Smallholders in rural Mozambique are typically characterized by low agricultural productivity, which is in part caused by very low levels of input usage. In the study area, four districts of Nampula province, farmers are generally far from towns where agricultural input providers are based and formal banking services are not available. Lacking these services, smallholders typically face liquidity constraints during the planting season when returns to input usage are the highest. In order to explore potential policy solutions to this challenge, the project combined training and incentives to use mobile money technology alongside targeted input marketing visits to promote formal saving strategies and increase take-up of basic inputs, primarily seeds, and fertilizer. The goal of the pilot project was to determine whether combining group-level trainings in mobile money technology with targeted direct marketing could increase input usage, and consequently boost agricultural productivity. In collaboration with Vodacom, IFPRI organized a series of trainings, first at the individual level with farm group leaders carried out in Nampula city in June 2014. This was followed by group trainings at local sites to which all farm group members were invited in July-August 2014. Input marketing visits were carried out by a local input provider, IKURU from October 2014-January 2015.

This dataset contains the percentage of population using Cellular Phones by province, 2015-2016. This data, derived from the National Socio-Economic Survey (SUSENAS March) that published through the People’s Welfare Statistic report by BPS. The data is available at province level (Admin 1) and downloadable in MS. Excel (XLS) format: https://www.bps.go.id/dynamictable/2018/05/21/1348/proporsi-individu-yang-menggunakan-telepon-genggam-2015---2016.html

Between 2015 and 2021, regardless of their age, the share of children owning a smartphone in the United States grew. During the 2021 survey, it was found that 31 percent of responding 8-year-olds owned a smartphone, up from only 11 percent in 2015.

The dataset provides temporary migration estimates at the (origin*destination*time)-level in Senegal derived from mobile phone data for the period 2013-2015. Origin and destination locations are comprised of 39 cities and 112 rural areas of third-level administrative units (i.e., districts), defining the spatial resolution of the dataset. Estimates are provided for each half-month period, defining the temporal resolution of the dataset.

This dataset (n=2,175, vars=95) is a Mozambique-specific dataset of adult (18+) household members’ use of mobile phones and awareness and use of mobile money. These variables are from sub-module D2 (Mobile Phone Use and Mobile Money) in the Mozambique 2015 ZOI Interim Survey. This dataset has multiple records per household. The unique identifiers in this file are pbs_id + idcode.

Smallholders in rural Mozambique are typically characterized by low agricultural productivity, which is in part caused by very low levels of input usage. In the study area, four districts of Nampula province, farmers are generally far from towns where agricultural input providers are based and formal banking services are not available. Lacking these services, smallholders typically face liquidity constraints during the planting season when returns to input usage are the highest. In order to explore potential policy solutions to this challenge, the project combined training and incentives to use mobile money technology alongside targeted input marketing visits to promote formal saving strategies and increase take-up of basic inputs, primarily seeds, and fertilizer. The goal of the pilot project was to determine whether combining group-level trainings in mobile money technology with targeted direct marketing could increase input usage, and consequently boost agricultural productivity. In collaboration with Vodacom, IFPRI organized a series of trainings, first at the individual level with farm group leaders carried out in Nampula city in June 2014. This was followed by group trainings at local sites to which all farm group members were invited in July-August 2014. Input marketing visits were carried out by a local input provider, IKURU from October 2014-January 2015.

This statistic displays the frequency of phone calls made or received on mobile phones in Poland in 2015. A seven percent share of respondents reported never using a mobile phone.

The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of * hours and ** minutes as of April 2022. This figure was expected to reach around * hours and ** minutes by 2024.

Locations of mobile phone operator sites in Barnet. These are included in an Excel workbook containing all sheets and descriptive metadata as well as a csv dataset.

The Afrobarometer survey measures the social political and economic atmosphere in than 30 countries in Africa, including Uganda. This dataset holds responses from 2400 Ugandans (a randomly selected nationally representative sample of citizens 18 years old and above), on their perceptions about the overall direction of the country, the economy, governance and other aspects of public affairs. It also includes their self-reported use of media and social media. It also holds enumerators' observations on the availability of services and facilities like schools, post offices, roads, cell phone service etc. in the areas studied. The data was collected in May 2015. Afrobarometer's summary of results, which is also a useful guide to the questions that were asked of respondents, is also included here.

This dataset (CSV) shows prices of iPhone 6S (and 3GS) mobile mobile phone including data in Australia in Sept 2015 (3GS in July 2009). An Excel version is linked below. Fields include: telco, price per month, data per month, price per GB of data (calculated), excess price per GB, description, link to URL. The iPhone 6G (and 3GS) is used as a comparison across several telco's pricing. Prices are included for several types of plans. Graphs of this dataset in an Microsoft Excel version of this data will be linked below. iPhone 3GS data at: http://dx.doi.org/10.6084/m9.figshare.1099008 Visualisation of iPhone 6S data at: (xls) http://dx.doi.org/10.6084/m9.figshare.1558198

Percentage of smartphone users by selected smartphone use habits in a typical day.

This workbook provides data and data dictionaries for the SFMTA 2015 Travel Decision SurveySFMTA Travel Decision Survey Data for 2015

On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano.

The primary goals of this study were to: • Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY2018 with a 95% confidence level and MOE +/- 5% or less. • Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. • Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.

The survey was conducted as a telephone study among 762 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, and Cantonese. Surveying was conducted via random digit dial (RDD) and cell phone sample. All three survey datasets incorporate respondent weighting based on age and home location; utilize the “weight” field when appropriate in your analysis.

The survey period for this survey is as follows: 2015: August – October 2015

The margin of error is related to sample size (n). For the total sample, the margin of error is 3.5% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes.

