Tag Archives: gis

Fall Data and Visualization Workshops

2017 Data and Visualization Workshops

Visualize, manage, and map your data in our Fall 2017 Workshop Series.  Our workshops are designed for researchers who are new to data driven research as well as those looking to expand skills with new methods and tools. With workshops exploring data visualization, digital mapping, data management, R, and Stata, the series offers a wide range of different data tools and techniques. This fall, we are extending our partnership with the Graduate School and offering several workshops in our data management series for RCR credit (please see course descriptions for further details).

Everyone is welcome at Duke Libraries workshops.  We hope to see you this fall!

Workshop Series by Theme

Data Management

09-13-2017 – Data Management Fundamentals
09-18-2017 – Reproducibility: Data Management, Git, & RStudio 
09-26-2017 – Writing a Data Management Plan
10-03-2017 – Increasing Openness and Reproducibility in Quantitative Research
10-18-2017 – Finding a Home for Your Data: An Introduction to Archives & Repositories
10-24-2017 – Consent, Data Sharing, and Data Reuse 
11-07-2017 – Research Collaboration Strategies & Tools 
11-09-2017 – Tidy Data Visualization with Python

Data Visualization

09-12-2017 – Introduction to Effective Data Visualization 
09-14-2017 – Easy Interactive Charts and Maps with Tableau 
09-20-2017 – Data Visualization with Excel
09-25-2017 – Visualization in R using ggplot2 
09-29-2017 – Adobe Illustrator to Enhance Charts and Graphs
10-13-2017 – Visualizing Qualitative Data
10-17-2017 – Designing Infographics in PowerPoint
11-09-2017 – Tidy Data Visualization with Python

Digital Mapping

09-12-2017 – Intro to ArcGIS Desktop
09-27-2017 – Intro to QGIS 
10-02-2017 – Mapping with R 
10-16-2017 – Cloud Mapping Applications 
10-24-2017 – Intro to ArcGIS Pro

Python

11-09-2017 – Tidy Data Visualization with Python

R Workshops

09-11-2017 – Intro to R: Data Transformations, Analysis, and Data Structures  
09-18-2017 – Reproducibility: Data Management, Git, & RStudio 
09-25-2017 – Visualization in R using ggplot2 
10-02-2017 – Mapping with R 
10-17-2017 – Intro to R: Data Transformations, Analysis, and Data Structures
10-19-2017 – Developing Interactive Websites with R and Shiny 

Stata

09-20-2017 – Introduction to Stata
10-19-2017 – Introduction to Stata 

 

 

 

 

 

 

 

 

 

 

 

 

Data and Visualization Spring 2016 Workshops

Spring 2016 DVS WorkshopsSPRING 2016: Data and Visualization Workshops 

Interested in getting started in data driven research or exploring a new approach to working with research data?  Data and Visualization Services’ spring workshop series features a range of courses designed to showcase the latest data tools and methods.  Begin working with data in our Basic Data Cleaning/Analysis or the new Structuring Humanities Data  workshop.  Explore data visualization in the Making Data Visual class.  Our wide range of workshops offers a variety of approaches for the meeting the challenges of 21st century data driven research.   Please join us!

Workshop by Theme

DATA SOURCES

DATA CLEANING AND ANALYSIS

DATA ANALYSIS

MAPPING AND GIS

DATA VISUALIZATION

* – For these workshops, no prior experience with data projects is necessary!  These workshops are great introductions to basic data practices.

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

 

New Year- New Data and Visualization Lab!

Data and Visualization Services is happy to announce our new Data and Visualization Lab in Duke Libraries new Edge research space.  Located on the first floor of the Bostock Library, the Brandaleone Family Lab for Data and Visualization Services offers a dedicated space for researchers working on data driven projects.

The lab features three distinct areas for supporting data driven research.

Data and Visualization Lab Space

Data and Visualization Lab Computing Zone

Our lab space features twelve high end workstations with dual monitors with the latest software for data visualization, digital mapping, statistics, and qualitative research.  All of the machines have two dedicated displays to encourage collaborative work and data consultations.  Additionally, all twelve machines have a dedicated power port located conveniently under the edge of the table for powering a laptop or usb powered device.

Bloomberg Professional “Bar”

bloom

Since the launch of our Bloomberg terminals, we have seen a steady increase in both individual and team based usage of Bloomberg financial data.  Our three Bloomberg Professional workstations are now located on a dedicated “bar” across from our lab machines.  The  new Bloomberg zone will facilitate collaborate work and provide a base for groups such as the Duke University Investment Club and Duke Financial Economics Center.

Consult and Collaborative SpaceCollaboration Zone

Our third lab space provides a set of four rolling tables for small groups to collaborate or for projects that don’t require a fixed computing space.   An 85″ flat panel display near this zone features data visualizations and other data driven research projects at Duke.

Come See Us!

With ample natural light,  almost 24/7 availability, and a welcoming staff eager to work with you on your next data driven project.  We look forward to working with you in the upcoming year!

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.