The percentage of households without an internet subscription at home.Source: American Community Survey Years Available: 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

The layer was derived and compiled from the U.S. Census Bureau’s 2013 – 2017 American Community Survey (ACS) 5-Year Estimates in order to assist 2020 Census planning purposes.

Source: U.S. Census Bureau, Table B28011 INTERNET SUBSCRIPTIONS IN HOUSEHOLD, 2013 – 2017 ACS 5-Year Estimates

Effective Date: December 2018

Last Update: December 2019

Update Cycle: ACS 5-Year Estimates update annually each December. Vintage used for 2020 Census planning purposes by Broward County.

The layer was derived and compiled from the U.S. Census Bureau’s 2013 – 2017 American Community Survey (ACS) 5-Year Estimates in order to assist 2020 Census planning purposes.

Source: U.S. Census Bureau, Table B28011 INTERNET SUBSCRIPTIONS IN HOUSEHOLD, 2013 – 2017 ACS 5-Year Estimates

Effective Date: December 2018

Last Update: December 2019

Update Cycle: ACS 5-Year Estimates update annually each December. Vintage used for 2020 Census planning purposes by Broward County.

US Broadband Usage Across Counties

Utilizing Microsoft's Data to Estimate Access

By Amber Thomas [source]

About this dataset

This dataset provides an estimation of broadband usage in the United States, focusing on how many people have access to broadband and how many are actually using it at broadband speeds. Through data collected by Microsoft from our services, including package size and total time of download, we can estimate the throughput speed of devices connecting to the internet across zip codes and counties.

According to Federal Communications Commission (FCC) estimates, 14.5 million people don't have access to any kind of broadband connection. This data set aims to address this contrast between those with estimated availability but no actual use by providing more accurate usage numbers downscaled to county and zip code levels. Who gets counted as having access is vastly important -- it determines who gets included in public funding opportunities dedicated solely toward closing this digital divide gap. The implications can be huge: millions around this country could remain invisible if these number aren't accurately reported or used properly in decision-making processes.

This dataset includes aggregated information about these locations with less than 20 devices for increased accuracy when estimating Broadband Usage in the United States-- allowing others to use it for developing solutions that improve internet access or label problem areas accurately where no real or reliable connectivity exists among citizens within communities large and small throughout the US mainland.. Please review the license terms before using these data so that you may adhere appropriately with stipulations set forth under Microsoft's Open Use Of Data Agreement v1.0 agreement prior to utilizing this dataset for your needs-- both professional and educational endeavors alike!

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How to use the dataset

How to Use the US Broadband Usage Dataset

This dataset provides broadband usage estimates in the United States by county and zip code. It is ideally suited for research into how broadband connects households, towns and cities. Understanding this information is vital for closing existing disparities in access to high-speed internet, and for devising strategies for making sure all Americans can stay connected in a digital world.

The dataset contains six columns: - County – The name of the county for which usage statistics are provided. - Zip Code (5-Digit) – The 5-digit zip code from which usage data was collected from within that county or metropolitan area/micro area/divisions within states as reported by the US Census Bureau in 2018[2].
- Population (Households) – Estimated number of households defined according to [3] based on data from the US Census Bureau American Community Survey's 5 Year Estimates[4].
- Average Throughput (Mbps)- Average Mbps download speed derived from a combination of data collected anonymous devices connected through Microsoft services such as Windows Update, Office 365, Xbox Live Core Services, etc.[5]
- Percent Fast (> 25 Mbps)- Percentage of machines with throughput greater than 25 Mbps calculated using [6]. 6) Percent Slow (< 3 Mbps)- Percentage of machines with throughput less than 3Mbps calculated using [7].

Research Ideas

• Targeting marketing campaigns based on broadband use. Companies can use the geographic and demographic data in this dataset to create targeted advertising campaigns that are tailored to individuals living in areas where broadband access is scarce or lacking.
• Creating an educational platform for those without reliable access to broadband internet. By leveraging existing technologies such as satellite internet, media streaming services like Netflix, and platforms such as Khan Academy or EdX, those with limited access could gain access to new educational options from home.
• Establishing public-private partnerships between local governments and telecom providers need better data about gaps in service coverage and usage levels in order to make decisions about investments into new infrastructure buildouts for better connectivity options for rural communities

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: broadband_data_2020October.csv

Acknowledgements

If you use this dataset in your research,...

The percentage of households without an internet subscription at home. Source: American Community Survey Years Available: 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

The percentage of households with internet access in Eastern Europe was forecast to continuously increase between 2024 and 2029 by in total 6.9 percentage points. After the twenty-eighth consecutive increasing year, the internet penetration is estimated to reach 96.38 percent and therefore a new peak in 2029. Notably, the percentage of households with internet access of was continuously increasing over the past years.Depicted is the share of housholds with internet access in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the percentage of households with internet access in countries like Southern Europe and Northern Europe.

