Are Jobs Evenly Spread Out Amongst Women in the US_SAD Reearch Paper

Are Jobs Evenly Spread Out Amongst Women In The US 1

Job Patterns for Women in Private Industry

Are Jobs Evenly Spread Out Amongst Women in the US?

Shirley A. Dash

Drexel University


Table of Contents

Abstract…………………………………………………………………………………………………………………………………………..3

Introduction…………………………………………………………………………………………………………………………………….4

Tools……………………………………………………………………………………………………………………………………………….4

Methods…………………………………………………………………………………………………………………………………………5-8

Results……………………………………………………………………………………………………………………………………………9

Interpretation…………………………………………………………………………………………………………………………………10

Discussion………………………………………………………………………………………………………………………………………10

Conclusion………………………………………………………………………………………………………………………………………10

References………………………………………………………………………………………………………………………………………11

Appendix A………………………………………………………………………………………………………………………………………12


Abstract

Women of all nationality work in various industries around the country and I thought it would be

interesting to use visualization to show where which race of woman show the most to the least

of jobs, and does location matter. The data used is from the government’s Equal Employment

Opportunity Commission dataset. The tools used are Tableau which is where the (source data

resides); Google Fusion Table which also helped created multiple visuals and Google Fusion

Map to display the locations. Now looking at the data I wonder if the questions should be focus

on location as oppose to the actual job. For instance, what part of the country has more jobs or

are they even throughout the states? Which race of women has the most or fewest jobs? If the

jobs are not evenly spread should the EEO look into this more thoroughly or more data is

needed from organizations and companies looking to add more jobs. And maybe more

information is needed from the women like education requirements.


Introduction

My objective is use visualization as a way to quickly speak to the audience to determine if the

data given has value to make a change in what’s being presented. In this case data is given to

show what kind of jobs are disperse throughout the country to women of race. Can we make a

determination if change is needed from what is being presented or is more data needed from

another data source, and if so what other data is needed.

Tools

I am going to name some of the tools I used and why I chose them for this report. First

of all I needed a tool that was user friendly and easily to download as an excel

workbook. Tableau is the first tool I used after downloading the data source because Tableau

can be used as live data (if data is changed on network or server your data will be updated). It

can also be downloaded to a destination folder on your computer but the data will not be

updated if changes are made. Tableau makes it easy to import my data and drop it on the

columns and rows for quick data interpretation. Once the data is in place there are multiple

versions of visual outputs like (bars, stacked bars, lines, and circle view) just to name a few

graphs.


Methods

Tableau also allows you to view previous work of multiples graphs on one page as

thumb prints without losing any work. Another good feature about Tableau is the Dashboard

which is great for comparing graphs at the same time. The below Dashboard is a picture of four

graphs from Tableau and the data is the same source as used with Google’s Fusion. The 1st

graph is a summary of Woman by race in the Computer and Electronic Product Manufacturing

field. This gives me an ideal of the majority of women that are in this field. The next graph is a

total of all other careers excluding the Computer and Electronic field, and not much change as

to which race of women has the most careers throughout the US. The next graph is a crosstab

displaying the average women of race by location in the Computer and Electronic field. The last

graph is a bar graph by women of race by location in the C&E field. Now questions started to

surface but I needed to see more visualization of the data, so my next focus was on Google’s

Fusion Table.

Women in Computer & Electronic Product Manufacturing Dashboard by Tableau


Google’s Fusion Table is a new tool I used to create a visual graph and summary. This

tool compared to Tableau graphs. Google’s Fusion Table is simple and straight to the point its

choice of graph is the (bar) and the following summary options [minimum, maximum, average

and summary]. Below is a table I used to display my data using Google’s Fusion Table in its

simplest form which is not too much of a difference from Tableau. It’s a screenshot of Women

of various races by average careers reported to the EEOC and to the North American Industry

Classification System (NAICS). The acronyms are as follow [WHfp10 = White Female, BLKfp10

= Black Female, HISPfp10 = Hispanic Female, Asianfp10 = American Asian Female, Tomrfp10

= Two or More Race Female, Nhopifp10 = Native Hawaiian Female, Minfp10 = Minority

Females and FTp10 = Female Total Percentage]. The number 10 is used as a sequence only,

regardless of the category if it has #10 behind it is the Total or Total Percentage for the group

represented.

