By 2025, forecasts suggest that there will be more than 75 billion Internet of Things (IoT) connected devices in use. This would be a nearly threefold increase from the IoT installed base in 2019.

What is the Internet of Things?

The IoT refers to a network of devices that are connected to the internet and can “communicate” with each other. Such devices include daily tech gadgets such as the smartphones and the wearables, smart home devices such as smart meters, as well as industrial devices like smart machines. These smart connected devices are able to gather, share, and analyze information and create actions accordingly. By 2023, global spending on IoT will reach 1.1 trillion U.S. dollars.

How does Internet of Things work?

IoT devices make use of sensors and processors to collect and analyze data acquired from their environments. The data collected from the sensors will be shared by being sent to a gateway or to other IoT devices. It will then be either sent to and analyzed in the cloud or analyzed locally. By 2025, the data volume created by IoT connections is projected to reach a massive total of 79.4 zettabytes.

Privacy and security concerns 

Given the amount of data generated by IoT devices, it is no wonder that data privacy and security are among the major concerns with regard to IoT adoption. Once devices are connected to the Internet, they become vulnerable to possible security breaches in the form of hacking, phishing, etc. Frequent data leaks from social media raise earnest concerns about information security standards in today’s world; were the IoT to become the next new reality, serious efforts to create strict security stands need to be prioritized.

This feature layer includes all OPM collected data at the town level.-------------The Connecticut Broadband Availability and Adoption Maps were created to help citizens and policymakers understand the strengths and weaknesses of broadband infrastructure in the state. Data is aggregated to the block, tract, and town (county subdivision) levels and includes counts of locations classified as unserved, underserved, and served as well as whether they meet the state goal of 1000Mbps/100Mbps. This application splits its visualizations into block, tract, and town layers for both unserved locations and progress to the state goal. This map uses OPM collected availability and adoption data. As of 2023, OPM collected availability data was submitted by internet service providers pursuant to PA 21-159 and processed by the GIS Office in the Office of Policy and Management, cleaned, and matched to the CostQuest _location fabric. Metadata: All feature layers, maps, and datasets including OPM's internal broadband availability data follows the same basic schema with additional fields added in some case for convenience. Fields named no service, unserved, underserved, served, and GigC are counts of locations where a particular level of broadband service is provided, No service locations are those where there is no reported service at all. Unserved locations are locations where there is a provider offering wireline service, but not at or above 25 Mbps download and 3 Mbps upload. Underserved locations are locations where at least one provider offers wireline service of 25 Mbps download and 3 Mbps upload, but there is no provider offering wireline service of 100 Mbps download and 20 Mbps upload. Served locations are locations where there is wireline service of at least 100 Mbps download and 20 Mbps upload. GigC denotes the count of locations that have service at 1000 Mbps download and 100 Mbps upload. Accordingly, total locations is equal to the sum of no service, unserved, underserved, served, and "GigC" locations. Availability also includes fields for average download and upload speeds. These are calculated at the relevant level of census geography based on the maximum for all locations. The final field included in all availability data is the provider list. OPM collected adoption data: OPM collected adoption data uses many of the same naming conventions as the availability data, but there are some notable differences. Fields named unserved_Sub, underserved_Sub, served_Sub, and GigC _Sub are counts of subscriptions where a particular level of broadband service is currently subscribed to, Unserved subscriptions are subscriptions that do not meet the standard of 25 Mbps download and 3 Mbps upload. Underserved subscriptions are subscriptions with speeds of 25 Mbps download and 3 Mbps upload, but not meeting 100 Mbps download and 20 Mbps upload. Served subscriptions are subscriptions where speeds are between 100 Mbps download and 20 Mbps upload and 1000 Mbps download and 100 Mbps upload. GigC denotes the count of locations that have a subscription at 1000 Mbps download and 100 Mbps upload or higher. For subscription data these locations are NOT included in the "served" field as this does not directly apply to FCC use of the terms.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..Examples of "Internet access without a subscription" include cases such as free Internet service provided by a respondent's town or city or free Internet service a university may provide for their students.."Internet access" refers to whether or not a household uses or connects to the Internet, regardless of whether or not they pay for the service to do so. Data about Internet access was collected by asking if the respondent or any member of the household accessed the Internet. The respondent then selected one of the following three categories: "Yes, by paying a cell phone company or Internet service provider"; "Yes, without paying a cell phone company or Internet service provider"; or "No access to the Internet at the house, apartment or mobile home". Only respondents who answered "Yes, by paying a cell phone company or Internet service provider" were asked the subsequent question about the types of service they had access to such as dial-up, broadband (high speed) service such as cable, fiber-optic, or DSL, a cellular data plan, satellite or some other service..In 2016, changes were made to the computer and Internet use questions, involving the wording as well as the response options. A crosswalk was used to map pre-2016 data to the post-2016 categories, enabling creation of 5-year data. For more detailed information about the 2016 changes, see the 2016 American Community Survey Content Test Report for Computer and Internet Use located at https://www.census.gov/library/working-papers/2017/acs/2017_Lewis_01.html or the user note regarding changes in the 2016 questions located at https://www.census.gov/programs-surveys/acs/technical-documentation/user-notes/2017-03.html. For more detailed information about the crosswalk, see the user note regarding the crosswalk located at https://www.census.gov/programs-surveys/acs/technical-documentation/user-notes.html..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the es...

