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.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Australia & Oceania and Asia.

Worldwide mobile data pricing: The cost of 1GB of mobile data in 228 countries

Data from 5,554 mobile data plans in 228 countries were gathered and analysed by Cable.co.uk between 3 February and 25 February 2020. The average cost of one gigabyte (1GB) was then calculated and compared to form a worldwide mobile data pricing league table.

Source

https://www.cable.co.uk/mobiles/worldwide-data-pricing/#resources

Image Source

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Mexico and Canada.

About this dataset

Context

The dataset illustrates the median household income in Mobile, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Mobile decreased by $1,596 (3.19%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 4 years and declined for 7 years.

https://i.neilsberg.com/ch/mobile-al-median-household-income-trend.jpeg" alt="Mobile, AL median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile median household income. You can refer the same here

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

RNA half-life estimates from uniformly reprocessed/reanalyzed, published, high quality nucleotide recoding RNA-seq (NR-seq; namely SLAM-seq and TimeLapse-seq) datasets. 12 human cell lines are represented. Data can be browsed at this website.

Analysis notes:

• Data was processed using fastq2EZbakR. All config files used are provided in fastq2EZbakR_config.tar.gz (as well as some for data not included in the final RNAdecayCafe due to QC issues). Some general notes:
  • Multi-mapping reads were filtered out completely. It is difficult to do anything accurate/intelligent with such reads (see discussion here for instance), so better to just get rid of these completely. Does mean that some classes of features rich in repetitive sequences will be underrepresented in this database.
  • Adapters were trimmed. For 3'-end data, 12 additional nucleotides were trimmed from the 5' end of the reads, as suggested by the developers of the popular Quant-seq kit, and as is done by default in SLAMDUNK. Quality score end trimming and polyX trimming is also done for all samples.
• Data was analyzed using EZbakR. Some general notes:
  • If -s4U data was included, this data was used to infer a global pold to stabilize pnew estimation (see Methods here for brief discussion). Dropout was also corrected using a previously devleoped strategy now implemented in EZbakR's CorrectDropout() function.
  • Half-lives are estimated on a gene-level. That is, all reads that map to exonic regions of a gene (i.e., regions that are exonic in at least one annotated isoform) are combined and used to estimate a half-life for that gene. Thus, you should think of these half-life estimates as a weighted average over all isoforms expressed from that gene, weighted by the relative abundances of those isoforms. Future releases may include isoform-resolution estimates as well, given that EZbakR can now perform this type of analysis.
• A unique feature of RNAdecayCafe is that it includes what I am referring to as "dropout normalized" half-life and kdeg estimates. As not all datasets analyzed include -s4U data, dropout correction is not possible for all samples. This can lead to global biases in the average time scale of half-lives that is unlikely to represent real biology (that is, two different K562 datasets may have median half-life estimates of 4 hours and 15 hours). To address this problem and faciltiate comparison across datasets, I developed a strategy (implemented in EZbakR's NormalizeForDropout() function) that uses a model of dropout to normalize estimates with respect to a low dropout sample.
  • These "donorm" estimates will often be a more accurate reflection of rate constants and half-lives in a given cell line.
  • This strategy can normalize out real global differences in turnover kinetics though, so interpret these values with care.

Relevant data provided in this repository are as follows:

1. hg38_Strict.gtf: annotation used for analysis. Filtered similarly to how is described here.
2. AvgKdegs_genes_v1.csv: Table of cell-line average half-lives and degradation rate constants (kdegs). Average log(kdeg)'s are calculated for all samples from a given cell line, weighting by the uncertainty in the log(kdeg) estimate. Columns in this table are as follows:
   1. feature_ID: Gene ID (symbol) from hg38_Strict.gtf
   2. cell_line: Human cell line for which averages are calculated
   3. avg_log_kdeg: Weighted log(kdeg) average
   4. avg_donorm_log_kdeg: Weighted dropout normalized log(kdeg) average.
   5. avg_log_RPKM_total: Average log(RPKM) value from total RNA data. A value of exactly 0 means that there was no total RNA data for this cell line (i.e., all data was 3'-end data).
   6. avg_log_RPKM_3pend: Average log(RPKM) value from 3'-end data. Technically no length normalization is performed as this is 3'-end data, so it is really an log(RPM). A value of exactly 0 means that there was no 3'-end data for this cell line (i.e., all data was for total RNA).
   7. avg_kdeg: e^avg_log_kdeg
   8. avg_donorm_kdeg: e^avg_donorm_log_kdeg
   9. avg_halflife: log(2)/avg_kdeg; can be thought of as average lifetime of the RNA.
   10. avg_donorm_halflife: log(2)/avg_donorm_kdeg
   11. avg_RPKM_total: e^avg_log_RPKM_total
   12. avg_RPKM_3pend: e^avg_log_RPKM_3pend.
3. FeatureDetails_gene_v1.csv: Table of details about each gene measured; information comes from hg38_Strict.gtf and the corresponding hg38 genome FASTA file.
   1. seqnames: chromosome name
   2. strand: strand on which gene is transcribed
   3. start: genomic start position for gene (most 5'-end coordinate; will be location of TES for - strand genes).
   4. end: end position for gene
   5. type: all "gene" for now, as all analyses are currently gene-level average half-life calculations
   6. exon_length: length of union of exons for a given gene. A read is considered exonic, and thus used for half-life estimation, if it exclusively overlaps with the region defined by the union of all annotated exons for that gene.
   7. exon_GC_fraction: fraction of nucleotides in union of exons that are Gs or Cs.
   8. end_GC_fraction: fraction of nucleotides in last 1000 nts of 3'end of transcript that are Gs or Cs. Useful for assessing GC biases in 3'-end data.
   9. feature_ID: Gene ID (symbol) from hg38_Strict.gtf
4. SampleDetails_v1.csv: Table of details about all samples represented in RNAdecayCafe
   1. sample: SRA accession ID for sample
   2. dataset: Citation-esque summary of the dataset of origin
   3. pnew: EZbakR estimated T-to-C mutation rate in reads from new (labeled) RNA. You can see these blogs (here and here) for some intuition as to how to interpret these. More technical explanations of the models involved can be found here and here.
   4. pold: EZbakR estimated T-to-C mutation rate in reads from old (unlabeled) RNA. Same citations for pnew apply. Best samples are those with the largest gap between the pold and pnew; can think of this like a signal-to-noise ratio
   5. label_time: How long (in hours) were cells labeled with s4U for?
   6. cell_line: Cell line used for that sample.
   7. threePseq: TRUE or FALSE; TRUE if 3'-end sequencing was used.
   8. total_reads: Total number of aligned, exonic reads in the sample.
   9. median_halflife: Median, unnormalized half-life estimate. Differences between cell lines could represent real biology, but could also be evidence of dropout (see here and here for discussion of this phenomenon).
5. RateConstants_gene_v1.csv
   1. sample: SRA accession ID for sample.
   2. kdeg: e ^ log_kdeg
   3. halflife: log(2) / kdeg. Can be thought of as the average lifetime of the RNA.
   4. donorm_kdeg: e ^ donorm_log_kdeg
   5. donorm_halflife: log(2) / donorm_kdeg
   6. log_kdeg: log degradation rate constant estimated by EZbakR
   7. donorm_log_kdeg: dropout normalized log degradation rate constant
   8. reads: number of reads that contributed to estimates
   9. donorm_reads: dropout normalization corrected read count
   10. feature_ID: Gene ID (symbol) from hg38_Strict.gtf
6. RNAdecayCafe_database_v1.rds: compressed RDS file that stores a list containing the above 4 tables in the following entries:
   1. kdegs = RateConstants_gene_v1.csv
   2. sample_metadata = SampleDetails_v1.csv
   3. feature_metadata = FeatureDetails_v1.csv
   4. average_kdegs = AvgKdegs_gene_v1.csv
7. RNAdecayCafe_v1_onetable.csv: Inner joining of all but the averages table in this database. Thus, is one mega table containing all sample-specific estimates, sample metadata, and feature information.

Datasets included:

1. Finkel et al. 2021 (Calu3 cells; PMID: 35313595)
2. Harada et al. 2022 (MV411 cells; PMID: 35301220)
3. Ietswaart et al. 2024 (K562 cells; PMID: 38964322); whole-cell data used
4. Luo et al. 2020 (HEK293T cells; PMID: 33357462)
5. Mabin et al. 2025 (HEK293T cells; PMID: 40161772); only dataset for which data is not yet publicly

The dataset consists of nitrogen dioxide, meteorological data, and traffic data for the period of January-June 2019 and January-June 2020, which were generated taking into account the spatial distribution of the monitoring stations. Using the ArcGIS Pro software, a grid was created (Top -4,486,449.725263 m; Bottom - 4,466,449.725263 m; Left - 434,215.234430 m; Right - 451,215.234430 m) with a cell size having a width and height equal to 1000 m. There are 340 cells in total (20 by 17). Each cell value includes nitrogen dioxide, meteorological, and traffic attributes from assigned stations at a certain time. The cell value without stations was assigned to zero. The generated grid was exported as Comma Separated Values (CSV) files. Overall, 4344 and 4368 CSV files were generated every hour during the first six months of 2019 and 2020, respectively. Meteorological data include ultraviolet radiation, wind speed, wind direction, temperature, relative humidity, barometric pressure, solar irradiance, and precipitation, traffic data includes intensity, occupation, load, and average speed. The datasets have an hourly rate. The data were obtained from the Open Data portal of the Madrid City Council. There are 24 air quality monitoring stations, 26 meteorological monitoring stations, and more than 4,000 traffic measurement points (the location of the measurement points was provided on a monthly basis as these points changed monthly).

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. 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).

In the fourth quarter of 2024, Samsung shipped around 52 million smartphones, a decrease from the both the previous quarter and the same quarter of the previous year. Samsung’s sales consistently place the smartphone giant among the top three smartphone vendors in the world, alongside Xiaomi and Apple. Samsung smartphone sales – how many phones does Samsung sell? Global smartphone sales reached over 1.2 billion units during 2024. While the global smartphone market is led by Samsung and Apple, Xiaomi has gained ground following the decline of Huawei. Together, these three companies hold more than 50 percent of the global smartphone market share.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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).