This dataset contains Iowa households by presence and type of internet subscriptions for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B28002.

Internet presence includes the following: Total, Yes, and No.

Internet subscription type includes the following: Total; All Subscriptions; Broadband - Cable Fiber Optic/DSL; Satellite Internet Service; Other Service, No Other Type; Broadband - Any Type; No Subscription; Broadband - Cable Fiber Optic/DSL, No Other Type; Cellular Data Plan; No Internet Access; Satellite Internet Service, No Other Type; Dial-up, No Other Type; and Cellular Data Plan, No Other Type.

Private dwellings and the internet including access to broadband, dial-up, other internet access and no internet access by LGA 2011, for the year 2011.

Percentage of Canadians who have access to the Internet at home.

This web map was created to show the number of households with no internet connection in the City of Dallas by census tract boundaries. Additionally, the City of Dallas "Neighborhood Associations" layer has been added to show the neighborhoods of the areas of interest. The map is symbolized to show the percentage of households with no internet connection.The web map feeds this web map application: Households with No Internet Access.The ACS layer used in this web map was created by Esri and is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Data Source: ACS Internet Connectivity Variables - Boundaries - Overview

Dataset Overview

This dataset was collected between September 2023 and September 2024 and focuses solely on Wi-Fi network performance for AT&T customers. The data includes critical network performance metrics such as download speeds, upload speeds, and the status of the internet connection (connected/disconnected) at various times of day. The dataset provides valuable insights into the performance of Wi-Fi networks across different environments, although the specific locations or device types are not specified.

Features:

Time: The exact timestamp (in UTC) when the measurement was taken. Download_Mbps: The download speed, measured in Megabits per second (Mbps), representing the speed at which data is received. Upload_Mbps: The upload speed, also in Megabits per second (Mbps), indicating the speed at which data is sent. Connected: A binary indicator (0 or 1) that shows whether the Wi-Fi was connected at the time of the test (1 = connected, 0 = disconnected).

Data Context:

This dataset is limited to Wi-Fi connections, representing the experiences of users across various environments such as homes, offices, or public spaces. Although the data lacks specific details about the geographic locations or device types, it can still provide meaningful insights into how Wi-Fi network performance fluctuates based on time and potential external factors. The absence of detailed source information suggests the data may have been gathered through an automated network monitoring tool, possibly as part of a routine diagnostic or performance test process within a specific set of environments using AT&T's services.

Possible Data Collection Scenario (Assumed):

The data could have been collected by Wi-Fi-enabled devices (such as laptops, smartphones, or routers) periodically running speed tests to measure both download and upload performance. These measurements likely occurred across a variety of settings, though no explicit details are provided about whether the environment is residential, commercial, or public. Given that the dataset focuses exclusively on Wi-Fi, it's reasonable to assume that this data could have been gathered either by end-users manually running tests or via automated diagnostics by a network monitoring system designed to track connectivity performance.

Limitations and Assumptions:

No Geographic Data: While the dataset includes performance metrics, it does not provide any information about the geographic locations or regions where the data was collected. Wi-Fi Focus: The data is solely related to Wi-Fi connections, and no other connection types (such as Ethernet or mobile data) are included. Unspecified Source: The source of the data is not clearly documented, which means the scope of the environments (home, public, or enterprise) and the methods of collection are unknown.

Potential Use Cases:

Wi-Fi Performance Analysis: This dataset can be used to analyze how download and upload speeds vary over time, which may reflect broader network trends such as congestion, interference, or router quality. Connectivity Prediction: Machine learning models can be developed to predict the likelihood of disconnections based on patterns in speed fluctuations. Time-Based Network Optimization: Telecom companies or network administrators can use the dataset to understand peak hours of usage and optimize network performance accordingly.

Insights to Explore:

Time of Day Analysis: Investigate whether certain times of day exhibit higher speeds or more frequent disconnections, potentially revealing periods of peak demand. Download vs. Upload Discrepancies: Explore how download and upload speeds differ and what this might imply for different types of internet usage (e.g., video streaming vs. video conferencing).

Network Reliability: Examine the proportion of time users were disconnected from the Wi-Fi and look for patterns that might suggest technical issues or environmental interference.