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

Context

Microblog sites are environments where people follow people. With this feature, a microblog site is a convenient environment for spreading an opinion or introducing a new product. The key point is determination of individuals who maximize the spreading. This problem is known as Influence Maximization (IM) and has attracted attention of many researchers. Many studies in the literature have modeled IM problem on graphs for different propagation models such as Independent Cascade (IC) and Linear Threshold (LT). However, microblogs like Twitter have their own features. Many works on IM in Twitter derive new metrics from user and tweet features; apply a greedy approach for selecting influencers. We adopted different approach for IM problem, and we dealt it as a classification problem. Firstly, we collected data on International Women Day 2018; empirically we labeled the users as either influencer candidates or non-influencers; then we applied classification methods for classifying users into one class with using features of users. By this way, we obtained an influencer candidates set, which is very smaller than entire dataset.

Content

Twitter provides enhanced APIs (Application Programming Interface) in order to collect, query, and update its data programmatically. Since we need a daily topic for this study, we have decided to fetch the tweets related to “International Women’s Day” which is a global event and celebrated on March 8 every year. On this day, a service implemented based on Java programming language which utilizes twitter4j1, an open source Java library for Twitter APIs, is executed in order to collect tweets through the Twitter Streaming API. Three keywords which are directly related to “International Women’s Day” and amongst to the trending topics about this day namely “InternationalWomensDay”, “IWD2018”, and “WomensDay” are used to filter streaming tweets. The language of tweets which are fetched through the Twitter Streaming API is set to English. With considering the time differences amongst on various continents, the developed service has run for 48 hours. During this time, 219,076 tweets have collected from 168.168 unique Twitter users.

How to cite?

If you use these datasets please cite the following paper: Şimşek, M , Kabakuş, A . (2018). Finding Influencers on Twitter with Using Machine Learning Classification Algorithms. Gazi Mühendislik Bilimleri Dergisi (GMBD) , 4 (3) , 183-196 . Retrieved from https://dergipark.org.tr/tr/pub/gmbd/issue/41439/468269. DOI: 10.30855/GJES.2018.04.03.005

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.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 number of internet users in countries like the Americas and Asia.

This collection comprises data covering meteorology, physical oceanography, transport of water, biogeochemistry, and parameters relevant to the carbon cycle, ocean acidification, the ecosystem, and geophysics. The data are collected from long-term, high-frequency observations at fixed locations in the open ocean. OceanSITES data are stored in netCDF files conforming to the OceanSITES Data Format Reference Manual. OceanSITES is a worldwide system of long-term, open-ocean reference stations measuring dozens of variables and monitoring the full depth of the ocean from air-sea interactions down to the seafloor. It is a network of stations or observatories measuring many aspects of the ocean's surface and water column using, where possible, automated systems with advanced sensors and telecommunications systems, yielding high time resolution, often in real-time, while building a long record.

This feature layer provides all OPM collected data at the tract level.------The Connecticut Broadband Availability and Adoption Maps were created to help citizens and policymakers understand the strengths and weaknesses of broadband infrastructure in the state. Data is aggregated to the block, tract, and town (county subdivision) levels and includes counts of locations classified as unserved, underserved, and served as well as whether they meet the state goal of 1000Mbps/100Mbps. This application splits its visualizations into block, tract, and town layers for both unserved locations and progress to the state goal.

This map uses OPM collected availability and adoption data.

As of 2023, OPM collected availability data was submitted by internet service providers pursuant to PA 21-159 and processed by the GIS Office in the Office of Policy and Management, cleaned, and matched to the CostQuest location fabric.

