Money Supply M0 in Singapore decreased to 64390.50 SGD Million in May from 64401.90 SGD Million in April of 2025. This dataset provides - Singapore Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M2 in Singapore increased to 856167.10 SGD Million in May from 851039.90 SGD Million in April of 2025. This dataset provides - Singapore Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Singapore Dollar 2

## Overview

Singapore Dollar 2 is a dataset for object detection tasks - it contains Money annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Singapore 10

## Overview

Singapore 10 is a dataset for object detection tasks - it contains Money annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset from Singapore Customs. For more information, visit https://data.gov.sg/datasets/d_a193b9d2ca09a24e21190a1573fa01ab/view

Singapore SG: Broad Money: % of GDP data was reported at 129.687 % in 2017. This records a decrease from the previous number of 131.348 % for 2016. Singapore SG: Broad Money: % of GDP data is updated yearly, averaging 82.360 % from Dec 1963 (Median) to 2017, with 55 observations. The data reached an all-time high of 132.642 % in 2009 and a record low of 52.853 % in 1963. Singapore SG: Broad Money: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank.WDI: Money Supply. Broad money (IFS line 35L..ZK) is the sum of currency outside banks; demand deposits other than those of the central government; the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler’s checks; and other securities such as certificates of deposit and commercial paper.; ; International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.; Weighted average; The derivation of this indicator was simplified in September 2012 to be current-year broad money divided by current-year GDP times 100.

Government Revenues in Singapore increased to 17755.40 SGD Million in May from 9705 SGD Million in April of 2025. This dataset provides - Singapore Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.

Singapore 100

## Overview

Singapore 100 is a dataset for object detection tasks - it contains Money annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data is used for article of macroeconomic of some Asian countries in long period which explained about four Asian countries, such as Indonesia, Malaysia, Singapore, and South Korea. This data has taken from World Bank Development Indicators (WDI) database and is formed by Vector Auto Regression (VAR) model, then empirical result is executed by Granger causality model on E-views 11 program to gauge the relationship between gross domestic product, exchange rate, inflation rate, foreign direct investment, net export, government expenditures, unemployment rate, and savings. The results showed that most of gross domestic product of sample and other macro-economy variables have not causality relationship.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Twenty-eight of 55 Planning Areas were excluded due to zero or small population size, accounting for less than 3 percent of the total population. In addition, individuals living in private condos or landed properties were excluded, representing approximately 20 percent of households in Singapore.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Singapore is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.0443 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.023 and 0.0015 (in million kms), corressponding to 51.9029% and 3.3001% respectively of the total road length in the dataset region. 0.0198 million km or 44.797% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0021 million km of information (corressponding to 10.7545% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Context

This dataset is in context of the real world data science work and how the data analyst and data scientist work.

Content

The dataset consists of four columns Year, Level_1(Ethnic group/gender), Level_2(Age group), and population

Acknowledgements

I would sincerely thank GeoIQ for sharing this dataset with me along with tasks. Just having a basic knowledge of Pandas and Numpy and other python data science libraries is not enough. How can you execute tasks and how can you preprocess the data before making any prediction is very important. Most of the datasets in Kaggle are clean and well arranged but this dataset thought me how real world data science and analysis works. Every data science beginner must work on this dataset and try to execute the tasks. It would only give them a good exposer to the real data science world.

Inspiration

1. Identify the largest Ethnic group in Singapore. Their average population growth over the years and what proportion of the total population do they constitute.
2. Identify the largest age group in Singapore. Their average population growth over the years and what proportion of the total population do they constitute.
3. Identify the group (by age, ethnicity and gender) that: a. Has shown the highest growth rate b. Has shown the lowest growth rate c. Has remained the same
4. Plot a graph for population trends

Singapore 5

## Overview

Singapore 5 is a dataset for object detection tasks - it contains Money annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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.

Modified data set ready for analysis (modifications done and source will be mentioned later) This dataset is related to energy consumption in Singapore buildings in 207 and 2018. And this data has 976 rows and 10 columns.

The columns are respectively: buildingaddress buildingtype greenmarkstatus greenmarkrating greenmarkyearaward buildingsize grossfloorarea 2017 energy use intensity 2018 energy intensity greenmarkrating_numeric buildingtype_color

The main source of the data set is: https://data.gov.sg/dataset/building-energy-performance-data

But its general flaws, including the following, have been fixed and are ready for analysis :

Empty and missing data have been corrected in two columns, and each column has been managed (corrected or deleted) according to the nature of the data and the importance of that missing data.

Singapore SG: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Secondary Education Or More: % of Population Aged 15+ data was reported at 99.059 % in 2017. This records an increase from the previous number of 96.751 % for 2014. Singapore SG: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Secondary Education Or More: % of Population Aged 15+ data is updated yearly, averaging 99.059 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 99.923 % in 2011 and a record low of 96.751 % in 2014. Singapore SG: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Secondary Education Or More: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted Average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

This record is part of 'The Customer Satisfaction Index of Singapore (CSISG) Annual Dataset Collection 2007-2022', providing raw data set, datamap and questionnaires for 2015. For related datasets across other years, refer to the full collection here: https://doi.org/10.25440/smu.c.6906043The Customer Satisfaction Index of Singapore (CSISG) is a landmark measure of customer satisfaction cutting across a variety of key sectors and sub-sectors in the services industry of Singapore. The study was produced and updated on an quarterly and annual basis from 2007 to 2022. First launched in April 2008, the CSISG is an independent and qualitative indicator of the Singapore economy. It covers 8 core economic sectors, more than 20 sub-sectors and numerous companies from the Air Transport Finance, Food & Beverage, Info-communications, Insurance, Land Transport, Retail, and Tourism industries. This national barometer of customer satisfaction in the Singapore economy serves as an objective gauge of service competitiveness between businesses, industries, and even countries. As it reports the overall customer satisfaction scores of every sector and sub-sector, including a ranking of the companies measured, the CSISG serves as an invaluable benchmarking tool across industries in the services sector.The methodological foundations of the Customer Satisfaction Index of Singapore can be traced to the American Customer Satisfaction Index (ACSI), developed by the National Quality Research Centre (NQRC) at the University of Michigan. The American Customer Satisfaction Index has been the standardised measure of customer satisfaction in the US economy since 1994.The Customer Satisfaction Index of Singapore is based on econometric modelling of data obtained from interviews with actual users of products and services.

Singapore SG: Proportion of Seats Held by Women in National Parliaments data was reported at 23.000 % in 2018. This records a decrease from the previous number of 23.800 % for 2017. Singapore SG: Proportion of Seats Held by Women in National Parliaments data is updated yearly, averaging 21.700 % from Mar 1991 (Median) to 2018, with 22 observations. The data reached an all-time high of 25.300 % in 2015 and a record low of 4.300 % in 2001. Singapore SG: Proportion of Seats Held by Women in National Parliaments data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank.WDI: Policy and Institutions. Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women.; ; Inter-Parliamentary Union (IPU) (www.ipu.org).; Weighted average; General cut off date is end-December. Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.

This dataset has total population of dingapore basing on their ethnicity,gender . It is raw data which has mixed entities in columns . from year 1957 to 2018 population data is given . The main aim in uploading this data is to get skilled in python pandas for exploratory data analysis.