Getis-Ord GI

Income 18 46 58 65 77 130Income Non High School Diploma 34 63 75 80 1000 Non High School DiplomaBroadband Per Household Non White

To gain a better understanding of the demographic make-up of the study areas, we first investigate social vulnerability indicators at the block level.

Drawing upon the CDC’s Social Vulnerability Index, we developed our own index comprised of the following census variables:

1. Median Household Income 2. Educational Attainment: Age 25+ without a highschool diploma 3. Children: Population age less than 18 4. Seniors: Population age 65 and above 5. Minority: Non-white Population 6. Unemployment 7. Household Broadband Subscription 8. Population Density: people per square mile

We understand that selecting different social vulnerability indicators is a subjective task, and that adding or removing certain variables could lead to different results. We view our selection as a crossover between commonly accepted indicators and additional variables such as levels of household broadband subscription that are uniquely relevant to this particular study.

Income

EDUCATION

Percentage of People over the age of 25 without a High School Diploma% 3 5 8 11 55 Broadband Per Household Broadband Per Household Less Than 18Non High School Diploma Non WhiteIncome Non High School Diploma

Income Broadband Per Household

MINORITY

%Less Than 18Broadband Per Household Non High School Diploma Percent of Non-White Households

2 5 8 15 58 Less Than 18 Non White Over 65

Income Non High School Diploma

Broadband Per Household

Income

CHILDREN

Less Than 18

Broadband Per Household Percent of Households with Children Over 65 under the age of 18

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

SENIORS

Percentage of Households with Seniors over the age of 65% 1.3 1.7 2.1 2.6 5.2 Less Than 18 Over 65 Population DensityNon White Over 65 Population Density

UNEMPLOYMENT

Percent of People Unemployed% 0 2.1 3.6 7.0 32 UnemploymentOver 65 Population Density

Non White Unemployment

Population Density

POPULATION DENSITY

Population per square-mileUnemployment # in each block group Over 65 20 70 140 760 90001900 Population Density Unemployment

We employed three methods to understand household accessibility to broadband Internet in Ontario and Livingston. As explained in the previous section, we defined accessibility in terms of availability (number of consumer broadband providers) and quality (average download speed) per block group. These methods strive to determine spatial clusters of high and low broadband accessibility, as well as the relationship between fixed Internet access and the social vulnerability variables defined earlier. We conducted this test to identify statistically significant clusters of hot and low values, determined by high and low z-scores, respectively.

Larger z-scores indicate clustering of high values of broadband providers and reported download Internet speeds around specific block groups, while smaller scores represent clustering of low values.

We first ran this test for each of the counties individually, and then for the two geographies combined. The results of the latter approach show that there are hotspots of Internet providers and reported speeds for Ontario county, and analogous coldspots for Livingston county.

As a way to contrast the results from the Getis-Ord GI* method, we employed the Anselin Local Moran’s I test to determine whether individual block groups were clustered around high and low values, and to identify potential spatial outliers.

The Locals Moran’s I method was used to compare previous hotspot findings and find clusters of block groups of outliers containing high/low numbers of broadband providers and reported download speed.

2. ANSELIN LOCAL MORAN’S I

NUMBER OF PROVIDERS

AVERAGE DOWNLOAD SPEED

NUMBER OF PROVIDERS

AVERAGE DOWNLOAD SPEED

Broadband hot/cold spots of accessibility in terms of availability and quality varies throughout Livingston and Ontario County illustrate an uneven spatial distribution of these two variables. The significance in the clustering of providers is notoriously high, and points at underlying differences in broadband access that can be expanded in terms of income, race, education attainment, among other social vulnerability variables.

We obtained mixed results: the results from the Getis-Ord GI* seemed to be confirmed by the Local Moran’s I for the hotspots and coldspots of providers, indicating a cluster of high number of providers to the east of our case study, in Ontario county, and a significant low number of providers in western Livingston county. However, the test for the average download speed did not yield similar findings. Instead, the Local Moran’s I indicated a number of outliers in both counties.

Cold Spot Level of Significance

99% 95% 90% n.s. 90% 95% 99%

High-Low Outlier High-High Cluster Low-Low Cluster Low-High Outlier Not Significant