Tag Archives: spatial humantities

Introducing Felipe Álvarez de Toledo, 2019-2020 Humanities Unbounded Digital Humanities Graduate Assistant

Felipe Álvarez de Toledo López-Herrera is a Ph.D. candidate at the Art, Art History, and Visual Studies Department at Duke University and a Digital Humanities Graduate Assistant for Humanities Unbounded, 2019-2020.  Contact him at askdata@duke.edu.

Over the 2019-2020 academic year, I am serving as a Humanities Unbounded graduate assistant in Duke Libraries’ Center for Data and Visualization Sciences. As one of the three Humanities Unbounded graduate assistants, I will partner on Humanities Unbounded projects and focus on developing skills that are broadly applicable to support humanities projects at Duke. In this blog post, I would like to introduce myself and give readers a sense of my skills and interests. If you think my profile could address some of the needs of your group, please reach out to me through the email above!

My own dissertation project began with a data dilemma. 400 years ago, paintings were shipped across the Atlantic by the thousands.  They were sent by painters and dealers in places like Antwerp or Seville, for sale in the Spanish colonies. But most of these paintings were not made to last. Cheap supports and shifting fashions guaranteed a constant renewal of demand, and thus more work for painters, in a sort of proto-industrial planned obsolescence.[1]As a consequence, the canvas, the traditional data point of art history, was not a viable starting point for my own research, rendering powerless many of the tools that art history has developed for studying painting. I was interested in examining the market for paintings as it developed in Seville, Spain from 1500-1700; it was a major productive center which held the idiosyncratic role of controlling all trade to the Spanish colonies for more than 200 years. But what could I do when most of the work produced within it no longer exists?

This problem drives my research here at Duke, where I apply an interdisciplinary, data-driven approach. My own background is the product of two fields: I obtained a bachelor’s degree in Economics in my hometown of Barcelona, Spain in 2015 from the Universitat Pompeu Fabra, and simultaneously attended art history classes in the University of Barcelona. This combination found a natural mid-way point in the study of art markets. I came to Duke to be a part of DALMI, the Duke, Art, Law and Markets Initiative, led by Professor Hans J. Van Miegroet, where I was introduced to the methodologies of data-driven art historical research.

Documents in Seville’s archives reveal a stunning diversity of production that encompasses the religious art for which the city is known, but also includes still lives, landscapes and genre scenes whose importance has been understated and of which few examples remain [Figures 1 & 2]. But analysis of individual documents, or small groups of them, yields limited information. Aggregation, with an awareness of the biases and limitations in the existing corpus of documents, seems to me a way to open up alternative avenues for research. I am creating a database of painters in the city of Seville from 1500-1699, where I pool known archival documentation relating to painters and painting in this city and extract biographical, spatial and productive data to analyze the industry. I explore issues such as the industry’s size and productive capacity, its organization within the city, reactions to historical change and, of course, its participation in transatlantic trade.

This approach has obliged me to become familiar with a wide range of digital tools. I use OpenRefine for cleaning data, R and Stata for statistical analysis, Tableau for creating visualizations and ArcGIS for visualizing and generating spatial data (see examples of my own work below [Figures 3-4]). I have also learned the theory behind relational databases and am learning to use MySQL for my own project; similarly, for the data-gathering process I am interested in learning data-mining techniques through machine learning. I have been using a user-friendly software called RapidMiner to simplify some of my own data gathering.

Thus, I am happy to help any groups that have a data set and want to learn how to visualize it graphically, whether through graphs, charts or maps. I am also happy to help groups think about their data gathering and storage. I like to consider data in the broadest terms: almost anything can be data, if we correctly conceptualize how to gather and utilize it realistically within the limits of a project. I would like to point out that this does not necessarily need to result in visualization; this is also applicable if a group has a corpus of documents that they want to store digitally. If any groups have an interest in text mining and relational databases, we can learn simultaneously—I am very interested in developing these skills myself because they apply to my own project.

I can:

  • Help you consider potential data sources and the best way to extract the information they contain
  • Help you make them usable: teach you to structure, store and clean your data
  • And of course, help you analyze and visualize them
    • With Tableau: for graphs and infographics that can be interactive and can easily be embedded into dashboards on websites.
    • With ArcGIS: for maps that can also be interactive and embedded onto websites or in their Stories function.
  • Help you plan your project through these steps, from gathering to visualization.

Once again, if you think any of these areas are useful to you and your project, please do not hesitate to contact me. I look forward to collaborating with you!

[1]Miegroet, Hans J. Van, and Marchi, ND. “Flemish Textile Trade and New Imagery in Colonial Mexico (1524-1646).” Painting for the Kingdoms. Ed. J Brown. Fomento Cultural BanaMex, Mexico City, 2010. 878-923.

