Introduction to Data Visualization
Data Visualization is a broad field that offers many possibilities for researchers in any discipline. This introduction will begin with an exploration of different categories of visualizations. The workshop will then address a typical workflow for visualization projects, including how to match the visualization type to the needs of a research project and a set of users. Finally, there will be a discussion of stylistic and formatting considerations that may arise during publication of visualizations.
Data processing and visualization for small projects can easily be accomplished by using Excel and the many chart options it offers. In this workshop, we will outline the types of charts available in Excel and identify the data needs for the major chart types. We will also cover basic data terminology and Excel functions that may be useful for some data transformations.
This class will show some ways that ArcGIS can be used for the analysis and visualization of historical spatial data. Topics discussed will be: sources for GIS layers reflecting the past; georeferencing a scanned historic map; creating new layers from scratch based on known locations of features; editing existing GIS layers to reflect former features; and vectorizing a scanned map to create editable features.
Introduction to Text Analysis
Many research projects involve textual data, and computational advances now provide the means to engage in various types of automated text analysis that can enhance these projects. Understanding what analysis techniques are available and where they can appropriately be applied is an important first step to beginning a text analysis project.
This hands-on approach to text analysis will give a quick overview of small- and large-scale text-based projects before addressing strategies for organizing and conducting text analysis projects. Tools for data collection, parsing, and eventual analysis will be introduced and demonstrated. The workshop will focus on acquiring and preparing text sources for small-scale projects and text-based visualizations, but many of the techniques will be useful for larger projects as well. For this introduction, the focus will primarily be on using Graphical User Interface (GUI) tools like Microsoft Excel and Google Refine, instead of programming languages and command line approaches.
Compare and contrast three products intended for quick geospatial visualization (e.g., a map to embed in a blog or PowerPoint, or for a poster session) and for some elementary GIS data analysis. (1) Google Earth: emphasis on its features particularly applicable in an academic setting; (2) GeoCommons: both a repository for spatial data as well as an analysis and visualization tool; and (3) Google Fusion Tables: merge datasets, filter and aggregate data, and visualize data by creating online maps and graphs.
GeoCommons serves as an open repository of spatial data and maps. In this workshop, we'll cover how to search for data stored by GeoCommons as well as how to upload your own spatial data to share with others. You can edit, filter, and combine your GeoCommons data. GeoCommons also serves as a spatial data visualization tool to build maps and animate your data based on time or space. The visualizations can be shared and embedded in other applications.
Do you want to find out how geographic information (GIS) software can aid your research? This class will provide an overview of how ArcGIS software can help you analyze or visualize digital data that has a locational component, as well as discuss starting points for obtaining data. Examples will focus on social science data, but attendees are encouraged to ask questions regarding their own needs and will be welcome to make one-on-one appointments later for more focused instruction.
Data Management Plans - Grants, Strategies, and Considerations
Fall 2012, Spring 2012, Fall 2011, Spring 2011
In the last few years granting agencies across the disciplines have increasingly required data management plans as part of a grant proposal that detail strategies to manage, share and preserve research data as part of a funded grant project. NSF, the NIH, the National Endowment for the Humanities and other organizations have similar requirements, and Duke policy requires that research records (including digital data) be kept for at least five years. How should researchers respond? In this presentation, we’ll give an overview of research data management challenges and opportunities, and describe some approaches for meeting them. We’ll ask the audience to share how they do data management now, and we’ll talk about planning underway for new services to help with data management at Duke.
Stata Review (Slides and Sample Data)
Fall 2012, Spring 2012, Fall 2011, Spring 2011
A basic overview of using Stata for social science research projects with a focus on common questions and procedures.
Introduction to Tableau Public
Fall 2012, Spring 2012
Tableau Public is a free service that allows researchers and students to create web based visualizations of their research data. The workshop explores how to load your data in tableau, popular visualizations, and the most appropriate uses of the software.
Introduction to Google Refine
Spring 2012, Fall 2011
Google Refine is a tool for working with messy data. It allows you to detect and fix inconsistencies in your data, transform your data from one structure to another, and provides a simple way to understand patterns in your data. If you've ever faced the task of trying to harmonize names, ISBNs, or titles in a spreadsheet, Refine provides an appealing way of dealing with these common challenges. In this hands-on class, we'll explore how Refine can help with common data cleaning challenges.
Google Earth Pro
Spring 2012, Fall 2011
Learn about some of the features of Google Earth and Google Earth Pro, with an emphasis on those features particularly applicable in an academic setting. Special attention will be given to GIS capabilities and comparison with ArcGIS.
Google Fusion Tables
Introduction to the features of Google Fusion Tables, which include merging datasets, filtering and aggregating data, and visualizing data by creating online maps and graphs. For certain tasks, it can serve as an alternative to using statistical software such as Stata or GIS software such as ArcGIS.
Working with Stata
A workshop exploring basic data management and analysis using Stata. This workshop is aimed at researchers new to stata or seeking a refresher on stata basics.
Adding Your Own Data to GIS Layers (Joins and Spatial Joins in ArcGIS)
We'll look at ways of adding variables to a layer's attribute table in ArcGIS (the spreadsheet data associated with the layer's features). These include joining your own data based on common attribute values as well as adding the attributes of another layer based on spatial location. We'll also touch on geo-referencing scanned maps or digital images to turn them into GIS layers, so that other layers will properly overlay.
Elementary Editing of Layers and Features
A discussion of techniques in ArcGIS to make layers smaller (clipping, selecting), combining layers (the merge and append tools), and combining features within the same layer (dissolve tool and the merge editing function).
Keeping Your Layers in Line: Projections, Coordinates, and Datums in GIS
Projections and coordinate systems are one of the most frequent stumbling blocks when trying to overlay GIS layers from different sources, to do proximity analysis, or to create attractive maps. This class will provide an overview of what these terms mean and how to deal with some common problems, focusing on ArcGIS software and touching on Google Earth, using example data from sources such as the US Census Bureau.
Tell Me if I am Close: Measure, Nearness, and Proximity Analysis in GIS
Focusing on ArcGIS software, this session will give an overview of some of the proximity tools, such as Near, Point Distance, and Buffer and take a look at some of the other measurement calculation techniques in the program.
Where Am I? ... Adding Address Data and Plotting Geographic Coordinate Data to GIS Maps
If you've got data that includes addresses, we'll look at how to plot the locations of your data points in Google Earth and in ArcGIS. We'll do the same for data that includes X/Y coordinates, such as longitude and latitude information.
Unless otherwise specified on this page, this work is licensed under a
Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.