset showing the major rail lines and railroad junctions for the state of Virginia.

GIS software creates, manages, analyzes, and visualizes geographically referenced data.  Environmental Systems Research Institute (ESRI) in Redlands, California, produces ArcGIS desktop, a GIS software suite.  The following examples use ArcGIS ArcMap software version 9.3.1 at the ArcInfo product level.  Instructions for accessing GIS software at the University of Virginia are here: http://guides.lib.virginia.edu/gis.  If you are not affiliated with UVA, contact your local IT support person or ESRI for information on accessing this GIS software.

Calculating polygon centroids – When working with polygon features (like county boundaries) in GIS it is often necessary to locate the geographic center or centroid of each polygon as a point feature.  In ESRI’s ArcGIS desktop software the ‘Feature To Point’ tool creates polygon centroids.

For more information see the ArcGIS online help for the ‘Feature To Point’ tool:

http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?id=1798&pid=1790&topicname=Feature_To_Point_%28Data_Management%29

Creating a point distance table – Given a set of point features, GIS software can calculate the straight-line distance from each point in the set to every other point in the set.   The output distance table contains one row for each point-to-point combination along with the calculated distance.  In ESRI’s ArcGIS desktop software the ‘Point Distance’ tool creates a point distance table.

For more information see the ArcGIS online help for the Point Distance tool: http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?id=1353&pid=1347&topicname=Point_Distance_%28Analysis%29

Example using the ‘Feature To Point’ and ‘Point Distance’ tools – Given a polygon dataset representing boundaries of a group of US counties, calculate the distance between each county in the group and every other county in the group.

1. After ensuring the county polygon boundary file is projected using a distance-preserving projection, select the county polygons for the study area.
2. Convert the selected county polygons to county polygon centroid points using the ‘Feature To Point’ tool.
3. Generate the point distance table for all county centroid points created in step 2 using the ‘Point Distance’ tool.  Distance is expressed in the linear unit of the input dataset, which is meters in our example.