Data Integration – Use It for Good

Digitizing and Exploring Qatar’s Population Censuses

June 24, 2024June 24, 2024 by mpcblog

By Shine Min Thant

Qatar, a small yet influential state in the Middle East, is a very interesting case study for demographic research because of its rapid development over the past thirty years. Qatar occupies a peninsula only slightly larger than the U.S. state of Rhode Island that juts out into the Persian Gulf from its border with Saudi Arabia. The country has experienced relatively rapid economic growth since the late 20th century, mainly due to its vast reserves of natural gas and oil. This newfound wealth allowed Qatar to invest heavily in its healthcare, infrastructure, and education – therefore making the country an ideal case study for social change and development. Additionally, a recent surge in Qatar’s immigrant population (which constitutes over 78 percent of the population) also makes it an ideal country to study social mobility and social change.

As part of the ISRDI Diversity Fellowship Program, I worked with Dr. Tracy Kugler, Professor Steven Manson, Professor Evan Roberts, and undergraduate student Rawan AlGahtani on a project to examine Qatar’s change using census data from 1984, 1997, and 2004. Summary tables from all three censuses were previously only available as printed documents. As a first step, we needed to transform the data from a hard-to-get printed format to widely accessible IPUMS IHGIS format. This process included multiple steps from conducting optical character recognition (OCR) to conducting data quality checks using R scripts (Figure 1).

Figure 1: IPUMS IHGIS Workflow

Bivariate Proportional Symbol Maps, Part 2: Design Tips with Instructions for ArcGIS Pro

February 22, 2024February 22, 2024 by mpcblog

By Jonathan Schroeder, IPUMS Research Scientist, NHGIS Project Manager

How to make effective bivariate proportional symbol maps

A map of the share of population under age 18 in the Miami area in 2020. There is one colored circle for each census tract. There are five colors ranging from dark blue (representing less than 15% under age 18) to light green (representing 20 to 25% under age 18) to brown (representing 30% or more). The circle sizes correspond to tract populations. Most circles have similar sizes, representing around 1,000 to 10,000 people. The circles cluster together forming groups where there are more tracts and more people. The circles in central Miami and along the coast are bluer than elsewhere. — A bivariate proportional symbol map.
Click map for larger version.

In Part 1 of this blog series, I introduced bivariate proportional symbol maps and shared some examples to demonstrate their advantages. In short, when they’re well designed, they can make it easy to see multiple dimensions of a population all at once: size, composition, and spatial distribution.

A key part of that statement is, “when they’re well designed.” Standard mapping tools can make it easy to get started, but getting all the way to a good design still takes some extra effort.

In this Part 2 post, I discuss some key design considerations for bivariate proportional symbol maps, and I provide specific instructions to help you get to a good design.

Software considerations

I used Esri’s ArcGIS Pro to create the examples here and in Part 1. The design tips I share below should be relevant for any mapping tool, but my instructions are specifically for ArcGIS Pro (version 3.2). I expect there are ways to achieve similar designs with QGIS, R, Python, etc., quite possibly more easily than with ArcGIS Pro. I can only say that it’s easier to create effective bivariate proportional symbols now with ArcGIS Pro than it was with its predecessor ArcMap.

As I proceed, I’ll flag which instructions pertain specifically to ArcGIS Pro. All other tips are “tool neutral.”

General tip: Match size to “size” and color to “character”

When selecting which features to map, a framework that works consistently well is to use symbol color to represent an intensive property—e.g., the share of population under 18 years old, average household size, median household income, or the share of votes cast for a candidate—and use symbol size to represent the number of cases to which the intensive property pertains—e.g., the total population (when color corresponds to a population share) or the count of households (when color corresponds to average household size or median household income).

This framework enables the map to illustrate both the spatial distribution of the mapped characteristics and the frequency distribution of the intensive property—e.g., not only where a candidate received large or small vote shares but also how many votes were cast in each of those areas. Other frameworks can also work well (e.g., see the change maps in Part 1), but it’s generally very helpful if the two mapped characteristics relate to each other in a way that corresponds intuitively with “size” and “color.”

Bivariate Proportional Symbol Maps, Part 1: An Introduction

February 22, 2024February 7, 2024 by mpcblog

By Jonathan Schroeder, IPUMS Research Scientist, NHGIS Project Manager

A powerful, underused mapping technique

The world could use a lot more bivariate proportional symbol maps. These maps pair two basic visual variables—size and (usually) color—to symbolize two characteristics of mapped features. When designed well, they convey multiple key dimensions of a population all at once: size and composition as well as spatial distribution and density.

Unfortunately, standard mapping software hasn’t made it easy to create good versions of these maps, and most introductions to statistical mapping stick to simpler strategies. As a result, bivariate proportional symbols aren’t used very often. With few examples and little guidance to go on, it’s understandable that mapmakers don’t realize how often they’re a viable, well-suited option.

This two-part blog series aims to spark more interest by providing a “few examples” (Part 1) and a “little guidance” (Part 2).

Picking up where I left off

In a previous blog post, I shared an example of a bivariate proportional symbol map and described some of the technique’s advantages. But that post focuses on a mapping resource (census centers of population) rather than on mapping techniques. Most of the examples in the post are also simply “proportional symbol maps,” without the more intriguing “bivariate” part.

To close that post, I suggested “a tantalizing next step” would be to use bivariate proportional symbols with small-area data (for census tracts or block groups), and I shared a few technical notes and design tips without much detail. I later expanded on those ideas in a conference talk, sharing some new examples with small-area data and going a little deeper with design tips.

In these new posts, I’m sharing and building on the examples and tips from the conference talk.