 

DVS Fall Workshops

GenericWorkshops-01Data and Visualization Services is happy to announce its Fall 2015 Workshop Series.  With a range of workshops covering basic data skills to data visualization, we have a wide range of courses for different interests and skill levels..  New (and redesigned) workshops include:

  • OpenRefine: Data Mining and Transformations, Text Normalization
  • Historical GIS
  • Advanced Excel for Data Projects
  • Analysis with R
  • Webscraping and Gathering Data from Websites

Workshop descriptions and registration information are available at:

library.duke.edu/data/news

 

Workshop
 

Date

OpenRefine: Data Mining and Transformations, Text Normalization
Sep 9
Basic Data Cleaning and Analysis for Data Tables
Sep 15
Introduction to ArcGIS
Sep 16
Easy Interactive Charts and Maps with Tableau
Sep 18
Introduction to Stata
Sep 22
Historical GIS
Sep 23
Advanced Excel for Data Projects
Sep 28
Easy Interactive Charts and Maps with Tableau
Sep 29
Analysis with R
Sep 30
ArcGIS Online
Oct 1
Web Scraping and Gathering Data from Websites
Oct 2
Advanced Excel for Data Projects
Oct 6
Basic Data Cleaning and Analysis for Data Tables
Oct 7
Introduction to Stata
Oct 14
Introduction to ArcGIS
Oct 15
OpenRefine: Data Mining and Transformations, Text Normalization
Oct 20
Analysis with R
Oct 20

 

ArcGIS Tutorial – Georeferencing Imagery

One of the limitations of computer mapping technology is that it is new. There is little historical imagery and data available as a result, although this has started to change. The integration of paper and imaged maps into computer mapping technology is possible, and this tutorial will walk through the process of georeferencing.

Georeferencing is the process of placing an image into two dimensional space. In essence, georeferencing pins a scanned map to particular geographical coordinates.

This tutorial will georeference a map of Durham County from 1955. In addition to the scanned map, we will use two current layers as referents: the Durham roads layer, and the Durham county boundary. Note that because the layers are more recent than the historical map, many roads will not exist in the image. Georeferencing historical imagery requires familiarity with geographic characteristics and changes.

 

Step 1: Enable Georeferencing

First, under the “Customize” Menu Bar option, navigate to “Toolbar” and select Georeferencing. The figure to the right displays the Georeferencing toolbar.

 

Step 2: Add Data and Image Layers

Next, add the shapefiles that you will use as referents for the image.

Once this is done, add the image to be georeferenced.  Note that you will almost certainly not see that image, as it lacks spatial coordinates. However, the image will appear in the Table of Contents.

In this example, I have added Durham County (blue polygon) and the Durham roads layer (blue lines).

 

Step 3: Fitting the Image to the Layers

The next step will relocate the image to the center of your current window and will expand the image only to the point where the entire image is visible. In this case, Durham County is taller than it is wide, so vertical space will be maximized.

First, it is a good idea to zoom, if necessary, so that your current view roughly matches where the image will be place. In this case, zooming to the full extent of the Durham county boundary will accomplish this.

Second, under the Georeferencing toolbar, click “Georeferencing” and select “Fit to Display.” The image should be roughly aligned to the data layers, though if not, this is not problematic.

As you can see from the image to the right, there is some distance between the county boundaries of today (red lines) to the hand-drawn county boundaries located in the image (white lines).

 

Step 4: Adjusting the Map

ArcGIS georeferences images through the addition of control points. The control points tool (to the right) operates through two mouse clicks: the first mouse click selects a point on the image, and the second mouse click pins that point to a location within a data layer.

For example, in the image to the right, I have selected a major intersection that likely has not changed in the last 60 years. After my first click, where I’ve selected a point near the top of the intersection, a green crosshair is placed. As I move the mouse, ArcGIS will pin my current crosshair to a proximate layer, in this case, the Durham roads layer.

Once you click a second time, the map will move to conform to the new control points. Control points work in combination, so as you add new control points, your image will (ideally) match more closely to your referents.

There is a limit to how much each subsequent control point will improve fit as more points are added. Generally, it’s a good idea to zoom in to improve accuracy and to create control points across the extent of the image.

After about 15 control points, we can compare the image to the included shapefiles. As you can see, if we assume that major roads have not changed, the green lines correspond well to the image, while the county boundary does to a lesser extent.

 

Step 5: Statistics and Transformations

Before saving the results, it is also a good idea to evaluate the results. Open the Table of Points to see each of your control points and the root mean squared error of all control points.

The Root Mean Square error (RMS) provides a rough guide to how consistent your control points are to one another with reference to the map.  Note that a low value does not mean that you’ve necessarily georeferenced the image well, it means you’ve georeferenced consistently.  High RMS errors indicate that your control points are less consistent with one another in comparison with a low RMS error.  One way to address this issue is to identify especially probelmatic control points and either replace or remove these points.  However, always reevaluate how well your image maps to the referent shapefiles.

You may delete control points or add new points at this stage. In addition, you may also try different transformations, although second- or third-order transformations are rarely needed.

 

Step 6: Saving the Results

Under the Georeferencing tab of the Georeferencing toolbar, select “Update Georeferencing.” Spatial information is saved in two new files that MUST accompany the image, an “.aux” file and a “.thw” file.

 

General Tips

– Zoom close to the layer resolution in order to improve accuracy

– Use more than 1 referent if possible. In this example, the county boundary provided a rough guide with respect to how far off the image initially is, but was not used to actually georeference the image.

– Georeference to accurate features. In this example, the county boundary was hand-drawn on the image and is not as precise as photographed features, like roads.