About this dataset

Context

The dataset tabulates the population of Rule by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Rule. The dataset can be utilized to understand the population distribution of Rule by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Rule. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Rule.

Key observations

Largest age group (population): Male # 65-69 years (42) | Female # 45-49 years (56). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Rule population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Rule is shown in the following column.
• Population (Female): The female population in the Rule is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Rule for each age group.

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 Rule Population by Gender. You can refer the same here

The City of Bloomington Utilities drinking water sampling for the unregulated contaminants monitoring rule (2023/2024). This rule was to monitor and measure PFAS and lithium

Dataset contains counts for territorial authority local board area (TALB) of usual residence by TALB of usual residence address one year ago and five years ago, and by life cycle age group, for the census usually resident population count, 2023 Census.

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This dataset compares usual residence at the 2023 Census with usual residence one and five years earlier to show population mobility and internal migration patterns of people within New Zealand.

‘Usual residence address’ is the address of the dwelling where a person considers that they usually live.

‘Usual residence one year ago address’ identifies an individual’s usual residence on 7 March 2022, which may be different to their current usual residence on census night 2023 (7 March 2023).

‘Usual residence five years ago address’ identifies an individual’s usual residence on 6 March 2018, which may be different to their current usual residence on census night 2023 (7 March 2023).

Note: This dataset only includes usual residence address information for individuals whose usual residence address one year ago and five years ago is available at TALB.

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Life cycle age groups are categorised as:

• under 15 years
• 15–29 years
• 30–64 years
• 65 years and over.

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This dataset can be used in conjunction with the following spatial files by joining on the TALB code values:

• Territorial authority local board (generalised)

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Rows excluded from the dataset

Rows show TALB of usual residence by TALB of usual residence one year ago and five years ago, by life cycle age group. Cells with a number less than six have been confidentialised. Responses to categories unable to be mapped, such as response unidentifiable, not stated, and Auckland (not further defined), have also been excluded from this dataset.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Age quality rating

Age is rated as very high quality.

Age – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Census usually resident population quality rating

The census usually resident population count is rated as very high quality.

Census usually resident population count – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Usual residence address quality rating

Usual residence address is rated as high quality.

Usual residence address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Usual residence one year ago quality rating

Usual residence one year ago area is rated as high quality.

Usual residence one year ago – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Usual residence five years ago quality rating

Usual residence five years ago area is rated as high quality.

Usual residence five years ago – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Number of households broken down by type of household. The variable is divided into single household types (including single residents), cohabiting and other households, all with or without children. Children are counted by kinship and not age. Households are household-dwelling units.All persons registered in the same apartment are counted to the same household. For more information on household statistics, see Statistics Sweden’s document “Register-based household statistics”. Data from the Register of Total Population (RTB). In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Source Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se.There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before.However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers.Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells.However, the values are included in summaries. In general, the following rules apply: * No statistics are reported for geographical areas with very few housing. * No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. * In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.

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The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated).

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The variables for part 1 of the dataset are:

• Census usually resident population count
• Census night population count
• Age (5-year groups)
• Age (life cycle groups)
• Median age
• Birthplace (NZ born/overseas born)
• Birthplace (broad geographic areas)
• Ethnicity (total responses) for level 1 and ‘Other Ethnicity’ grouped by ‘New Zealander’ and ‘Other Ethnicity nec’
• Māori descent indicator
• Languages spoken (total responses)
• Official language indicator
• Gender
• Cisgender and transgender status – census usually resident population count aged 15 years and over
• Sex at birth
• Rainbow/LGBTIQ+ indicator for the census usually resident population count aged 15 years and over
• Sexual identity for the census usually resident population count aged 15 years and over
• Legally registered relationship status for the census usually resident population count aged 15 years and over
• Partnership status in current relationship for the census usually resident population count aged 15 years and over
• Number of children born for the sex at birth female census usually resident population count aged 15 years and over
• Average number of children born for the sex at birth female census usually resident population count aged 15 years and over
• Religious affiliation (total responses)
• Cigarette smoking behaviour for the census usually resident population count aged 15 years and over
• Disability indicator for the census usually resident population count aged 5 years and over
• Difficulty communicating for the census usually resident population count aged 5 years and over
• Difficulty hearing for the census usually resident population count aged 5 years and over
• Difficulty remembering or concentrating for the census usually resident population count aged 5 years and over
• Difficulty seeing for the census usually resident population count aged 5 years and over
• Difficulty walking for the census usually resident population count aged 5 years and over
• Difficulty washing for the census usually resident population count aged 5 years and over.

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Download lookup file for part 1 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

​

Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.