At the 95% confidence level is: • n = 762(Total Sample). Margin of error = +/- 3.5% • n = 382. Margin of error = +/- 4.95% • n = 100. Margin of error = +/- 9.80%

Abstract

Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional quasi-free modality, namely, the wireless signal emitted by individuals' cell phones, referred to as RGB-W. The received signal strength acts as a rough proxy for depth and a reliable cue on a person's identity. Although the measured signals are noisy, we demonstrate that the combination of visual and wireless data significantly improves the localization accuracy. We introduce a novel image-driven representation of wireless data which embeds all received signals onto a single image. We then evaluate the ability of this additional data to (i) locate persons within a sparsity-driven framework and to (ii) track individuals with a new confidence measure on the data association problem. Our solution outperforms existing localization methods. It can be applied to the millions of currently installed RGB cameras to better analyze human behavior and offer the next generation of high-accuracy location-based services.

Conference Paper

PDF: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Alahi_RGB-W_When_Vision_ICCV_2015_paper.pdf

Metadata

+----------------+-----------------+-----------+-----------+--------------+----------+ | Sequence Name | Length (mm:ss) | # Frames | # People | # W Devices | Download | +----------------+-----------------+-----------+-----------+--------------+----------+ | conference-1 | 01:53 | 1,697 | 5 | 5 | 116 MiB | | conference-2 | 05:18 | 4,782 | 12 | 12 | 379 MiB | | conference-3 | 23:31 | 21,165 | 1 | 2 | 1.3 GiB | | conference-4 | 06:27 | 4,832 | 1 | 2 | 357 MiB | | conference-5 | 06:03 | 4,525 | 2 | 2 | 290 MiB | | patio-1 | 07:22 | 6,636 | 4 | 4 | 474 MiB | | patio-2 | 04:36 | 4,144 | 2 | 2 | 258 MiB | | Full Dataset | 55:10 | 47,781 | -- | -- | 3.2 GiB | +----------------+-----------------+-----------+-----------+--------------+----------+

Citation

If you would like to cite our work, please use the following.

Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. International Conference on Computer Vision (ICCV). Santiago, Chile. IEEE.

@inproceedings{alahi2015rgb, title={RGB-W: When vision meets wireless}, author={Alahi, Alexandre and Haque, Albert and Fei-Fei, Li}, booktitle={International Conference on Computer Vision}, year={2015} }

Poland Mobile Phone: Centres data was reported at 58.000 Unit in 2017. This records a decrease from the previous number of 59.000 Unit for 2016. Poland Mobile Phone: Centres data is updated yearly, averaging 74.000 Unit from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 122.000 Unit in 2010 and a record low of 56.000 Unit in 2015. Poland Mobile Phone: Centres data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.TB001: Mobile Phone Statistics.

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.

Original datasource The Mobile phone activity dataset is a part of the Telecom Italia Big Data Challenge 2014, which is a rich and open multi-source aggregation of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino (Italy). The original dataset has been created by Telecom Italia in association with EIT ICT Labs, SpazioDati, MIT Media Lab, Northeastern University, Polytechnic University of Milan, Fondazione Bruno Kessler, University of Trento and Trento RISE. In order to make it easy-to-use, here we provide a subset of telecommunications data that allows researchers to design algorithms able to exploit an enormous number of behavioral and social indicators. The complete version of the dataset is available at the following link: http://go.nature.com/2fz4AFr We kindly ask people who use this dataset to cite the following paper, where this aggregation comes from: Citation Barlacchi, Gianni, Marco De Nadai, Roberto Larcher, Antonio Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli, Alessandro Vespignani, Alex Pentland, and Bruno Lepri. "A multi-source dataset of urban life in the city of Milan and the Province of Trentino." Scientific data 2 (2015).

Brazil BR: Mobile Account: % Aged 15+ data was reported at 0.858 % in 2014. Brazil BR: Mobile Account: % Aged 15+ data is updated yearly, averaging 0.858 % from Dec 2014 (Median) to 2014, with 1 observations. The data reached an all-time high of 0.858 % in 2014 and a record low of 0.858 % in 2014. Brazil BR: Mobile Account: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Banking Indicators. Mobile account denotes the percentage of respondents who report personally using a mobile phone to pay bills or to send or receive money through a GSM Association (GSMA) Mobile Money for the Unbanked (MMU) service in the past 12 months; or receiving wages, government transfers, or payments for agricultural products through a mobile phone in the past 12 months.; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

This data set forms the basis of the paper 'The Hibernating Mobile Phone: Dead Storage as a Barrier to Efficient Electronic Waste Recovery'.

These results are from an online, self-completion questionnaire with mobile phone owners; distributed to a non-probability, purposive sample (i.e. aged between 18-25 years old, living and studying at a UK University, and owning a mobile phones. The survey was conducted during July 2015.

For a full description of the data collection techniques and our analysis of the data, please refer to the above paper.

Abstract

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform policy. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques.

The data set contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing.

The data is uploaded in three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_id’. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.

The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 4,061 Kenyan households Wave 2: July 16 to September 18, 2020; 4,492 Kenyan households Wave 3: September 28 to December 2, 2020; 4,979 Kenyan households Wave 4: January 15 to March 25, 2021; 4,892 Kenyan households Wave 5: March 29 to June 13, 2021; 5,854 Kenyan households Wave 6: July 14 to November 3, 2021; 5,765 Kenyan households Wave 7: November 15, 2021, to March 31, 2022; 5,633 Kenyan households Wave 8: May 31 to July 8, 2022: 4,550 Kenyan households

The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Household, Individual

Sampling procedure

The COVID-19 RRPS with Kenyan households has two samples. The first sample consists of households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was administered in English and is provided as a resource in pdf format. Additionally, questionnaires for each wave are also provided in Excel format coded for SCTO. The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/