Metadata:

All feature layers, maps, and datasets including OPM's internal broadband availability data follows the same basic schema with additional fields added in some case for convenience.

Fields named no service, unserved, underserved, served, and GigC are counts of locations where a particular level of broadband service is provided, No service locations are those where there is no reported service at all. Unserved locations are locations where there is a provider offering wireline service, but not at or above 25 Mbps download and 3 Mbps upload. Underserved locations are locations where at least one provider offers wireline service of 25 Mbps download and 3 Mbps upload, but there is no provider offering wireline service of 100 Mbps download and 20 Mbps upload. Served locations are locations where there is wireline service of at least 100 Mbps download and 20 Mbps upload. GigC denotes the count of locations that have service at 1000 Mbps download and 100 Mbps upload. Accordingly, total locations is equal to the sum of no service, unserved, underserved, served, and "GigC" locations. Availability also includes fields for average download and upload speeds. These are calculated at the relevant level of census geography based on the maximum for all locations.

The final field included in all availability data is the provider list.

OPM collected adoption data:

OPM collected adoption data uses many of the same naming conventions as the availability data, but there are some notable differences.

Fields named unserved_Sub, underserved_Sub, served_Sub, and GigC _Sub are counts of subscriptions where a particular level of broadband service is currently subscribed to, Unserved subscriptions are subscriptions that do not meet the standard of 25 Mbps download and 3 Mbps upload. Underserved subscriptions are subscriptions with speeds of 25 Mbps download and 3 Mbps upload, but not meeting 100 Mbps download and 20 Mbps upload. Served subscriptions are subscriptions where speeds are between 100 Mbps download and 20 Mbps upload and 1000 Mbps download and 100 Mbps upload. GigC denotes the count of locations that have a subscription at 1000 Mbps download and 100 Mbps upload or higher. For subscription data these locations are NOT included in the "served" field as this does not directly apply to FCC use of the terms.

Data in this digital opportunity map comes from students' and families' answers to the Internet Access at Home Survey, which school districts use to gather data on home internet and learning device access for students in their districts. While this is an optional data collection, DPI encouraged districts to collect this information and push it to WISEdata to help drive statewide initiatives to improve digital learning opportunity in Wisconsin. Data is given in percentages to protect student privacy. View statewide digital opportunity data on the WISEdash Public Portal.The digital opportunity questions are the result of a coordinated effort with the Council of Chief State School Officers (CCSSO), Education SuperHighway, and the Ed-Fi Alliance (affiliated with the Dell Foundation). In May 2021, the US Department of Education added these questions as data elements to the Common Educational Data Standard (CEDS). CEDS is the federal government’s framework for all education data, adding significant validation to the questions and items. See the questions DPI provided to districts to use in their surveys here.

Long-term monitoring data of geomorphic, hydrological, and biological characteristics of landscapes. This information provides an effective means of relating observed change to possible causes of the change. Identification of changes in basin characteristics, especially in arid areas where the response to altered climate or land use is generally rapid and readily apparent, might provide the initial direct indications that factors such as global warming and cultural impacts have affected the environment. The Vigil Network provides an opportunity for earth and life scientists to participate in a systematic monitoring effort to detect landscape changes over time, and to relate such changes to possible causes. This data release includes 70 sites and basins used to monitor landscape features. This data release includes information for Vigil Network sites monitored in the United States. The data and information in this data release are historical and were obtained from original documents. This data release has been updated to include a table of summary characteristics.

This feature layer includes all data available at the block level - no adoption data is available at this level.

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The Connecticut Broadband Availability and Adoption Maps were created to help citizens and policymakers understand the strengths and weaknesses of broadband infrastructure in the state. Data is aggregated to the block, tract, and town (county subdivision) levels and includes counts of locations classified as unserved, underserved, and served as well as whether they meet the state goal of 1000Mbps/100Mbps. This application splits its visualizations into block, tract, and town layers for both unserved locations and progress to the state goal.

This map uses OPM collected availability and adoption data.

As of 2023, OPM collected availability data was submitted by internet service providers pursuant to PA 21-159 and processed by the GIS Office in the Office of Policy and Management, cleaned, and matched to the CostQuest location fabric.

Metadata:

All feature layers, maps, and datasets including OPM's internal broadband availability data follows the same basic schema with additional fields added in some case for convenience.