Google's Fusion Table

I like creating new graphs in Google’s Fusion Table because everything is within the

same application. For example, they have tabs at top when creating a table, maps, cards or a

summary. It also allows you to move back and forth between the tabs to see your work, similar

to (Microsoft Excel Workbook where their worksheet tabs are at the bottom). However, with

Google’s Fusion Table the more data you have the less you will be able to visually see on the

screen unless you use their bottom and right scroll bars to view your data. The next screenshot

will display the careers by table and Bar graph. Another problem I have with Google’s Fusion

Table is there is no option to export your work as an image or pdf file like in Tableau or


Microsoft Excel, and the more data you add the smaller your visual. It is important to know what

data to use if using Google’s Fusion Table with bar graphs and a summary. Tableau I believe is

better because they have tabs at the bottom and a dashboard that allows you to look at multiple

visuals at same time. Please see Appendix A for Tableau’s Women in Computer & Electronic

Summary_Map Dashboard.

Google's Fusion Table w/Summary

Now, I realized I didn’t need another table from another application to show me where

their jobs were located; however by comparing Tableau to Google’s Fusion Table I started

thinking about questions that my visual aid should be able to answer. So I created a map in

Google’s Fusion which I found really easy to do. It also has a programming feature where you

can add code to display data from your table especially if someone hovers over one of the

locations this could also make it easy to answer questions your audience might have. After

creating the map from my data.gov data collection the data became much more interesting. The

first thing the map revealed was the location of the many red dots making the US map look one

sided. For instance the East, North, Northeast, South, and Southeast has most of all the red


dots meaning this is the majority of jobs where the women work. I could start researching this

side of the map first then narrow it down, but first I need to make sure the data collection from

the EEOC website has all the information I need.

Computer and Electronic Product Manufacturing by Google’s Fusion Map


Results

I must admit I did not use all of the data that came along with the (Commission &

Magrogan, 2015) data.gov collection. What I am saying is I used Shneiderman, B. (1996) tasks

without any hesitation. I overviewed the data then zoom in on the information regarding women

then I focused on particular careers of the women throughout the US, and filtered out the jobs I

didn’t want to use for visuals. However, the other tasks, [Details-on-demand, Relate, History

and Extract] were still available for me during and after I created the (1 and 2 dimensional)

visual data types. Now before you create your visuals in either application Tableau or Google

Fusion if you want to create a map for later make sure you have city, state, zip code, address in

your tables. Now the data I used was downloaded from “Job Patterns For Minorities And

Women In Private Industry, 2008 EEO-1 CBSA Aggregate by NAICS-3 Report - Data.gov”

(Commission & Magrogan, 2015). Both applications accepted the CSV file for downloading with

no problem. Once you filter and exclude you are good to go to create your tables and maps.

I am looking for my visualization graphs to automatically tell a story once looking at it

and if the viewer has a question I expect for the data to answer the question. I expect for both

applications to provide graphs that will both raise questions and answer. However, if one

application is takes longer to use or is not friendly then the result of the graph will probably not

be as sharp; however, it is up to the person putting out the visuals to make sure they are

understandable and readable.

Now, looking at the visuals I noticed my objectives are not being answered because the

map left me with questions. What area of the United States show where there are less women

working? If the data only provides information such as race, gender, job title, city/state, total

and percentage of men and total and percentage of women is this enough information or do you

need to know their highest education, salary, family and marital status. How about if the person

is a veteran or not? The first graph on the Tableau Dashboard showed me at first glance which

race of women had the most Computer and Electronic Manufacturing Production job. The

second graph on the dashboard went the opposite direction and shows me the race of women

with the most jobs excluding the Computer and Electronic Manufacturing Production, but I am

not sure if that was information I needed. The fourth and fifth visual graphs are hard to display

as they both show details of the jobs by location by race of women, and that information is too

large to show on those visuals chosen.


