The Promise, the Trouble & the Question

3. GEOGRAPHICALLY WEIGHTED REGRESSION

18 46 58 65 77 130Income

Income Non High School Diploma 0 0.05 0.19Non High School Diploma 34 630 Non White 75 80 100 Broadband Per Household Non White Population Density Less Than 18 0 0.01 0.15 Population Density

Income

EDUCATION

%Income Percentage of People over the age of 25 without a High School Diploma Broadband Per Household

3 5 8 11 55 Broadband Per Household Non High School DiplomaNon High School Diploma Less Than 180.02 0.11 0.35

0

Non White 0.05 0.23 Less Than 18 Non White

SENIORS

Percentage of Households with Seniors over the age of 65% 1.3 1.7 2.1 2.6 5.2 Over 65 Over 65 Population DensityPopulation Density Unemployment Unemployment 0.02 0 0.09 0.05 0.24 0.40

Income

GWR . Download Speed

MINORITY

Percent of Non-White Households

% 2 5 Broadband Per HouseholdBroadband Per Household Non High School Diploma 8 15 58 Less Than 18 Non White Less Than 18

Income Non High School Diploma 0.008 0.05 0.22

0

Over 65 Population Density 0.02 0.09 Non White Over 65

The influence of educational attainment is illustrated with more clarity in the following set of maps. Educational levels have the highest correlation with the reported download Internet speeds. This connection could hint to overlapping forms of exclusion, which can only be exacerbated by the sudden transition we have experienced to online forms of learning and working.

However, the remaining local R-squared maps show that in block groups with low values, the GWR model is performing poorly, and other variables need to be studied and included to refine it.

Broadband Per Household Less Than 18

Broadband Per Household

CHILDREN

%Income Percent of Households with Children Over 65 under the age of 18

1.3 1.8 2.1 2.4 3.7 Less Than 18Non High School Diploma

Unemployment 0.007 0.05

Over 65 0.19

0 0.02 0.10

Non White

UNEMPLOYMENT

Percent of People Unemployed% 0 2.1 3.6 7.0 32 UnemploymentUnemployment

Population Density

POPULATION DENSITY

Population per square-mile in each block group # 7020 760 90001900140 Unemployment Population Density 0.02 0.08 0.32

0 0.05 0.12

0.02 0.12 0.51

0 0.06 0.26

The Covid-19 pandemic has proven that Internet use and reliability are foundational not only to work, shop or play but also to learn. Lack of accessibility to this service in certain areas can impose a considerable burden on the education of kids and teenagers, and erase the gains of years of social policy that aimed to bridge pre-existing disparities.

Among our main findings, we discovered that areas with low access to broadband providers are collocated with areas where a larger proportion of adults have no high school diploma, which could increase educational (and socioeconomic) gaps in the new hybrid education model the U.S. and the rest of the world are experiencing.

While these issues tend to be concentrated in lowerincome households, they are not necessarily limited to them. As we observed in the results, population density has the highest spatial correlation with the number of broadband providers per block group. In consequence, residents of less dense areas, usually in rural zones of the state, can also experience higher difficulties to access reliable broadband connections.

As broadband has become an essential service for economic mobility and social opportunities, lowincome families living in less dense counties need urgent access to high-speed, reliable connections. Further research could be aimed at understanding the affordability variable of Internet accessibility. Households living in poverty not only need fast connections and a range of providers to choose from, but also a broadband service provided at an affordable price. Overlapping forms of social differentiation could further drive apart connectivity rates in the two counties we analyzed. Our approach can allow policymakers to understand unserved markets and develop specific public policies to foster connectivity in specific geographic areas or for people who lack adequate connectivity. At the same time, our methodology highlighted several concerns of measuring accessibility - usually operationalized as a distance to a destination, instead of the possibility to connect to a public service. We added a different angle to this concept, using the spatial distribution of Internet providers and the rate of connected households as ways to pin down the supply and demand of broadband services.

However, this research can be expanded with additional questions about dispersion and density of households and population, using the conceptual dichotomy of ‘urban vs rural’ to determine the effects of density to Internet connectivity at a larger (e.g., statewide) scale. On a different scale (the neighborhood level, perhaps) issues of segregation may arise when some communities are compared to others.

Understanding the nuances of these problems, both in their methodological development as well as in their spatial materiality, will be a crucial element of expanding infrastructure and encouraging a more equitable distribution of resources to connect communities.

@discoversavsat | Unsplash.com

Ookla. (2021). Speedtest® by Ookla® Global Fixed and Mobile Network Performance Maps. Based on analysis by Ookla of Speedtest Intelligence® data for January 1, 2020, to April 1, 2020. [shapefile] Provided by Ookla and retrieved on May 2021 from: https://github.com/teamookla/ookla-open-data

U.S. Federal Communications Commission. (2021). Fixed Broadband Deployment Data: December 2019, New York State.[dataset] Retrieved April 2021 from: https://opendata.fcc.gov/Wireline/FixedBroadband-Deployment-Data-December-2019/ whue-6pnt

U.S. Census Bureau. (2019). 2019 TIGER/ Line Shapefiles: Block Groups. [shapefile] Retrieved April 2021 from: https://www. census.gov/cgi-bin/geo/shapefiles/index. php?year=2019&layergroup=Block+Groups

U.S. Census Bureau. (2021). 2015-2019 American Community Survey 5-year estimates [dataset]. Retrieved April 2021 from Social Explorer. Bauer, S., Clark, D. D., and Lehr, W. (2010) Understanding Broadband Speed Measurements. Massachusetts Institute of Technology (MIT). TPRC 2010, Available at SSRN: https://ssrn.com/ abstract=1988332

Beynon, M.J., Crawley, A., and Munday, M. (2016) “Measuring and understanding the differences between urban and rural areas.” Environment and Planning B: Planning and Design, Vol. 43(6): 11361154.

Tomer, A., Kneebone, E., and Shivaram, R. (2017) Signs of digital distress: Mapping broadband availability and subscription in American neighborhoods. Brookings Institution. https://www. brookings.edu/research/signs-of-digital-distressmapping-broadband-availability/

Advanced Spatial Analysis . Spring 2021 Professor Leah Meisterlin

Columbia University, Graduate School of Architecture, Planning and Preservation MS Urban Planning