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Symbol

-997 Not available

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Source Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB).The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population.Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: * No statistics are reported for geographical areas with very few housing. * No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. * In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Sources Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: No statistics are reported for geographical areas with very few housing. No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What is the 5% rule for stock portfolio?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract

Introduction

The Annual Survey of Industries (ASI) is one of the large-scale sample survey conducted by Field Operation Division of National Sample Survey Office for more than three decades with the objective of collecting comprehensive information related to registered factories on annual basis. ASI is the primary source of data for facilitating systematic study of the structure of industries, analysis of various factors influencing industries in the country and creating a database for formulation of industrial policy.

The main objectives of the Annual Survey of Industries are briefly as follows:

(a) Estimation of the contribution of manufacturing industries as a whole and of each unit to national income.

(b) Systematic study of the structure of industry as a whole and of each type of industry and each unit.

(c) Casual analysis of the various factors influencing industry in the country: and

(d) Provision of comprehensive, factual and systematic basis for the formulation of policy.

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

The ASI is the principal source of industrial statistics in India and extends to the entire country except Arunachal Pradesh, Mizoram & Sikkim and the Union Territory of Lakshadweep. It covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to census scheme is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948.

Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 2000-01 is a Circular Systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All the complete enumeration States namely, Manipur, Meghalaya, Nagaland, Tripura and Andaman & Nicobar Islands. b) For the rest of the States/ UT's., (i) units having 100 or more workers, and (ii) all factories covered under Joint Returns.

Rest of the factories found in the frame constituted Sample sector on which sampling was done. Factories under Biri & Cigar sector were not considered uniformly under census sector. Factories under this sector were treated for inclusion in census sector as per definition above (i.e., more than 100 workers and/or joint returns). After identifying Census sector factories, rest of the factories were arranged in ascending order of States, NIC-98 (4 digit), number of workers and district and properly numbered. The Sampling fraction was taken as 12% within each stratum (State X Sector X 4-digit NIC) with a minimum of 8 samples except for the State of Gujarat where 9.5% sampling fraction was used. For the States of Jammu & Kashmir, Himachal Pradesh, Daman & Diu, Dadra & Nagar Haveli, Goa and Pondicherry, a minimum of 4 samples per stratum was selected. For the States of Bihar and Jharkhand, a minimum of 6 samples per stratum was selected. The entire sample was selected in the form of two independent sub-sample using Circular Systematic Sampling method.

Sampling deviation

There was no deviation from sample design in ASI 2000-01

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

Validation checks are carried out on data files. Code list, State code list, Tabulation program and ASICC code are may be refered in the External Resources which are used for editing and data processing as well..

B. Tabulation procedure

The tabulation procedure by CSO(ISW) includes both the ASI 2000-01 data and the extracted data from ASI 99-00 for all tabulation purpose. For extracted returns, status of unit (Block A, Item 12) would be in the range 17 to 20. To make results comparable, users are requested to follow the same procedure. For calculation of various parameters, users are requested to refer instruction manual/report. Please note that a separate inflation factor (Multiplier) is available for each unit against records belonging to Block-A for ASI 2000-01 data. The multiplier is calculated for each stratum (i.e. State X NIC'98(4 Digit)) after adjusting for non-response cases.

C. Merging of unit level data

As per existing policy to merge unit level data at ultimate digit level of NIC'98 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC'98(5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC'98(5 Digit) ending with '9' which do not figure in the book of NIC '98. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-98) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise loss of information.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula (Pl ease refer to Estimation Procedure document in external resources). Programs developed in Visual Faxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

According to estimates, the majority of TikTok Shop's gross merchandise value (GMV) originates from Asia. As of **********, nearly ** percent of TikTok Shop's GMV came from the Asia region. In comparison, TikTok Shop's GMV from North America accounted for an estimated ** percent. However, the share of open job roles was much higher in North America than in Asia, with ** percent compared to ** percent. Europe accounted for the remaining GMV share and open job positions.

Question Paper Solutions of chapter Scoping Rules of Data Analysis with R, 2nd Semester , Bachelor of Computer Application 2023-2024

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Sources Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: • No statistics are reported for geographical areas with very few housing. No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. • In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

About this dataset

Context

The dataset tabulates the population of Rule by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Rule across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.68% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Rule is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Rule total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Rule Population by Race & Ethnicity. You can refer the same here

Abstract

Introduction

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

The ASI extends to the entire country except the States of Arunachal Pradesh, Mizoram, and Sikkim and Union Territory of Lakshadweep. It covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948 i.e. those factories employing 10 or more workers using power; and those employing 20 or more workers without using power. The survey also covers bidi and cigar manufacturing establishments registered under the Bidi & Cigar Workers (Conditions of Employment) Act, 1966 with coverage as above. All electricity undertakings engaged in generation, transmission and distribution of electricity registered with the Central Electricity Authority (CEA) were covered under ASI irrespective of their employment size. Certain servicing units and activities like water supply, cold storage, repairing of motor vehicles and other consumer durables like watches etc. are covered under the Survey. Though servicing industries like motion picture production, personal services like laundry services, job dyeing, etc. are covered under the Survey but data are not tabulated, as these industries do not fall under the scope of industrial sector defined by the United Nations.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Merging of unit level data As per existing policy to merge unit level data at ultimate digit level of NIC'08 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC-08 (5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC-08 (5 Digit) ending with '9' that do not figure in the book of NIC '08. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-09) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise the loss of information.

