No More “Half-Fast”: Improving US Broadband Download Speed

Georgetown University – 2015 Data Science Capstone Brittne Nelson PhD, Amgad Sirag, Ernest S.

Approach and Overview What? •  Broadband Data Story •  Research Problem •  Data Science Pipeline

So What? •  Data Visualization Story •  Findings •  Lessons Learned

Now What? •  Future Research •  Conclusions

WHAT?

There were communities with no broadband access

Every day, residents and businesses had limited or no access to resources, services, content, new customers, and new technology limiting opportunities and community empowerment

One day, the US government created the SBI to facilitate the integration of broadband and information technology into state and local economies

Because of that, states did more to quickly expand broadband to more areas

Because of that, the SBI, decision makers, and researchers- including us- were able to assess how broadband is being implemented across the US

Until finally, residents and businesses gained more access to resources services, content, new customers, and technology that empowered and gave them a competitive edge

Data Story

Benefits of Broadband •  Increased job opportunities •  Increased employment opportunities due to telework •  Higher pay •  Increased economic security •  Recruitment of job seekers, especially in rural areas •  Increased access to and quality of healthcare •  Availability of a wide variety of entertainment •  Increased participation in everyday economic, social, and community life •  Improved social connections to existing friends and acquaintances •  Creation of new relationships based on common interests •  Improved social integration of minority populations •  More positive attitudes toward aging •  Higher levels of perceived social support and connectivity among seniors •  Lower prices for online purchases •  Improved variety of items available for purchase •  Better purchasing decisions based on online information •  Savings in time and money for online vs. paper-based activities •  Improved connectivity for social or political action

Sources: Center for Social Inclusion,. (2010). The Promise and Challenge of Community Broadband Models. New York City: Center for Social Inclusion. Analytics ASR,. (2014). Final Report: Social and Economic Impacts of the Broadband Technology Opportunities Program. Potomac Maryland.

Research Problem •  Does  broadband  availability  and  speed  make  a  state’s  economy  and  it’s  residents  compe55ve?  

•  When  will  every  state  reach  98%  broadband  connec5vity?    

•  How  are  community  economic  features  impac5ng  or  related  to  broadband  development?

Hypotheses •  Broadband  speed  and  accessibility  will  cluster  in  urban  areas  

•  Areas  with  more  broadband  speed  will  have  lower  unemployment,  more  businesses,  and    larger  popula5ons    

•  Broadband  growth  is  not  consistent  across  all  coun5es  

•  Based  on  past  growth,  broadband  coverage  is  not  expected  to  be  available  in  98%  of  all  coun5es  in  2016  

Data Sources •  Na5onal  Broadband  Map  Maximum  and  Minimum  Download  Speed  by  

County,  June  2011-­‐June  2014  –  Na5onal  Telecommunica5ons  and  Informa5on  Administra5on  

hRp://www.broadbandmap.gov/data-­‐download      

•  Labor  Force  Data  by  County  Annual  Average,  2011-­‐2013  –  U.S.  Department  of  Labor  Local  Area  Unemployment  Sta5s5cs  

hRp://www.bls.gov/lau/      •  Demographic  Popula5on  by  County,  2010  

–  U.S  Census  Bureau  hRp://fac[inder.census.gov/faces/nav/jsf/pages/index.xhtml    

•  Total  Number  of  Business  Establishments,  2011-­‐2012    –  U.S  Census  Bureau  

hRp://fac[inder.census.gov/faces/nav/jsf/pages/index.xhtml    

Resources

Data Science Pipeline

Sources: Ojeda, T., Murphy, S., Bengfort, B., & Dasgupta, A. (2014). Practical data science cookbook. Birmingham: Packt Publishing.

SO WHAT?

Switch to Tableau Data Visualization

Hypotheses  Results •  Broadband  speed  and  accessibility  will  cluster  in  urban  areas  

•  “URBAN”  TOO  DIFFICULT  TO  DEFINE  GIVEN  PROJECT  TIMELINE,  NOT  ANALYZED  

•  Areas  with  higher  broadband  speed  have  lower  unemployment,  more  businesses,  and  larger  popula5ons  •  NOT  TRUE    

•  Broadband  growth  is  not  consistent  across  all  coun5es  •  TRUE  

•  Based  on  past  growth,  broadband  coverage  is  not  expected  to  be  available  in  98%  of  all  coun5es  in  2016    •  NOT  ENOUGH  DATA  TO  COMFORTABLY  FORECAST  

Summary of Findings •  Iden5fied  economic  features  are  mild  drivers  of  technology  implementa5on  specifically  broadband  speed.  

•  Broadband  availability  makes  a  state  economy  and  it’s  residents  compe55ve.  

•  Implemen5ng  broadband  is  not  the  silver  bullet  to  community  development  or  economic  growth,  it  should  be  incorporated  with  other  economic  and  social  features.  

Lessons Learned •  Quantity of data is important for forecasting •  Source of data is important. SBI reports data from

providers which makes it somewhat difficult to assess

•  Plan a significant amount of time for data wrangling

•  Master each step of the data science pipeline before moving on

•  Operationalize more factors to provide a clear picture of relationships when identifying hypotheses

NOW WHAT?

Future Research •  Develop a matched pairs analysis framework that

compares changes in the availability of broadband at the state level between counties

•  Measure how much of the growth in availability within these counties occurred due to funding (Grants, Federal Government, Private Organizations)

•  Examine broadband’s long-term quantitative extrapolations and impact on social and economics

•  Index and model additional community factors such as education, adoption, tax rate, etc in order to broadly define economic impact

Conclusions •  There is a business case for continued focus on

broadband improvement

•  Broadband improves the overall communities •  Drives economic development and shared

opportunities •  Improve quality of life across the United

States

Thank You to the Georgetown University 2015 Data Science Program Faculty

Benjamin Bengfort Allen Leis

Sacha Litman Laura Lorenz Salil Mehta Tony Ojeda

(and lady!)

Questions?

ADDENDUM: TABLEAU STORY