Fields named no service, unserved, underserved, served, and GigC are counts of locations where a particular level of broadband service is provided, No service locations are those where there is no reported service at all. Unserved locations are locations where there is a provider offering wireline service, but not at or above 25 Mbps download and 3 Mbps upload. Underserved locations are locations where at least one provider offers wireline service of 25 Mbps download and 3 Mbps upload, but there is no provider offering wireline service of 100 Mbps download and 20 Mbps upload. Served locations are locations where there is wireline service of at least 100 Mbps download and 20 Mbps upload. GigC denotes the count of locations that have service at 1000 Mbps download and 100 Mbps upload. Accordingly, total locations is equal to the sum of no service, unserved, underserved, served, and "GigC" locations. Availability also includes fields for average download and upload speeds. These are calculated at the relevant level of census geography based on the maximum for all locations.

The final field included in all availability data is the provider list.

OPM collected adoption data:

OPM collected adoption data uses many of the same naming conventions as the availability data, but there are some notable differences.

Fields named unserved_Sub, underserved_Sub, served_Sub, and GigC _Sub are counts of subscriptions where a particular level of broadband service is currently subscribed to, Unserved subscriptions are subscriptions that do not meet the standard of 25 Mbps download and 3 Mbps upload. Underserved subscriptions are subscriptions with speeds of 25 Mbps download and 3 Mbps upload, but not meeting 100 Mbps download and 20 Mbps upload. Served subscriptions are subscriptions where speeds are between 100 Mbps download and 20 Mbps upload and 1000 Mbps download and 100 Mbps upload. GigC denotes the count of locations that have a subscription at 1000 Mbps download and 100 Mbps upload or higher. For subscription data these locations are NOT included in the "served" field as this does not directly apply to FCC use of the terms.

The Oxford Internet Survey, 2019 (OxIS 2019) is a representative survey of British internet use in 2019. Data were collected via in-home interviews with respondents. It includes both internet users and non-users. The dataset contains almost 700 variables measuring internet activities, attitudes and effects.

Further information about the OxIS, including publications, is available from the Oxford Internet Surveys webpages.

Users should note the data are only available in Stata format.

This study is Open Access. It is freely available to download and does not require UK Data Service registration.

Data given in this domain are collected annually by the National Statistical Institutes and are based on Eurostat's annual model questionnaires on ICT (Information and Communication Technologies) usage in households and by individuals.

Large part of the data collected are used in the context of the follow up of the Digital Single Market process (Monitoring the Digital Economy & Society 2016-2021). This conceptual framework follows the 2011 - 2015 benchmarking framework, the i2010 Benchmarking Framework and the eEurope 2005 Action Plan. ICT usage data are also used in the Consumer Conditions Scoreboard (purchases over the Internet) and in the Employment Guidelines (e-skills of individuals).

The aim of the European ICT surveys is the timely provision of statistics on individuals and households on the use of Information and Communication Technologies at European level. Data for this collection are supplied directly from the surveys with no separate treatment.

Coverage:

The characteristics to be provided are drawn from the following list of subjects:

• access to and use of ICTs by individuals and/or in households,
• use of the Internet and other electronic networks for different purposes by individuals and/or in households,
• ICT security and trust,
• ICT competence and skills,
• barriers to the use of ICT and the Internet,
• perceived effects of ICT usage on individuals and/or on households,
• use of ICT by individuals to exchange information and services with governments and public administrations (e-government),
• access to and use of technologies enabling connection to the Internet or other networks from anywhere at any time (ubiquitous connectivity).

Breakdowns (see details of available breakdowns):

Relating to households:

• by region of residence (NUTS 1, optional: NUTS 2)
• by geographical location: less developed regions, transition regions, more developed regions
• by degree of urbanisation (till 2012: densely/intermediate/sparsely populated areas; from 2012: densely/thinly populated area, intermediate density area)
• by type of household
• by households net monthly income (optional)

Relating to individuals:

• by region of residence (NUTS1, optional: NUTS 2)
• by geographical location: less developed regions, transition regions, more developed regions
• by degree of urbanisation: (till 2012: densely/intermediate/sparsely populated areas; from 2012: densely/thinly populated area, intermediate density area)
• by gender
• by country of birth, country of citizenship (as of 2010, optional in 2010)
• by educational level: ISCED 1997 up to 2013 and ISCED 2011 from 2014 onwards.
• by occupation: manual, non-manual; ICT (coded by 2-digit ISCO categories)/non-ICT (optional: all 2-digit ISCO categories)
• by employment situation
• by age (in completed years and by groups)
• legal / de facto marital status (2011-2014, optional)