Universe

The survey cover factories registered under the Factory Act 1948. Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI. The geographical coverage of the Annual Survey of Industries, 2008-2009 has been extended to the entire country except the states of Arunachal Pradesh, Mizoram and Sikkim and Union Territory of Lakshadweep.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 2008-09 is a stratified circular systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All industrial units belonging to the six less industrially developed states/ UT's viz. Manipur, Meghalaya, Nagaland, Tripura, Sikkim and Andaman & Nicobar Islands.
b) For the rest of the twenty-six states/ UT's., (i) units having 100 or more workers, and (ii) all factories covered under Joint Returns. c) After excluding the Census Sector units as defined above, all units belonging to the strata (State by 4-digit of NIC-04) having less than or equal to 4 units are also considered as Census Sector units.

Remaining units, excluding those of Census Sector, called the sample sector, are arranged in order of their number of workers and samples are then drawn circular systematically considering sampling fraction of 20% within each stratum (State X Sector X 4-digit NIC) for all the states. An even number of units with a minimum of 4 are selected and evenly distributed in two sub-samples. The sectors considered here are Biri, Manufacturing and Electricity.

Sampling deviation

There was no deviation from sample design in ASI 2008-09.

Mode of data collection

Face-to-face [f2f]

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A.IDENTIFICATION PARTICULARS BLOCK B. PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C: FIXED ASSETS BLOCK D: WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H: INPUT ITEMS (indigenous items consumed) BLOCK I: INPUT ITEMS – directly imported items only (consumed) BLOCK J: PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

A list of validation checks carried out on data files is given in External Resources "Validation checks, ASI 2008-09". Code list, State code list, Tabulation program and ASICC code are also may be refered in the External Resources which are used for editing and data processing as well.

Response rate

No. of units to be surveyed No. of units responded No. of units non-responded Response rate (in %)

  58300             52376                  5924             89.84

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula (Pl ease refer to Estimation Procedure document in external resources). Programs developed in Visual Foxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

The experiments described in our manuscript have generated a substantial amount of data, which we cannot directly include in a document. Parts of this data are made available in the supporting_information archive, while the entire raw classification data is available on request. Below we briefly describe the content of this archive: Table set S1. Basic rule set statistics on the ART corpus, including the sizes of the rule sets for each ranking mechanism we have used, for each rhetorical type at different thresholds: 1%, 5%, 10% and 15%. Table set S2. Basic rule set statistics on the Wilbur corpus, including the sizes of the rule sets for each ranking mechanism we have used, for each rhetorical type at different thresholds: 1%, 5%, 10% and 15%. Table set S3. Corpus-driven inter-type comparison on the ART corpus, looking at the overlaps between the rule sets of the different rhetorical types. Table set S4. Corpus-driven inter-type comparison on the Wilbur corpus. Table set S5. Corpus-driven intra-type comparison on the ART corpus, looking at the overlaps between the different serialization types in the context of a rhetorical type. Table set S6. Corpus-driven intra-type comparison on the Wilbur corpus. Table set S7. Cross-corpus type comparison, listing the overlaps in rule sets between the rhetorical types of the ART and Wilbur corpora. (ZIP)

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Source Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included.Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se.There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total.In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells.However, the values are included in summaries. In general, the following rules apply: * No statistics are reported for geographical areas with very few housing. * No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. * In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

About this dataset

Context

The dataset tabulates the Rule population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Rule. The dataset can be utilized to understand the population distribution of Rule by age. For example, using this dataset, we can identify the largest age group in Rule.

Key observations

The largest age group in Rule, TX was for the group of age 45 to 49 years years with a population of 89 (12.06%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Rule, TX was the 85 years and over years with a population of 7 (0.95%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Rule is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Rule total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Rule Population by Age. You can refer the same here

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Source Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: * No statistics are reported for geographical areas with very few housing. * No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. * In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

Shows the number of passenger cars registered on natural persons in the area. Data from the Vehicle Register. In this file, statistics are broken down by Malmö’s different areas over time. Sources Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: • No statistics are reported for geographical areas with very few housing. No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. • In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Source Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: * No statistics are reported for geographical areas with very few housing. * No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. * In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.