Smart homes contain programmable electronic devices (mostly IoT) that enable home au- tomation. People who live in smart homes benefit from interconnected devices by controlling them either remotely or manually/autonomously. However, high interconnectivity comes with an increased attack surface, making the smart home an attractive target for adversaries. NCC Group and the Global Cyber Alliance recorded over 12,000 attacks to log into smart home devices maliciously. Recent statistics show that over 200 million smart homes can be subjected to these attacks. Conventional security systems are either focused on network traffic (e.g., firewalls) or physical environment (e.g., CCTV or basic motion sensors), but not both. A key challenge in de- veloping cyber-physical security systems is the lack of datasets and test beds. For cyber-physical datasets to be meaningful, they need to be collected in real smart home environments. Due to the inherited difficulties and challenges (e.g. effort, costs, test-bed availability), such cyber-physical smart home datasets are quite rare. This paper aims to fill this gap by contributing a dataset we collected in a real smart home with annotated labels. This paper explains the process we followed to collect the data and how we organised them to facilitate wider use within research communities.A related article can be found at https://doi.org/10.3389/friot.2023.1275080

Discounts for Internet service through the Affordable Connectivity Program (ACP) ended June 1, 2024 due to lack of additional funding. Whether the program will receive additional funding in the future is uncertain. Please see ACP program information from the FCC for more details.The Affordable Connectivity Program (ACP) households data set summarizes household enrollments and subscriptions by month and zip code for beneficiary households located in Detroit zip codes. The Affordable Connectivity Program (ACP) is a U.S. government program to help low-income households pay for Internet services and connected devices. Households that participate in ACP receive discounts on qualifying broadband Internet services of up to $30 per month and can also receive a one-time discount of up to $100 to purchase a laptop, desktop computer, or tablet. Households can qualify for ACP based on participation in Lifeline or other service provider programs for low-income households, income at or below 200% of the federal poverty guidelines, participation in other Lifeline-qualifying programs such as SNAP or Medicaid, or participation in free and reduced-price school lunch and breakfast programs. Additionally, service providers can ask the FCC to approve an alternative verification process and use that approved process to check consumer eligibility. ACP program discounts first became available to eligible enrolled households on January 1, 2022. The ACP claims process is built on the Lifeline Claims System and this data set is derived from snapshots of all subscribers entered in the National Lifeline Accountability Database (NLAD) as of the first of each month. The ACP was created under the Infrastructure Investment and Jobs Act, also known as the Bipartisan Infrastructure Law, and is administered by the independent not-for-profit Universal Service Access Co. under the direction of the Federal Communications Commission (FCC). Eligible beneficiaries who participated in the Emergency Broadband Benefit (EBB) program that was funded by the Coronavirus Aid, Relief, and Economic Security (CARES) Act, were transitioned to ACP between January 1 and March 1, 2022. EBB was ACP's predecessor program and ran from May 12, 2021 until it was phased out on February 28, 2022. Due to the granularity of available data, households located in communities adjacent to Detroit that share a zip code such as Hamtramck and Highland Park are included in this data set.Fieldsprogram - Associated program for the data (ACP or EBB)data_month - Data month is associated with the subscriber snapshot for each claim month. If data month is listed as '5/1/2022', then the subscriber snapshot was captured on June 1, and the data represents the number of households in ACP as of June 1. This is the universe of subscribers that providers can claim for the May 2022 data month.zipcode - Zip code where the enrolled household is located.net_new_enrollments_alternative_verification_process - Difference between the current month Total Subscribers who qualified using an alternative verification process and prior month Total Subscribers who qualified using an alternative verification process.net_new_enrollments_verified_by_school - Difference between the current month Total Subscribers who qualified using school lunch program verification and prior month Total Subscribers who qualified using school lunch program verification.net_new_enrollments_lifeline - Difference between the current month Total Subscribers who qualified using the Lifeline program and prior month Total Subscribers who qualified using the Lifeline program.net_new_enrollments_national_verifier_application - Difference between the current month Total Subscribers who qualified using a National Verifier application and prior month Total Subscribers who qualified using a National Verifier application.net_new_enrollments_total - Difference between the total number of subscribers in the current and prior months. Calculated based on the sum of net new monthly enrollments verified by the school, lifeline, alternative verification process, and national verifier application programs.total_alternative_verification_process - Number of households in the ACP on the first of the month snapshot whose eligibility was determined via an FCC-approved alternative verification process. total_verified_by_school - Number of households in the ACP on the first of the month snapshot whose eligibility was verified based on participation in a school lunch program.total_lifeline - Number of households in the ACP on the first of the month snapshot whose eligibility was determined based on participation in Lifeline, a federal program that lowers the monthly cost of phone or Internet services.total_national_verifier_application - Number of households in the ACP on of the first of the month snapshot whose eligibility was determined via the National Eligibility Verifier (National Verifier) system.total_subscribers - Number of total households participating in ACP on the first of the month snapshot. If, for example, there were 100 subscribers enrolled as of the June 1, 2022 snapshot, then Total Subscribers for the 05/01/2022 (May 2022) data month would be 100.