Interpretation

It is clear that the Tableau Dashboard only works when the data is telling a story from

the beginning to the end, but what if you take that same information and add it to Googles

Fusion Table you get the same results because the data is too large to display as a table unless

it’s in a summary format. Once the Google Fusion Map was utilized it became much clearer

and I was able to move on with the story. I still needed more information because I knew which

race of women with the most C&E jobs, and the map showed where the majority of the women

job location. However, it still didn’t show me the total number of women in those locations.

I think in order to see if jobs should be hiring more minorities and women of color in this

job category I will need to see which race is predominantly working in that city and state. The

Tableau 2nd Dashboard displays a summary of women working in Computer and Electronic

Product Manufacturing and a map which shows the totals of the city and state. Now this is

getting me closer to the information I need, but I still need to see the details by women of race

which will break it down even further. I believe this is the furthest this information will take me

because the data collection is limited and this was not discovered until looking at the first

Tableau dashboard.

Discussion

This project could have gone on forever from looking for the collection data to using

visualization tools to display the data. I have learned that I am always using Shneiderman’s

seven tasks at the job, in school and at home. It was really amazing to find out those seven

task have names and responsibilities. I used this report to not prove right or wrong but to show

how you can have a lot of data and if data is missing any component you will have a long road

ahead. I like the way of using maps to tell a story because I only looked at maps for weather

although I have seen data maps before. It’s different when you are the one using it for the first

time. I will continue to look for other applications where I can use maps with or without

programming.

Conclusion

I want to start using other data types to display my visual information. After these few weeks of

reading, watching videos, and gathering information for these projects I believe I can create

visuals at a mid to higher lever. I really like how they speak what you are saying especially if

create right. I also would love to share these visual applications to people who I know would be

happy to use them instead of regular charts from Microsoft Excel. At the end of the day my

appreciation for visual information has grown quite a bit over these few weeks as well as my

understanding of it wanting to learn more.


References

Commission, U., & Magrogan, M. (2015). Job Patterns For Minorities And Women In Private

Industry, 2008 EEO-1 CBSA Aggregate by NAICS-3 Report - Data.gov. Catalog.data.gov.

Retrieved 22 January 2015, from http://catalog.data.gov/dataset/job-patterns-for-minorities-and-

women-in-private-industry-2008-eeo-1-cbsa-aggregate-by-nai-fb87f

News Apps Blog,. (2010). Quickly visualize and map a data set using Google Fusion Tables.

Retrieved 21 January 2015, from http://blog.apps.chicagotribune.com/2010/03/04/quickly-

visualize-and-map-a-data-set-using-google-fusion-tables/

Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information

visualizations. Proceedings 1996 IEEE Symposium On Visual Languages.

doi:10.1109/vl.1996.545307

Support.google.com,. (2015). About Fusion Tables - Fusion Tables Help. Retrieved 26 January

2015, from https://support.google.com/fusiontables/answer/2571232

Tableau Software,. (2015). Tableau Software. Retrieved 18 January 2015, from

http://www.tableausoftware.com/

http://catalog.data.gov/dataset/job-patterns-for-minorities-and-women-in-private-industry-2008-eeo-1-cbsa-aggregate-by-nai-fb87f

http://catalog.data.gov/dataset/job-patterns-for-minorities-and-women-in-private-industry-2008-eeo-1-cbsa-aggregate-by-nai-fb87f

http://blog.apps.chicagotribune.com/2010/03/04/quickly-visualize-and-map-a-data-set-using-google-fusion-tables/

http://blog.apps.chicagotribune.com/2010/03/04/quickly-visualize-and-map-a-data-set-using-google-fusion-tables/

http://www.tableausoftware.com/


Appendix A

Women in Computer & Electronic Product Manufacturing Dashboard from Tableau

Documents

Are Jobs Evenly Spread Out Amongst Women in the US_SAD Reearch Paper