Regional breakdowns (NUTS) are available only for a selection of indicators disseminated in the regional tables in Eurobase (Regional Information society statistics by NUTS regions (isoc_reg):

• Households with access to the internet at home
• Households with broadband access
• Individuals who have never used a computer
• Individuals who used the internet, frequency of use and activities
• Individuals who used the internet for interaction with public authorities
• Individuals who ordered goods or services over the internet for private use
• Individuals who accessed the internet away from home or work

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This dataset contains all of the programs and the unrestricted data used for our research:Reversing the U: new evidence on the Internet and democracy. The relationship between the internet and democratic developments has long been a controversial topic, hampered in part by the lack of empirical evidence. This study is undertaken to investigate the effects of Internet penetration on democratization based on the panel data of 125 countries gathered from 1993 to 2014. The authors apply machine learning method (i.e. random forest) to effectively screen the variables that are more closely related to democracy. The results of different estimation models reveal an inverted U-shaped relationship between Internet penetration and democratization, and also distinguish the impacts of the Internet on advanced and less advanced democracies. Then, we arrive at the conclusion that Internet penetration brings a late-starting advantage in the development of democracy for less advanced democracies. These conclusions are further confirmed by robust test.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show populations with computer and internet access by Regional Commission in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

TotalHH_e

# Total households, 2017

TotalHH_m

# Total households, 2017 (MOE)

WithAComputer_e

# Households with a computer, 2017

WithAComputer_m

# Households with a computer, 2017 (MOE)

pWithAComputer_e

% Households with a computer, 2017

pWithAComputer_m

% Households with a computer, 2017 (MOE)

WithBroadband_e

# Households with broadband Internet, 2017

WithBroadband_m

# Households with broadband Internet, 2017 (MOE)

pWithBroadband_e

% Households with broadband Internet, 2017

pWithBroadband_m

% Households with broadband Internet, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This dataset consists of data of different types of television shows based on video games from years mentioned in the title. The data has been used in articles and conference presentations before (e.g. Kerttula 2019; Kerttula 2020). The data is free to use in any future publications with proper references to the author and the original data. Should the data be used in further research, it is to be noted that the dataset is not 100% complete. The reasons to this are difficulties with language and cultural barriers. It also needs to be mentioned, that some of the television shows and production companies have probably being forgotten over time, which means that a complete list would quite likely prove to be very difficult to gather. Some of the data included is missing classification information. This is because in these cases, the data needed was not available or hard to figure out. For example, the time slot data was missing for these shows, or there was not enough information available to make conclusions about the structure of the show. This applies only for a handful of shows, however. This data does not compromise or endanger any copyrights or personal information. All the data gathered here is publically available from different internet sources. No personal information, such as addresses, phone numbers or contact persons was recorded in the data. Some shows feature episodes from video depositories around internet, but if the production company wants to take the episodes offline, it does not harm the dataset.

Introduction

This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.

NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

Datasets

The firts dataset was colleted to train the detection models (D1) and other collected using different attacks than those used in training to test the models and ensure their generalization (D2).

The datasets contain both benign and malicious traffic. All collected datasets are balanced.

The version of NetFlow used to build the datasets is 5.

    Dataset
    Aim
    Samples
    Benign-malicious
    traffic ratio




    D1
    Training
    400,003
    50%


    D2
    Test
    57,239
    50%

Infrastructure and implementation

Two sets of flow data were collected with DOROTHEA. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor ipt_netflow installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.

DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)

Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).

The malicious traffic collected (SQLI attacks) was performed using SQLMAP. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.

The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.

    Parameters
    Description




    '--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'
    Enumerate users, password hashes, privileges, roles, databases, tables and columns


    --level=5
    Increase the probability of a false positive identification


    --risk=3
    Increase the probability of extracting data


    --random-agent
    Select the User-Agent randomly


    --batch
    Never ask for user input, use the default behavior


    --answers="follow=Y"
    Predefined answers to yes

Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the MYSQL or SQLServer database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).

The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24. The malicious traffic in the test sets was collected under different conditions. For D1, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.

However, for D2, BlindSQL SQLIAs were performed against the web form connected to a PostgreSQL database. The IP address spaces of the networks were also different from those of D1. In D2, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.

To run the MySQL server we ran MariaDB version 10.4.12. Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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 population share with mobile internet access in countries like Caribbean and Europe.