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome:

This layer Technology Access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total households in given census tract.Layer includes:Key demographicsTotal Households % With an Internet subscription% Dial-up with no other type of Internet subscription% Broadband of any type% Cellular data plan% Broadband such as cable, fiber optic or DSL% Satellite Internet service% Without an Internet subscriptionCurrent Vintage: 2017-2021ACS Table(s): S2801 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: Dec 8, 2022Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

This statistic shows a ranking of the estimated number of households with internet access at home in 2020 in Africa, differentiated by country.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Context

https://images.unsplash.com/photo-1554098415-4052459dc340?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1398&q=80" width="400">

The data contains household internet connection information for European Union. The time span is from 2003 to current days.

The dataset contains a composite field and several year values.

The composite field is:

indic_is,unit,hhtyp,geo\time

The meaning of each component in the composed field is:

• indic_is - type of connection;
• unit - unit of measure - can be either % per household or % per person;
• hhtyp - household type;
• geo - geography - can be either country or EU region/period.

There are as well several years columns, from 2019 to 2003.

For the type of connection, the possible types are:

• H_BBFIX - Household internet connection type: fixed broadband;
• H_BBMOB - Household internet connection type: mobile broadband;
• H_BBOTH - Household internet connection type: other broadband;
• H_BROAD - Household internet connection type: broadband;
• H_DIALUP - Household internet connection type: modem or ISDN;
• H_DIALUP1 - Household internet connection type: dial-up access via normal telephone line or ISDN;
• H_DSL - Household internet connection type: DSL;
• H_MPHNAR - Household internet connection type: mobile phone over narrowband;
• H_MPHNAR - Household internet connection type: mobile phone over narrowband (as of 2014)

Extendend list of type of connections can be find on EU data portal, here.

For the household type, the possible values, according to the nomenclator are:

• F1_CH - Household composed of lone mother living with at least one child
• F1_YCH - Household composed of lone mother with at least one resident child under 25 years
• F1_OCH - Household composed of lone mother with at least one resident child 25 years or older
• P1_CH - Household composed of lone mother or father living with at least one child
• FAM_GE2 - Household composed of two-or-more-family nucleus
• NFAM - Household composed of non-family nucleus
• P1 - One-person household
• A1 - Household composed of one adult
• A1_DCH - Household composed of one adult with dependent children
• A2 - Household composed of two adults
• A2_DCH - Household composed of two adults with dependent children
• A_GE2_DCH - Household composed of two or more adults with dependent children
• A_GE3 - Household composed of three or more adults
• A_GE3_DCH - Household composed of three or more adults with dependent children
• MULTI - Multiperson household other than family nucleus
• DCH - Households with dependent children
• NDCH - Households without dependent children
• A1_GE65 - Household composed of one adult 65 years or over
• A2_GE1_GE65 - Household composed of two adults, at least one aged 65 years or over
• OTH - Other
• NRP - No response
• UNK - Unknown

Acknowledgements

The data provenance is EU Open Data Portal

Inspiration

Analyze the distribution of internet access per household type, connection type, country and years.

Context

House Price Prediction based on city zipcode...

Content

A home is often the largest and most expensive purchase a person makes in his or her lifetime. Ensuring homeowners have a trusted way to monitor this asset is incredibly important. The Zestimate was created to give consumers as much information as possible about homes and the housing market, marking the first time consumers had access to this type of home value information at no cost.

Acknowledgements

“Zestimates” are estimated home values based on 7.5 million statistical and machine learning models that analyze hundreds of data points on each property. And, by continually improving the median margin of error (from 14% at the onset to 5% today), Zillow has since become established as one of the largest, most trusted marketplaces for real estate information in the U.S. and a leading example of impactful machine learning.

Inspiration

Zillow Prize, a competition with a one million dollar grand prize, is challenging the data science community to help push the accuracy of the Zestimate even further. Winning algorithms stand to impact

This dataset contains Iowa households by household income in last 12 months, and presence and type of internet subscriptions for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B28004. Household income includes the following: Total; Less than $10,000; $10,000 to $19,000; $20,000 to $34,999; $35,000 to $49,999; $50,000 to $74,999; and $75,000 or more. Presence and type of internet service includes the following: Total, Dial-up Alone, Broadband, and No Subscription.

Private dwellings and the internet including access to broadband, dial-up, other internet access and no internet access by LGA 2011, for the year 2011.