Introduction
This lab project explored the global female fertility rates between 1995-2005 according to the United Nations Statistics Division. In this project females ranging in ages between 15-49 years, in 195countries, over a 10 year period (specifically between 1995-2000 and 2000-2005) were examined. I selected this project out of general curiosity. For years I have heard comments about reports that fertility rates within the United States were declining. Therefore, I used this lab to try to answer my own curiosity. The objectives of this lab report were to determine:
- If there was a general increase or decrease in the global fertility rates of women
- If there was a increase or decrease in the fertility rates of women inhabiting specific Countries and Regions
- What age ranges women globally were most fertile and least fertile
- If there was a increase or decrease in the fertility of women of specific age ranges
Materials
The dataset used in this lab project, titled “Age-specific fertility rate“, was taken from the United Nations Data website (data.un.org), specifically from the Gender Info database of the United Nations Statistics Division and was produced by the UNDP World Population Prospects in 2006. This dataset was applied to Tableau Public to generate the Information Visualizations of this lab project.
Methods
Once I had selected the dataset for this project, I began my search for three visualization examples that would inspire my visualizations. As my dataset was concerned with global female fertility rates, I looked for graphs and visualizations that similarly displayed data about fertility, birth or population rates. The first visualization that informed my design was a choropleth map visualization from the UN Work Bank databank that depicted the global life expectancy totals per year since 2000. I liked the simplicity of the map design and how easy it communicated information from the dataset. As my dataset similarly included global and geographic information, I thought this would be a good example to help inspire one of my visualizations.
This is next inspiration visualization from the CDC/NCHS Data Visualization Gallery, illustrates “Natality Trends in the US from 1909-2013”. As its subject is similar to my dataset and was generated through Tableau, I thought it would be a great example to consult. I liked how this one line graph visualization, “Birth Rates, By Age of Mother: United States, 1940-2013,” highlighted a specific but important piece of its dataset. As my dataset similarly has a category which indicates fertility rates by age, I wanted to use this line graph as inspiration, being aware of its scale, filters and colors applied.
This last visualization is from OurWorldinData.org. It is a side bar graph that clearly shows how long it took for global fertility to decline from 6 to fewer than 3 children per women. I liked this graph because it was easy to understand and clear in indicating greater or lesser values in comparison to one another.
Before inputting my dataset into Tableau Public and creating visualizations, I made a few minor format adjustments to the dataset within Excel. In Excel, I removed the descriptive word “female” from my the age range category column. I removed this information from the dataset as the word “female” would be visually distracting as well as redundant in any of my visualizations that closely examined the differences of fertility within the age groups. Within Excel I also edited the date ranges “1995-2000” and “2000-2005” that were provided within the dataset. As Tableau Public would not correctly display this data as is, I had to reformat each date range to signify the 5 year period of “1995-2000” with a start date of “1/1/1995” and similarly “2000-2005” as “1/1/2000”. I did this by using the Excel command controls (Left+) and (Concatenate+). Thus, my data now represented two start dates, in 1995 and 2000, the data still describes fertility rates over a 10 year range.
With my dataset ready to be imported into Tableau Public I began to play around and experiment with layouts and its formatting features. Before making any concrete visualizations, I thought it would be interesting and important to group the 195 countries within my dataset according to their geographic regions. Following the UN’s organization and classification of countries within regions, I used the group function within Tableau to individually organize each country and area into 7 regions (East Asia & Pacific; Europe & Central Asia; Latin America & Caribbean; Middle East & north Africa; North America; South Asia; Sub-Saharan Africa). This tool would later become useful as a Filter for users to apply to several of the visualizations included in my Dashboard.
Results & Discussion
As I began experimenting with different layouts within Tableau Public, I quickly realized that my dataset would not be useful if applied to certain types of visualization designs, such as line graphs. Because my data contains only two date ranges collectively taken over a 10 year period, there are not enough data-points to visually show any significance within a line graph that illustrates the data over a period time (as seen below).
While I was unable to apply one of my three visualization examples into this lab because of the type of data I used, my dataset was useful when similarly applied the choropleth map visualization example. The choropleth map visualization I created provides an visual overview of global female fertility rates over a 10 year period between 1995-2005. Specifically showing the difference in fertility according to its geographic location and neighboring countries through temperature driven color ranges. The interactive visualization allows for countries and areas on the map to be zoomed in and out of, selected, as well as filtered according to age group, country(-ies) and region(s). However, even without utilizing any of the filters or interactive applications, this choropleth map provides a broad but clear understanding of which countries have higher or lower fertility rates in comparison to one another.
The next visualization I created on my Dashboard was the numerical heat map of countries ranked according to lowest to highest fertility rates in women of combined age groups. This sorted list enables users to easily scroll and identify which in which countries fertility rates have generally decreased or increase from 1995-2005 through the numerical color coordinated rate. As this visualization illustrates, Oman (-297) has the most decrease in fertility whereas the Netherlands (+25.4) is the country with the largest increase in fertility rates. In addition, this visualization highlights that overall there are more countries which have decreased fertility rates and at a larger proportion to those that have increase.
In addition, I created a Bar Chart that compares fertility rates within each age group from 1995-2000 to 2000-2005. This comparative visualization clearly shows the global decrease in female fertility rates over this 10 year period. It also indicates that while there has been an overall global decrease in female fertility rates, the ages at which fertility rates are still relatively high are women within age ranges 25-29, followed by 20-24, and 30-34.
The last visualization on my dashboard is a numerical heat map showing the total change in fertility rates from 1995-2005 according to age and country. As this visualization is sorted alphabetically by country and the numerical fertility rates are exhibited through the standard temperature color coordinated range, it is easy to discern which age groups of women within each countries have experienced an increase or decrease in fertility.
It is important to note that I chose to use a numerical heat map for this visualization rather than a standard heat map. I did not use a standard heat map visualization because it did not allow me as much control in its design and had to many distracting colors that were unimportant to the visualization (as seen below).
I decided to organize my visualizations within the dashboard according to a note that was made during a class lecture. It seems appropriate to feature the most general and widest information visualization at the top of the dashboard, such as the World Heat Map, and then work down through the dashboard with more specific visualizations from which users can make meaning. In addition, I chose to keep my key and filter tools in the top right corner, so that they would not take away from the visualizations.
Future Directions
A possibility for future work on this lab project would be to add data to this dataset or include similar or more specific data on global fertility rates. It may be interesting to include the global fertility rates of men of various ages. With a larger and diverse dataset, the visualizations could more clearly demonstrate a wider and distinct change of global fertility rates. Moreover, I would be able to experiment with different visualization graphs that I was unable to utilize in this one project.