Mobile data usage in Singapore reached an average of around 96 petabytes (96,000 terabytes) per month during the fourth quarter of 2024. Despite the expansion of public Wireless Local Area Networks (WLANs), mobile data use remained on the rise during the observed period. Data pricing trend in SingaporeSingapore has always had one of the most expensive data prices in Southeast Asia. However, new technologies such as 5G, as well as the competitive telecommunications market, led to price reductions over the past few years. In 2023, one gigabyte of mobile data cost around 0.63 U.S. dollars on average, indicating a decrease of more than two dollars compared to 2019. Despite the foreseeable trend of decreasing data prices, it would still remain relatively high compared to neighboring Indonesia's 0.28 U.S. dollars per gigabyte. Singapore’s mobile data providersThe telecommunications market in Singapore is shared by three main providers, Singtel, Starhub and M1. Among the leading providers in the country, Singtel had the best 5G coverage experience, as of December 2024.

In the first quarter of 2021, the average Singaporean mobile internet user consumed about **** GB of data per month. With the increasing demand of online video and social media content this figure is expected to further grow over the next few years.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_2bc812dbfd1e485638435fcdf7aac196/view

New Home Sales in Singapore decreased to 272 Units in June from 312 Units in May of 2025. This dataset provides - Singapore New Home Sales- actual values, historical data, forecast, chart, statistics, economic calendar and news.

We have created Singapore Maritime Dataset, using Canon 70D cameras around Singapore waters. All the videos are acquired in high definition (1080X1920 pixels). We divide the dataset into parts, on-shore videos and on-board videos, which are acquired by a camera placed on-shore on a fixed platform and a camera placed on-board a moving vessel, respectively. The videos are acquired at various locations and routes and thus do not necessarily capture the same scene. The third part is Near Infrared (NIR) videos which are also captured using another Canon 70D camera with the hot mirror removed and Mid-Opt BP800 Near-IR Bandpass filter.

Acknowledgement- Dataset has been captured by Dilip K. Prasad and annotated by student volunteers. Dataset has been captured on various environmental conditions like before sunrise (40 min before sunrise), sunrise, mid-day, afternoon, evening, after sunset (2hrs after sunset), Haze and Rain from July 2015 to May 2016.

Optical lens used for all the 3 sub-dataset - Canon EF 70-300mm f/4-5.6 IS USM

Following papers to be cited for using this dataset

D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabaly, and C. Quek, "Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey," IEEE Transactions on Intelligent Transportation Systems (IEEE), 18 (8), 1993 - 2016, 2017. (preprint PDF)

Other papers on this dataset from our group

D. K. Prasad, H. Dong, D. Rajan, and C. Quek, "Are object detection assessment criteria ready for maritime computer vision?," IEEE Transactions on Intelligent Transportation Systems, 2019.

D. K. Prasad, C.K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, "Object detection in maritime environment: Performance evaluation of background subtraction methods," IEEE Transactions on Intelligent Transportation Systems, 22 (5), 1787-1802, 2019.

D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, “MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images,” Journal of Optical Society America A, vol. 33, issue 12, pp. 2491-2500, 2016.

D. K. Prasad, C.K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, “Maritime situational awareness using adaptive multisensory management under hazy conditions,” 5th International Maritime-Port Technology and Development Conference (MTEC 2017), Singapore, 26-28 April, 2017.

D. K. Prasad, C.K. Prasath, D. Rajan, C. Quek, L. Rachmawati, and E. Rajabally, “Challenges in video based object detection in maritime scenario using computer vision,” 19th International Conference on Connected Vehicles, Zurich, 13-14 January, 2017.

D. K. Prasad, D. Rajan, C. Krishna Prasath, L. Rachmawati, E. Rajabally, and C. Quek, “MSCM-LiFe: Multi-Scale Cross Modal Linear Feature for Horizon Detection in Maritime Images,” IEEE TENCON, Singapore,22-25 Nov, 2016.

Definition:

Type of internet connection the households used

Introduction

The Annual Survey on Infocomm Usage in Households (“Survey”) has been conducted by IDA since the 1990s. The objective of the Survey is to assess the extent of infocomm adoption in Singapore resident households1 and residents.

Methodology:

Data was collected from about 3,500 households and about 3,500 residents via face-to-face interviews. The sample of addresses was provided by the Singapore Department of Statistics based on a random selection using a two-stage stratified design by geographical location and housing type.

Home Ownership Rate in Singapore increased to 90.80 percent in 2024 from 89.70 percent in 2023. This dataset provides the latest reported value for - Singapore Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Singapore Household Access to Internet: % of Access to Internet data was reported at 98.000 % in 2019. This stayed constant from the previous number of 98.000 % for 2018. Singapore Household Access to Internet: % of Access to Internet data is updated yearly, averaging 84.000 % from Dec 2003 (Median) to 2019, with 17 observations. The data reached an all-time high of 98.000 % in 2019 and a record low of 65.000 % in 2004. Singapore Household Access to Internet: % of Access to Internet data remains active status in CEIC and is reported by Infocomm Media Development Authority of Singapore. The data is categorized under Global Database’s Singapore – Table SG.TB002: Telecommunication Industry Statistics.

Acknowledgements

The use and distribution of the data are governed by the Singapore Open Data Licence. More information can be found at < https://data.gov.sg/open-data-licence#acceptance >

Source of the raw data < https://www.tablebuilder.singstat.gov.sg/publicfacing/initApiList.action >

Code

If you are interested in the source code to extract the data, visit my GitHub page for more information.

Mexico Exports of household or laundry-type washing machines to Singapore was US$5 during 2010, according to the United Nations COMTRADE database on international trade. Mexico Exports of household or laundry-type washing machines to Singapore - data, historical chart and statistics - was last updated on May of 2025.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

The per capita consumer spending on education ranking is led by Singapore with 1,640.84 U.S. dollars, while Australia is following with 1,290.37 U.S. dollars. In contrast, Ethiopia is at the bottom of the ranking with 0.68 U.S. dollars, showing a difference of 1,640.16 U.S. dollars to Singapore. Consumer spending, in this case education-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group tenth As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.

Singapore Home Ownership Rate among Res Households: HDB: 5-Room & Exec Flats data was reported at 97.300 % in 2017. This records a decrease from the previous number of 97.400 % for 2016. Singapore Home Ownership Rate among Res Households: HDB: 5-Room & Exec Flats data is updated yearly, averaging 97.400 % from Dec 1980 (Median) to 2017, with 21 observations. The data reached an all-time high of 98.800 % in 1980 and a record low of 94.400 % in 2010. Singapore Home Ownership Rate among Res Households: HDB: 5-Room & Exec Flats data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.H054: Resident Households .

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_66ad6d9200e5223a4773df8a615f0c5f/view

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_71282469d7642447115f4f58cb936753/view

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_73cdb2f88a57c7406d1955098395c734/view

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_4a02b0c381d4dbc9370cbb3f0ce255f2/view

Dataset from Ministry of Social and Family Development. For more information, visit https://data.gov.sg/datasets/d_b85f15701092578c2aa0ae8cd263b210/view

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_ad4a8ccbdab03d16c486a9ee6988289d/view

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_ce1804c655566f5089fff370bec37c2d/view